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Seven homogeneous zones of India (NMI, NWI, NCI, NEI, EPI, WPI and SPI) as identified by Sontakke et al. (2008)
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Present study has assessed different sources of uncertainties in multi-model precipitation projection using Global Climate Models (GCMs) from coupled model inter-comparison project phase five (CMIP5) experiment over seven homogeneous rainfall zones of India namely North Mountainous India (NMI), Northwest India (NWI), North Central India (NCI), Nort...
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The Quantile Mapping (QM) bias correction (BC) technique was applied for the first time to address biases in the simulated precipitation over Vietnam from the Regional Climate Model (RegCM) driven by five Coupled Model Intercomparison Project Phase 5 (CMIP5) Global Climate Model (GCM) products. The QM process was implemented for the period 1986-200...
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... Global climate models (GCMs) are commonly used to forecast future precipitation. Many GCMs have produced precipitation outputs for future climate scenarios as part of the coupled model intercomparison programs (CMIPs) [4][5][6]. After the Intergovernmental Panel on Climate Change (IPCC) 4th Assessment Report (AR4) and CMIP3 GCMs, future projection studies have gradually increased [7]. ...
Understanding how temporal precipitation variability will behave in the future is crucial. Change point detection and sub-trend analysis were done on monthly total precipitation data acquired from the Coordinated Regional Climate Downscaling Experiment project in this study. Sub-periods have been determined based on the natural structure of the data set, as opposed to standard sub-period determination approaches. For the period 1971-2100, six different time series were used under three global climate models (CNRM-CERFACS-CNRM-CM5, ICHEC-EC-EARTH, MPI-M-MPI-ESM-LR) and two emission scenarios (Representative Concentration Pathway (RCP) 4.5 and RCP 8.5). The study area has been selected to be the Konya Endorheic Basin in Turkey, which has been significantly influenced by climate change and human impacts. For change point detection, the Pruned Exact Linear Time method (PELT) and Binary Segmentation (BS) were employed, for trend analysis, Mann Kendall (MK) and Şen-Innovative Trend Analysis (Şen-ITA), and for both change point detection and sub-trend analysis, the Onyutha Trend Test (OTT) was utilized. The findings revealed four change points and five sub-periods. Summer precipitation has reduced considerably at a 95% significance level. There is strong evidence that precipitation has decreased between 1981 and 2070. Precipitation decreases similarly in the middle basin for the 1981-2000 and 2041-2070 years.
... However, all the GCMs have some assumptions about the climate system, which result in uncertainties in their outputs (Woldemeskel et al. 2012Das et al. 2017). The uncertainties associated with the climate projections from the GCMs involve intermodal differences, internal climate variability, and a wide range of possible emission scenarios (Deser et al. 2012;Hingray and Saïd, 2014;Akhter et al. 2018). Future trajectories of greenhouse gases, aerosols, land use change, and ozone concentrations are some of the external factors influencing the climate system and, thus, are also included in the emission scenarios used by the models. ...
Selection of suitable general circulation models (GCMs) increases the reliability in the assessment of the impact of climate change. In a recent study, we introduced the concepts of complex networks for evaluating the performance of GCMs in simulating rainfall using clustering coefficient, which quantifies the tendency of a network to cluster. However, since networks have different properties, it is important to use any additional measure to verify the consistency in the outcomes for more reliable interpretations and conclusions. To this end, in the present study, we employ the shortest path length to evaluate the performance and ranking of GCMs for rainfall simulation. The shortest path length quantifies the efficiency of a network in transmitting information between the nodes in the network. It is generally a better measure, as its calculation involves every pair of nodes in the entire network, rather than only the nodes that are in ‘clusters’ with a given node of interest. We evaluate the ability of 49 GCMs from the Coupled Model Intercomparison Project phase 6 (CMIP6) to simulate the monthly rainfall in India for the period 1961–2014. We consider each grid, of the 288 grids of 1° × 1° spatial resolution across India, as a network. For each network, we treat the higher-dimensional vectors reconstructed from the scalar rainfall time series as the nodes and the connections between the vectors as the links. We determine the optimum dimension for reconstruction using the false nearest neighbor method. We consider two cases for the evaluation of GCMs: (1) Case 1—whole-year rainfall (January-December); and (2) Case 2—summer monsoon rainfall (June–September). For each grid, we rank the 49 GCMs based on the difference in the average shortest path length between the observed rainfall network and the GCM-simulated rainfall network. For each of Case 1 and Case 2, we employ the group decision-making (GDM) methodology to rank the GCMs for the entire study area, considering all the 288 grids. We then use the comprehensive rating metric (RM) value to combine the ranks obtained for the GCMs for Case 1 and Case 2 and to identify the final ranking of the GCMs. For the whole-year rainfall, the models CMCC-ESM2, NorCPM1, GFDL-ESM4, CMCC-CM2-SR5, and CESM2-WACCM, in order, occupy the top five positions. For the summer monsoon rainfall, the models CESM2-FV2, E3SM-1-1-ECA, CMCC-ESM2, EC-Earth3-Veg-LR and CMCC-CM2-HR4, in order, are the top five. The results from the RM values suggest that the models CMCC-ESM2, CESM2-WACCM, CMCC-CM2-HR4, E3SM-1-1-ECA, and BCC-ESM1 are, in order, the five best-performing models. We find that the ranks obtained for the GCMs based on the shortest path length analysis are in reasonably good agreement and consistent with those obtained using clustering coefficient, especially for the whole-year rainfall.
... As all the GCMs have some common bases and associated assumptions about the climate system, there are uncertainties in the resulting climate outputs (e.g., Yip et al. 2011;Woldemeskel et al. 2012Woldemeskel et al. , 2014Das et al., 2017). The uncertainties associated with the climate projections involve the range of possible emission scenarios, intermodal differences, and internal climate variability (e.g., Deser et al. 2012;Hingray and Saïd 2014;Akhter et al. 2018). Future trajectories of land use change, greenhouse gases (GHGs), ozone concentrations, and aerosols are some of the external factors influencing the climate system and are included in the emission scenarios. ...
Selection of an appropriate ensemble of General Circulation Models (GCMs) is vital to properly assess the impacts of climate change in a region. Several methods exist for the ranking and selection of GCMs. The present study compares the commonly-used Compromise Programming (CP) method and the recently-developed Global Performance Indicator (GPI) technique for ranking the GCMs. The ability of 20 GCMs to simulate the observed monthly rainfall over the period 1961–2005 in the Upper Godavari River basin in India is assessed, and a grid-wise analysis is performed. The SPAtial EFficiency (SPAEF) metric and Kling–Gupta Efficiency (KGE) are used as the performance indicators to evaluate the 20 GCMs. Further, both the CP method and the GPI technique are applied to integrate the values of the performance indicators into one. The group decision-making (GDM) approach is employed to make a collective decision about the rank of the 20 GCMs considering all the grids. The results from the CP method suggest that the best models are MPI-ESM-P, MPI-ESM-LR, and CNRM-CM5-2, whereas those from the GPI technique indicate that the best-performing GCMs are NorESM1-M, FIO-ESM, and MPI-ESM-LR. An analysis is also performed to examine whether the ranking of the GCMs identified based on the grid-wise analysis also holds true when the average rainfall over the entire basin is considered for ranking. To this end, the average rainfall value across all the 12 grids in the basin is used for the evaluation. The ensemble of GCMs identified by the grid-wise study of GCMs using the CP method provides better SPAEF and KGE values when compared to that using the GPI technique. These results suggest that the GCMs have to be evaluated at the individual grids, and then collective information has to be taken to identify the ensemble of the best-performing GCMs.
... As all the GCMs have some common bases and associated assumptions about the climate system, there are uncertainties in the resulting climate outputs (e.g., Yip et al. 2011;Woldemeskel et al. 2012Woldemeskel et al. , 2014Das et al., 2017). The uncertainties associated with the climate projections involve the range of possible emission scenarios, intermodal differences, and internal climate variability (e.g., Deser et al. 2012;Hingray and Saïd 2014;Akhter et al. 2018). Future trajectories of land use change, greenhouse gases (GHGs), ozone concentrations, and aerosols are some of the external factors influencing the climate system and are included in the emission scenarios. ...
Selection of an appropriate ensemble of General Circulation Models (GCMs) is vital to properly assess the impacts of climate change in a region. Several methods exist for the ranking and selection of GCMs. The present study compares the commonly-used Compromise Programming (CP) method and the recently-developed Global Performance Indicator (GPI) technique for ranking the GCMs. The ability of 20 GCMs to simulate the observed monthly rainfall over the period 1961-2005 in the Upper Godavari River basin in India is assessed, and a grid-wise analysis is performed. The SPAtial EFficiency (SPAEF) metric and Kling-Gupta Efficiency (KGE) are used as the performance indicators to evaluate the 20 GCMs. Further, both the CP method and the GPI technique are applied to integrate the values of the performance indicators into one. The group decision-making (GDM) approach is employed to make a collective decision about the rank of the 20 GCMs considering all the grids. The results from the CP method suggest that the best models are MPI-ESM-P, MPI-ESM-LR, and CNRM-CM5-2, whereas those from the GPI technique indicate that the best-performing GCMs are NorESM1-M, FIO-ESM, and MPI-ESM-LR. An analysis is also performed to examine whether the ranking of the GCMs identified based on the grid-wise analysis also holds true when the average rainfall over the entire basin is considered for ranking. To this end, the average rainfall value across all the 12 grids in the basin is used for the evaluation. The ensemble of GCMs identified by the grid-wise study of GCMs using the CP method provides better SPAEF and KGE values when compared to that using the GPI technique. These results suggest that the GCMs have to be evaluated at the individual grids, and then collective information has to be taken to identify the ensemble of the best-performing GCMs.
... Hence, remaining skill (potential − actual) in predictability can be achieved by improving the models' ability to capture the pacific SST of erroneous years like 1997, IOD-monsoon relationship, and Monsoon-El-Niño Modoki relationship efficiently by improving model parameterizations and representation of physical processes [1,50,59,101,152]. Decadal predictions, those at 10 to 30-year time scale as per Smith et al. [137], are often full of uncertainty that mainly stems from internal variability and model variability, which are the spreads of the climate predictions of same model but with different initial conditions (different realizations of the same model) and predictions from different models (also known as inter-model uncertainty), respectively [144,7,47]. At a longer time scale, such as centennial projections, uncertainties linked to likelihood of future forcing are more important. ...
... At a longer time scale, such as centennial projections, uncertainties linked to likelihood of future forcing are more important. Akhter et al. [7] found that the internal variability (defined as standard deviation of detrended variations or simply noise in simulations) accounts for about 70-80% of total uncertainty in precipitation projections and dominate the inter-model variability (defined as standard deviation of multi-model projections at a particular time) [20-30% of total uncertainty]. This contribution reduces to 60% and 50% by the end of the century under RCP 4.5 and RCP 8.5, respectively. ...
... This contribution reduces to 60% and 50% by the end of the century under RCP 4.5 and RCP 8.5, respectively. They also noted higher model spread for changes in precipitation in 3 future periods (2006-35, 2036-65, 2066-95) under RCP 8.5 (~14%, 17%, 23%) compared to RCP 4.5 (13%, 15%, 20%) [7]. In contrast to internal variability, which is inherent to models and can hardly be reduced by ensemble method, inter-model uncertainty can be reduced by weighing the model based on past performance and bias correction of models [144,7,133]. ...
A major section of India’s economy is directly linked with water-dependent food and energy systems. Skillful predictions of droughts play a pivotal role in sustainable water management and evading serious damages to agriculture production and economy of a region. Recent decades have witnessed valuable advances in scientific understanding and prediction of droughts in India. In this review, we synthesize major sources of drought predictability over different regions in India. We find that a few large-scale atmospheric and oceanic circulation patterns and regional scale hydrometeorological variables are key to understanding and predicting drought occurrences. We also present a concise summary of major statistical and dynamical forecasting-based modelling efforts in drought predictions. Although major strides have been taken in drought prediction in the recent decades, important gaps remain in understanding the onset and spatio-temporal dynamics of droughts.
Further, many opportunities of improving the skill of drought prediction over India are envisaged, and many impending challenges are highlighted. The overall picture is that significant efforts and investments are critical for understanding and skillfully predicting droughts over India.
... Empirical quantile mapping approach has adopted in the present study where CDFs of observed and modeled precipitation are estimated using empirical percentiles and values in between the percentiles are approximated using linear interpolation (Gudmundsson et al. 2012). Next, signal time series have been obtained from biascorrected GCMs using dynamic trends as prescribed by Akhter et al. (2018b). In Fig. 3, the percentage anomaly of signal time series for both the variables over three different plant locations has been depicted. ...
... Moderate increments of precipitation (3.37% for RCP 4.5 and 11.21% for RCP 8.5) have been found over Narora. Akhter et al. (2017Akhter et al. ( , 2018b have also found increasing rainfall projection by CMIP5 models over this part of the country. The possible causes of increasing precipitation may be attributed due to increased amount of atmospheric moisture under elevated future warming as per Clausius-Clapeyron equation as found by various researchers over different parts of India (Ghimire et al. 2018;Rai et al. 2019;Dimri et al. 2019;Sannan et al. 2019). ...
... In general, uncertainties have increased with time for both RCPs. Similar results have been shown by Akhter et al. (2018b) over this northwestern part of this country. ...
The present study has assessed the possible water stress scenarios over six nuclear power plant locations (inland plants at Kakrapar, Kota, and Narora and coastal plants at Kodankulam, Kalpakkam, and Tarapur) of India based on downscaled climatic products from 12 coupled model inter-comparison project 5 (CMIP5) simulations. Firstly, statistically downscaled scenarios over power plant locations for water temperature, precipitation, evapotranspiration, and sea surface temperature (SST) have been developed using various statistical downscaling methods. Secondly, the water stress has been quantified by formulating a multivariate standardized water stress index (MSWSI) based on water temperature and freshwater availability (precipitation minus evapotranspiration) over inland plants and a univariate index from the SST for coastal plants. Results have indicated that three inland power plants are not expecting any scarcity of freshwater availability. However, they have been projected to face high to severe water stress from middle to end of the century due to a higher warming rate of water temperature under global warming conditions. Similarly, three coastal plants have also been projected to prevail high to severe water stress through enhanced SST warming. Therefore, the efficiency and productivity of the nuclear plants may reduce under changing climatic conditions.
... A vast majority of studies estimate ToE at global scales (Giorgi and Bi 2009;Hawkins and Sutton 2012), or at large scales including China (Sui et al. 2014) and India (Akhter et al. 2018), with maps showing the local impacts at a broad scale. However, management decisions and adaptation strategies are implemented locally (for instance at the river basin scale), where climate variability can differ considerably from that of the GCM projections. ...
... Lehner et al. (2017) analyze different sources of uncertainty and demonstrated that GCM biases in variability significantly impact ToE detection. Few studies have incorporated these biases to estimate ToE, mostly by using a quantile mapping bias correction (QM; Akhter et al. 2018;Leng et al. 2016;Bureau of Reclamation 2014;Zhou et al. 2018;Zhuan et al. 2018). Because QM can change the GCM trend and produce an effect comparable to other sources of uncertainty such as variability among GCMs (Maraun 2013;Pierce et al. 2013), a procedure for unbiased mapping of GCMs changes to local stations should be evaluated and eventually used in the detection of ToE. ...
The time at which climate change signal can be clearly distinguished from noise is known as time of emergence (ToE) and is typically detected by a general circulation model (GCM) signal-to-noise ratio exceeding a certain threshold. ToE is commonly estimated at large scales from GCMs, although management decisions and adaptation strategies are implemented locally. This paper proposes a methodology to estimate ToE for both precipitation and temperature at local scales (i.e., river basin). The methodology considers local climatic conditions and unbiased GCM projections to estimate ToE by using the statistical power to find when the climate significantly differs from the historical one. The method suggests that ToE for temperature already occurred in three Chilean basins (Limarí, Maipo, and Maule). However, in terms of precipitation, an earlier ToE is clearly identified for the Maule basin, indicating that risk assessment and adaptation measures should be implemented first in this basin.
... Each of the 12 PPQM models has outperformed PP models over NMI, NWI, NEI, EPI and WPI whereas 11 and 10 PPQM models have shown superior Performance metrics of 12 PP downscaled GCMs over seven homogeneous zones of India Overall assessment suggests that although some of the PPQM models have shown slightly larger bias than PP models, they have been more skilful in simulating observed variance, PDF and CDFs over each zone. Some other studies have also reported improved results after bias correction (Sachindra et al., 2014;Akhter et al., 2016;Akhter et al., 2017) ...
Statistical downscaling through perfect prognosis (PP) method is widely utilized to bridge the gap between large‐scale global climate model (GCM) simulations and regional scale or local scale observed predictands. Present study has assessed the performances of PP‐based downscaled CMIP5 GCMs in simulating observed monsoon precipitation over seven homogeneous zones of India, namely, North Mountainous India (NMI), Northwest India (NWI), North Central India (NCI), Northeast India (NEI), West Peninsular India (WPI), East Peninsular India (EPI) and South Peninsular India (SPI). Firstly, PP models have been constructed through principal component regression (PCR) using large‐scale atmospheric predictors from National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis. Secondly, GCM predictors have been imposed on the PP models to downscale large scale GCM simulations at regional scale. Four performance metrics namely percent bias (PB), interquartile relative fractions (IRF), Perkins skill score (PS) and Kuiper metric (KM) have been considered to evaluate skills of downscaled GCMs in reproducing mean, variance, probability distribution function (PDF) and cumulative distribution functions (CDF) of observed precipitation, respectively. As per results of several metrics, PP models have performed relatively better over NCI and SPI zones. However, they have shown poor skills in reproducing the observed variance over all zones. Further to improve the performances of PP models, quantile mapping has been embedded to form hybrid (PPQM) models, which have shown superior skills over all the zones. In addition, PPQM models have also shown their applicability to provide more reliable added value information over sub‐regional scale compared to raw GCMs.
... Climate projections developed for South Asia region show that India might witness an increase in mean annual temperature by 2-3°C in midterm and by 3-5°C in long-term (2081-2100) future (IPCC 2013). The increase in temperature is expected to be more in northern India with overall projected increase in mean annual precipitation with a shift in rainfall pattern (Jain and Kumar 2012;Auffhammer et al. 2012;Akhter et al. 2017;Basha et al. 2017). A trend analysis for Uttar Pradesh shows a significant increase in annual mean and minimum temperature by 0-1.5% and 1.5-3% for the period of 107 years decreases in rainfall by 0-8% in east UP and 16 to 24% in west UP though insignificant for the period of 141 years (1871-2011) (Mondal et al. 2015). ...
The paper aims to explore the biasness in the RegCM climate model outputs for diverse agro-climatic zones of Uttar Pradesh, India, with emphasis on wheat (Rabi growing season) and rice (Kharif growing season) yields with and without bias correction using quantile mapping approach for the baseline period of 1971–2000. The result shows that RCM highly underestimated the maximum and minimum temperature. There exists a bias towards lower precipitation in annual and Kharif and higher in Rabi along with strikingly low intense warm (maximum temperature > 45 °C and 40 °C) and high cold events (maximum temperature < 20 °C and minimum temperature < 5 °C) in the RCM simulation and biased towards low extreme rainfall > 50 mm/day. Bias correction through quantile mapping approach, however, showed excellent agreement for annual and seasonal maximum and minimum temperature and satisfactory for extreme temperatures but drastically failed to correct rainfall. The study also quantified the biasness in the simulated potential, irrigated, and rainfed wheat and rice yield using DSSAT (Decision Support System for Agro-technology Transfer) crop model by employing observed, RCM baseline, and RCM baseline bias-corrected weather data. The grain yields of RCM-simulated wheat and rice were high while the bias-corrected yield has shown good agreement with corresponding observed yield. Future research must account for the development of more reliable RCM models and explicitly bias correction method in specific to complement future analysis.
... We also conduct a sliding trend analysis using different start years from 1933 through 1992, but with a fixed end year (2012) (see Fig. S2), showing generally consistent detection and attribution results to those we obtain from the sliding trend analysis using different end years but with a fixed start year. Such sliding trend analyses have been used previously in climate change detection studies to assess the robustness of findings (e.g., Knutson et al. 2013) or time scale of emergence of detectable trends (Akhter et al. 2018). As shown in Knutson et al. (2013), the 5th or 95th percentile range of control run trend magnitudes tends to be larger for shorter trend durations. ...
Over regions where snowmelt runoff substantially contributes to winter-spring streamflows, warming can accelerate snowmelt and reduce dry-season streamflows. However, conclusive detection of changes and attribution to anthropogenic forcing is hindered by the brevity of observational records, model uncertainty, and uncertainty concerning internal variability. In this study, the detection/attribution of changes in midlatitude North American winter-spring streamflow timing is examined using nine global climate models under multiple forcing scenarios. Robustness across models, start/end dates for trends, and assumptions about internal variability are evaluated. Marginal evidence for an emerging detectable anthropogenic influence (according to four or five of nine models) is found in the north-central United States, where winter-spring streamflows have been starting earlier. Weaker indications of detectable anthropogenic influence (three of nine models) are found in the mountainous western United States/southwestern Canada and in the extreme northeastern United States/Canadian Maritimes. In the former region, a recent shift toward later streamflows has rendered the full-record trend toward earlier streamflows only marginally significant, with possible implications for previously published climate change detection findings for streamflow timing in this region. In the latter region, no forced model shows as large a shift toward earlier streamflow timing as the detectable observed shift. In other (including warm, snow free) regions, observed trends are typically not detectable, although in the U.S. central plains we find detectable delays in streamflow, which are inconsistent with forced model experiments.
