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Predictions of Climate Change over Europe using Statistical and Dynamical Downscaling techniques
International Journal of Climatology
Abstract
Statistical and dynamical downscaling predictions of changes in surface temperature and precipitation for 2080–2100, relative to pre-industrial conditions, are compared at 976 European observing sites, for January and July. Two dynamical downscaling methods are considered, involving the use of surface temperature or precipitation simulated at the nearest grid point in a coupled ocean–atmosphere general circulation model (GCM) of resolution ∼300 km and a 50 km regional climate model (RCM) nested inside the GCM. The statistical method (STAT) is based on observed linear regression relationships between surface temperature or precipitation and a range of atmospheric predictor variables. The three methods are equally plausible a priori, in the sense that they estimate present-day natural variations with equal skill.
... This study follows the recommendations of previous studies (Murphy, 1999(Murphy, , 2000Diaz-Nieto and Wilby, 2005) through the convergence of the two downscaling techniques by using the DM alongside statistical downscaling techniques, thus using their advantages in a complementary way. By definition the DM applies relative changes obtained from GCM or RCM into the local observations, as a result the method only accounts for the change in mean, ignoring changes in distribution for the variable being downscaled. ...
... Since it is based on differences and ratios between current and future simulated climates, the DM is known for bias removal, assuming that biases are systematic (Bosshard et al., 2011;Lenderink et al., 2007b). This study follows the recommendations of previous studies (Murphy, 1999(Murphy, , 2000Diaz-Nieto and Wilby, 2005) through the convergence of the two downscaling techniques by using the DM alongside Statistical Downscaling (SD) techniques, thus using their advantages in a complementary way. By definition the DM applies relative changes obtained from global or RCM's to the local observations. ...
More frequent heat waves and longer and more frequent dry spells are expected worldwide. As well as wetter winters with less but more intense rainfall. Climate change is also expected to affect agriculture in Europe and across the world. Expected effects depend on current climatic, soil and economic conditions. By 2050, the urban areas worldwide are expected to increase and concentrate 66% of the world population according to the United Nations (UN), thus increasing the exposure to flood, increase of surface temperature and frequency of heat waves. The hydrological cycle and water resources regimes are particularly threatened by environmental change. The aim of this thesis is to understand the impact of socio-economic changes in the (i) land use, (ii) climate and (iii) demography at a local scale. This thesis work focuses on developing socio-economic scenarios for the land use, climate and demography of the Black Sea catchment (BSC). As well as policy-driven scenarios of land use for the the Cantons of Vaud and Valais in Western Switzerland, covering the upper Rhône basin.
... These generally take the form of typical weather years, created from hourly historic observations at a specific location [15]. However, the need to adapt buildings to the impacts of likely future climate change has created a requirement to incorporate climate change projections into these weather files, either by morphing the weather data or synthetically generating it [16,17]. Weather files representing 'extreme' years (i.e. the selection of observed weather occurrences far from the norm) have also been introduced to analyse a building design's response in case of severe weather conditions [18]. ...
... The alternative to using climate projections to prime a weather generator is to adjust (morph) current weather files [16] or even raw time series data taken at weather stations [107]. The starting point of this method is obtaining high-resolution weather data for a specific site. ...
This article provides the first comprehensive assessment of methods for the creation of weather variables for use in building simulation. We undertake a critical analysis of the fundamental issues and limitations of each methodology and discusses new challenges, such as how to deal with uncertainty, the urban heat island, climate change and extreme events. Proposals for the next generation of weather files for building simulation are made based on this analysis. A seven-point list of requirements for weather files is introduced and the state-of-the-art compared to this via a mapping exercise. It is found that there are various issues with all current and suggested approaches, but the two areas most requiring attention are the production of weather files for the urban landscape and files specifically designed to test buildings against the criteria of morbidity, mortality and building services system failure.
Practical application: Robust weather files are key to the design of sustainable, healthy and comfortable buildings. This article provides the first comprehensive assessment of their technical requirements to ensure buildings perform well in both current and future climates.
... These can lead to inaccurate predictions on the spatial scales that are relevant for regional climate change impact assessments, such as studies investigating the impacts on the hydrological cycle (Boé et al., 2009), mountain glaciers (Mutz et al., 2016;Mutz and Aschauer, 2022), air quality (e.g., Colette et al., 2012), and agriculture (e.g., Shahhosseini et al., 2020). Therefore, GCM-based predictions are downscaled by performing dynamical downscaling or statistical downscaling, with empirical-statistical downscaling (ESD) being one type of statistical downscaling (Murphy, 2000;Schmidli et al., 2007;Wilby and Dawson, 2013). ...
The nature and severity of climate change impacts vary significantly from region to region. Consequently, high-resolution climate information is needed for meaningful impact assessments and the design of mitigation strategies. This demand has led to an increase in the application of empirical-statistical downscaling (ESD) models to general circulation model (GCM) simulations of future climate. In contrast to dynamical downscaling, the perfect prognosis ESD (PP-ESD) approach has several benefits, including low computation costs, the prevention of the propagation of GCM-specific errors, and high compatibility with different GCMs. Despite their advantages, the use of ESD models and the resulting data products is hampered by (1) the lack of accessible and user-friendly downscaling software packages that implement the entire downscaling cycle, (2) difficulties reproducing existing data products and assessing their credibility , and (3) difficulties reconciling different ESD-based predictions for the same region. We address these issues with a new open-source Python PP-ESD modeling framework called pyESD. pyESD implements the entire down-scaling cycle, i.e., routines for data preparation, predictor selection and construction, model selection and training, evaluation , utility tools for relevant statistical tests, visualiza-tion, and more. The package includes a collection of well-established machine learning algorithms and allows the user to choose a variety of estimators, cross-validation schemes, objective function measures, and hyperparameter optimization in relatively few lines of code. The package is well-documented, highly modular, and flexible. It allows quick and reproducible downscaling of any climate information, such as precipitation, temperature, wind speed, or even short-term glacier length and mass changes. We demonstrate the use and effectiveness of the new PP-ESD framework by generating weather-station-based downscaling products for precipitation and temperature in complex mountainous terrain in southwestern Germany. The application example covers all important steps of the downscaling cycle and different levels of experimental complexity. All scripts and datasets used in the case study are publicly available to (1) ensure the reproducibility and replicability of the modeled results and (2) simplify learning to use the software package.
... For downscaling precipitation data, a variety of statistical techniques have been employed, including multilinear regression (MLR) [19,20], exponential regression [21], artificial neural network (ANN) [20,22], and random forest model [23,24]. These models are spatially invariant and ignore the spatially heterogeneity of the interactions between precipitation and environmental variables, which may lead to local overfitting and interscale matching errors [25]. ...
Satellite precipitation data downscaling is gaining importance for climatic and hydrological studies at basin scale, especially in the data-scarce mountainous regions, e.g., the Upper Indus Basin (UIB). The relationship between precipitation and environmental variables is frequently utilized to statistically data and enhance spatial resolution; the non-stationary relationship between precipitation and environmental variables has not yet been completely explored. The present work is designed to downscale TRMM (Tropical Rainfall Measuring Mission) data from 2000 to 2017 in the UIB, using stepwise regression analysis (SRA) to filter environmental variables first and a geographically weighted regression (GWR) model to downscale the data later. As a result, monthly and annual precipitation data with a high spatial resolution (1 km × 1 km) were obtained. The study’s findings showed that elevation, longitude, the Normalized Difference Vegetation Index (NDVI), and latitude, with the highest correlations with precipitation in the UIB, are the most important variables for downscaling. Environmental variable filtration followed by GWR model downscaling performed better than GWR model downscaling directly when compared with observation data. Generally, the SRA and GWR method are suitable for environmental variable filtration and TRMM data downscaling, respectively, over the complex and heterogeneous topography of the UIB. We conclude that the monthly non-stationary relationships between precipitation and variables exist and have the greatest potential to affect downscaling, which requires the most attention.
... In order to obtain local-scale climatic information, dynamic downscaling approaches employ regional climate models (RCMs) nested in Fo) TA B L E 3 ETCCDI extreme indices used during the GCM-RCM selection procedure. GCMs (Murphy, 2000). Dynamic downscaling techniques are associated with high computational costs (Sun & Chen, 2012) due to the complex physics-based structure of the RCMs. ...
Morocco is located in a region vulnerable to the impacts of climate change, which can have profound effects on its social, economic and environmental systems. This makes studies aimed at forecasting these impacts in future using climate models particularly important. However, the generally coarse spatial resolution of models, combined with a large number of models, imposes a limitation on the models, allowing the selection of the most appropriate ones for climate change impact assessments in a specific region. In this study, 38 GCMs and GCM‐RCMs from CMIP5 ensemble and CORDEX project were downscaled and bias‐corrected for use in projecting climate change over Morocco under the RCP4.5 and RCP8.5 scenarios. A three‐step sequential process was adopted, involving in that order the selection of models based on: (i) projection of climate means; (ii) projection of climate extremes; and (iii) ability of the models to simulate the baseline climate. Climate projections show precipitation decreases of up to 10% by the beginning of the century, with decreases of more than 20% under RCP8.5 projected by 2100, with the central and northern mountainous regions of the country being the most affected. Seasonal projections showed autumn months likely to experience the greatest decline in precipitation, up to 36.56% by the end of the century. Temperature projections revealed an upward trend in mean, maximum and minimum temperatures, with increases of up to 3°C predicted by mid‐century over most of the country, particularly in the winter months. Our results point to a concerning future, with impacts related to decreased precipitation and increased temperatures expected to be many and varied across the country. Nevertheless, they can help provide a knowledge base for efforts to mitigate and adapt to expected changes.
... Dynamic downscaling applies the output results of a global climate model to a high-resolution numerical model, which requires substantial computing power. Statistical methods are based on observed linear regressions between precipitation and a range of atmospheric variables 1,2 . Statistical methods are often used to estimate daily or monthly precipitation from observed and simulated data rather than to estimate hourly precipitation based on climate model simulations, unlike the dynamic downscaling method. ...
Ensemble simulations of climate models are used to assess the impact of climate change on precipitation, and require downscaling at the local scale. Statistical downscaling methods have been used to estimate daily and monthly precipitation from observed and simulated data. Downscaling of short-term precipitation data is necessary for more accurate prediction of extreme precipitation events and related disasters at the regional level. In this study, we developed and investigated the performance of a downscaling method for climate model simulations of hourly precipitation. Our method was designed to recognize time-varying precipitation systems that can be represented at the same resolution as the numerical model. Downscaling improved the estimation of the spatial distribution of hourly precipitation frequency, monthly average, and 99th percentile values. The climate change in precipitation amount and frequency were shown in almost all areas by using the 50 ensemble averages of estimated precipitation, although the natural variability was too large to compare with observations. The changes in precipitation were consistent with simulations. Therefore, our downscaling method improved the evaluation of the climatic characteristics of extreme precipitation events and more comprehensively represented the influence of local factors, such as topography, which have been difficult to evaluate using previous methods.
... Dynamic downscaling applies the output results of a global climate model to a high-resolution numerical model, which requires substantial computing power. Statistical methods are based on observed linear regressions between precipitation and a range of atmospheric variables 1,2 . Statistical methods are often used to estimate daily or monthly precipitation from observed and simulated data rather than to estimate hourly precipitation based on climate model simulations, unlike the dynamic downscaling method. ...
Ensemble simulations of climate models are used to assess the impact of climate change on precipitation, and require downscaling at the local scale. Statistical downscaling methods have been used to estimate daily and monthly precipitation from observed and simulated data. Downscaling of short-term precipitation data is necessary for more accurate prediction of extreme precipitation events and related disasters at the regional level. In this study, we developed and investigated the performance of a downscaling method for climate model simulations of hourly precipitation. Our method was designed to recognize time-varying precipitation systems that can be represented at the same resolution as the numerical model. Downscaling improved the estimation of the spatial distribution of hourly precipitation frequency, monthly average, and 99th percentile values. The climate change characteristics of precipitation was clearly shown by using the 50 ensemble averages of estimated precipitation, although the natural variability was too large to compare with observations. The changes in precipitation were consistent with simulations. Therefore, our downscaling method improved the evaluation of the climatic characteristics of extreme precipitation events and more comprehensively represented the influence of local factors, such as topography, which have been difficult to evaluate using previous methods.
... • Scénario 1yA : Ce scénario prévoit une augmentation de 50% de la variabilité saisonnière des pluies (Déqué et al., 1998 ;Murphy, 2000 ;Giorgi et al., 2004 ;Christensen et al., 2007, Fowler & Kilsby, 2007Jacob et al., 2014et Caloiero et al., 2018. Le réchauffement climatique induit une augmentation des précipitations mensuelles moyennes d'hiver (novembre à mars, jusqu'à +40%). ...
Nous ne comprenons pas encore complètement le rôle que les caractéristiques physiques des surfacesexercent sur la réponse hydro-sédimentaire des bassins versants aux précipitations. L’objectif de ce travailde thèse est donc de comprendre de quelle manière les flux hydro-sédimentaires, générés par le climat, sontmodulés par les caractéristiques des surfaces continentales, à court et long terme. Cette question a étéabordée en considérant de nombreux bassins versants aux conditions climatiques et aux caractéristiquesphysiques diverses. L'approche retenue est basée sur l’application conjointe de deux outils : la modélisationet les méthodes de traitement du signal. Les méthodes de traitement du signal (fonction d’autocorrélation etspectre de Fourier) ont permis de caractériser la variabilité temporelle des flux hydro-sédimentaire. Elles ontpermis d’établir un lien entre le comportement hydro-sédimentaire des bassins versants et leurs propriétésphysiques et climatiques. Le fonctionnement hydro-sédimentaire de deux cents quarante-trois bassinsversants synthétiques, simulé avec le modèle distribué à base physique CAESAR-Lisflood/CLiDE (Coulhardet al., 2013), a commencé par être analysé. Ces bassins versants, dont les caractéristiques physiques sontparfaitement contrôlées, ont permis une analyse de sensibilité méthodique. Outre la taille et la forme dubassin versant, la procédure de génération de bassins synthétiques, spécialement développée pour ce travailde thèse, a permis d'ajuster cinq paramètres de manière indépendante : la densité de drainage, l'intégralehypsométrique, la pente moyenne du chenal principal, l'occupation des sols et la granulométrie. Uncomportement plus lisse et moins intermittent du flux sédimentaire est mis en évidence pour des bassinsvégétalisés à forte pente, hypsométrie et densité de drainage et à granulométrie fine et homométrique. Encomplément, le modèle a été appliqué à cinq bassins versants réels (le Laval, le Brusquet, l’Orgeval, le Dunet l’Austreberthe) afin d’analyser les processus et les flux hydro-sédimentaires se produisant dans desenvironnements plus complexes. L’analyse des mesures in-situ de flux hydro-sédimentaire disponibles pources bassins a permis de corroborer certains des résultats obtenus pour les bassins synthétiques.Parallèlement, la capacité de CAESAR-Lisflood/CLiDE, après calibration, à reproduire la dynamique hydrosédimentaire des cinq bassins a été évaluée. La version calibrée du modèle a finalement été employée pourexplorer, pour les bassins versants du Dun et de l’Austreberthe, des scénarios de changementsenvironnementaux comme la modification du mode d'occupation des surfaces ou bien la modification dusignal climatique d'entrée. Les résultats montrent une évolution importante des flux hydro-sédimentaires pourles scénarios climatiques testés.
... The highest one-hour temperature being 40.8°C during the one-in-twenty-year EEW across the UK. These values might seem high but are in line with the most recent Met Office results [47] of a peak summertime temperature in 2080 of 41°C in London when using a similar emissions scenario and R = 20. ...
As events like the 2003 European heatwave showed (where 14,000 people died in Paris alone), it is in the extremes of weather, not the mean climate, where much climate change risk lies. Communication with the public, or the testing of natural and human-made environments via simulation, has focused however on the mean situation. To many, a future 2 or even 4 °C rise in mean temperature will seem modest and hence fail to convey the scale of the issue, thereby creating a gap between reality and expectation. Here we use the idea of presenting an audience with a week-long time series of future local extreme weather as a way of bridging this gap. A week has both vernacular currency and covers the length of many heatwaves. We generate UK future weeks in 2030, 2050 and 2080 at a 5 km interval, thereby allowing interested parties to visualise for the first time likely future heatwaves in their locality. Future heatwaves of similar form as the 2003 Paris event are found, but with even higher temperatures, suggesting the likelihood of largescale mortality. We apply the approach to the conditions within a UK home under future heatwaves with return periods of 10–50 years. Conditions far beyond adaptive comfort limits are found. Weather files containing the extreme weeks for 11,326 locations have been prepared and are made available. These will be of use to those trying to explain the likely impacts of climate change, governments setting resilience policy and those using computer modelling.
... Moreover, for the last couple of decades, downscaling methods have been used for future climate projection and long-term weather forecasting [30]. In reality, by coupling GCMs and hydrological models, downscaling methods can be divided into dynamical downscaling and statistical downscaling [31,32]. Statistical downscaling models are computationally convenient and widely used for regional climate change impact evaluation [24,33]. ...
This study presents an evaluation of climate and land-use changes induced impacts on water resources of Multan City, Pakistan. Statistical Down Scaling Model (SDSM) and Geographical Information System (GIS) are used for climate change scenario and spatial analyses. Hydrologic Engineering Center's Hydraulic Modeling System (HEC-HMS) model is used for rainfall-runoff simulation. The investigated results show significant changes in climatological parameters, i.e., an increase in temperature and decrease in precipitation over the last 40 years, and a significant urban expansion is also observed from 2000 to 2020. The increase in temperature and urbanization has reduced the infiltration rate into the soil and increased the runoff flows. The HEC-HMS results indicate that surface runoff gradually increased over the last two decades. Consequently, the depth of the water table in the shallow aquifer has declined by about 0.3 m/year. Projected climate indices stipulate that groundwater depletion will occur in the future. Arsenic levels have exceeded the permissible limit owing to unplanned urban expansion and open dumping of industrial effluents. The results can help an efficient water resources management in Multan.
... Due to climate change and rising air temperatures within Europe, cold winters are becoming infrequent and even rare (Murphy, 2000;Jacob et al., 2014). Despite this, extremely cold winters and their effects on wood boring insects should not be overlooked (Boggs, 2016;Forrest, 2016;Lehmann et al., 2020). ...
The capability of a non-native species to withstand adverse weather is indicative of its establishment in a novel area. An unusually cold winter of 2016/2017 that occurred in the West Carpathians of Slovakia and other regions within Europe provided an opportunity to indirectly assess survival of the invasive ambrosia beetle Xylosandrus germanus (Coleoptera: Curculionidae, Scolytinae). We compared trap captures of this species in the year preceding and succeeding the respective cold winter. Ethanol-baited traps were deployed in 24 oak dominated forest stands within the southern and central area from April to August 2016, and again from April to August 2017 to encompass the seasonal flight activity of X. germanus and to get acquainted with temporal changes in the abundance of this species in these two distant areas. Dispersing X. germanus were recorded in all surveyed stands before and after the aforementioned cold winter. Their total seasonal trap captures were lower in the southern area following low winter temperatures, but remained similar in the central area. Our results suggest that X. germanus can withstand adverse winter weather in oak dominated forests of the West Carpathians within altitudes of 171 and 450 m asl. It is likely that minimum winter temperatures will not reduce the establishment or further spread of this successful invader in forests in Central Europe.
... In addition, the ocean-atmosphere coupled model of Hadley Centre Coupled Model version 3 (Had CM3) proposed by the Hadley Centre for Climate Prediction and Research was used to conduct downscaling of the main wheat production area of the piedmont plain area in Hebei Province and to generate its future climate. According to studies, the SDSM model can accurately simulate a future climate [23][24][25]. In this paper, the future climate prediction for the piedmont plain area in Hebei Province was mainly based on local historical meteorological data, global atmosphere re-analysis daily data, and global atmosphere circulation model. ...
The statistical downscaling tool of a statistical downscaling model (SDSM) to generate the future climate of the piedmont plain area in Hebei Province for a 30-year period. The Xinji city was selected as a typical example of this area. The crop growth model of the decision support system for agrotechnology transfer (DSSAT) was adopted to estimate the changing trends of the water footprint of winter wheat production in this area under future climate conditions, and to obtain the optimal irrigation scheme of winter wheat for an ‘acceptable yield’. According to the test results, all the temperature indices of the piedmont plain area increased in the two selected future climate scenarios. In addition, the effective precipitation exhibited a slight decrease in scenario A2 and a remarkable increase in scenario B2. Both the total water footprint and green water footprint increased. A yield of 500 kg per mu was taken as the acceptable yield. In scenario A2, to achieve this acceptable yield, it was required to irrigate once in the jointing period with an irrigation rate of 105 mm. In scenario B2, one-time irrigation with an amount of 85 mm was sufficient to reach the acceptable yield.
... Several studies have been carried out to compare dynamical and statistical downscaling methods for various atmospheric phenomena over different regions worldwide. Kidson and Thompson (1998) Similarly, Murphy (1999) found that a regression model for monthly temperature and precipitation anomalies has a comparable performance to a RCM for stations in Europe, but scenarios developed from statistical and dynamical methods differed significantly (Murphy, 2000). Schmidli et al (2007) tested statistical and dynamical methods over European Alps region and stated that none of these methods is superior and performance varies significantly from region to region and season to season. ...
Reliable high resolution weather forecasts are in increasing demand to the
governments, industry, traffic, media, farming community and risk management
departments of most of the countries worldwide (Majewski, 1997). The Indian Summer
Monsoon Rainfall (ISMR) forecast for two weeks to one month in advance is one of the
most challenging task to the scientific community due to complex interactions between
land-air-sea and also small scale convective activities with large scale flow. The
summer monsoon season (June to September) contributes more than 70% of the
annual rainfall over India (Parthasarathy et al. 1994). The active equatorial intraseasonal oscillations (ISO) enhanced the convective activity over the north Indian
Ocean and moves northward to the Indian landmass. The onset of Indian Summer
Monsoon (ISM) over the southern tip of Indian peninsula marks the beginning of rainfall
season and ending of hot summer over the India. Though onset of ISM over Kerala on
1st June is considered the normal date for onset of ISMR according to India
Meteorological Department (IMD), it generally occurs during end of May or early June.
After the onset of ISM over Kerala, the monsoonal system marches towards the north
associated with rainfall. One of the important features of monsoon is the monsoon
trough, which, in general passes through the northern part of India such as Punjab,
Rajasthan, Uttar Pradesh, Bihar, West Bengal & north of Bay of Bengal. The fluctuation
of the ISMR mainly depends on the oscillation of the monsoon trough. In the active phase of monsoon the trough shifts to south of its mean position causing good amount
of rainfall over the country; on the other hand when it shifts to foothills of Himalaya, the
rainfall reduces over the central parts of the country and monsoon break occurs. In the
second half of September strength of monsoonal westerlies gradually decreases
leading to the withdrawal of southwest monsoon.
The Indian summer monsoon plays a crucial role for the agro economic country
like India. Major parts of the Indian population (more than 70%) explicitly depend on the
agriculture and their economies are highly dependant on the crop productions during
the summer monsoon season. Though the monsoonal system is a regular
phenomenon, but the Indian summer monsoon has a large abnormality in the global
climate systems. This abnormality varies from region to region and time to time. The
advance intimation of likely behavior of monthly and seasonal rainfall helps the farmer
to avail the opportunities and to make decisions that could enhance the farm
productivity and maximize returns or minimize the loss. Among various types of
forecasts made for different temporal scales viz. short range, medium range and long or
extended range, the extended range forecasts are highly valuable to the farming
community, government, industry for long term planning, decision making, management
and mitigation. The extended range prediction of monsoon rainfall over smaller regions
such as met-subdivision scale (Parthasarathy et al. 1994) is one of the challenging
tasks to the scientific communities.
The forecast products from General Circulation Models (GCMs) are being
effectively used all over the world for generating seasonal forecasts. The GCMs are the
important tools to simulate the atmospheric circulation. Present day, most of the GCMs
are coupled with oceanic models to take into account the interactions between the
oceans and the atmosphere. Although these numerical tools are required to understand
complex interactions between land-ocean-atmosphere systems globally, they are
computationally intensive and then, can only produce relatively low spatial resolution
simulations which in turn provide data in coarse resolutions on model spatial grid.
Therefore, direct application of GCMs output is often inadequate because of their limited representation of mesoscale atmospheric processes, topography and land sea
distribution in GCMs (Cohen 1990; von Storch et al., 1993). Consequently, the
performance of these model are poor in capturing small scale physical processes which
drive some important local/regional surface variables and their high resolution
properties such as precipitation (frequency of occurrence and intensity) and its strong
variability (Wood et al, 2004). Also, it is difficult to compare GCMs output to local
present observations (Vrac et al., 2007) and even more for extreme climate/weather
events (Vrac and Naveau, 2007) due to coarse resolution of GCMs. However,
comparison between local observations with the model simulation output is essential to
understand physical and dynamical processes of the atmospheric circulation in local
scale. In order to overcome these scale issues, it is important to reproduce information
from GCMs output in higher resolutions for better understanding the regional/local
weather/climatic phenomena though, this reproduced information for specific
geographic location may not coincide with the model grid.
A number of methods are used to convert GCMs output to required region. The
simplest method is to consider the nearby model grid points as the representative points
of the target region. This method often is not able to reproduce realistic features since
the representative points are in general, far away from the targeted region and the
surface characteristics of the representative points are also different. To improve the
nearest point forecast, a number of procedures are present that fall in general
calibration and downscaling techniques (Barnston and Smith, 1996; Goddard et al.,
2001; Landman and Goddard, 2002; Stephenson et al., 2005). These downscaling
techniques work as the bridge between climate forecasts and weather (Wilby and
Wigley, 1997; Huth and Kysely, 2000). In other words, downscaling is a technique which
links the state of some variables representing large space to the state of some variables
representing a much smaller space (Benestad et al., 2008). The field of downscaling is
divided into two approaches namely a) “Dynamical downscaling” based on nesting of
high-resolution regional climate models (RCMs) to simulate finer scale physical
processes consistent with large scale weather evaluation prescribed from a GCM
(Giorgi et al., 2001; Mearns et al., 2004; Lim el al., 2007) and b) “Statistical downscaling” adopts statistical relationships between the regional climate and statistical
characteristics of desired fields from the coarse resolution of GCM data (von Storch et
al., 1993; Wilby et al., 2004; Goodess et al., 2007). The downscaled high resolution
data can be used for forecast and as input into other types of numerical simulation tools
such as hydrological, agricultural and ecological models. Therefore, use of proper
downscaling techniques is the key issue for extended range prediction systems.
A brief overview has been given in 10.2 on the Extended Range Forecast
System (ERFS) and its present status with skill evaluated by various scientists
worldwide. Descriptions and methodologies of different downscaling techniques for
ERFS have been discussed in 10.3. Preliminary efforts with some experimental results
have been given in 10.4. Finally, the conclusions of this study have been presented in
10.5.
... To provide the required weather forcing data for building energy simulations at the appropriate scale, many approaches have been taken. These address different issues related to the source of data and spatial scale (e.g., dynamic modelling/downscaling [28]), and the lack of data (e.g. stochastic weather generator [29]; morphing [30]). ...
Despite building energy use being one of the largest global energy consumers, building energy simulations rarely take the actual local neighbourhood scale climate into account. A new globally applicable approach is proposed to support buildings energy design. ERA5 (European Centre Reanalysis version 5) data are used with SUEWS (Surface Urban Energy and Water balance Scheme) to obtain (in this example case) an urban typical meteorological year (uTMY) that is usable in building energy modelling. The predicted annual energy demand (heating and cooling) for a representative four-storey London residential apartment using uTMY is 6.9% less (cf. conventional TMY). New vertical profile coefficients for wind speed and air temperature in EnergyPlus are derived using SUEWS. EneryPlus simulations with these neighbourhood scale coefficients and uTMY data, predict the top two floors have ~10% larger energy demand (cf. the open terrain coefficients with uTMY data). Vertical variations in wind speed have a greater impact on the simulated building energy than equivalent variations in temperature. This globally appliable approach can provide local meteorological data for building energy modelling, improving design for the local context through characterising the surrounding neighbourhood.
... Thus, every different RCMs is expected to give a variety of different, so-called ensemble predictions. The pros and cons of these two fundamental downscaling approaches have been widely discussed (Murphy, 1998(Murphy, , 2000Wilby & Wigley, 1997) as well as their impacts on the resulting simulations (Hellstrom et al., 2001;Haylock et al., 2006;Schmidli et al., 2006). Xu et al. (2005) and Fowler et al. (2007), reviewed climate data downscaling methods and techniques, including combination of them, for hydrological modeling, concluding that there is generally no clear evidence to propose a specific downscaling technique or method (dynamic or statistical) as better for use in hydrological and water resources management studies but the combination of both downscaling methodologies is suggested for climate change impact studies (Turco et al., 2011) while Teutschbein & Seibert, 2010 suggest that ensembles approach should perform better than using the single RCM approach. ...
This thesis deals with the groundwater resources management of Kastoria basin of Western Macedonia, Greece though the use of FeFLOW v.7.2 groundwater flow modelling package in con-junction with ARCGIS v.10.6 and MATLAB, which were used to manage all the data used in this thesis and also to prepare the input data in the structure and format required by FeFLOW. Kastoria basin houses an extensive alluvial aquifer system, which is of fluvio-torrential origin and consists of alternating layers of coarse and fine-grained layers of small spatial and vertical extent. Precipita-tion is the main recharge element of the aquifer, where a representative annual value for the former is 770 mm. The modelled area consisted of the main body of the alluvial aquifer as a 100 m thick layer. Such an environment provides an inherit uncertainty regarding to the hydraulic properties, to alleviate a part of this uncertainty a stochastic approach used through PEST. The groundwater flow model was successfully calibrated first manually and then finally by PEST in all aspects, yielding RMSE=0.758 for the entire transient simulation. The water balance elements produced resemble those presented by previous studies. To further assess the evolution of the groundwater resources at Kastoria basin precipitation and temperature data of regional climate models were extracted from the EURO-CORDEX project, which provides the needed high spatial resolution (EUR-11) to spa-tially represent the future climate condition at the study area, for three 20 year long sub-periods be-tween 2019-2078. After bias-correction of the 53 extracted EUR-11 region climate models, ten of those were selected and used further. Bias-correction was performed with the linear scaling method and monthly observed data of the 1986-2005 period. This showed improvements at the bias-corrected RCM data in comparison to raw data in representing observed patterns. The analysis pre-sented that, as mean annual values, precipitation will reduce by 7.23% while temperature will in-crease by 1.23°C throughout the projected period. Based on the selected RCM data, ten groundwa-ter scenarios were simulated by feeding the bias-corrected RCM data to the calibrated groundwater flow model. In summary of the results of the former, the groundwater level will practically remain the same. Kastoria lake water level will drop, if left unchecked, due to higher evaporation, reduced precipitation and lower lateral flow from Kastoria alluvial aquifer reduction while surface runoff will slightly increase. This will lead to the reduction of the total runoff or discharge through the Gkioli stream by a mean annual value of 8.22 x 106 m3 or 14.79%. Finally, options to counter this change at the water balance of the basin were also proposed.
... 3. One major limitation of traditional statistical downscaling is its accuracy of extrapolation 58,[60][61][62] . A fundamental assumption of the application of statistical downscaling is that the derived predictor/predictand relationship based on local observations remains valid outside of the training region and/or in a changing future climate. ...
Effective urban planning for climate-driven risks relies on robust climate projections specific to built landscapes. Such projections are absent because of a near-universal lack of urban representation in global-scale Earth system models. Here, we combine climate modelling and data-driven approaches to provide global multi-model projections of urban climates over the twenty-first century. The results demonstrate the inter-model robustness of specific levels of urban warming over certain regions under climate change. Under a high-emissions scenario, cities in the United States, Middle East, northern Central Asia, northeastern China and inland South America and Africa are estimated to experience substantial warming of more than 4 K—larger than regional warming—by the end of the century, with high inter-model confidence. Our findings highlight the critical need for multi-model global projections of local urban climates for climate-sensitive development and support green infrastructure intervention as an effective means of reducing urban heat stress on large scales.
... The essence of the statistical downscaling model is to establish a significant relationship between large-scale meteorological circulation factors and regional climatic variables using long-term observation data (Hellström and Chen 2003;Murphy 2000). The output of large-scale, low-temporal resolution GCM or regional climate model (RCM) is converted into a small-scale and high-resolution model using a statistical or dynamic method, which is an effective way to solve the scale mismatch problem Maraun et al. 2010). ...
Reference crop evapotranspiration (ET0) is of great importance in assessing the potential impacts of climate changes on hydrological cycles and the global energy balance. The spatiotemporal change of ET0 and the drought response over Poyang Lake watershed of China from 2011 to 2100 are the main concern in this work. Based on the meteorological data and the output of the general circulation model (GCM) from the Coupled Model Intercomparison Project Phase 5 (CMIP5), we used the Penman-Monteith formula and downscaling model to calculate the history and future ET0 in Poyang Lake watershed, respectively. Major results are drawn as follows. First, the annual average ET0 decreased during 1961-2014 and the average ET0 of the basin is high in the north and south, but low in the middle. The ET0 was most dominated by sunshine duration in the spring, summer, and fall and by relative humidity in the winter. Second, the downscaling model has a good simulation effect, and the GCM data-downscaling simulation results are significantly improved after the deviation correction. Third, under the representative concentration pathway (RCP) 4.5 and RCP 8.5 scenarios, ET0 in the Poyang Lake watershed will increase over the next three periods, with the middle future (2041-2070) as the largest increase period. The spatial distribution of ET0 is generally high in the east and low in the west. Fourth, under the RCP 8.5 scenario, the drought index (DI) of the watershed showed an increasing trend, the seasonal distribution of DI is fall > summer > spring > winter, and the Ganjiang River subbasin will be the key prevention area for future drought risks. The results can provide basic data support for the optimal management of water resources and scientific response to the impact of climate change on agricultural production in the watershed for associated policymakers and stakeholders.
... Dynamical downscaling and ESD should support each other (Oshima et al., 2002). Murphy (2000) have argued that the confidence in estimates of regional climate change will only be improved by the convergence between dynamical and statistical predictions or by the emergence of clear evidence supporting the use of a single preferred method. ...
Empirical-statistial downcaling,which is a range of analysis techniques for infering theeffect of a global warming on the local climate.
... Since it is based on differences and ratios between current and future simulated climates, the DM is known for bias removal, assuming that biases are systematic (Bosshard et al. 2011;Lenderink et al. 2007b). This study follows the recommendations of previous studies (Murphy 1999(Murphy , 2000Diaz-Nieto and Wilby 2005) through the convergence of the two downscaling techniques by using the DM alongside statistical downscaling (SD) techniques, thus using their advantages in a complementary way. By definition, the DM applies relative changes obtained from global or RCM's to the local observations. ...
This dataset improves on previous products in its spatial resolution, spatial extent and time period covering the Black Sea catchment (BSC). The spatial prediction of daily datasets was performed using the integrated nested Laplace approximation (INLA) methodology. The results show that for minimum and maximum temperature, the model with the elevation and distance to shorelines predictors is the best fitted model. The best fitted model for precipitation is obtained with the elevation predictor. The downscaling of climate change scenarios is based on HIRHAM regional climate model (RCM) from the European project PRUDENCE. The downscaling was made by means of a modified delta method. The modified delta method applied in this study explicitly considers the spatial differences of the climate scenarios and the monthly variability. For each grid point, the delta method is applied according to the rank order of values in the monthly distribution of the closest RCM grid point. The results show that the delta method gives satisfying results when considering the monthly variability. Impacts on minimum temperature, maximum temperature, precipitation, number of days without precipitation, dry spell length, number and length of days above maximum temperature above 30∘C, under conditions of climate change are also examined for this region.
... Another issue is the coarse resolution of the NWP forecast fields that are often not detailed enough to be applied over the relatively small reservoir catchments. To address this scale limitation, dynamic downscaling technique can be used to resolve the atmospheric processes at finer spatial scales [30][31][32]. To the best of our knowledge, there has not been any study to explore the value of dynamically downscaled NWP based-forecasts specifically for hydropower maximization. ...
This study explores the maximization of hydropower generation by optimizing reservoir operations based on short-term inflow forecasts derived from publicly available numerical weather prediction (NWP) models. Forecast fields from the NWP model of Global Forecast System (GFS) were used to force the Variable Infiltration Capacity (VIC) hydrologic model to forecast reservoir inflow for 1-16 days lead time. The optimization of reservoir operations was performed based on the forecast of inflow. The concept was demonstrated for two dams in the United States. Results showed that a significantly greater amount additional hydroelectric energy benefit can be derived consistently than the traditional operations without optimization and weather forecasts. Goals of flood control and dam safety were also not compromised when exploring opportunities for hydropower maximization. An alternate data-based technique was also demonstrated to improve the forecasting skill and efficiency. The study clearly underscores the additional value of weather forecasts that are available publicly and globally from NWP models for any dam location for hydropower maximization. Given the on-going effort to coordinate strategies for sustainable energy production from renewable energy sources, it is timely that this concept be expanded further to current hydropower dam sites around the world.
... Several review papers deal with downscaling concepts, their prospects and limitations, e.g. Hewitson and Crane (1996), Wilby and Wigley (1997), Gyalistras et al. (1998), Murphy (1999Murphy ( , 2000), Zorita and von Storch (1999), . ...
... There have been many statistical downscaling methods proposed by past researchers. The simplest form is linear regression, which estimates target predictand using an optimized linear combination of local circulation features [19][20][21]. The features are usually represented as the leading Principal Components (PC) of moisture, pressure, and wind field. ...
Precipitation downscaling is widely employed for enhancing the resolution and accuracy of precipitation products from general circulation models (GCMs). In this study, we propose a novel statistical downscaling method to foster GCMs' precipitation prediction resolution and accuracy for the monsoon region. We develop a deep neural network composed of a convolution and Long Short Term Memory (LSTM) recurrent module to estimate precipitation based on well-resolved atmospheric dynamical fields. The proposed model is compared against the GCM precipitation product and classical downscaling methods in the Xiangjiang River Basin in South China. Results show considerable improvement compared to the European Centre for Medium-Range Weather Forecasts (ECMWF)-Interim reanalysis precipitation. Also, the model outperforms benchmark downscaling approaches, including (1) quantile mapping, (2) the support vector machine, and (3) the convolutional neural network. To test the robustness of the model and its applicability in practical forecasting, we apply the trained network for precipitation prediction forced by retrospective forecasts from the ECMWF model. Compared to the ECMWF precipitation forecast, our model makes better use of the resolved dynamical field for more accurate precipitation prediction at lead times from 1 day up to 2 weeks. This superiority decreases with the forecast lead time, as the GCM's skill in predicting atmospheric dynamics is diminished by the chaotic effect. Finally, we build a distributed hydrological model and force it with different sources of precipitation inputs. Hydrological simulation forced with the neural network precipitation estimation shows significant advantage over simulation forced with the original ERA-Interim precipitation (with NSE value increases from 0.06 to 0.64), and the performance is only slightly worse than the observed precipitation forced simulation (NSE = 0.82). This further proves the value of the proposed downscaling method, and suggests its potential for hydrological forecasts.
... Many studies have been conducted to compare the two downscaling methods (Haylock et al. 2006;Gutmann et al. 2012;Flaounas et al. 2013). Murphy (2000) and Spak et al. (2007) produced present climate and future climate change scenarios using both methods. They found that the methods were equally robust in estimation of the present climate. ...
Mediterranean region is identified as a primary hot-spot for climate change due to the expected temperature and rainfall changes. Understanding the potential impacts of climate change on the hydrology in these regions is an important task to develop long-term water management strategies. The aim of this study was to quantify the potential impacts of the climate changes on local hydrological quantities at the Goksu Watershed at the Eastern Mediterranean, Turkey as a case study. A set of Representative Concentration Pathways (RCP) scenarios were used as drivers for the conceptual hydrological model J2000 to investigate how the hydrological system and the underlying processes would respond to projected future climate conditions. The model was implemented to simulate daily hydrological quantities including runoff generation, Actual Evapotranspiration (AET) and soil-water balance for present (2005–2015) and future (up to 2100). The results indicated an increase of both precipitation and runoff throughout the region from January to March. The region showed a strong seasonally dependent runoff regime with higher flows during winter and spring and lower flows in summer and fall. The study provides a comparative methodology to include meteorological-hydrological modelling integration that can be feasible to assess the climate change impacts in mountainous regions.
... As seen in Table 3 and similar to other studies (Murphy 1999(Murphy , 2000Maurer and Hidalgo 2008), the DD method required more RAM (32GB) and hard drive space (150GB, due to precise settings such as boundary conditions and convection scheme) than the SD Delta method (RAM 3GB and hard 3GB). RAM is an acronym for Random Access Memory. ...
Precise evaluations of climate model precipitation outputs are valuable for making decisions regarding agriculture, water resource, and ecosystem management. Many downscaling techniques have been developed in the past few years for projection of weather variables. We need to apply dynamical and statistical downscaling (DD and SD) to bridge the gap between the coarse resolution general circulation model (GCM) outputs and the need for high-resolution climate information over a semi-arid region. We compare the requirements of DD (RegCM4) and SD (Delta) approaches, evaluate the historical run of NNRP1 data in comparison with station data, and analyze the changes in wet days and precipitation values through both methods during 1990-2010. In this study, we did not want to use prediction data under different scenarios of climate change, and we have just applied observed data to assess the amount of precise of NNRP1 data, over the observed period. SD method requires less time and computing power than DD. The DD approach performs better over the evaluation period according to efficiency criteria. In general, the Pearson correlation in DD with observation data in evaluation period was higher than (r>0.72 and R 2 >0.52) SD (r>0.65 and R 2 >0.41) over three study stations. Similarly, MAE and NSE show better results from DD relative to SD. SD underestimates the number annual mean wet-days for all three stations examined. DD overestimates a number of annual mean wet-days, but with less deviation from the observed mean. (https://link.springer.com/article/10.1007%2Fs13143-019-00112-1)
... Climatic studies have worked on statistical downscaling concepts, their projections and their limitations (Wilby and Wigley, 1997;Zorita and von Storch, 1997;Murphy, 1999Murphy, , 2000Widmann and Bretherton, 2000). The advantages of this technique, as aforementioned, include computationally inexpensive and can be easily applied for analysing output data of different GCM trials. ...
Choosing downscaling techniques is crucial in obtaining accurate and reliable climate change predictions, allowing for detailed impact assessments of climate change at regional and local scales. Traditional statistical methods are likely inefficient in downscaling precipitation data from multiple sources or complex data patterns, so using deep learning, a form of nonlinear models, could be a promising solution. In this study, we proposed to use deep learning models, the so‐called long short‐term memory and feedforward neural network methods, for precipitation downscaling for the Vietnamese Mekong Delta. Model performances were assessed for 2036–2065 period, using original climate projections from five climate models under the Coupled Model Intercomparison Project Phase 5, for two Representative Concentration Pathway scenarios (RCP 4.5 and RCP 8.5). The results exhibited that there were good correlations between the modelled and observed values of the testing and validating periods at two long‐term meteorological stations (Can Tho and Chau Doc). We then analysed extreme indices of precipitation, including the annual maximum wet day frequency (Prcp), 95th percentile of precipitation (P95p), maximum 5‐day consecutive rain (R5d), total number of wet days (Ptot), wet day precipitation (SDII) and annual maximum dry day frequency (Pcdd) to evaluate changes in extreme precipitation events. All the five models under the two scenarios predicted that precipitation would increase in the wet season (June–October) and decrease in the dry season (November–May) in the future compared to the present‐day scenario. On average, the means of multiannual wet season precipitation would increase by 20.4 and 25.4% at Can Tho and Chau Doc, respectively, but in the dry season, these values were projected to decrease by 10 and 5.3%. All the climate extreme indices would increase in the period of 2036–2065 in comparison to the baseline. Overall, the developed downscaling models can successfully reproduce historical rainfall patterns and downscale projected precipitation data.
... To date, there are many methods proposed for statistical downscaling using different techniques such as stochastic weather generators [2][3][4][5], weather typing method [6][7][8], resampling methods [9][10][11], and regression methods. The regression methods are attracting more attention and preferred to apply due to their flexibility and straightforwardness. ...
A statistical downscaling approach for improving extreme rainfall simulation was proposed to predict the daily rainfalls at Shih-Men Reservoir catchment in northern Taiwan. The structure of the proposed downscaling approach is composed of two parts: the rainfall-state classification and the regression for rainfall-amount prediction. Predictors of classification and regression methods were selected from the large-scale climate variables of the NCEP reanalysis data based on statistical tests. The data during 1964–1999 and 2000–2013 were used for calibration and validation, respectively. Three classification methods, including linear discriminant analysis (LDA), random forest (RF), and support vector classification (SVC), were adopted for rainfall-state classification and their performances were compared. After rainfall-state classification, the least square support vector regression (LS-SVR) was used for rainfall-amount prediction for different rainfall states. Two rainfall states (i.e., dry day and wet day) and three rainfall states (dry day, non-extreme-rainfall day, and extreme-rainfall day) were defined and compared for judging their downscaling performances. The results show that RF outperforms LDA and SVC for rainfall-state classification. Using RF for three-rainfall-states classification and LS-SVR for rainfall-amount prediction can improve the extreme rainfall downscaling.
... Hence it does not seems to be sufficient for precipitation simulation because precipitation mechanisms are based on thermodynamics and vapor content. Therefore, humidity has recently been used to downscale precipitation (Wilby and Wigley, 1997;Murphy, 2000;Beckmann and Adri Buishand, 2002;Cavazos and Hewitson, 2005) assessed 29 NCEP reanalysis variables by applying an artificial neural network downscaling method in 15 locations. Geopotential heights and specific humidity predictors had good correlation with predictands in all locations and seasons. ...
This book is PhD dissertation conducting at Technical University of Munich.
The copyright is belong to the first author and TUM Library has first right to publish it in the media system of TUM via this link
https://mediatum.ub.tum.de/?id=1449466
or https://nbn-resolving.org/urn/resolver.pl?urn:nbn:de:bvb:91-diss-20190115-1449466-1-7
... Along with the advancement of general circulation models (GCMs), many precipitation PP methods have been developed. The simplest form is linear regression, which estimates precipitation using an optimized linear combination of the local circulation features ( Hannachi et al., 2007;Jeong et al., 2012;Li & Smith, 2009;Murphy, 2000). The predictors usually consist of ...
Precipitation process is generally considered to be poorly represented in numerical weather/climate models. Statistical Downscaling (SD) methods, which relate precipitation with model resolved dynamics, often provide more accurate precipitation estimates compared to model’s raw precipitation products. We introduce the convolutional neural network (CNN) model to foster this aspect of SD for daily precipitation prediction. Specifically, we restrict the predictors to the variables that are directly resolved by discretizing the atmospheric dynamics equations. In this sense, our model works as an alternative to the existing precipitation-related parameterization schemes for numerical precipitation estimation. We train the model to learn precipitation-related dynamical features from the surrounding dynamical fields by optimizing a hierarchical set of spatial convolution kernels. We test the model at 14 geogrid points across the contiguous United States. Results show that provided with enough data, precipitation estimates from the CNN model outperform the reanalysis precipitation products, as well as SD products using linear regression, nearest neighbor, random forest, or fully-connected deep neural network. Evaluation for the test set suggests that the improvements can be seamlessly transferred to numerical weather modeling for improving precipitation prediction. Base on the default network, we examine the impact of the network architectures on model performance. Also, we offer simple visualization and analyzing approaches to interpret the models and their results. Our study contributes to the following two aspects: Firstly, we offer a novel approach to enhance numerical precipitation estimation; secondly, the proposed model provides important implications for improving precipitation-related parameterization schemes using a data-driven approach.
... Our analyses indicate that X. germanus is also spreading vertically into higher altitudes. Climate change within Europe [119,120], and specifically mild winters, could be assisting the spread of X. germanus. Similarly, freeze stress events following mild winters could also increase the availability of suitable host material and lead to an increased incidence of attacks [97,98]. ...
The black timber bark beetle Xylosandrus germanus (Blandford) is an invasive ambrosia beetle that originates from Southeast Asia and has become successfully established within Europe and North America. Herein, we provide a review of the spread and distribution of this tree and timber pest species across Europe, before and after 2000, along with a review of its habitat preferences. Since the spread of X. germanus across Europe has accelerated rapidly post-2000, emphasis is placed on this period. X. germanus was first recorded in Germany in 1951 and since then in 21 other European countries along with Russia. Ethanol-baited traps were deployed in oak, beech, and spruce forest ecosystems in the Western Carpathians, Central Europe, Slovakia, to characterize the distribution and habitat preferences of this non-native ambrosia beetle. Captures of X. germanus within Slovakia have been rising rapidly since its first record in 2010, and now this species dominates captures of ambrosia beetles. X. germanus has spread throughout Slovakia from south-southwest to north-northeast over a period of 5–10 years, and has also spread vertically into higher altitudes within the country. While living but weakened trees in Europe and North America are attacked by X. germanus, the greatest negative impact within Slovakia is attacks on recently felled logs of oak, beech and spruce trees, which provide high quality timber/lumber. We suggest that the recent rapid spread of X. germanus in Central Europe is being facilitated by environmental changes, specifically global warming, and the increasing frequency of timber trade. Recommendations for the management of X. germanus in forest ecosystems are proposed and discussed, including early detection, monitoring, sanitary measures, etc.
... Therefore a second downscaling step is required to introduce a more realistic and greater degree of spatial variability. There are two main approaches to downscaling climate model data: statistical methods (e.g., Wilby and Wigley, 1997;Wilby et al., 1998;Wood et al., 2002;Bouwer et al., 2004) and dynamical approaches using regional circulation models (RCMs) nested within the coarser climate model (e.g., Cocke and LaRow, 2000;Kim et al., 2000;Murphy, 2000;Yarnal et al., 2000;Wood et al., 2002). The results of these two approaches have been found to have similar levels of skill (e.g. ...
We have coupled a climate model (ECBilt-CLIO-VECODE) and a hydrological model (STREAM) offline to simulate palaeodischarge of nineteen rivers (Amazon, Congo, Danube, Ganges, Krishna, Lena, Mackenzie, Mekong, Meuse, Mississippi, Murray-Darling, Nile, Oder, Rhine, Sacramento-San Joaquin, Syr Darya, Volga, Volta, Zambezi) for three time-slices: Early Holocene (9000-8650 BP), Mid-Holocene (6200-5850 BP) and Recent (1750-2000 AD). To evaluate the model's skill in retrodicting broad changes in mean palaeodischarge we have compared the model results with palaeodischarge estimates from multi-proxy records. We have compared the general trends inferred from the proxy data with statistical differences in modelled discharge between the three periods, thereby developing a technique to assess the level of agreement between the model and proxy data. The quality of the proxy data for each basin has been classed as good, reasonable or low. Of the model runs for which the proxy data were good or reasonable, 72% were in good agreement with the proxy data, and 92% were in at least reasonable agreement. We conclude that the coupled climate-hydrological model performs well in simulating mean discharge in the time-slices studied. The discharge trends inferred from the proxy and model data closely follow latitudinal and seasonal variations in insolation over the Holocene. For a number of basins for which agreement was not good we have identified specific mechanisms which could be responsible for the discrepancy, primarily the absence of the Laurentide ice sheet in our model. In order to use the model in an operational sense within water management studies it would be useful to use a higher spatial resolution and a daily time-step.
... Uncertainties about the effects of climate change on ecosystems highlight the need for thorough studies of species and communities (Campbell et al., 2005;Bale & Hayward, 2010;Bellard et al., 2012). The predictions of climate change within Europe for 2080 and 2100 show a robust and signifi cant increase in both January and annual mean temperatures (Murphy, 2000;Jacob et al., 2014). In the continental climate of Central Europe ladybirds are expected to experience increasing fl uctuation in winter weather both within and between years. ...
We surveyed ladybirds (Coleoptera: Coccinellidae) in 10 stands of Scots pine (Pinus sylvestris), all monoculture stands 5–100 years old, in western Slovakia, Central Europe, over two successive periods, October 2013 – March 2014 and October 2014 – March 2015. The winter in each period was exceptionally mild. Ladybirds were collected from the lower branches of pine trees using beating trays and were present in 61% of the 1040 samples (one sample containing ladybirds from 20 branches, 1 m long each). In total 3965 individuals of 20 species were recorded. Non-conifer dwelling species associated with broadleaved trees or herbaceous plants prevailed (45% of species), followed by conifer specialists (40%) and generalists (15%). Although 13 species were found at least in one winter month, December, January or February, only four of them, Exochomus quadripustulatus, Coc-cinella septempunctata, Harmonia axyridis and Hippodamia variegata, were recorded continually during both winters. The number of species, the abundance of all ladybirds and the abundance of dominant species (E. quadripustulatus, C. septempunctata and H. axyridis)decreased from late autumn towards winter and remained lowest during this most adverse time of the year for lady-birds. Overwintering species assemblages of ladybirds changed over time and varied with age of pine stand. Our results suggest that Scots pine in Central Europe supports species rich assemblages of ladybirds from late autumn to early spring and, being widely distributed, it could be suited to winter surveying of ladybirds at large spatial scales to reveal behavioural and ecological responses of species to changing weather or different climates
... Dynamical downscaling, through the use of regional climate models that apply the full physics of global climate models at a fine-scale (Murphy, 2000), have the advantage that they can generate internally consistent data for variables and represent synoptic systems. ...
Climate is of fundamental importance to the ecology and evolution of all organisms. However, studies of climate–organism interactions usually rely on climate variables interpolated from widely spaced measurements or modelled at coarse resolution, whereas the conditions experienced by many organisms vary over scales from millimetres to metres.
To help bridge this mismatch in scale, we present models of the mechanistic processes that govern fine‐scale variation in near‐ground air temperature. The models are flexible (enabling application to a wide variety of locations and contexts), can be run using freely available data and are provided as an R package.
We apply a mesoclimate model to the Lizard Peninsula in Cornwall to provide hourly estimates of air temperature at resolution of 100 m for the period Jan‐Dec 2010. A microclimate model is then applied to a 1 km ² region of the Lizard Peninsula, Caerthillean Valley (49.969°N, 5.215°W), to provide hourly estimates of near‐ground air temperature at resolution of 1 m ² during May 2010.
Our models reveal substantial spatial variation in near‐ground temperatures, driven principally by variation in topography and, at the microscale, by vegetation structure. At the meso‐scale, hours of exposure to air temperatures at 1 m height in excess of 25°C ranged from 23 to 158 hr, despite this temperature never being recorded by the weather station within the study area during the study period. At the micro‐scale, steep south‐facing slopes with minimal vegetation cover experienced temperatures in excess of 40°C.
The microclima package is flexible and efficient and provides an accurate means of modelling fine‐scale variation in temperature. We also provide functions that facilitate users to obtain and process a variety of freely available datasets needed to drive the model.
... A key feature in climate change impact studies is the choice of best methods for downscaling of the outputs of global circulation models GCMs, especially precipitation (Groppelli, Soncini, Bocchiola, & Rosso, 2011). Benchmarking of some studies entailing use of both statistical and dynamic (e.g., nested regional climate models) methods for downscaling showed that statistical methods may lead to equivalent results with less mathematical burden (Kidson & Thompson, 1998;Murphy, 1999Murphy, , 2000. Wilby and Wigley (1997) and Wilby et al. (1998) Here, for downscaling of precipitation, we used a statistical approach based on stochastic space random cascades, which we developed for the purpose (see full account in Bocchiola, 2007;Groppelli, Soncini, et al., 2011). ...
We assess the effects of prospective climate change until 2100 on water management of two major reservoirs of Iran, namely Dez (3.34x10⁹ m³), and Alavian (6x10⁷ m³). We tune the Poly‐Hydro model suited for simulation of hydrological cycle in high altitude snow fed catchments. We assess optimal operation rules (ORs) for the reservoirs using three algorithms under dynamic and static operation, and linear and nonlinear decision rules during control run (CR,1990‐2010 for Dez and 2000‐2010 for Alavian). We use projected climate scenarios (plus statistical downscaling) from three general circulation models (GCMs), EC‐Earth, CCSM4 and ECHAM6, and three emission scenarios, or representative concentration pathways (RCPs), RCP2.6, RCP4.5 and RCP8.5, for a grand total of 9 scenarios, to mimic evolution of the hydrological cycle under future climate until 2100. We subsequently test the ORs under the future hydrological scenarios (at half century, and end of century), and the need for re‐optimization. Poly‐Hydro model, when benchmarked against historical data well mimics the hydrological budget of both catchments, including the main processes of evapotranspiration and stream flows. Teaching‐learning based optimization (TLBO) deliver the best performance in both reservoirs according to objective scores, and is used for future operation. Our projections in Dez catchment depict decreased precipitation along the XXI century, with ‐1% on average (of the nine scenarios) at half century, and ‐6% at the end of century, with changes in stream flows on average ‐7% yearly, and ‐13% yearly, respectively. In Alavian precipitation would decrease by ‐10% on average at half century, and ‐13% at the end of century, with stream flows ‐14% yearly, and ‐18% yearly, respectively. Under the projected future hydrology reservoirs’ operation would provide lower performance (i.e. larger lack of water) than now, especially for Alavian dam. Our results provide evidence of potentially decreasing water availability, and less effective water management in water stressed areas like Northern Iran here during this century.
... Downscaling technology is a bridge that connects GCMs low-resolution output to high-resolution meteorological elements for hydrological model (Wilby et al., 2002). Statistical downscaling methods are widely used for easy to construct, diverse methods, and flexible form (Tareghian and Rasmussen, 2013;Murphy, 2000). Li et al. (2010) proposed an equidistant cumulative distribution function matching method (EDCDFm) based on the quantile function method, which consider the differences between the projected climatic factors and the historical statistical cumulative distribution of climatic factors. ...
The Yellow River Basin (YRB) is the largest river basin in northern China,
which has suffering water scarcity and drought hazard for many years.
Therefore, assessments the potential impacts of climate change on the future
streamflow in this basin is very important for local policy and planning on
food security. In this study, based on the observations of 101 meteorological
stations in YRB, equidistant CDF matching (EDCDFm) statistical downscaling
approach was applied to eight climate models under two emissions scenarios
(RCP4.5 and RCP8.5) from phase five of the Coupled Model Intercomparison
Project (CMIP5). Variable infiltration capacity (VIC) model with
0.25° × 0.25° spatial resolution was developed based
on downscaled fields for simulating streamflow in the future period over YRB.
The results show that with the global warming trend, the annual streamflow
will reduced about 10 % during the period of 2021–2050, compared to the
base period of 1961–1990 in YRB. There should be suitable water resources
planning to meet the demands of growing populations and future climate
changing in this region.
... A Statistical Downscaling Model (SDSM) should be established that relates the predictions on local scale, such as precipitation and the maximum and minimum daily temperatures and large-scale predictors such as mean sea-level pressure and surface vorticity (WILBY; DAWSON, 2007). The established relations are then applied to simulated circulation by MCG, in order to generate local weather forecasts, motivated by the assumption that MCGs are more efficient in simulating atmospheric circulation on a large scale than in simulating surface climate variables (MURPHY;, MARAUN et al., 2010. In this study, the selected SDSM was the analogous method, which compares the large-scale atmospheric circulation simulated by an MCG with each of the historical observations, and the most similar pattern with the observations is chosen as its equivalent (ZORITA et al., 1995, ZORITA;STORCH, 1999). ...
The performance of four global models, together with two scenarios of climate change, was evaluated in five municipalities of the State of Alagoas, for precipitation, minimum and maximum temperature. The input data of the model were obtained through the conventional meteorological stations of the National Institute of Meteorology (INMET), arranged between 1961 and 2016. Estimation of corn yield was obtained through the theoretical model which relates losses in productivity and water deficiency during the phenological phases of the crop. A post-processing technique of global climate model outputs (statistical downscaling) was used, thus, a better visualization in time and space. The precipitation and temperature series were used for the period 2021-2080 estimating the yield losses of maize, comparing to the historical average values of the period 1961-2016, evaluating the impacts of possible climatic changes on crop yield. The scenarios have valuesof losses very close to and indicate a prediction of increased productivity loss in the period 2021-2080 for Água Branca, Pão de Açúcar and Palmeira dos Índios, and decrease of losses, that is, increase of productivity, for Maceió and Mainly Porto de Pedras. This result is directly associated to the predictions of rainfall reduction in the interior of the State, encompassing the cities of Água Branca, Pão de Açúcar and Palmeira dos Índios, a slight increase in precipitation for Maceió and a more significant increase in precipitation in Porto de Pedras.
... 136 Фиг. 35 ➢ Хидроложка информация, необходима за изпълнение на следващите етапи 2 и 3 (към настоящия момент в процес на разработка). ...
17 Декември 2014 г.
... This method integrates atmospheric chemistry composition, allowing for extrapolation of future atmospheric conditions (Nolte et al., 2008). However, the high computational demand (due to high-resolution, full-chemistry simulations) limits the application to multiple GCM outputs and reduces the availability of these methods (Gao et al., 2013;Gao et al., 2012;Murphy, 2000). Previous studies have used dynamical downscaling methods to study the impact of climate change on future O 3 and air quality. ...
Overwhelming evidence has shown that, from the Industrial Revolution to the present, human activities influence ground-level ozone (O3) concentrations. Past studies demonstrate links between O3 exposure and health. However, knowledge gaps remain in our understanding concerning the impacts of climate change mitigation policies on O3 concentrations and health. Using a hybrid downscaling approach, we evaluated the separate impact of climate change and emission control policies on O3 levels and associated excess mortality in the US in the 2050s under two Representative Concentration Pathways (RCPs). We show that, by the 2050s, under RCP4.5, increased O3 levels due to combined climate change and emission control policies, could contribute to an increase of approximately 50 premature deaths annually nationwide in the US. The biggest impact, however, is seen under RCP8.5, where rises in O3 concentrations are expected to result in over 2,200 additional premature deaths annually. The largest increases in O3 are seen in RCP8.5 in the Northeast, the Southeast, the Central, and the West regions of the US. Additionally, when O3 increases are examined by climate change and emissions contributions separately, the benefits of emissions mitigation efforts may significantly outweigh the effects of climate change mitigation policies on O3-related mortality.
... Wilby and Wigley, 1997;Fowler et al., 2007;Maraun et al., 2010;Winkler et al., 2011) and it is nowadays recognized that both are complementary in many practical applications. Nevertheless, whereas both approaches have been extensively used and critically assessed in climate change studies (see, e.g., Murphy, 2000;Spak et al., 2007, for an example over Europe and North America, respectively), their added value for seasonal forecasting is not well understood yet, and there is a lack of comprehensive intercomparison studies over Europe-the few existing experiences are quite inconclusive or provide limited information for this region (see, e.g. Shukla and Lettenmaier, 2013). ...
This work describes the results of a comprehensive intercomparison experiment of dynamical and statistical downscaling methods performed in the framework of the SPECS (http://www.specs-fp7.eu) and EUPORIAS (http://www.euporias.eu) projects for seasonal forecasting over Europe, a region which exhibits low-to-moderate seasonal forecast skill. We considered a 15-member hindcast provided by the EC-EARTH global model (similar to ECMWF System 4, but using bias corrected SST) for the period 1991–2012. In particular, we focus on summer mean temperature and evaluate the added value of downscaling for representation of the local climatology (mean values and extremes), improvement of model skill and performance in particular heatwave episodes. Whereas the suitability of dynamical downscaling for reducing the orographic biases of the global model depends on the region and model considered, statistical downscaling can systematically reduce errors in different order moments, from the mean to the extremes (as represented by the 95th percentile here). However, both dynamical and statistical methods lead to similar skill patterns with about the same overall performance as the global model, which shows higher values in south-eastern Europe. Therefore, no relevant added value is found in terms of model skill improvement. Finally, when focusing on the heatwaves of 2003, 2006, 2010 and 2012, the limitations of the global model to detect these hot episodes are inherited by all dynamical and statistical downscaling methods so no added value is neither found in this aspect. This work provides, to our knowledge, the largest and most comprehensive intercomparison of statistical and dynamical downscaling for seasonal forecasting over Europe.
Climate change is one of the most pressing challenges faced by developing countries due to their lower adaptive capacity, with far-reaching impacts on agriculture. The mid-century period is widely regarded as a critical moment, during which adaptation is deemed essential to mitigating the associated impacts. This study presents future climate projections across Tanzania using the latest generation of global climate models (CMIP6) combined with a high-resolution regional climate model. The findings indicate that, the trends in temperature and precipitation in Tanzania from 1991 to 2020, minimum temperatures showed the highest variability with a trend of 0.3 °C, indicating significant fluctuations in minimum temperature over the decades. Maximum temperatures also showed high variability with a trend of 0.4 °C. There is a range of variability in precipitation per decade for different regions in Tanzania, with some regions experiencing significant decreases in precipitation of up to − 90.3 mm and − 127.6 mm. However, there were also regions that experienced increases in precipitation, although these increases were generally less than 4.8 mm over the decades. The projections of minimum and maximum temperatures from 2040 to 2071 under the Shared Socioeconomic Pathways (SSP) 2–4.5 and SSP 5–8.5 are projected to increase by 0.14 °C to 0.21 °C per decade, across different regions. The average projected precipitation changes per decade vary across regions. Some regions are projected to experience increases in precipitation. Other regions are projected to show decreases in precipitation within the range of − 0.6 mm to 15.5 mm and − 1.5 mm to 47.4 mm under SSP2–4.5 and SSP5–8.5 respectively. Overall, both scenarios show an increase in projected temperatures and precipitation for most regions in Tanzania, with some areas experiencing more significant increases compared to others. The changes in temperatures and precipitation are expected to have significant impacts on agriculture and water resources in Tanzania.
Air temperature and precipitation outputs from 10 CMIP6 GCMs were input to the Precipitation-Runoff Modeling System hydrologic model to evaluate water and energy responses in three headwater basins to projected climate change over the twenty-first century. The headwater basins (398–801 km ² ) are located within the Mataura River basin in the South Island of New Zealand. CMIP6 datasets included two emission scenarios [shared socioeconomic pathways (SSPs) SSP2-4.5 and SSP5-8.5]. Half of the 10 GCMs selected in the study have equilibrium climate sensitivity (ECS) values above 4.5°C, which has been considered the upper end of equilibrium climate sensitivity. Modeling results included increased annual air temperature, evapotranspiration, and precipitation by the end of the twenty-first century for both SSP emissions scenarios, both high- and low-ECS GCMs, and all three headwater basins. Monthly precipitation and evapotranspiration totals also increased for all or most months. Monthly streamflow changes generally corresponded with monthly precipitation changes. Snowpack decreased significantly in depth and seasonal duration in all basins. However, streamflow increased for all SSP and ECS groups and basins because increased precipitation was consistently greater than increased evapotranspiration losses. Sources of uncertainty include the GCMs, climate sensitivity, downscaling, bias adjustment, emission scenarios, and the hydrologic model. Simulated hydrologic responses based on climate data from GCMs with ECS values of greater than 4.5°C could be less plausible since previous studies have suggested true ECS ranges from 1.5° to 4.5°C.
The article, on the base of Yenoqavan community of Tavush region (Republic of Armenia) case study, analyzes and presents the importance of assessing the potential of certain forest and agroecosystem services in order to stimulate the development of ecotourism in the region. According to the results of the research, only the recreational and ecotourism services of community forest ecosystems accounts to 36 million AMD, but in 2019 only 55.6% or 20 million AMD were used from those services. Meanwhile, this value should be integrated in the general system of nature use and economy, aiming to redirect the financial means for the forest and agro-landscapes improvement and protection. At the same time, the study revealed that unlike the ecotourism, agro-tourism is poorly developed, the main reasons for which are associated with degraded natural grasslands, low yields of arable lands, low productivity of livestock. The treatment means and measures are recommended in the study. The proposed improvement interventions can significantly increase the yield of fields and livestock productivity, also stimulate the development of ecotourism in the community.
The impact of climate change on precipitation and maximum and minimum temperatures patterns in the Northeast region of Brazil is investigated based on the mean results of four global climate models, ECHAM5-OM from Germany, HADGEM2-ES from the UK, BCM2 from Norway and the CNRM-CM3 of France, for two scenarios of greenhouse gas emissions, A1B and A2, that had their future projections regionalized for the period 2021-2080 using the statistical downscaling model. The ability of the models to simulate present climate conditions was checked for the 1961-1990 control period, presenting very satisfactory results, and validated for the period 1991-2000. The analogues method was employed to perform statistical downscaling and to find predictor-prediting relationships. The results point to a significant reduction in rainfall in the respective rainy periods of the northeastern subregions, and the highest temperatures increase in the first semester, with a tendency to decrease in large areas of the northern Northeast sector in the second semester, mainly for scenario A2. For the minimum temperatures the results show a tendency of increase in all the year with highlight for the winter months.
Predicting the temporal and spatial distribution of Reference crop Evapotranspiration (ET0) in the Poyang Lake Watershed in the future climate scenario can provide optimal management of water resource in the watershed and provide basic data support for scientific response to the impact of climate change on agricultural production. In this paper, the daily meteorological data of 14 meteorological stations in Poyang Lake Watershed from 1961 to 2014 were used to calculate the historical ET0 by the Penman-Monteith formula; Based on the reanalysis data of the US Environmental Center (NCEP) and the CNRM-CM5 model in CMIP5 from 2006 to 2100. The prediction data in the RCP4.5 and RCP8.5 scenarios are predicted by the statistical downscaling model (SDSM) and the bias correction to predict the future ET0 of the basin; Through M-K test, ordinary kriging interpolation and spatial autocorrelation, the temporal and spatial evolution characteristics of ET0 in the basin from 1961 to 2100 were analyzed. The results show that: the simulation effect of NCEP reanalysis data and the stepwise regression scale reduction model established by ET0 of the basin is good, and the accuracy of the scale reduction simulation results of CNRM-CM5 model is significantly improved after the deviation correction, which is suitable for the estimation of future ET0 of the basin. The overall ET0 of Poyang lake watershed during the base period from 1961 to 2010 shows a decreasing trend, and the spatial distribution is high in the north and south, and low in the middle, showing obvious spatial differences. Under the scenario of RCP4.5 and RCP8.5, the ET0 of Poyang lake basin in the next three periods shows an increasing trend in different degrees compared with the base period. ET0 has strong spatial autocorrelation in both benchmark period and future scenario. In RCP8.5 scenario, the aridity index from 1961 to 2100 in Poyang lake watershed, showing a more obvious rise and increased with time, the valley of drought conditions in most of river basin from 2011 to 2100 by the humid to semi-humid areas, aridity index decreases from south to north, the Gan river basin will be the focus of the Poyang lake watershed in the future drought risk prevention area.
Daylighting is a parallel universe to Architecture, where architects benefit greatly from daylight prediction techniques, which have witnessed a paradigm shift from simple methods to more sophisticated computational simulation tools. Still, such accumulating complexities made many designers disinclined to integrate what they consider difficult methods into their practices, even hindered the casual use of simulation tools, due to the lack of essential knowledge, among other complications. Herein, this research aims to provide a comprehensive review of over 100 years of growing fundamental directions to predict the amount of daylight inside buildings, with a particular focus on tracing sky models, weather datasets, building geometry and daylight calculation methods, which drove the progress of performance metrics and simulation tools, considering detailed descriptions of 50 prevalent simulation tools. This historical review is conducted with the architects’ nature in mind to underline existing knowledge gaps in the research domain and reveal future perspectives. Another implication of this research is to remove ambiguity of unfamiliar terms and technicalities, helping practitioners, especially young architects, of different backgrounds and expertise to grasp the essential daylight-related topics, guiding their decisions on suitable tools to use in building design.
The properties of geographical phenomena vary with changes in the scale of measurement. The information observed at one scale often cannot be directly used as information at another scale. Scaling addresses these changes in properties in relation to the scale of measurement, and plays an important role in Earth sciences by providing information at the scale of interest, which may be required for a range of applications, and may be useful for inferring geographical patterns and processes. This paper presents a review of geospatial scaling methods for Earth science data. Based on spatial properties, we propose a methodological framework for scaling addressing upscaling, downscaling and side-scaling. This framework combines scale-independent and scale-dependent properties of geographical variables. It allows treatment of the varying spatial heterogeneity of geographical phenomena, combines spatial autocorrelation and heterogeneity, addresses scale-independent and scale-dependent factors, explores changes in information, incorporates geospatial Earth surface processes and uncertainties, and identifies the optimal scale(s) of models. This study shows that the classification of scaling methods according to various heterogeneities has great potential utility as an underpinning conceptual basis for advances in many Earth science research domains.
The black timber bark beetle Xylosandrus germanus (Blandford) is an invasive ambrosia beetle originating from Southeastern Asia that has become successfully established within Europe and North America. Herein, we provide a review of the spread and distribution of this pest of trees and timber across Europe before and after 2000, along with a review of its habitat preferences. Since the spread of X. germanus across Europe has accelerated rapidly post-2000, emphasis is placed on this period. X. germanus was first recorded in Germany in 1951 and since then in 21 European countries along with Russia. Ethanol-baited traps were deployed in oak, beech, and spruce forest ecosystems in the Western Carpathians, Central Europe, Slovakia, to characterize the distribution and habitat preference of this non-native ambrosia beetle. Captures of X. germanus within Slovakia have been rising rapidly since its first record in 2010, and now this species dominates captures of native ambrosia beetles. X. germanus has spread throughout the whole Slovakia from the south-southwest to the north-northeast over the period of 5–10 years and has also spread vertically into higher altitudes within this country. While living but weakened trees in Europe and North America are attacked by X. germanus, the greatest negative impact within Slovakia is attacks on recently felled logs of oak, beech and spruce trees providing high quality timber/lumber. We suggest that the recent rapid spread of X. germanus in Central Europe is being facilitated by environmental changes, specifically global warming, and the increasing frequency of timber trade. Recommendations for management of X. germanus in forest ecosystems are proposed and discussed, including early detection, monitoring, sanitary measures, etc.
Climate change and impacts studies are gaining importance in wake of changing climate and its impact. Climate models namely General Circulation Models have been developed by different research groups to study the impact of climate at Global Scale and they are the primary dataset available for modelling global climate change in the future. However, owing to their coarse spatial resolution, GCM models are not appropriate for impact studies at local scale having the finer spatial resolution. Therefore, for impact studies, climate models available at global scale are correlated with atmospheric and climate conditions like temperature and precipitation at local scale through downscaling process. Different downscaling techniques ranging from simple to dynamic downscaling techniques have been developed by the researchers to develop the mathematical models that correlate the GCM outputs with local observations.
Among these downscaling techniques, statistical downscaling techniques are most widely used techniques owing to easy of its implementation through computer based tools. SDSM is one of the widely used software for statistical downscaling that utilizes statistical downscaling technique for downscaling the GCM data-set. However, the available statistical downscaling software tools are not appropriate to automate the downscaling process for multiple grids of a given area of interest (AOI). Using the existing downscaling tools, manual intervention is required to downscale the GCM data at local scale for large AOIs having the sizeable spatial extent.
In this research work, a novel generalized downscaling model namely Efficient Multi-site Statistical Downscaling Model (EMSDM) based on the multivariate regression technique has been developed to automate the downscaling process for multiple grids. EMSDM can be applied to automate the downscaling of GCM data to multiple local grids of a AOI. Internal procedures of EMSDM are programmed in platform independent C programming language for efficiently handling large quantum of GCM and local observation data and carrying out the complex mathematical computations like inversion of large matrices. For demonstrating, the applicability of the model, GCM model namely second generation Canadian Earth System Model (CanESM2) (CanESM2) developed by the Canadian Centre for Climate Modelling and Analysis (CCCma) of Environment and Climate Change Canada and local daily precipitation and temperature data-set acquired from Indian Meteorological Department (IMD) have been used for carrying out downscaling using the proposed model. India has been selected as AOI.
On basis of analysis of downscaling results generated by the model, it can be concluded that proposed model can efficiently be used to carry out statistical downscaling the AOI (comprising of multiple grids) irrespective of its extent. Results generated by the proposed model can be utilized by investigators to carry out climate impacts studies for AOI having large spatial extent.
Moreover, in order to facilitate the spatial geo-visualization of downscaling results, a web GIS based framework has been developed to geo-visualize the time series data generated by EMSDM. In addition of the downscaling, EMSDM is able to generate valuable spatial data-set pertaining to local observation and GCM outputs of given area of interest. These spatial date-set can utilized by the decision makers to investigate spatial distribution of climatological parameters like temperature, precipitation etc.
The objective of the present study was to analyse the impacts of climate change on streamflow in the Mae Klong basin in Thailand using improved daily gridded precipitation data of the ECHAM4/OPYC global climate model. Bias correction and spatial downscaling methods are frequently used to improve global climate model precipitation. For bias correction, gamma-gamma transformation and ratio methods were used to correct the frequency and quantity of raw global circulation model (GCM) precipitation data. Spatial disaggregation, based on a multiplicative random cascade, was applied to downscale coarse resolution climate projected precipitation spatially. A rainfall-runoff model (Hydrologic Engineering Center Hydrologic Modelling System, HEC-HMS) was used to simulate future streamflow scenarios for three 5 year windows: the 2025s, the 2050s and the 2095s. The downscaling model parameters (β and σ²) were estimated separately for each month using the Mandelbrot-Kahane-Peyrière function. Both methods have proved useful in improving the quality, compared to historical trends and rainfall variability, of raw GCM precipitation data by reducing bias. There will not be much impact on water resources and water availability due to climate change in the coming decades in the Mae Klong River. There is some chance that, in the future, the monsoons will start before their regular period. There is also a chance of an increase in streamflow in the dry period and a decrease of streamflow in the wet period in the basin.
According to criteria and proposals of S. Rivas-Martínez, S. Rivas-Sáenz and A. Penas (Glob Geobot 1(1):1–634, 2011a) with which they established a world bioclimatic classification system, two macrobioclimates (Temperate and Mediterranean), eight bioclimates (temperate hyperoceanic, temperate oceanic, temperate xeric, mediterranean pluviseasonal-oceanic, mediterranean pluviseasonal-continental, mediterranean xeric-oceanic and mediterranean desertic-oceanic), two bioclimatic variants (steppic and submediterranean), 11 thermotypes (thermotemperate, mesotemperate, supratemperate, orotemperate, cryorotemperate, inframediterraean, thermomediterranean, mesomediterranean, supramediterranean, oromediterranean and cryoromediterraean) and seven ombrotypes (arid, semiarid, dry, subhumid, humid, hyperhumid and ultrahyperhumid) are recognized in the Iberian Peninsula and Balearic Islands. In addition to this, six types of continentality (euhyperoceanic, subhyperoceanic, semihyperoceanic, euoceanic, semicontinental and subcontinental) are also recognized. Furthermore, relationships between potential natural vegetation (sigmeta, geosigmeta, permasigmata, minorisigmeta and geopermasigmeta) and the bioclimatic units existing in the Iberian Peninsula and Balearic Islands are set.
As a direct consequence of warmer temperatures, the hydrologic cycle will undergo significant impact with accompanying changes in the rates of precipitation and evaporation. Climate change will cause changes in climate variable such as precipitation, temperature, sunshine hours, wind speed and etc. So as a result of climate variable change, the related variable such as potential evapotranspiration will change too. As the soft computing skills increased in recent decades, more number of climate models has been developed for weather and climate predictions which have significantly improved the quality and quantity of projections. This notable increase in number of climate models has enabled the scientists to estimate a wide range of main climate variables such as precipitation and temperature in fine temporal and spatial resolutions. Although the uncertainty in model outputs still remains a main challenge. Upon the release of new scenarios based on radiative forcing which are known as Representative Concentration Pathway scenarios (RCP scenarios), by Intergovernmental panel on climate change (IPCC) in fifth assessment report (AR5), a new set of 42 global climate models (GCMs) have been proposed for future climate projections. Apart from increased number of available models, three main sources of uncertainty including: Measurement error, variability, and model structure, that have been explained and studied in AR5.The aim of the current study is to investigate of changes of potential evapotranspiration (ET) over Mashhad plain, Northeast of Iran in future period 2021-2070 under two RCP scenarios i.e. RCP4.5 and RCP8.5. The main synoptic station in the region is Mashhad Station located at 59- 38 E, 36- 18 N, with elevation of 990 m. above M.S.L. The required meteorological data including maximum and minimum temperature, sunshine hours,wind speed for period of 1991 to 2005 were obtained from Iran Meteorological Organization for ET calculation using FAO Penman-Monteith (hereafter, FAO-PM) equation. Besides, the downscaled historical data of potential evapotranspiration provided by Swedish Meteorological and Hydrological Institute (SMHI) have been retrieved for the baseline period of 1991-2005.Then these historical estimated data were compared with those estimated using FAO-PM equation. The historical ET values were post-processed using a statistical proposed method for more accuracy. By completion of this part, the accuracy of historical dataset provided by SMHI was confirmed and used for further comparisons. In the second section the ET variations for future period of 2021 to 2070 under two RCP scenarios of 4.5 and 8.5 was studied. The results showed better estimation of ET during warm months. Statistical comparisons using T-Test revealed significant differences between historical and estimated values of ET in months of February, March and December. The correlation coefficient between post processed and observed data showed similar results as in T-Test. Since the historical dataset of potential evapotranspiration provided by SMHI was acceptable, it was used for the analysis during future period (2021-2070) under RCP4.5 and RCP8.5 scenarios compared to baseline observed data. The result of this part showed that the highest increase of potential evapotranspiration would be for January by 15.4% and 16.4% under RCP4.5 and RCP8.5 scenarios respectively and October would experience lowest decrease by -12.5% and -10.0% decrease, respectively. In general ET increase will be more under RCP 8.5 scenario comparing to RCP 4.5.
The validation of climate model simulations creates substantial demands
for comprehensive observed climate datasets. These datasets need not
only to be historically and geographically extensive, but need also to
be describing areally averaged climate, akin to that generated by
climate models. This paper addresses one particular difficulty found
when attempting to evaluate the daily precipitation characteristics of a
global climate model, namely the problem of aggregating daily
precipitation characteristics from station to area.Methodologies are
developed for estimating the standard deviation and rainday frequency of
grid-box mean daily precipitation time series from relatively few
individual station time series. Temporal statistics of such areal-mean
time series depend on the number of stations used to construct the areal
means and are shown to be biased (standard deviations too high, too few
raindays) if insufficient stations are available. It is shown that these
biases can be largely removed by using parameters that describe the
spatial characteristics of daily precipitation anomalies. These spatial
parameters (the mean interstation correlation between station time
series and the mean interstation probability of coincident dry days) are
calculated from a relatively small number of available station time
series for Europe, China, and Zimbabwe. The relationships that use these
parameters are able to successfully reproduce the statistics of grid-box
means from the statistics of individual stations. They are then used to
estimate the statistics of grid-box means as if constructed from an
infinite number of stations (for standard deviations) or 15 stations
(for rainday frequencies), even if substantially fewer stations are
actually available. These estimated statistics can be used for the
evaluation of daily precipitation characteristics in climate model
simulations, and an example is given using a simulation by the
Commonwealth Scientific and Industrial Research Organisation atmosphere
general circulation model. Applying the authors' aggregation methodology
to observed station data is a more faithful form of model validation
than using unadjusted station time series.
To improve understanding of the mechanisms responsible for COâ-induced, midcontinental summer dryness, several integrations were performed using a GCM with idealized geography. The simulated reduction of soil moisture in middle latitudes begins in late spring, caused by excess of evaporation over precipitation. Increase of carbon dioxide and the associated increase of atmospheric water vapor enhances the downward flux of terrestrial radiation at the continental surface at all latitudes. However, the increase in the downward flux of terrestrial radiation is larger in the equatorward side of the rain belt, making more energy available there for both sensible and latent heat. Evaporation increases more than precipitation over the land surface in the equatorward side of the rain belt during spring and early summer and initiates the drying of the soil there. As the rain belt moves poleward from spring to summer, the soil moisture decreases in middle latitudes, reducing the rate of evaporation. This reduction of evaporation, in turn, causes a corresponding decrease of precipitation in middle latitudes, keeping the soil dry throughout the summer. In high latitudes, there is also a tendency for increased summer dryness. Earlier removal of highly reflective snow cover in spring enhances the evaporation in the late spring, lengthening the period of drying during the summer season. The drying of soil is also enhanced by the land surface-cloud interaction. Solar radiation absorbed by the continental surface increases, enhancing evaporation and further reducing the soil moisture in summer. A greater fraction of radiative energy occurs as sensible heat rather than latent heat due to the colder surface temperature, causing evaporation to increase less than precipitation. Because of increased evaporation from the oceanic surface upstream, precipitation over most of the continent increases substantially.
Empirical downscaling procedures relate large-scale atmospheric features with local features such as station rainfall in order to facilitate local scenarios of climate change. The purpose of the present paper is twofold: first, a downscaling technique is used as a diagnostic tool to verify the performance of climate models on the regional scale; second, a technique is proposed for verifying the validity of empirical downscaling procedures in climate change applications.
The case considered is regional seasonal precipitation in Romania. The downscaling model is a regression based on canonical correlation analysis between observed station precipitation and European-scale sea level pressure (SLP). The climate models considered here are the T21 and T42 versions of the Hamburg ECHAM3 atmospheric GCM run in “time-slice” mode. The climate change scenario refers to the expected time of doubled carbon dioxide concentrations around the year 2050.
The downscaling model is skillful for all seasons except spring. The general features of the large-scale SLP variability are reproduced fairly well by both GCMs in all seasons. The climate models reproduce the empirically determined precipitation–SLP link in winter, whereas the observed link is only partially captured for the other seasons. Thus, these models may be considered skillful with respect to regional precipitation during winter, and partially during the other seasons. Generally, applications of statistical downscaling to climate change scenarios have been based on the assumption that the empirical link between the large-scale and regional parameters remains valid under a changed climate. In this study, a rationale is proposed for this assumption by showing the consistency of the 2 × CO2 GCM scenarios in winter, derived directly from the gridpoint data, with the regional scenarios obtained through empirical downscaling. Since the skill of the GCMs in regional terms is already established, it is concluded that the downscaling technique is adequate for describing climatically changing regional and local conditions, at least for precipitation in Romania during winter.
For climate change impact analyses, local scenarios of surface variables at the daily scales are frequently required. Empirical transfer functions are a widely used technique to generate scenarios from GCM data at these scales. For successful downscaling, the impact analyst should take into account certain considerations. First, it must be demonstrated that the GCM simulations of the required variable are unrealistic and therefore that downscaling is required. Second, it must be shown that the GCM simulations of the selected predictor variables are realistic. Where errors occur, attempts must be made to compensate for their effect on the transfer function-generated predictions or, where this is not possible, the effect on the transfer function-generated climate series must be understood. Third, the changes in the predictors between the control and perturbed simulation must be examined in the light of the implications for the change in the predicted variable. Finally, the effect of decisions made during the development of the transfer functions on the final result should be explored. This study, presented in two parts, addresses these considerations with respect to the development of local scenarios for daily maximum (TMAX) and minimum (TMIN) temperature for two sites, one in North America (Eau Claire, Michigan) and one in Europe (Alcantarilla, Spain).Part I confirms for a selected GCM that simulations of daily TMAX and TMIN, whether taken from the nearest land grid point, or obtained by interpolation to the site location, are inadequate. Differences between the GCM 1 × CO2 and observed temperature series arise because of a 0°C threshold in the model data. At both sites, variability is suppressed during periods affected by the threshold. The thresholds persist into the perturbed simulation, affecting not only GCM-predicted 2 × CO2 temperatures but also, because the duration and timing of the threshold effect changes in the perturbed simulation, the magnitude and seasonal distribution of the 2 × CO2 -1 × CO2 GCM differences.Comparison of modeled and observed 500-hPa geopotential height (Z500) and sea level pressure (SLP) shows that, although systematic errors of the type associated with the 0°C threshold in the temperature data are absent, significant errors do occur in certain seasons at both sites. For example, SLP is poorly modeled at Alcantarilla, where the control and observed means differ significantly in every season. The worst results at both sites are in summer. These results will affect the performance of the transfer functions when initialized with model data. Whereas little change is found to occur in SLP at either site between the 1 × CO2 and 2 × CO2 simulation, there is a noticeable increase in Z500. Other things being equal, therefore, the temperature changes predicted by the transfer functions are likely to be greatest when Z500 contributes the most to the explained variances.In Part II, a range of transfer functions are developed from the free atmosphere variables and validated, using observations. The performance of these transfer functions when initialized with model data is evaluated in the light of the findings in Part I. The sensitivity of the perturbed climate scenarios to a range of user decisions is explored.
The severe impacts of climate variability and climate hazards on society reveal the increasing need for improving regional- and local-scale climate diagnosis. A new downscaling approach for climate diagnosis is presented here. It is based on artificial neural network (ANN) techniques that derive relationships from the large- and local-scale atmospheric controls to the local winter climate. This study documents the large-scale conditions associated with extreme precipitation events in northeastern Mexico and southeastern Texas during the 1985-93 period, and demonstrates the ability of ANN to simulate realistic relationships between circulation-humidity fields and daily precipitation at local scale.The diagnostic model employs a neural network that preclassifies the winter circulation and humidity fields into different patterns. The results from this neural network classification approach, known as a self-organizing map (SOM), indicate that negative (positive) anomalies of winter precipitation over the study area are associated with 1) a weaker (stronger) Aleutian low, 2) a stronger (weaker) North Pacific high, 3) a negative (positive) phase of the Pacific-North American pattern, and 4) cold (warm) ENSO events. The atmospheric patterns classified with the SOM technique are then used as input to another neural network (feed-forward ANN) that captures over 60% of the daily rainfall variance over the region. This further reveals that the SOM preclassification of days with similar atmospheric conditions succeeded in emphasizing the differences of the atmospheric variance that are conducive to extreme precipitation. This resulted in a downscaling model that is highly sensitive to local- and large-scale weather anomalies associated with ENSO warm events and cold air outbreaks.
A statistical strategy to deduct regional-scale features from climate general circulation model (GCM) simulations has been designed and tested. The main idea is to interrelate the characteristic patterns of observed simultaneous variations of regional climate parameters and of large-scale atmospheric flow using the canonical correlation technique. The large-scale North Atlantic sea level pressure (SLP) is related to the regional, variable, winter (DJF) mean Iberian Peninsula rainfall. The skill of the resulting statistical model is shown by reproducing, to a good approximation, the winter mean Iberian rainfall from 1900 to present from the observed North Atlantic mean SLP distributions. It is shown that this observed relationship between these two variables is not well reproduced in the output of a general circulation model (GCM). The implications for Iberian rainfall changes as the response to increasing atmospheric greenhouse-gas concentrations simulated by two GCM experiments are examined with the proposed statistical model. In an instantaneous [open quotes]2 CO[sub 2][close quotes] doubling experiment, using the simulated change of the mean North Atlantic SLP field to predict Iberian rainfall yields, there is an insignificant increase of area-averaged rainfall of I mm/month, with maximum values of 4 mm/month in the northwest of the peninsula. In contrast, for the four GCM grid points representing the lberian Peninsula, the change is - 10 mm/month, with a minimum of - 19 mm/month in the southwest. In the second experiment, with the IPCC scenario A ([open quotes]business as usual[close quotes]) increase of CO[sub 2], the statistical-model results partially differ from the directly simulated rainfall changes: in the experimental range of 100 years, the area-averaged rainfall decreases by 7 mm/month (statistical model), and by 9 mm/month (GCM); at the same time the amplitude of the interdecadal variability is quite different. 17 refs., 10 figs.
Simulations of the intraseasonal oscillation (ISO) in the Indian summer monsoon by a general circulation model (GCM) and a nested regional climate model (RCM) are described. The ISO is the leading mode of subseasonal variability in both models. It is shown to be associated with circulation and precipitation anomalies that propagate northward from the equatorial Indian Ocean to the foothills of the Himalayas on the 30-50-day timescale. The spatial structure, timescale, and propagation characteristics of the simulated ISO are found to be similar to those of the leading observed intraseasonal mode. In particular, both of the simulated versions and the observed version all involve periodic deepening and filling of the monsoon trough resulting from northward propagation of troughs and ridges from the equatorial region. Some differences do occur, however: the GCM version of the ISO is too zonally symmetric and the ISO is too strong in both models. During the positive phase of the ISO (i.e.. when the ISO acts to enhance the monsoon trough), composite low-level circulation anomalies in the monsoon trough region are found to be somewhat weaker in the RCM than in the GCM because the RCM signal is obscured to a greater degree by noise associated with other modes of variability. In the GCM, large precipitation anomalies are found to be associated with the positive and negative phases of the ISO in many areas, particularly at the latitudes of the monsoon trough. However, the use of a fine-resolution nested RCM leads to the identification of important spatial detail not present in the GCM distributions. This is particularly true in mountainous regions, most notably in the foothills of the Himalayas: here the RCM simulates a strong precipitation signal, which appears to represent an orographic component of the response to circulation anomalies associated with the ISO. whereas this precipitation signal is absent in the GCM. The use of a nested RCM also allows the phase relationship between the oscillations in the two models to be studied. The relationship is found to be close in most years, suggesting that the regional ISO in the RCM is modulated by the driving GCM circulation via the lateral boundary forcing on the 30-50-day timescale. Several examples are also found, however, where the GCM and RCM diverge, showing that the northward-propagating mode can occur independently of any global forcing on the same timescale, in agreement with observational evidence.
Extreme events act as a catalyst for concern about whether the climate is changing. Statistical theory for extremes is used to demonstrate that the frequency of such events is relatively more dependent on any changes in the variability (more generally, the scale parameter) than in the mean (more generally, the location parameter) of climate. Moreover, this sensitivity is relatively greater the more extreme the event. These results provide additional support for the conclusions that experiments using climate models need to be designed to detect changes in climate variability, and that policy analysis should not rely on scenarios of future climate involving only changes in means.
Seasonal simulations of the Indian summer monsoon using a 50-km regional climate model (RCM) are described. Results from three versions of the RCM distinguished by different domain sizes are compared against those of the driving global general circulation model (AGCM). Precipitation over land is 20% larger in the RCMs due to stronger vertical motions arising from finer horizontal resolution. The resulting increase in condensational heating helps to intensify the monsoon trough relative to the AGCM. The RCM precipitation distributions show a strong orographically forced mesoscale component (similar in each version). This component is not present in the AGCM. The RCMs produce two qualitatively realistic intraseasonal oscillations (ISOs) associated respectively with monsoon depressions which propagate northwestward from the Bay of Bengal and repeated northward migrations of the regional tropical convergence zone. The RCM simulations are relatively insensitive to domain size in several respects: (1) the mean bias relative to the AGCM is similar for all three domains; (2) the variability simulated by the RCM is strongly correlated with that of the driving AGCM on both daily and seasonal time scales, even for the largest domain; (3) the mesoscale features and ISOs are not damped by the relative proximity of the lateral boundaries in the version with the smallest domain. Results (1) and (2) contrast strongly with a previous study for Europe carried out with the same model, probably due to inherent differences between mid-latitude and tropical dynamics.
The climatic effects of doubling the carbon dioxide concentration in a highly simplified three-dimensional general circulation model were analyzed. The concentration of CO/sub 2/ may increase as much as 20 percent during the latter half of this century as a result of fossil fuel combustion. The model contains these simplifications: a limited computational domain, an idealized topography, no heat transport by ocean currents, and fixed cloudiness. The CO/sub 2/ increases the temperature of the model troposphere, but it lowers that of the model stratosphere. The tropospheric warming is somewhat larger than that expected from a radiative-convective equilibrium model. The increase of surface temperature in higher latitudes is magnified due to the recession of the snow boundary and thermal stability of the lower troposphere which limited convective heating to the lowest layer. The doubling of CO/sub 2/ significantly increases the intensity of the hydrologic cycle of the model.
The respective merits of statistical and regional modeling techniques for downscaling GCM predictions have been evaluated over New Zealand, a small mountainous country surrounded by ocean. The boundary conditions were supplied from twice-daily European Centre for Medium-Range Weather Forecasts analyses at 2.5 degrees resolution for the period 1980-94, which were taken as the output of a "perfect" climate model. Daily and monthly estimates of minimum and maximum temperature and precipitation From both techniques were validated against readings from a network of 78 climate stations. The statistical estimates were made by a screening regression technique using the EOFs of the regional height fields at 1000 and 500 hPa, and local variables derived from these fields, as predictors. The model interpolations made use of the RAMS model developed at Colorado State University running at 50-km resolution for 1990-94 only. The model values at the nearest grid point to each station were rescaled using a simple linear regression to give the best fit to the station values. The results show both methods to have comparable skill in estimating daily and monthly station anomalies of temperatures and rainfall. Statistical estimates of monthly departures were better obtained directly from monthly mean forcing than from a combination of daily estimates; however, daily values are needed if one wishes to estimate variability. While there are good physical grounds for using the modeling technique to estimate the likely effects of climate change, the statistical technique requires considerably less computational effort and may be preferred for many applications.
International model comparisons of cloud-climate feedbacks have
typically been restricted to assessing only the radiative effect of
changes in clouds and have not attempted to explain the mechanisms for
differences in cloud feedbacks. This paper uses different versions of
the U.K. Meteorological Office GCM run at the Hadley Centre to
illustrate the usefulness of a detailed comparison of microphysical
cloud properties in understanding cloud feedback mechanisms and their
effect on the regional distribution of the predicted warming in
simulations of climate change. The inclusion of interactive cloud
radiative properties explains much of the difference in the spatial
patterns of cloud feedback and leads to a marked difference in the
response of the large-scale circulation and in the resulting meridional
gradient of surface temperature changes. In the model versions that
include interactive radiative properties, the strength of the related
feedback is determined by the water path of the cloud in the control
experiment. Difficulties in performing such a detailed comparison on a
wider range of models may arise from the lack of diagnostics in a common
format being available from different models and because of the range of
assumptions about how clouds are treated by different radiation schemes.
A suggestion is put forward for a possible common format that would
enable comparison of such diagnostics.
A limited-area grid point model is nested at resolutions of 250 km and 125 km within a spectral general circulation model for simulations of perpetual January climate over the Australian region. The spectral model is run for 300 days and provides the lateral boundary conditions for the nested model runs. In these experiments the physical parameterizations are kept as similar as possible. The results show that the limited-area model significantly improves the simulation of January climate, particularly in regions of steep topography. Some problems are described regarding the integration of the two models, including the generation of spurious precipitation near the boundaries of the nested model and limitations of the soil temperature scheme. Problems relating to the applicability of the technique to tropical domains are also discussed. The simulation in the interior of the domain is good; in particular, the simulation of precipitation is significantly improved compared to that of the general circulation model.
An attempt is made to estimate the temperature changes resulting from doubling the present CO2 concentration by the use of a simplified three-dimensional general circulation model. This model contains the following simplications: a limited computational domain, an idealized topography, no beat transport by ocean currents, and fixed cloudiness. Despite these limitations, the results from this computation yield some indication of how the increase of CO2 concentration may affect the distribution of temperature in the atmosphere. It is shown that the CO2 increase raises the temperature of the model troposphere, whereas it lowers that of the model stratosphere. The tropospheric warming is somewhat larger than that expected from a radiative-convective equilibrium model. In particular, the increase of surface temperature in higher latitudes is magnified due to the recession of the snow boundary and the thermal stability of the lower troposphere which limits convective beating to the lowest layer. It is also shown that the doubling of carbon dioxide significantly increases the intensity of the hydrologic cycle of the model.
The degree to which human conditions and the natural environment are vulnerable to the potential effects of climate change is a key concern for governments and the environmental science community worldwide. This book from the Intergovernmental Panel on Climate Change (IPCC) provides the best available base of scientific information for policymakers and public use. The Regional Impacts of Climate Change: An Assessment of Vulnerability reviews state-of-the-art information on potential impacts of climate change for ecological systems, water supply, food production, coastal infrastructure, human health, and other resources for ten global regions. It also illustrates that the increasing costs of climate and climate variability, in terms of loss of human life and capital due to floods, storms, and droughts, are a result of the lack of adjustment and response in society's policies and use of resources. This book points to management options that would make many sectors more resilient to current variability in climate and thus help these sectors adapt to future changes in climate. This book will become the primary source of information on regional aspects of climate change for policymakers, the scientific community, and students.
The present-day precipitation climatology of Tasmania, Australia, has been simulated for January and July using a limited area model with multiple nesting down to a resolution of 60 km. The results are superior to simulations at lower resolutions, although the high resolution model produces excessive precipitation in regions of steep orography. A preliminary precipitation climatology is constructed for 2×CO2 conditions from the results of the simulations for January and July. In the simulations, climate change causes precipitation to decrease over Tasmania during January, while it is increased during July. Examination of daily precipitation frequency indicates that precipitation events in January become less intense, but those in July become more intense. The changes in precipitation are related to modifications to the low-level synoptic fields. Some strengthening of low-level winds as a result of climate change is also noted for July.
This paper presents a validation analysis of the climatology of a version of the National Center for Atmospheric Research-Pennsylvania State University limited-area model (MM4) developed for application to regional climate simulation over the western United States. Two continuous multiyear simulations, for the periods 1 January 1982-31 December 1983 and 1 January 1988-25 April 1989, were performed over this region with the MM4 driven by ECMWF analyses of observations and run at a horizontal resolution of 60 km. The model used includes horizontal diffusion on terrain-following [sigma] coordinates, a Kuo-type cumulus parameterization, sophisticated radiative transfer and surface physics-soil hydrology packages, and a relaxation boundary-conditions procedure. Model-produced surface air temperatures, precipitation, and snow depths were compared with observations from about 390 stations distributed throughout the western United States. The base-model run reproduced the seasonal cycle of temperature and precipitation well. The effects of topography on the regional distribution of precipitation were well reproduced. When regionally averaged, absolute model-precipitation biases were mostly in the range of 10%-50% of observations. The model generally simulated precipitation better in the cold season than in the warm season, and over coastal regions than in the continental interior. The simulated seasonal cycles of snowpack formation and melting were realistic. Over the Rocky Mountain regions the model reproduced wintertime precipitation amounts well but over-predicted summertime precipitation. However, modifications were found to provide an improved simulation of summer precipitation while not substantially altering wintertime precipitation. This work shows that it is feasible to apply current limited-area models to climate studies. 30 refs., 12 figs., 5 tabs.
An assessment is made of downscaling estimates of screen temperature and precipitation observed at 976 European stations during 1983-94. A statistical downscaling technique, in which local values are inferred from observed atmospheric predictor variables, is compared against two dynamical downscaling techniques, based on the use of the screen temperature or precipitation simulated at the nearest grid point in integrations of two climate models. In one integration a global general circulation model (GCM) is constrained to reproduce the observed atmospheric circulation over the period of interest, while the second involves a high-resolution regional climate model (RCM) nested inside the GCM.The dynamical and statistical methods are compared in terms of the correlation between the estimated and observed time series of monthly anomalies. For estimates of temperature a high degree of skill is found, especially over western, central, and northern Europe; for precipitation skill is lower (average correlations ranging from 0.4 in summer to 0.7 in winter). Overall, the dynamical and statistical methods show similar levels of skill, although the statistical method is better for summertime estimates of temperature while the dynamical methods give slightly better estimates of wintertime precipitation. In general, therefore, the skill with which present-day surface climate anomalies can be derived from atmospheric observations is not improved by using the sophisticated calculations of subgrid-scale processes made in climate models rather than simple empirical relationships. It does not necessarily follow that statistical and dynamical downscaling estimates of changes in surface climate will also possess equal skill.By the above measure the two dynamical techniques possess approximately equal skill; however, they are also compared by assessing errors in the mean and variance of monthly values and errors in the simulated distributions of daily values. Such errors arise from systematic biases in the models plus the effect of unresolved local forcings. For precipitation the results show that the RCM offers clear benefits relative to the GCM: the simulated variability of both daily and monthly values, although lower than observed, is much more realistic than in the GCM because the finer grid reduces the amount of spatial smoothing implicit in the use of grid-box variables. The climatological means are also simulated better in the winter half of the year because the RCM captures some of the mesoscale detail present in observed distributions. The temperature fields contain a mesoscale orographic signal that is skillfully reproduced by the RCM; however, this is not a source of increased skill relative to the GCM since elevation biases can be largely removed using simple empirical corrections based on spatially averaged lapse rates. Nevertheless, the average skill of downscaled climatological mean temperature values is higher in the RCM in nearly all months. The additional skill arises from better resolution of local physiographical features, especially coastlines, and also from the dynamical effects of higher resolution, which generally act to reduce the large-scale systematic biases in the simulated values. Both models tend to overestimate the variability of both daily and monthly mean temperature. On average the RCM is more skillful in winter but less skillful in summer, due to excessive drying of the soil over central and southern Europe.The downscaling scores for monthly means are compared against scores obtained by using a predictor variable consisting of observations from the nearest station to the predictand station. In general the downscaling scores are significantly worse than those obtained from adjacent stations, indicating that there remains considerable scope for refining the techniques in future. In the case of dynamical downscaling progress can be made by reducing systematic errors through improvements in the representation of physical processes and increased resolution; the prospects for improving statistical downscaling will depend on the availability of the observational data needed to provide longer calibration time series and/or a wider range of predictor variables.
Important surface observations such as the daily maximum and minimum
temperature, daily precipitation, and cloud ceilings often have
localized characteristics that are difficult to reproduce with the
current resolution and the physical parameterizations in
state-of-the-art General Circulation climate Models (GCMs). Many of the
difficulties can be partially attributed to mismatches in scale, local
topography. regional geography and boundary conditions between models
and surface-based observations. Here, we present a method, called
climatological projection by model statistics (CPMS), to relate GCM
grid-point flee-atmosphere statistics, the predictors, to these
important local surface observations. The method can be viewed as a
generalization of the model output statistics (MOS) and perfect prog
(PP) procedures used in numerical weather prediction (NWP) models. It
consists of the application of three statistical methods: 1) principle
component analysis (FICA), 2) canonical correlation, and 3) inflated
regression analysis. The PCA reduces the redundancy of the predictors
The canonical correlation is used to develop simultaneous relationships
between linear combinations of the predictors, the canonical variables,
and the surface-based observations. Finally, inflated regression is used
to relate the important canonical variables to each of the surface-based
observed variables.We demonstrate that even an early version of the
Oregon State University two-level atmospheric GCM (with prescribed sea
surface temperature) produces free-atmosphere statistics than can, when
standardized using the model's internal means and variances (the
MOS-like version of CPMS), closely approximate the observed local
climate. When the model data are standardized by the observed
free-atmosphere means and variances (the PP version of CPMS), however,
the model does not reproduce the observed surface climate as well. Our
results indicate that in the MOS-like version of CPMS the differences
between the output of a ten-year GCM control run and the surface-based
observations are often smaller than the differences between the
observations of two ten-year periods. Such positive results suggest that
GCMs may already contain important climatological information that can
be used to infer the local climate.
General circulation models (GCMs) suggest that rising concentrations of greenhouse gases may have significant consequences for the global climate. What is less clear is the extent to which local (subgrid) scale meteorological processes will be affected. So-called 'downscaling' techniques have subsequently emerged as a means of bridging the gap between what climate modellers are currently able to provide and what impact assessors require. This article reviews the present generation of downscaling tools under four main headings: regression methods; weather pattern (circulation)-based approaches; stochastic weather generators; and limited-area climate models. The penultimate section summarizes the results of an international experiment to intercompare several precipitation models used for downscaling. It shows that circulation-based downscaling methods perform well in simulating present observed and model-generated daily precipitation characteristics, but are able to capture only part of the daily precipitation variability changes associated with model-derived changes in climate. The final section examines a number of ongoing challenges to the future development of climate downscaling.
Artificial neural nets are used in an empirical down-scaling procedure to derive daily subgrid-scale precipitation from general circulation model (GCM) geopotential height and specific humidity data. The neural net-based transfer functions are developed using a 2°×2·5° gridded data assimilation product from the Goddard Space Flight Center, applied to a 4×4 matrix of grid-cells centred on the Susquehanna river basin. The down-scaled precipitation is a close match to the observed data (temporal correlations at individual grid-points range from 0·6 to 0·84). Doubled CO2 climate change scenarios are produced by applying the same transfer functions to the geopotential height and specific humidity fields from 1×CO2 and 2×CO2 simulations of version II of the GENESIS climate model. The analysis indicates a 32 per cent increase in spring and summer rainfall over the basin, resulting from changes in both moisture availability and the orientation of the storm track over the region. The down-scaled precipitation increases, derived from the change in the GCM's circulation and humidity fields, are considerably larger than the change in the model's actual computed precipitation. © 1998 Royal Meteorological Society.
Because of the coarse resolution of general circulation models (GCM), ‘downscaling’ techniques have emerged as a means of relating meso-scale atmospheric variables to grid- and sub-grid-scale surface variables. This study investigates these relationships. As a precursor, inter-variable correlations were investigated within a suite of 15 potential downscaling predictor variables on a daily time-scale for six regions in the conterminous USA, and observed correlations were compared with those based on the HadCM2 coupled ocean/atmosphere GCM. A comparison was then made of observed and model correlations between daily precipitation occurrence (a time series of zeroes and ones) and wet-day amounts and the 15 predictors. These two analyses provided new insights into model performance and provide results that are central to the choice of predictor variables in downscaling of daily precipitation. Also determined were the spatial character of relationships between observed daily precipitation and both mean sea-level pressure (mslp) and atmospheric moisture and daily precipitation for selected regions. The question of whether the same relationships are replicated by HadCM2 was also examined. This allowed the assessment of the spatial consistency of key predictor–predictand relationships in observed and HadCM2 data. Finally, the temporal stability of these relationships in the GCM was examined. Little difference between results for 1980–1999 and 2080–2099 was observed.
Results are assessed from a 10‐year simulation of the equilibrium response to doubled carbon dioxide (CO 2 ) over Europe made with a nested high‐resolution regional‐climate model (RCM). the simulated changes are compared against those produced by the driving global general‐circulation model (GCM). the domain‐averaged increases in temperature and moisture content are similar in both models. Because of a stronger hydrological cycle the increases in precipitation and evaporation are larger in the RCM than in the GCM, whereas the reductions in lower and middle tropospheric relative humidity and cloud cover are smaller.
The frequency of intense precipitation events increases substantially in both models; however, the fractional changes are significantly smaller in the RCM. the proportion of precipitation associated with convection also increases in both models; however, much of the increase in intense events is explained simply by increased atmospheric moisture concentrations, especially in the RCM.
The time‐averaged precipitation changes in the RCM contain a substantial mesoscale component on scales not resolved by the GCM. Attempts are made to reconstruct this component from the changes in the large‐scale atmospheric circulation using empirical relationships calibrated from the 1 x CO 2 integration. These are largely unsuccessful, indicating that simple downscaling schemes to generate high‐resolution scenarios of climate change from coarse‐grid GCM data may be of only limited applicability. Further statistical calculations suggest that longer integrations (∼ 30 years) are needed to reduce the sampling uncertainty associated with the simulated mesoscale component to an acceptable level.
The large‐scale patterns of change of surface temperature and precipitation reveal significant regional contrasts which are influenced both by changes in atmospheric circulation and regional physical feedbacks. the RCM changes are similar to those of the driving GCM except in summer. the differences in the summer changes are traced to differences between the 1 x CO 2 integrations, in which the influence of the lateral boundary forcing on the RCM simulation is found to be anomalously weak. It is argued that any development of significant divergence between the RCM and GCM solutions on scales resolved by the latter may imply the need to refine or replace the one‐way nesting technique currently used in many regional modelling experiments.
Using one of the smaller domains, a 10-year RCM simulation was carried out, driven by a coupled atmosphere/mixed-layer-ocean version of the GCM. Over the region of interest the general circulation and daily synoptic variability is realistically simulated by the GCM and, therefore, also by the RCM (see above). Stronger vertical motions in the RCM lead to a general increase in dynamical precipitation relative to the GCM, and thus a drier and warmer troposphere and reduced convective cloud and precipitation. Layer-cloud cover is also reduced in the RCM, due to a time-step dependence in the treatment of the dissipation of ice cloud. Significant changes occur in the surface heat balance. the spatial patterns of surface air temperature and precipitation over Europe are well simulated by both the GCM and the RCM on scales resolved by the former. At finer scales the RCM contains a strong signal which is related to orographic height. Validation against a detailed observed climatology for Great Britain demonstrates that this signal contains considerable skill.
This study describes a new coupled ocean-atmosphere general circulation model (OAGCM) developed for studies of climate change
and results from a hindcast experiment. The model includes various physical and technical improvements relative to an earlier
version of the Hadley Centre OAGCM. A coupled spinup process is used to bring the model to equilibrium. Compared to uncoupled
spinup methods this is computationally more expensive, but helps to counter climate drift arising from inadequate sampling
of short time scale coupled variability when the components are equilibrated separately. Including sea ice advection and enhancing
reference surface salinities in high southern latitudes in austral winter to promote bottom water formation during spinup
appears to have stabilized the high-latitude drift exhibited in the earlier model’s control run. In the present study, the
atmospheric control climate is stable on multi-century time scales with a drift in global average surface air temperature
of only +0.016 K/century, despite a small residual drift in the deep ocean. The control climate is an improvement over the
earlier model in several respects, notably in its variability on short time scales. Two anomaly runs are presented incorporating
estimated forcing changes over the period 1860 to 1990 arising from greenhouse gases alone and from greenhouse gases plus
the radiative scattering effect of sulphate aerosols. These allow validation of the model against the instrumental climate
record. Inclusion of aerosol forcing gives a significantly better simulation of historical temperature patterns, although
comparisons against recent sea ice trends are equivocal. These studies emphasize the potential importance of including additional
forcing terms apart from greenhouse gases in climate simulations, and refining estimates of their spatial distribution and
magnitude.
Two ten-year simulations made with a European regional climate model (RCM) are compared. They are driven by the same observed
sea surface temperatures but use different lateral boundary forcing. For one simulation, RCM AMIP, this forcing is obtained
from a standard integration of a global general circulation model (GCM AMIP), whereas for the other simulation, RCM ASSIM,
it is derived from a time series of operational analyses. The archive of analysis fields (surface pressure plus winds and
temperatures on various pressure levels) is not sufficiently comprehensive to provide directly the full set of driving fields
required for the RCM (in particular, no moisture fields are present), so these are obtained via a GCM integration, GCM ASSIM,
in which the model is continuously relaxed towards the analysis fields using a data assimilation technique. Errors in RCM
AMIP can arise either from the internal RCM physics or from errors in the lateral boundary forcing inherited from GCM AMIP.
Errors in RCM ASSIM can arise from the internal RCM physics or the boundary moisture forcing but not from the driving circulation.
Although previous studies have considered RCM integrations driven either by output from standard GCM integrations or operational
analyses, our study is the first to compare parallel integrations of each type. This allows the total systematic error in
an RCM integration driven by standard GCM output to be partitioned into components arising from the driving circulation and
the internal RCM physics. These components indicate the scope for reducing regional simulation biases by improving either
the driving GCM or the RCM itself. The results relate mainly to elements of surface climate likely to be influenced by both
the driving circulation and regional physical processes operating in the RCM. For cloud cover, errors are found to arise largely
from the internal RCM physics. Values are too low despite a positive relative humidity bias, indicating shortcomings in the
parametrisation scheme used to calculate cloud cover. In summer, surface temperature and precipitation errors are also explained
principally by regional processes. For example excessive solar heating leads to anomalously high surface temperatures over
southern Europe and excessive drying of the soil reduces precipitation in the south eastern sector of the domain. The lateral
boundary forcing reduces precipitation in the south eastern sector of the domain. The lateral boundary forcing also exerts
some influence, mainly via a tropospheric cold bias which partially offsets the warming over southern Europe and also increases
precipitation. In other seasons the lateral boundary forcing and the regional physics both contribute significantly to the
errors in surface temperature and precipitation. In winter the boundary forcing (apart from moisture) is responsible for about
60% of the total error variance for temperature and about 40% for precipitation, due to the cold bias and circulation errors
such as a southward shift in the storm track. The remaining errors arise from the regional physics, although for precipitation
an excessive supply of moisture from the lateral boundaries also contributes. The skill of the mesoscale component of the
surface temperature and precipitation distributions exceeds previous estimates, due to more realistic observed climatology.
The mesoscale patterns are very similar in the two RCM simulations indicating that errors in the simulation of fine scale
detail arise mainly from inadequate representations of local forcings rather than errors in the large-scale circulation. Circulation
errors in RCM AMIP (e.g. cold bias, southward shift of storm track) are also present in GCM AMIP, but are largely absent in
RCM ASSIM except in summer. This confirms evidence from previous work that the key to reducing most circulation errors in
the RCM lies in improving the driving GCM. Regional processes only make a major contribution to circulation errors in summer,
when reduced advection from the boundaries allows errors in surface temperature to be transmitted more effectively into the
troposphere. Finally, we find evidence of error balances in the GCM which act to minimise biases in important climatological
variables. This reflects tuning of the model physics at GCM resolution. In order to achieve simultaneous optimisation of the
RCM and GCM at widely differing resolutions it may be necessary to introduce explicit scale dependences into some aspects
of the physics.
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