General Circulation Model Output Research Articles

The state and fate of lake ice thickness in the Northern Hemisphere

Lake ice thickness (LIT) is important for regional hydroclimate systems, lake ecosystems, and human activities on the ice, and is thought to be highly susceptible to global warming. However, the spatiotemporal variability in LIT is largely unknown due to the difficulty in deriving in situ measurements and the lack of an effective remote sensing platform. Despite intensive development and applications of lake ice models driven by general circulation model output, evaluation of the global LIT is mostly based on assumed “ideal” lakes in each grid cell of the climate forcing data. A method for calculating the actual global LIT is therefore urgently needed. Here we use satellite altimetry to retrieve ice thickness for 16 large lakes in the Northern Hemisphere (Lake Baikal, Great Slave Lake, and others) with an accuracy of ∼0.2 m for almost three decades. We then develop a 1-D lake ice model driven primarily by remotely sensed data and cross-validated with the altimetric LIT to provide a robust means of estimating LIT for lakes larger than 50 km2 across the Northern Hemisphere. Mean LIT (annual maximum ice thickness) for 1313 simulated lakes and reservoirs covering ∼840,000 km2 for 2003–2018 is 0.63 ± 0.02 m, corresponding to ∼485 Gt of water. LIT changes are projected for 2071–2099 under RCPs 2.6, 6.0, and 8.5, showing that the mean LIT could decrease by ∼0.35 m under the worst concentration pathway and the associated lower ice road availability could have a significant impact on socio-economic activities.

Summertime precipitation in Hokkaido and Kyushu, Japan in response to global warming

We analyzed a large ensemble dataset called the database for Policy Decision Making for Future climate change (d4PDF), which contains 60-km resolution atmospheric general circulation model output and 20-km resolution dynamical downscaling for the Japanese domain. The increase in moisture and precipitation, and their global warming response in June–July–August were described focusing on the differences between Hokkaido and Kyushu; Hokkaido is cool and dry and Kyushu is hot and humid in the current climate. The results suggested that the specific humidity increased almost following the Clausius Clapeyron relation, but the change in stationary circulation suppressed the precipitation increase, except for in western Kyushu. The + 4 K climate in Hokkaido would be as hot and humid as the present climate in Kyushu. The circulation change related to the southward shift of the jet stream and an eastward shift of the Bonin high weakened the moisture flux convergence via a stationary field over central Japan including eastern Kyushu. The transient eddy activity counteracted the increase in humidity, so that the moisture flux convergence and precipitation did not change much over Hokkaido. Because the contribution of tropical cyclones to the total precipitation was at most 10%, the decrease in the number of tropical cyclones did not explain the predicted change in precipitation.

A High-Resolution Regional Climate Model Physics Ensemble for Northern Sub-Saharan Africa

While climate information from General Circulation Models (GCMs) are usually too coarse for climate impact modelers or decision makers from various disciplines (e.g., hydrology, agriculture), Regional Climate Models (RCMs) provide feasible solutions for downscaling GCM output to finer spatiotemporal scales. However, it is well known that the model performance depends largely on the choice of the physical parameterization schemes, but optimal configurations may vary e.g., from region to region. Besides land-surface processes, the most crucial processes to be parameterized in RCMs include radiation (RA), cumulus convection (CU), cloud microphysics (MP), and planetary boundary layer (PBL), partly with complex interactions. Before conducting long-term climate simulations, it is therefore indispensable to identify a suitable combination of physics parameterization schemes for these processes. Using the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis product ERA-Interim as lateral boundary conditions, we derived an ensemble of 16 physics parameterization runs for a larger domain in Northern sub-Saharan Africa (NSSA), northwards of the equator, using two different CU-, MP-, PBL-, and RA schemes, respectively, using the Weather Research and Forecasting (WRF) model for the period 2006–2010 in a horizontal resolution of approximately 9 km. Based on different evaluation strategies including traditional (Taylor diagram, probability densities) and more innovative validation metrics (ensemble structure-amplitude-location (eSAL) analysis, Copula functions) and by means of different observation data for precipitation (P) and temperature (T), the impact of different physics combinations on the representation skill of P and T has been analyzed and discussed in the context of subsequent impact modeling. With the specific experimental setup, we found that the selection of the CU scheme has resulted in the highest impact with respect to the representation of P and T, followed by the RA parameterization scheme. Both, PBL and MP schemes showed much less impact. We conclude that a multi-facet evaluation can finally lead to better choices about good physics scheme combinations.

Assessing the impacts of climate change on drought-prone regions in Bhima sub-basin (India) using the Standard Precipitation Index

Abstract In Bhima sub-basin, the water sector is at high demand and in critical stress due to rapid urbanization. The past few decades have witnessed extreme events and seasonal shifts due to anthropogenic activity triggered climate change. Thus, to evaluate the variability of extreme events, assessing the historical and future trends of precipitation in climate change scenarios is vital for developing comprehensive mitigation and adaptation strategies. This paper examines the drought-prone regions by studying spatio-temporal variation of drought scenarios using the Standard Precipitation Index (SPI). The change factor method is used to downscale precipitation data from general circulation model (GCM) outputs under four Representative Concentration Pathway (RCP) scenarios to project future downscaled precipitation, to be input to examine the drought for 12 months. GCM and scenario uncertainty in climate change impact assessments are examined using box-whisker plots. Temporal variation for 12-month SPI shows significant changes over RCP scenarios. For the beginning of the period, 2021 precipitation is scanty for RCP 2.6 and 4.5 scenarios. Mild to moderate and severe drought events for the RCP 2.6 scenario are more predominant. Severe drought events under the RCP 6.0 scenario dominate over others. Lastly, the inconsistent pattern of drought events for RCP 8.5 is reported.

Impact of global climate change on temperature seasonal and annual of Arak city in the 2040s

Introduction: Global warming is the most important challenge facing man in the 21st century. Warmer weather will increase evapotranspiration, which will exacerbate droughts. One of the main causes of global warming is man himself. Humans have accelerated the Earth's climate change by producing large amounts of greenhouse gases. For this reason, information about changes in the earth's temperature in the next decades has always been considered. The results of the researchers show that climate change has obvious and significant effects on temperature and rainfall in different parts of Iran in the next decades. By predicting and estimating the extent of these effects, climate change impacts can be mitigated with adequate preparedness, low cost, and greater speed.Material and methods: In this study, the effect of climate change on the mean maximum and minimum annual and seasonal temperatures in Arak under the emission scenarios of RCP2.6, RCP4.5, RCP8.5 for the 2040s was investigated. To use the output of General Circulation Models at regional and local scales is that they are using downscaling models, are downscaled. In this study, Statistical Downscaling Model (SDSM) to downscale output of General Circulation Models CanESM2 were used. This model had an acceptable ability to simulate the average maximum and minimum seasonal and annual temperatures in the study area. Results and discussion: According to the obtained results, the mean maximum temperature in winter and spring will decrease under all three scenarios RCP2.6, RCP4.5, RCP8.5, which can indicate that the daily temperature will be cooler in these seasons. However, the mean maximum temperature will increase in summer and autumn, which may indicate that the daily temperature will be warmer in these seasons. The mean minimum temperature in winter and spring under all three scenarios RCP2.6, RCP4.5, RCP8.5 will decrease and increase in summer seasons. These results show that in the 2040s, the city of Arak has colder night temperatures in winter and spring and warmer temperatures in summer and spring. Due to the fact that warmer weather increases the demand for water and electricity, and because Arak is an industrial city with a dry climate, it can face serious challenges of water and electricity shortage in the future.Conclusion: According to the results obtained in this study, in the 2040s, Arak will have colder winters and springs, and warmer summers and autumns. The highest effect of climate change on the temperature of Arak is related to the average minimum temperature in autumn, which under the scenarios of RCP2.6, RCP4.5, RCP8.5, the average minimum temperature in autumn increased by 206.88, 196.37 and 192.27 percent, respectively. The mean annual maximum and minimum temperature under all three scenarios will increase in the 2040s. The highest increase in the mean annual maximum and minimum temperature is related to RCP8.5 and RCP2.6 scenarios, respectively, which they are equal to 4.14 and 4.38.

Estimation of Watershed Hydrochemical Responses to Future Climate Changes Based on CMIP6 Scenarios in the Tianhe River (China)

The impacts of future climate changes on watershed hydrochemical processes were assessed based on the newest Shared Socioeconomic Pathways (SSP) scenarios in Coupled Model Intercomparison Project Phase 6 (CMIP6) in the Tianhe River in the middle area of China. The monthly spatial downscaled outputs of General Circulation Models (GCMs) were used, and a new Python procedure was developed to batch pick up site-scale climate change information. A combined modeling approach was proposed to estimate the responses of the streamflow and Total Dissolved Nitrogen (TDN) fluxes to four climate change scenarios during four future periods. The Long Ashton Research Station Weather Generator (LARS-WG) was used to generate synthetic daily weather series, which were further used in the Regional Nutrient Management (ReNuMa) model for scenario analyses of watershed hydrochemical process responses. The results showed that there would be 2–3% decreases in annual streamflow by the end of this century for most scenarios except SSP 1-26. More streamflow is expected in the summer months, responding to most climate change scenarios. The annual TDN fluxes would continue to increase in the future under the uncontrolled climate scenarios, with more non-point source contributions during the high-flow periods in the summer. The intensities of the TDN flux increasing under the emission-controlled climate scenarios would be relatively moderate, with a turning point around the 2070s, indicating that positive climate policies could be effective for mitigating the impacts of future climate changes on watershed hydrochemical processes.

Adapting the COSP Radar Simulator to Compare GCM Output and GPM Precipitation Radar Observations

AbstractComparisons of precipitation between general circulation models (GCMs) and observations are often confounded by a mismatch between model output and instrument measurements, including variable type and temporal and spatial resolutions. To mitigate these differences, the radar-simulator Quickbeam within the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP) simulates reflectivity from model variables at the subgrid scale. This work adapts Quickbeam to the dual-frequency precipitation radar (DPR) on board the Global Precipitation Measurement (GPM) satellite. The longer wavelength of the DPR is used to evaluate moderate to heavy precipitation in GCMs, which is missed when Quickbeam is used as a cloud radar simulator. Latitudinal and land–ocean comparisons are made between COSP output from the Community Atmospheric Model version 5 (CAM5) and DPR data. Additionally, this work improves the COSP subgrid algorithm by applying a more realistic, nondeterministic approach to assigning GCM gridbox convective cloud cover when convective cloud is not provided as a model output. Instead of assuming a static 5% convective cloud coverage, DPR convective precipitation coverage is used as a proxy for convective cloud coverage. For example, DPR observations show that convective rain typically only covers about 1% of a 2° grid box, but that the median convective rain area increases to over 10% in heavy rain cases. In our CAM5 tests, the updated subgrid algorithm improved the comparison between reflectivity distributions when the convective cloud cover is provided versus the default 5% convective cloud-cover assumption.

Multimodel Ensemble Projections of Wave Climate in the Western North Pacific Using CMIP6 Marine Surface Winds

For decades, the western North Pacific (WNP) has been commonly indicated as a region with high vulnerability to oceanic and atmospheric hazards. This phenomenon can be observed through general circulation model (GCM) output from the Coupled Model Intercomparison Project (CMIP). The CMIP consists of a collection of ensemble data as well as marine surface winds for the projection of the wave climate. Wave climate projections based on the CMIP dataset are necessary for ocean studies, marine forecasts, and coastal development over the WNP region. Numerous studies with earlier phases of CMIP are abundant, but studies using CMIP6 as the recent dataset for wave projection is still limited. Thus, in this study, wave climate projections with WAVEWATCH III are conducted to investigate how wave characteristics in the WNP will have changed in 2050 and 2100 compared to those in 2000 with atmospheric forcings from CMIP6 marine surface winds. The wave model runs with a 0.5° × 0.5° spatial resolution in spherical coordinates and a 10-min time step. A total of eight GCMs from the CMIP6 dataset are used for the marine surface winds modelled over 3 h for 2050 and 2100. The simulated average wave characteristics for 2000 are validated with the ERA5 Reanalysis wave data showing good consistency. The wave characteristics in 2050 and 2100 show that significant decreases in wave height, a clockwise shift in wave direction, and the mean wave period becomes shorter relative to those in 2000.

Evaluation of the climate change impact on the extreme rainfall amounts using modified method of fragments for sub‐daily rainfall disaggregation

AbstractClimate change has a dramatic effect on the hydrologic variables including extreme rainfall amounts. To evaluate the climate change effects, general circulation models (GCMs) have been developed. However, due to the daily temporal scale of GCM outputs which could be insufficient for some hydrological studies, disaggregation models are introduced. The available disaggregation models which are almost useful in producing time series of finer scale than a day, cannot accurately estimate some statistical characteristics such as extreme events. The method of fragments (MOF) is one of the disaggregation models which considers daily rainfall as the only input. In the present study, in addition to daily rainfall, other influential factors on the rainfall distribution during a day such as weather variables and sub‐daily characteristics have been considered to improve the disaggregation results especially extreme events estimation in the MOF model. The two introduced approaches have been examined for a case study in Tehran, Iran and indicated that weather variables and sub‐daily characteristics are effective in the daily rainfall disaggregation during the dry and wet seasons, respectively. These approaches seem to be much better than the basic MOF in sub‐daily rainfall disaggregation. Hence, the modified disaggregation approaches have been used to evaluate the climate change impacts on the sub‐daily rainfall distribution. The obtained results indicated an increase in the extreme value statistics such as mean and standard deviation of the 95th percentile data compared with the historical ones.

Projection Pursuit Regression (PPR) on Statistical Downscaling Modeling for Daily Rainfall Forecasting

Rainfall forecasting has an important role in people's lives. Rainfall forecasting in Indonesia has complex problems because it is located in a tropical climate. Rainfall prediction in Indonesia is difficult due to the complex topography and interactions between the oceans, land and atmosphere. With these conditions, an accurate rainfall forecasting model on a local scale is needed, of course taking into account the information about the global atmospheric circulation obtained from the General Circulation Model (GCM) output. GCM may still be used to provide local or regional scale information by adding Statistical Downscaling (SD) techniques. SD is a regression-based model in determining the functional relationship between the response variable and the predictor variable. Rainfall observations obtained from the Meteorology Climatology and Geophysics Council (BMKG) are a response variable in this study. The predictor variable used in this study is the global climate output from GCM. This research was conducted in a place, namely Kupang City, East Nusa Tenggara because it has low rainfall. The Projection Pursuit Regression (PPR) will be used in this SD method for this study. In PPR modeling, optimization needs to be done and model validation is carried out with the smallest Root Mean Square Error (RMSE) criteria. The expected results must have a pattern between the results of forecasts and observations showing or approaching the observational data. The PPR model is a good model for predicting rainfall because The results of the forecast and observation show that the results of the rainfall forecast are observational data.

Station‐based non‐linear regression downscaling approach: A new monthly precipitation downscaling technique

ABSTRACTStatistical downscaling is an effective way to downscale General Circulation Model (GCM) outputs to a finer temporal and spatial scale. Here, we proposed and trained a new station‐based non‐linear regression downscaling (SNRD) approach, and quantitatively demonstrated its better performance than linear regression model. In addition, we also compared the SNRD method with the Bias Correction Spatial Disaggregation (BCSD) method, which has been widely applied on downscaling precipitation. Results indicated that RMSEs and residuals of the BCSD method are 4.6–31 times as ones of SNRD in each region of China during validation period (2004–2015). Besides, with historical in‐situ observed monthly mean precipitation as the benchmark, SNRD downscaled model modified the over‐estimation (1.25–2 times as observed precipitation) flaw of BCSD model in rainy season (June–August) of China, which could provide assistance on the further researches on drought events in rainy season in future. Further, we also surprisingly found that the 2,500 m was the threshold height for statistical downscaling models. Both BCSD and SNRD downscaled precipitation displayed more steadily and accurately above 2,500 m, since there are less intense human activities in China. Thus, we recommend further studies should fully consider the height element in downscaling models to improve the accuracies.

Projection pursuit regression in statistical downscaling model using artificial neural network for rainfall prediction

Rainfall prediction is important for farmers to be used in making policies, especially in areas of agricultural production, include in Indonesia. The availability of information about rainfall requires an accurate forecasting method. The General Circulation Model (GCM) is used in dynamic prediction to obtain rainfall information for one month, but with its low resolution, this model cannot be used to obtain information on a small scale so that a statistical downscaling (SD) model is needed. The Projection Pursuit Regression (PPR) used in this SD includes nonparametric and nonlinear approaches to processing large dimensional data that can describe small dimensions through a projection process. This research is further explained using a neural network-based approach, that is Artificial Neural Network (ANN) in a statistical downscaling model with applications for analysis of events related to rainfall prediction. In this case, the data will be part of the model formation of statistical downscaling. The SD prediction model uses several predictors, where some of these predictors have a physical relationship between the atmosphere and rainfall. The predictor variables are taken from the GCM output, the predictor variables used precipitation.

Impact of global warming on heating and cooling degree days in major Indian cities

Global warming induced rise in ambient temperatures would significantly impact the space heating and cooling (H/C) energy requirements in buildings. This investigation assessed the impact of global warming on space H/C energy requirements in eight major Indian cities covering all of the county’s climate zones. By using historical weather records and general circulation model outputs, we quantified the historical (1969–2017) and future (2018–2100) trends in annual mean temperatures together with heating and cooling degree days (HDDs and CDDs), which are well-known metrics for quantifying buildings’ H/C requirements. The investigation demonstrated that annual temperatures would be higher by 0.1–1.1 °C in the 2020 s, by 0.6–2.8 °C in the 2050 s, and by 1.0–4.6 °C in the 2080 s, depending on the city and the emission scenario. Due to rising temperatures, CDDs would also increase by 2.9–22.9% in the 2020 s, by 8.3–54.1% in the 2050 s, and by 11.89–83.0% in the 2080 s; thus, increasing the cooling requirements by a similar amount. In contrast, HDDs would decrease by 8.1–30.3% in the 2020 s, by 17.6–83.3% in the 2050 s, and by 19.3–97.1% in the 2080 s, thereby reducing the heating requirements.

Generalized Linear Mixed Model and Lasso Regularization for Statistical Downscaling

Rainfall is one of the climatic elements in the tropics which is very influential in agriculture, especially in determining the growing season. Thus, proper rainfall modeling is needed to help determine the best time to start cultivating the soil. Rainfall modeling can be done using the Statistical Downscaling (SDS) method. SDS is a statistical model in the field of climatology to analyze the relationship between large-scale and small-scale climate data. This study uses response variables as a small-scale climate data in the form of rainfall and explanatory variables as a large-scale climate data of the General Circulation Model (GCM) output in the form of precipitation. However, the application of SDS modeling is known to cause several problems, including correlated and not stationary response variables, multi-dimensional explanatory variables, multicollinearity, and spatial correlation between grids. Modeling with some of these problems will cause violations of the assumptions of independence and multicollinearity. This research aims to model the rainfall in Indramayu Regency, West Java Province using a combined regression model between the Generalized linear mixed model (GLMM) and Least Absolute Selection and Shrinkage Operator (LASSO) regulation (L1). GLMM was used to deal with the problem of independence and Lasso Regulation (L1) was used to deal with multicollinearity problems or the number of explanatory variables that is greater than the response variable. Several models were formed to find the best model for modeling rainfall. This research used the GLMM-Lasso model with Normal spread compared to the GLMM model with Gamma response (Gamma-GLMM). The results showed that the RMSE and R-square GLMM-Lasso models were smaller than the Gamma-GLMM models. Thus, it can be concluded that GLMM-Lasso model can be used to model statistical downscaling and solve the previously mentioned constraints. Received February 10, 2021Revised March 29, 2021Accepted March 29, 2021

Modeling and predicting suspended sediment load under climate change conditions: a new hybridization strategy

Abstract In the present study, for the first time, a new strategy based on a combination of the hybrid least-squares support-vector machine (LS-SVM) and flower pollination algorithm (FPA), average 24 general circulation model (GCM) output, and delta change factor method has been developed to achieve the impacts of climate change on runoff and suspended sediment load (SSL) in the Lighvan Basin in the period (2020–2099). Also, the results of modeling were compared to those of LS-SVM and adaptive neuro-fuzzy inference system (ANFIS) methods. The comparison of runoff and SSL modeling results showed that the LS-SVM-FPA algorithm had the best results and the ANFIS algorithm had the worst results. After the acceptable performance of the LS-SVM-FPA algorithm was proved, the algorithm was used to predict runoff and SSL under climate change conditions based on ensemble GCM outputs for periods (2020–2034, 2035–2049, 2070–2084, and 2085–2099) under three scenarios of RCP2.6, RCP4.5, and RCP8.5. The results showed a decrease in the runoff in all periods and scenarios, except for the two near periods under the RCP2.6 scenario for runoff. The predicted runoff and SSL time series also showed that the SSL values were lower than the average observation period, except for 2036–2039 (up to an 8% increase in 2038).

General Circulation Model Selection Technique for Downscaling: Exemplary Application to East Africa

AbstractDownscaling is widely used in studies of local and/or regional climate as it yields a greater spatial resolution than general circulation models (GCMs) can provide. It utilizes GCM output or reanalysis data, which is transformed using mathematical relationships or used to force the lateral boundaries of a regional climate model. However, there is no set selection technique to determine which GCM realization(s) to employ. Here, a comprehensive yet easily applicable model selection technique for studies requiring GCM data as a constraint was developed. The technique evaluates, with respect to a reanalysis product and/or observational data, the ability of GCM realizations to reconstruct the mean state of the climate and the space‐time climatic anomalies for the atmospheric state variables at three distinct pressure levels. It was applied to the region of East Africa, where GISS‐E2‐H r6i1p3 was found to perform the strongest. The top ranked realizations were found to better capture processes when evaluated for the example of the Indian Ocean Dipole. Furthermore, the surface air temperature and precipitation from three 10‐year regional climate model simulations, one forced by the Modern‐Era Retrospective Analysis for Research and Applications version 2 reanalysis, one forced by the top ranked GCM, and one by the lowest ranked one, were compared to gridded observations. Results show that using a top ranked GCM for the boundary conditions leads to a better dynamical downscaling simulation than a low‐ranked GCM, suggesting the potential of the proposed technique for future downscaling techniques.

Statistical downscaling using principal component regression for climate change impact assessment at the Cauvery river basin

Abstract Climate change impact studies are generally carried out with higher resolution general circulation model (GCM) outputs, which are usually for a global scale, and it is difficult to use the same for a regional scale. GCM simulations require downscaling to get a coarser scale output for local climate impact studies. In this study, an improvised principal component regression (PCR) downscaling technique is adapted to downscale 26 Coupled Model Intercomparison Project Phase 5 (CMIP5) GCM historical outputs. A massive river basin named Cauvery with 35 observation stations is categorized into three subbasins to study the regional climate impacts. In this case, the PCR model performed remarkably well compared to other conventional machine learning models with half the computational time than usual. The test statistics state that the validation of the proposed model illustrates a variance in calibration results of the PCR model, which ranges between 2 and 5%, and a variance in validation, which is less than 7% throughout the study area. Since it is desired to prioritize GCMs to choose the merely suitable models for a strategic climate study, the models were selected based on the PCR model performance. Furthermore, CCSM4, inmcm4, and EC-EARTH model's performance in recreating precipitation statistics over the study area are exceptional.

An assessment of LARS-WG’s model to forecast meteorological parameters for climatic zones of cotton-harvested areas over Iran

Background and Objectives: Nowadays, climatologists and researchers are interested in the long-term forecast of climatic variables to be informed about the extent of their variations and to take measures to mitigate the adverse effects of climate change. Accordingly, general circulation models (GCMs) of atmosphere have been developed to predict climatic parameters. LARS-WG is a model to downscale the output of GCMs. It was used in the present research to generate data of daily precipitation, radiation, and minimum and maximum temperature across the cotton cultivation climatic zones (Meteorological stations) in Iran. The objective of this study is to estimate climatic factors (rainfall, radiation and minimum and maximum temperature) in the future period in major climatic cotton cultivation areas in Iran, which may be used for optimized water resource preservation and management in these regions. Also, the 5th IPCC report was used in this study, in contrast with the majority of investigations that use the 4th report. Materials and Methods: In this research, the important areas under cotton cultivation in Iran are the target area. The data of 23 synoptic stations were used in these areas. The required meteorological data recorded in the stations were daily rainfall, minimum temperature, maximum temperature and sunshine. The present study assessed the performance of five different GCM models in simulating the data of precipitation, radiation, minimum temperature, and maximum temperature in nine synoptic stations (SABZEVAR, GHOOCHAN, GHOM, HASHMABAD, LAR, BILESOWAR, EDAREGORGAN, HASANABADEDARAB, and MASHHAD) from 2011 to 2016, firstly. Finally, two GCMs (MIROC5 and GFDL-CM3) were selected for the research purpose based on the results of t-test, F-test, and the Kolmogorov-Smirnov non-parametric test. Then, the parameters were predicted by the selected GCM models for 20 years (2041-2060) under the emission scenarios of RCP4.5 and RCP8.5. Results: According to employing the scenarios of RCP4.5 and RCP8.5, the predicted solar radiation does not show a significant change in the future period versus the base period of 1981-2010 in all studied regions based on both GCM models. The highest change (0.249 MJ / m2 / day) is based on the MIROC5 model and the RCP8.5 scenario in climate zone 6102 (HASHMABAD station). The predictions revealed that the parameters of maximum and minimum temperature would be ascending for all the climatic regions over the future period. The highest variations in the average long-term annual minimum and maximum temperatures versus the base period would be 2.67 and 2.75°C happening in Climatic Region 6002 Includes stations (HASANABADEDARAB, KHAF, HAJI ABAD and GONBAD) over the 2041-2060 period under Scenario RCP8.5. For both scenarios RCP4.5 and RCP8.5, the highest temperature increase was observed in the climatic zones where located in south of Alborz and the east of Zagros mountain chains as well as the Central Iranian Dry Plateau and the lowest increasing was in the climatic regions located around the Caspian Sea, the Persian Gulf and the south of Iran. For the total annual precipitation over the future period (2041-2060), the highest amplification was predicted by MIROC5 to be 98.5 mm under Scenario RCP4.5 in Climatic Region 6202 (EDAREGORGAN station), and the greatest loss of precipitation was predicted by the same model to be -29.8 mm in Scenario RCP8.5 in Climatic Region 6102. Conclusion: Given the prediction of rising average annual minimum and maximum temperature and the decline of precipitation over the future period (2041-2060) in the climatic regions leading to the arid plain of central and southern Iran, it can be concluded that the variations of meteorological parameters induced by climate change will be significant in the cotton-growing climatic regions of Iran over the future decades.

Development of hydro-meteorological drought index under climate change – Semi-arid river basin of Peninsular India

Univariate meteorological drought indices are inadequate to represent the complexity of hydrological conditions under the intensification of hydrological cycle due to climate change at catchment scale. In this study, Standardised Precipitation Actual Evapotranspiration Index (SPAEI) was proposed, which can combine both meteorological and hydrological drought characteristics at catchment scale. The proposed new drought index considers the hydrologically calibrated AET to account for the water use in addition to meteorological effect. The proposed hydrometeorological drought index was potential in identifying meteorological and hydrological drought events accounting for the time-lag effects and comparable with Global Land Evaporation Amsterdam Model (GLEAM) remote sensing AET data-based drought index. The PET based drought index of SPEI, which is based on energy demand, has shown intensified drought characteristics compared to SPAEI, which is based on both energy demand and available moisture supply and can be a promising variable in the drought estimation. The climate change projections of precipitation and temperatures downscaled using statistical downscaling model based on K-means clustering, Classification and Regression Trees and Support Vector Regression were used using three General Circulation Model outputs. Intensified drought characteristics under climate change has been predicted over Krishna River basin, India, in terms of increase of drought areal extent of about 25%-31%, with increase of drought frequency as 5 years per 20 years and durations as 4–5 months based on the proposed hydrometeorological drought index of SPAEI.

Comparing Flood Projection Approaches Across Hydro‐Climatologically Diverse United States River Basins

AbstractThe challenge of estimating design flood magnitude under climate change has led to the development of multiple approaches to long‐term flood projection: stationarity, informed‐parameter (composed of both trend informed and climate informed), and hydrologic simulation. This is the first study to compare these approaches across a large set of hydro‐climatologically diverse basins located throughout the contiguous United States, grouped into distinct clusters based on catchment and flood characteristics. The comparison is achieved using a split‐sample test conducted over the historic period; the climate (specifically, precipitation) informed and hydrologic simulation (specifically, Sacramento Soil Moisture Accounting model) approaches are forced with observations and downscaled general circulation model (GCM) simulations. The results provide a quantitative perspective on key long‐term flood projection issues. The precipitation informed approach can be informative for projections of regional change; the hydrologic simulation approach can be informative for direction of change for a single basin. When forced with GCM outputs, precipitation informed is both more accurate and more precise than hydrologic simulation, after accounting for model error. For hydrologic simulation, forcing with GCMs will add positive bias for basins in the Rocky Mountains and Southwest and will add negative bias with large uncertainty across GCMs for basins in the lower Midwest, the South, and the mid‐Atlantic seaboard. The results illustrate the need for continued improvement in long‐term flood projection approaches and for design paradigms incorporating uncertainty.