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Global average annual surface temperature change (from baseline 1971-2000 period) during the 1850 to 2005 period for the historical (all forcing) and four RCP simulations for 2006 to 2100 period. Observational changes are also shown.
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Fifth phase of the Climate Model Intercomparison Project (CMIP5) is the principal framework for coordinated climate modeling experimentation supporting the preparation of the IPCC 5 th Assessment Report to be released in 2013. About 20 modeling groups from around the world are undertaking the CMIP5 experiments and model data is being hosted on the...
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... rate was highest in high latitudes and over land in the simulation with all forcing. This model simulated warming was smaller than the observed change which is 0.45 o C ( Brohan et al., 2006) and can be attributed to model mean annual surface temperature being warmer than observed during the 1900 to 1960 period (see Figure 4). during the 1981 to 2005 period. ...
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... RCPs and the Mk3.6 climate model simulates substantial warming during the 21 st century, especially for the RCP8.5 (Fig 4). Climate change projection data using the four RCPs has been used to compute projected changes in mean annual surface temperature during the 2006 to 2030 period ( Figure 5). ...
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A convolution-based method of spectral nudging of atmospheric fields is developed in the Australian Community Climate and Earth Systems Simulator (ACCESS) version 1.3 which uses the UK Met Office Unified Model version 7.3 as its atmospheric component. The use of convolutions allow for flexibility in application to different atmospheric grids. An ap...
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... This model is updated from its recent predecessors Mk3.0 and Mk3.5. In Mk3.6, an interactive aerosol scheme has been added to explicitly treat sulfate, sea salt, dust, and carbonaceous aerosol (Syktus et al. 2011). Additionally, the radiation scheme and atmospheric physical components have been updated. ...
... Additionally, the radiation scheme and atmospheric physical components have been updated. That enables this model to investigate the impact of aerosol on climate (Syktus et al. 2011). Within this context, Rotstayn et al. (2011) reported that mineral dust played a key role in simulating rainfall variability that corresponded with ENSO in Australia. ...
Climate models are mathematical representations of the Earth’s climate system. Besides representing the fundamental controls on Earth’s climate in the form of solar radiation, surface and atmospheric albedo, and terrestrial infrared radiation, climate models are able to simulate the interactions and feedback processes of the physical, chemical, and biological features of the climate system at varying levels of detail. Running on powerful computers, they can be employed to predict future climate conditions.
... Future monthly temperature and precipitation data for the year 2050 under the CSIRO MK3.6 (SRES A1B) model were downloaded from "climond.org" database in the .GRD format and processed to produce a spreadsheet with these variables on a monthly basis [68,69] (Table S2). Seedlings were grown under a common temperature scheme and two different watering treatments (frequent watering (FR): watering every 7 days; and non-frequent watering (NF): watering every 20 days) simulating different monthly precipitation patterns. ...
Seasonality, rather than annual precipitation levels, is expected to affect the adaptive responses of plant populations under future climate change. To estimate adaptive traits’ variation, we conducted a common garden experiment with two beech populations from contrasting climatic origins (Evros with longer drought intervals during summer and higher precipitation seasonality, and Drama representing a more temperate ecosystem). We simulated two different watering treatments (frequent vs. non-frequent) on beech seedlings, according to predicted monthly precipitation levels expected to prevail in 2050 by the CSIRO MK3.6 SRESA1B model, considering as reference area a natural beech stand in Mt. Rodopi, Greece. A series of morphological and stem anatomical traits were measured. Seedling survival was greater for the Evros population compared to that of Drama under non-frequent watering, while no difference in survival was detected under frequent watering. Leaf morphological traits were not generally affected by watering frequency except for leaf circularity, which was found to be lower under non-frequent watering for both populations. Stomata density in leaves was found to be higher in the Evros population and lower in the Drama population under non-frequent watering than frequent. Stem anatomical traits were higher under non-frequent watering for Evros but lower for the Drama population. Multivariate analyses clearly discriminated populations under non-frequent rather than frequent watering, indicating genetic adaptation to the population’s environment of origin.
... All the physics and calculation of non-linear term require spectral transformation onto reduced Gaussian grid (Voldoire et al. 2013). The CSIRO and the Queensland Climate Change Center of Excellence (QCCCE) depend on spectral model (T63) that utilizes the flux form of dynamical equations (Syktus et al. 2011;Jeffrey et al. 2013). The Institute Pierre-Simon Laplace (IPSL) is also selected for this study and used medium range resolution with 144 × 142 points (2.5 • × 1.25 • ). ...
Regional climate models (RCM) are commonly used to downscale the coarse resolution general circulation models (GCMs) to produce climate variables at spatially high-resolution grids. The quality of the downscaled data depends on the skills of both GCMs and RCMs. In this study, 10 GCMs are used to constrain the boundary and provide initial conditions of three RCMs. A total of 18 GCM-RCMs combinations are employed to produce simulations over East Africa (EA). The accuracy of simulated rainfalls is evaluated with respect to Climate Research Unit (CRU) rainfall to identify the best GCM-RCM combinations. Bias, root mean squared error (RMSE), correlation coefficient, and MAE-based model skill score have shown that MPI-REMO, MIROC-REMO, MPI-RCA4, IPSL-RCA4, CCCMA-RCA4, MOHC-CCLM, MOHC-REMO, and CNRM-RCA4 during spring season; ICHEC-REMO, MIROC-REMO, MOHC-REMO, MIROC-RCA4, CSIRO-RCA4, and MPI-REMO during autumn season; CSIRO-RCA4, MIROC-RCA4, CCCMA-RCA4, MIROC-REMO, CNRM-RCA4, and MOHC-RECA during boreal summer; and ICHEC-REMO, NOAA-RCA4, MOHC-REMO, MOHC-CCLM, MIROC-REMO, MPI-REMO, and IPSL-RCA4 during boreal winter season are the best performing GCM-RCM combination. It is also evident that the skills of the models are better in autumn than their skills in boreal spring and summer. Moreover, summer rain in EA is the most difficult for models to simulate. Comparison of annual mean with the CRU rainfall shows that MPI-REMO, MIROC-REMO, CSIRO-RCA4, MOHC-REMO, CCCma-RCA4, IPSL-RCA4, and CNRM-RCA4 are also the best GCM-RCM combinations as observed from strong significant spatial correlation, as well as low bias, RMSE, and positive skill score as high as 0.7. Therefore, the GCM-RCM combinations that exhibit superior performance over EA in most seasons as well as in capturing observed annual mean are CCCMA-RCA4, MIROC-REMO, MPI-REMO, IPSL-RCA4, CSIRO-RCA4, MOHC-REMO, and MIROC-RCA4. The difference in skills between models as well as variation of the same model skill both spatially and seasonally implies the role of several factors such as local topography, vegetation, and surface type as well as robustness of model physics in capturing small scale processes such as mesoscale convection in boreal summer (e.g., over Ethiopian highlands).
... In March 2013, the surviving normal seedlings of both provenances were put in a growth chamber under simulated temperature and precipitation levels estimated from the CSIRO MK3 CGM model, according to the expected conditions in the year 2050 (downloaded from Climong.org) (Kriticos et al., 2012) (Supplement 1). The specific model was selected for its relevance with the summer drought periods in the Mediterranean region (Marcos and Tsimplis, 2008;Syktus et al., 2011;Ziv et al., 2013;Pulvento et al., 2015). The reference area for the climate simulation in the growth chamber was the location "Agios Georgios" (Drama, Greece) that corresponds to population D4 in this study. ...
The ability of beech ( Fagus sylvatica L.) populations to adapt to the ongoing climate change is crucial for the maintenance of economic and social benefits and for the conservation of biodiversity in Europe and especially in the southeastern part of the continent, where environmental change is expected to be more intense. Beech populations in the region cover multiple ecological conditions at a small geographical range and have a complex biogeographical background involving several postglacial lineages originating from distant or local refugia. In this study, we tested the existing adaptive potential of eight beech populations from two provenances in N.E. Greece (Evros and Drama), under simulated controlled climate change conditions in a growth chamber and in the field. In the growth chamber, simulated conditions of temperature and precipitation for the year 2050 were applied for three years, under two different irrigation schemes, a non-frequent (A1) and a frequent one (A2). Seedling survival, growth and leaf phenological traits were used as adaptive traits. The results showed that beech seedlings were generally able to survive under climate change conditions and showed adaptive differences among provenances and populations. Furthermore, beech genotypes demonstrated an impressive phenotypic plasticity by changing the duration of their growing season allowing them to avoid environmental stress and high selection pressure. Different populations and provenances were connected with different adaptation strategies, that relate mainly to the temporal distribution patterns of precipitation and temperature, rather than the average annual or monthly values of these measures. Additionally, different adaptive strategies appeared among beech seedlings when the same amount of water was distributed differently within each month. This indicates that the physiological response mechanisms of beech individuals are very complex and depend on several interacting parameters. For this reason, the choice of beech provenances for translocation and use in afforestation or reforestation projects should consider the small scale ecotypic diversity of the species and view multiple environmental and climatic parameters in connection to each other.
... In March 2013, the surviving containerized normal seedlings of both provenances were put in a growth chamber under simulated temperature and precipitation levels estimated from the CSIRO MK3 CGM model, according to the expected conditions in the year 2050 (downloaded from Climong.org) (Kriticos et al., 2011) (Supplement Table 2). The specific model was selected for its relevance with the summer drought periods in the Mediterranean region (Marcos and Tsimplis, 2008;Syktus et al., 2011;Ziv et al., 2013;Pulvento et al., 2015). The reference area for the climate simulation in the growth chamber was the location "Agios Georgios" (Drama, Greece) that corresponds to population D4 in this study. ...
The ability of beech (Fagus sylvatica L.) populations to adapt to the ongoing climate change is especially important in the southern part of Europe, where environmental change is expected to be more intense. In this study, we tested the existing adaptive potential of eight beech populations from two provenances in N.E. Greece (Evros and Drama) that show differences in their environmental conditions and biogeographical background. Seedling survival, growth and leaf phenological traits were selected as adaptive traits and were measured under simulated controlled climate change conditions in a growth chamber. Seedling survival was also tested under current conditions in the field. In the growth chamber, simulated conditions of temperature and precipitation for the year 2050 were applied for 3 years, under two different irrigation schemes, where the same amount of water was distributed either frequently (once every week) or non-frequently (once in 20 days). The results showed that beech seedlings were generally able to survive under climate change conditions and showed adaptive differences among provenances and populations. Furthermore, changes in the duration of the growing season of seedlings were recorded in the growth chamber, allowing them to avoid environmental stress and high selection pressure. Differences were observed between populations and provenances in terms of temporal distribution patterns of precipitation and temperature, rather than the average annual or monthly values of these measures. Additionally, different adaptive strategies appeared among beech seedlings when the same amount of water was distributed differently within each month. This indicates that the physiological response mechanisms of beech individuals are very complex and depend on several interacting parameters. For this reason, the choice of beech provenances for translocation and use in afforestation or reforestation projects should consider the small scale ecotypic diversity of the species and view multiple environmental and climatic parameters in connection to each other.
... Models with a dynamical core are required in order to calculate aerosol-cloud feedbacks (Meehl et al., 2013). General circulation models that do model aerosol-cloud feedbacks parameterise the interactions between soluble aerosols and clouds through schemes which compute the effect the physical changes from aerosol on clouds (Ghan et al., 2016;Bentsen et al., 2012;Syktus et al., 2011). ...
The effects of aerosols on the climate system are a major source of uncertainty in past and future simulations of climate. The role played by carbonaceous aerosols is particularly uncertain. Black carbon, which absorbs shortwave radiation, exerts a positive radiative forcing of the climate system, warming the Earth. This warming is offset by co-emitted organic carbon, which scatters shortwave radiation back to space. However, a subset of organic carbon aerosols, called brown carbon, may be absorbing, further complicating the issue. Carbonaceous aerosols also influence cloud formation and properties, potentially contributing an additional cooling of Earth’s surface temperatures.
This work aims at improving the simulation of carbonaceous aerosols in the UK Met Office’s HadGEM3-UKCA model with the GLOMAP-mode aerosol scheme. That model does not always compare well with regional multi-variable observations, often underestimating surface concentrations and aerosol optical depth. Although single scattering albedo, a measure of aerosol absorption, is well replicated in biomass burning regions, it is overestimated in regions where fossil- and bio-fuel emissions are dominant. To improve the comparison, we propose a range of changes to modelled carbonaceous aerosol emissions, refractive index and density. An analysis of the sensitivity of the model to these changes shows that emissions alone cannot be responsible for the discrepancies between observations and models. The analysis also shows that while the black carbon mass absorption coefficient needs to be high in fossil fuel combustion regions, black carbon absorption must be reduced in biomass burning areas to leave room for brown carbon absorption.
A selected combination of changes leads to improvements in the simulation of carbonaceous aerosols in most regions compared with observations. Those improvements do not however lead to a significant change in the total aerosol effective radiative forcing, but reduces the contribution of aerosol-radiation interactions. Using an internally-mixed aerosol scheme means that changes in one aerosol species affects the simulation of other aerosol types. Therefore, aerosol model improvements require an integrated consideration of all aerosol species.
... The atmospheric model is a spectral model (T63) that utilizes the flux form of the dynamical equations. The atmospheric model has 18 vertical levels and horizontal resolution of approximately 1.875°Â 1.875°(spectral T63) (Syktus et al., 2011;Rotstayn et al., 2013). ...
Both drought and aridity indicate imbalance in water availability. While drought is a natural temporal hazard, aridity is a constant climatic feature. This paper investigates the changes in drought characteristics across different aridity zones with and without consideration of potential evapotranspiration (PET), as a means to better assess drought in a warming climate. Two drought indexes are employed: (1) Standardized precipitation index (SPI), which is solely based on precipitation; and (2) Reconnaissance drought index (RDI), which, in addition to precipitation, takes PET into account. The two indexes are first employed to observed precipitation and PET data for the period 1960–2009 from the CRU (Climate Research Unit, University of East Anglia) TS 3.1 database. The results indicate that although all the aridity zones experience both downward and upward drought trends, no significant trend is found over large parts of the zones. However, the agreement between SPI and RDI reduces from the hyper-arid zone on one extreme toward the humid zone on the other. In the three more humid zones (i.e. semi-arid, sub-humid, and humid), the indexes exhibit different trends, with RDI showing more decreasing trends (i.e. becoming drier). While SPI generally shows more drought prone areas than RDI for the pre-1998 period, the opposite is observed for the post-1998 period. Given the known changes to PET in observed records, and also expected increases as global warming intensifies, these results suggest that RDI will be consistently different to the SPI as global warming intensifies. This hypothesis is further tested for historic and future climate projections from the CSIRO (Commonwealth Scientific and Industrial Research Organisation, Australia) Mk3.6 global climate model (GCM), with use of the fifth phase of the Coupled Model Intercomparison Project (CMIP5) and RCP8.5 (Representative Concentration Pathways). In this case, PET is calculated using FAO56-PM model for assessment of RDI. The results suggest that agreement between SPI and RDI is affected and decreases remarkably over time (between 1850 and 2100). All these lead to the conclusion that, in the face of climate change, PET, an important component in the hydrologic cycle, should not be ignored in drought modeling.
... The model also includes dynamic sea ice and a soil-canopy scheme with prescribed vegetation properties, but no carbon cycle. For further details of the model see Rotstayn et al. (2011Rotstayn et al. ( , 2012 and, for preliminary assessments of its performance see Rotstayn et al. (2010) and Syktus et al. (2011). ...
We assess the performance of 30 CMIP5 and two CMIP3 models using metrics based on an all-Australia average rainfall and NINO3.4 sea surface temperatures (SSTs). The assessment provides an insight into the relative performance of the models at simulating long-term average monthly mean values, interannual variability and the seasonal cycles. It also includes a measure of the ability to capture observed rainfall–NINO3.4 SST correlations. In general, the rainfall features are reasonably simulated and there is relatively little difference amongst the models but the NINO3.4 SST features appear more difficult to simulate as evidenced by the greater range in metric scores. We find little evidence of consistency in the sense that a relatively good metric score for one feature does not imply a relatively good score for another related (but independent) feature. The assessment indicates that more recent models perform slightly better than their predecessors, especially with regard to the NINO3.4 metrics. We also focus on the ability of models to reproduce the observed seasonal cycle of rainfall–SST correlations since this is a direct indicator of a model's potential utility for seasonal forecasting over Australia. This indicates some relatively good models (CNRM, HadGEM2-ESM, MPI-ESM-LR and MPI-ESM-MR) and some relatively poor models (CSIRO-Mk3.5, FGOALS, GISS-E2-HP1 and INMCM4). We find that the ACCESS1.3 and CSIRO-Mk3.6 models rank as near median performers on this metric and represent improvements over their predecessors (ACCESS1.0, CSIRO-Mk3.0 and CSIRO-Mk3.5).
To meet increasing demand for information on future drought hazard to help Australia build resilience and preparedness under a changing climate, we developed new information on drought projections for Australia and four sub-regions based on the natural resources management (NRM) zones. The information reported here includes: two drought indices (the Standardised Precipitation Index, SPI, and the Standardised Soil Moisture Index, SSMI); four drought metrics (percent time spent in droughts, mean drought duration, mean drought frequency, and mean drought intensity); and two drought categories (drought and extreme drought). The projections are developed from CMIP5 global climate model simulations of rainfall and soil moisture for the historical (1900–2005) and future (2006–2 100) climates.
The multi-model results project significant increases in all the drought hazard metrics, except frequency, with larger changes in the SSMI compared to SPI. The more severe drought hazard under climate change is apparent over a larger area than previously indicated, particularly in southern and eastern Australia. Although the majority of modelling results indicate more severe drought conditions, the range in the results is large, mainly because of the uncertainty in the global climate model rainfall projections. A projected decrease in rainfall results in a projected increase in drought severity (which is further enhanced by the increase in potential evapotranspiration), and a projected increase in rainfall results in a projected decrease in drought severity (moderated by the increase in potential evapotranspiration). The assessment of the ability of models to reproduce historical observations does not show clusters of models that best simulate all the different drought metrics. Unlike previously assumed, the results show that the models that best reproduce the observed rainfall are not necessarily best in simulating the drought metrics. For this reason, all the models are used here to estimate the multi-model median and range of results. The large uncertainty in the projections can be confusing to end users and present challenges in adapting to climate change. The presentation and communication of projections here will also go some way towards overcoming this challenge.
In this study we investigated changes in the Tsushima Warm Current (TWC) under the global warming scenario RCP 4.5 by analysing the results from the World Climate Research Program's (WCRP) Coupled Model Intercomparison Project Phase 5 (CMIP5). Among the four models that had been employed to analyse the Tsushima Warm Current during the 20th Century, in the CSIRO-Mk3.6.0 and HadGEM2-CC models the transports of the Tsushima Warm Current were 2.8 Sv and 2.1 Sv, respectively, and comparable to observed transport, which is between 2.4 and 2.77 Sv. In the other two models the transports were much greater or smaller than the observed estimates. Using the two models that properly reproduced the transport of the Tsushima Warm Current we investigated the response of the current under the global warming scenario. In both models the volume transports and the temperature were greater in the future climate scenario. Warm advection into the East Sea was intensified to raise the temperature and consequently the heat loss to the air.
