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The STatistical Analogue Resampling Scheme (STARS) statistical approach was recently used to project changes of climate variables in Germany corresponding to a supposed degree of warming. We show by theoretical and empirical analysis that STARS simply transforms interannual gradients between warmer and cooler seasons into climate trends. According to STARS projections, summers in Germany will inevitably become dryer and winters wetter under global warming. Due to the dominance of negative interannual correlations between precipitation and temperature during the year, STARS has a tendency to generate a net annual decrease in precipitation under mean German conditions. Furthermore, according to STARS, the annual level of global radiation would increase in Germany. STARS can be still used, e.g., for generating scenarios in vulnerability and uncertainty studies. However, it is not suitable as a climate downscaling tool to access risks following from changing climate for a finer than general circulation model (GCM) spatial scale. [ABSTRACT FROM AUTHOR]

The impacts of climate change increasingly show themselves in many forms in our everyday lives such as heatwaves and droughts. Drought is one of the critical events today for increasing drought frequency. This study focuses on meteorological drought because it directly affects other drought types. Hence, this study focuses on how the future drought conditions will vary under climate change effects in the Mediterranean basin (Turkey). In doing so, this study utilizes precipitation data from three General Circulation Models (GCMs) and three Regional Circulation Models (RCMs). The GCMs are CNRM-CM6, GFDL-CM4, and MPI-ESM1, while the RCMs are (RCA4)-CNRM-CM5, (Reg CM4)-GFDL-ESM2M, and (RCA4)-MPI-ESM-MR. Mitigating biases of the climate models, this study utilizes four statistical downscaling methods (SD), linear scaling (LS), local intensity scaling (LOCI), power transformation (PT), and distribution mapping (DM). Here, the study has two purposes. The main aim of the paper here is to compare the performance of SD methods in improving the representation of observed climate variables in climate models. In addition, the study shows how different methods will affect the spatial drought distribution in the area under the SSP2 4.5 and SSP5 8.5 scenarios. Consequently, the study uses the standardized precipitation index (SPI) and Z-score index (ZSI) to quantify future drought conditions and reaches the following results. The study reveals that mild drought conditions are prevalent in the basin for future periods, and drought indices go down to − 0.55. The study also shows that different SD methods affect the results obtained by each climate model diversely. For example, while the LS method causes the most drought conditions on the results based on CNRM-CM5 and CNRM-CM6, the DM method has a similar impact on outcomes based on GFDL-CM4 and GFDL-ESM2M and causes the most drought conditions. [ABSTRACT FROM AUTHOR]

General circulation models (GCM) show projections of climate variables that when downscaled can be applied to analyse future behaviour in different areas or places. Using them is possible not just to obtain expected values of climate variables but also to calculate their distributions and use those values to assess the effects of climate change at a local level. However, these calculations depend on the GCM selected. In this paper, daily maximum near-surface air temperatures from 21 climate models under representative concentration pathway (RCP) scenarios RCP 4.5 and RCP 8.5 and historic daily maximum temperatures (1990–2019) from nine cities in southern Spain are used with two objectives: first, to investigate past behaviour broken down into a deterministic part and a stochastic part; second, to compare historical data (2006–2019) with the information extracted from the 21 GCMs based on calculating goodness of fit in the period for both deterministic and stochastic parts. The methodology proposed may be useful in selecting a model or a range of models for use in a specific study. The results show positive historical and future trends in maximum daily temperature for these cities. The GCMs with the best fit for each city in this specific case are also presented. [ABSTRACT FROM AUTHOR]

Application of seasonal forecasts in agriculture has significant potential and realized utility. Other sectors that may also benefit from using seasonal forecasts include (but are not limited to) health, hydrology, water, and energy. This paper shows that seasonal forecast model data, satellite Pour l'Observation de la Terre (SPOT), dry matter productivity (DMP) data (proxy of grass biomass) along with other sets of data are effectively used to estimate grazing capacity (GC) over a 12-year test period (1998/1999–2009/2010) in Limpopo Province. GC comprises a vital consideration in agricultural activities, particularly for a province in South Africa like Limpopo, due to its varying climate. The Limpopo Province capitalizes on subsistence farming, including livestock and crop production. Grazing should thus be regulated in order to conserve grass, shrubs, and trees, thereby ensuring sustainability of rangelands. In a statistical downscaling model, the predictor is the 850 geopotential height fields of a coupled ocean–atmosphere general circulation (CGCM) over Southern Africa to predict seasonal DMP values. This model shows that the mid-summer rainfall totals are important predictors for the November through April (NDJFMA) DMP (as well as grazing capacity) growing season. Forecast verification is conducted using the relative operating characteristics (ROC) and reliability diagrams. The CGCM model shows skill in discriminating high and low DMP (GC) seasons in the Limpopo Province, as well as reliability in the probabilistic forecasts. This paper demonstrates the development of a tailored forecast, an avenue that should be explored in enhancing relevance of forecasts in agricultural production. [ABSTRACT FROM AUTHOR]

Changes in patterns of meteorological parameters, like precipitations, temperature, wind, etc., are causing significant increases in various extreme events. And these extreme events, i.e., floods, heatwaves, hurricanes, droughts, etc., lead to a shortage of water resources, crop failures, wildfires, and economic losses. However, Global Circulation Models (GCMs) are considered the most important tools for quantifying climate change. Therefore, we selected 20 different GCMs of precipitation in our research, as the frequency of extreme events, like drought and flood, is highly related to changes in precipitation patterns. However, this research introduced a new weighting scheme — MCFSAWS-Ensemble: Monte Carlo Feature Selection Adaptive Weighting Scheme to Ensemble multiple GCMs, whereas, Monte Carlo Feature Selection (MCFS) is one of the most popular algorithms for discovering important variables. However, the proposed weighting scheme (MCFSAWS-Ensemble) is mainly based on two sources. Initially, it evaluates the prior performance of each GCM model to define their relative importance using MCFS. Then, it computes value by value difference between the observed and simulated model. In addition, the application of this paper is based on the monthly time series data of precipitation in the Tibet Plateau region of China. In addition, we used twenty GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to analyze the implications of the MCFSAWS-Ensemble. Further, we compared the performance of the MCFSAWS-Ensemble scheme with Simple Model Averaging (SMA) through Mean Average Error (MAE) and correlation statistics. The results of this research indicate that the proposed weighting scheme (MCFSAWS-Ensemble) is more accurate than the SMA approach. Consequently, we recommend the use of advanced machine learning algorithms such as MCFS for making accurate multi-model ensembles. [ABSTRACT FROM AUTHOR]

This paper studies the Upper Tigris River (UTR) drainage basin in Turkey for climate change impacted runoff estimations. Statistical downscaling method (SDM) is used by taking into consideration spatial dependence function (SDF) for the scenario precipitation projections at a set of available meteorology stations. Temporal adjustment between the climate scenarios and precipitation record time series is achieved by the white Markov (WM) stochastic process. Although various climate research center scenario data are considered, herein, only the general circulation model (GCM) A2 scenario data are adapted from the Hadley Center, England. The precipitation and runoff results are presented in decadal groups starting from 2001 to 2050 as cumulative monthly precipitation (CMP) and cumulative monthly runoff (CMR) graphs. It is observed that after 2021, precipitation decreases at about 12.5% and after 2030, it is 26%. Runoff projections indicate that they may decrease at about 30% especial after 2040. [ABSTRACT FROM AUTHOR]

Weather generators (WGs) are one of the major downscaling tools for assessing regional climate change impacts. However, some deficiencies in the performance of WGs have limited their usage. This paper presents a method for correcting the low-frequency variability (LFV) of precipitation in the improved weather generator (IWG) model. The method is based on bias correction in the monthly precipitation distribution of the generated daily series. The performance of the modified model was tested directly by comparing the statistics of generated and observed weather data for 14 stations, and also indirectly by comparing the characteristics of simulated stream-flows of a basin from the simulation run based on generated and observed weather data. The results showed that the method not only corrected the LFV of precipitation but also improved the reproduction of many other statistics. The provided IWG2 model can serve as a useful tool for the downscaling of general circulation model (GCM) scenarios to assess regional climate change impacts, especially hydrological effects. [ABSTRACT FROM AUTHOR]

This paper demonstrates a climate change impact study on the hydrological process of a data-scarce Greek watershed. The Soil and Water Assessment Tool (SWAT) and, particularly, the ArcSWAT interface was used for the watershed simulation. The ERA-Interim reanalysis climate data regarding the period from 1981 to 2000 were used for the historical simulation of the watershed. The ArcSWAT simulated data were evaluated against the observed discharge data for the periods with the available data. The statistical evaluation confirmed the ArcSWAT model's capability in simulating the hydrological process of the research area. The climate change consequences on the hydrological components of the research area until the end of the twenty-first century were estimated by driving the ArcSWAT model with the Regional Climate Model Version 4 (RegCM4) forcing data under the extreme RCP 8.5 scenario, namely the simulations of the MPI and HadGEM2 general circulation models (GCMs), resulted from the spatio-temporal kriging approach. Based on the results, the increase in the minimum and the maximum temperature contributed to an increase in the actual evapotranspiration and the surface runoff. In contrast, the temperature increase caused a reduction in the infiltration. An increase (reduction) in the precipitation led to an increase (reduction) in the hydrological components. The climate change impact analysis of the Greek watershed showed that not only the precipitation changes but the temperature changes as well directly influence the water balance components of the research area and particularly the infiltration. [ABSTRACT FROM AUTHOR]

In this paper, trends of minimum and maximum temperatures in Iran were studied using time series of daily minimum and maximum temperatures of 45 meteorological stations from 1976 to 2005 (as the baseline period). Mann-Kendall test, for maximum and minimum temperature, was obtained 1.85 and 3.56, respectively, which was positive and significant. The slope of the trend line for maximum and minimum temperature was obtained 0.23 and 0.39 °C decade−1, respectively. In this study, the trend of extreme temperature indicators was also evaluated. According to the obtained results, in annual terms, TX10, FDO, TN10, and IDO indices have had a negative trend at most stations, but TX90, TR20, TNx, TNx, TXn, TN90, SDI, and SU25 indices showed a positive trend. In the seasonal scale, the indices of cold days (TX10) and cold nights (TN10) showed significant negative trends in most seasons. Warm days (TX90) and warm nights (TN90) showed significant positive trends at most stations. The results of future simulations using several general circulation models in different time periods showed that the highest increase in maximum and minimum temperature related to the RCP8.5 scenario in periods of 2071 to 2099. The results also showed that northwest of Iran would have the highest temperature rise. The results also showed that the probability density function of the minimum and maximum temperatures will shift to warmer temperatures. This could be an indication of climate change in the future decades in Iran. [ABSTRACT FROM AUTHOR]

The study of climatic variables in large scales with surface meteorological stations is limited due to the low density of these stations in many regions, possible sources of errors related to missing data, and uncertainties about the calibration sensors. Global gridded data (GD) systems can minimize these problems. Thus, studies that validate GDs with "ground truth" are important for several applications such as climate change. The objective of this study was to compare long series of surface data with 10-day estimates of average air temperature (T) and precipitation (P) using data from the European Center for Medium-Range Weather Forecast (ECMWF) and the National Aeronautics and Space Administration (NASA) for important agricultural locations in the states of Minas Gerais and São Paulo in Brazil. Despite the different spatial resolutions between ECMWF and NASA, the purpose of this paper was to evaluate the two data sources as they are readily available. The GD performance was evaluated by linear regression analysis. Analyses were performed for each meteorological variable for entire years and separated by seasons. The estimates of T from both ECMWF and NASA systems were accurate with the minimum Willmott concordance index (d) and RMSEp of 0.86, 0.37 °C, respectively, and precision with R2 0.61. The estimates of P had a minimum R2, d, and RMSEp of 0.48, 0.79, 2.15 °C respectively. The decreasing orders of (R2) were autumn > winter > spring > summer for T and winter > autumn > spring > summer for P, varying from 0.93 to 0.61 for T and from 0.77 to 0.48 for P. [ABSTRACT FROM AUTHOR]

In this paper, artificial neural network (ANN) was used for statistically downscale the outputs of general circulation models (GCMs) to assess future changes of precipitation and mean temperature in Tabriz synoptic station at north-west of Iran. Since one of the significant subjects in statistical downscaling of GCMs is to select the most dominant large scale climate variables (predictors) among huge number of potential predictors, the predictors screening methods including decision tree, mutual information (MI) and correlation coefficient (CC) were used to statistically downscale mean monthly precipitation and temperature. Three GCMs were used, including Can-ESM2 and BNU-ESM from IPCC AR5 models and CGCM3 from IPCC AR4 models. The results of downscaling in the base period (1951–2000) indicated that among feature extraction methods decision tree had superiority to MI and CC techniques. Therefore, the future projection of precipitation and mean temperature during 2020–2060 was implemented using ANN-based simulation according to the most efficient downscaling model (i.e., decision tree-based calibration). Different results according to different GCMs and scenarios were obtained for precipitation projection. In this way, the Can-ESM2 model under RCP8.5 showed 29.78% decrease in annual precipitation and CGCM3 model under B1 indicated 1.06% increase of annual precipitation. Temperature projection outcomes denoted that annual mean temperature will increase over the region and the most increase in mean temperature was determined by BNU-ESM model under RCP8.5. [ABSTRACT FROM AUTHOR]

In this paper, a novel data-driven approach is used to investigate the presence of spatial differences in the dynamic linkage between temperature and atmospheric carbon dioxide concentrations. This linkage seems to be latitude dependent. The main findings of the study are as follows. In the latitude belts surrounding the equator (0°− 24° N and 0°− 24° S), the link seems very similar. On the opposite, the patterns of the temperature CO2 link in the Arctic is very distant from those concerning the equatorial regions and other latitude bands in the South Hemisphere. This big distance is consistent with the so-called Arctic amplification phenomenon. Further, it is important to underline that this observational data-based analysis provides an independent statistical confirmation of the results from global circulation modelling. [ABSTRACT FROM AUTHOR]

Most often than not, incomplete datasets or short-term recorded data in vast regions impedes reliable climate and water studies. Various methods, such as simple correlation with stations having long-term time series, are practiced to infill or extend the period of observation at stations with missing or short-term data. In the current paper and for the first time, the hypothesis on the feasibility of extending the downscaling concept to backcast local observation records using large-scale atmospheric predictors is examined. Backcasting is coined here to contrast forecasting/projection; the former is implied to reconstruct in the past, while the latter represents projection in the future. To assess our hypotheses, daily and monthly statistical downscaling models were employed to reconstruct past precipitation data and lengthen the data period. Urmia and Tabriz synoptic stations, located in northwestern Iran, constituted two case study stations. SDSM and data-mining downscaling model (DMDM) daily as well as the group method of data handling (GMDH) and model tree (Mp5) monthly downscaling models were trained with National Center for Environmental Prediction (NCEP) data. After training, reconstructed precipitation data of the past was validated against observed data. Then, the data was fully extended to the 1948 to 2009 period corresponding to available NCEP data period. The results showed that DMDM performed superior in generation of monthly average precipitation compared with the SDSM, Mp5, and GMDH models, although none of the models could preserve the monthly variance. This overall confirms practical value of the proposed approach in extension of the past historic data, particularly for long-term climatological and water budget studies. [ABSTRACT FROM AUTHOR]

The aim of this paper is to identify relative roles of different land-atmospheric conditions, apart from sea surface temperature (SST), in determining early vs. late summer monsoon intensity over India in a high resolution general circulation model (GCM). We find that in its early phase (June-July; JJ), pre-monsoon land-atmospheric processes play major role to modulate the precipitation over Indian region. These effects of pre-monsoon conditions decrease substantially during its later phase (August-September; AS) for which the interannual variation is mainly governed by the low frequency northward propagating intraseasonal oscillations. This intraseasonal variability which is related to mean vertical wind shear has a significant role during the early phase of monsoon as well. Further, using multiple linear regression, we show that interannual variation of early and late monsoon rainfall over India is best explained when all these land-atmospheric parameters are taken together. Our study delineates the relative role of different processes affecting early versus later summer monsoon rainfall over India that can be used for determining its subseasonal predictability. [ABSTRACT FROM AUTHOR]

This paper assesses the uncertainties involved in the projections of seasonal temperature and precipitation changes over South America in the twenty-first century. Climate simulations generated by 24 general circulation models are weighted according to the reliability ensemble averaging (REA) approach. The results show that the REA mean temperature change is slightly smaller over South America compared to the simple ensemble mean. Higher reliability in the temperature projections is found over the La Plata basin, and a larger uncertainty range is located in the Amazon. A temperature increase exceeding 2 °C is found to have a very likely (>90 %) probability of occurrence for the entire South American continent in all seasons, and a more likely than not (>50 %) probability of exceeding 4 °C by the end of this century is found over northwest South America, the Amazon Basin, and Northeast Brazil. For precipitation, the projected changes have the same magnitude as the uncertainty range and are comparable to natural variability. [ABSTRACT FROM AUTHOR]

This paper analyzes the climate change impact on water diversion plan of Melamchi Water Supply Project (MWSP) in Nepal. The MWSP is an interbasin water transfer project aimed at diverting water from the Melamchi River of the Indrawati River basin to Kathmandu Valley for drinking water purpose. Future temperature and precipitation of the basin were predicted using the outputs of two regional climate models (RCMs) and two general circulation models (GCMs) under two representative concentration pathway (RCP) scenarios which were then used as inputs to Soil and Water Assessment Tool (SWAT) to predict the water availability and evaluate the water diversion strategies in the future. The average temperature of the basin is projected to increase by 2.35 to 4.25 °C under RCP 4.5 and RCP 8.5, respectively, by 2085s. The average precipitation in the basin is projected to increase by 6-18 % in the future. The annual water availability is projected to increase in the future; however, the variability is observed in monthly water availability in the basin. The water supply and demand scenarios of Kathmandu Valley was also examined by considering the population increase, unaccounted for water and water diversion from MWSP in the future. It is observed that even with the additional supply of water from MWSP and reduction of unaccounted for water, the Kathmandu Valley will be still under water scarcity in the future. The findings of this study can be helpful to formulate water supply and demand management strategies in Kathmandu Valley in the context of climate change in the future. [ABSTRACT FROM AUTHOR]

This paper describes the projection of climate change scenarios under increased greenhouse gas emissions, using the results of atmospheric-ocean general circulation models in the Coupled Model Intercomparison Project phase 3 dataset. A score is given to every model based on global and regional performance. Four out of 20 general circulation models (GCMs) were selected based on skill in predicting observed annual temperature and precipitation conditions. The ensemble of these four models shows superiority over the individual model scores. These models were subjected to increases in future anthropogenic radiative forcings for constructing climate change scenarios. Future climate scenarios for Tamil Nadu were developed with MAGICC/SCENGEN software. Model results show both temperature and precipitation increases under increased greenhouse gas scenarios. Northeast and northwest parts of Tamil Nadu show a greater increase in temperature and precipitation. Seasonally, the maximum rise in temperature occurred during the MAM season, followed by DJF, JJA, and SON. Decreasing trends of precipitation were observed during DJF and MAM. [ABSTRACT FROM AUTHOR]

In order to better understand the effect associated with global climate change on Iran's climate condition, it is important to quantify possible shifts in different climatic types in the future. To this end, monthly mean minimum and maximum temperature, and precipitation from 181 synoptic meteorological stations (average 1970-2005) have been collected from the meteorological organization of Iran. In this paper, to study spatial changes of Iran's climatic zones affected by climate changes, Extended De Martonne's classification (originally formulated by De Martonne and extended by Khalili ()) was used. Climate change scenarios were simulated in two future climates (average conditions during the 2050s and the 2080s) under each of the SRES A1B and A2, for the CSIRO-MK3, HadCM3, and CGCM3 climate models. Coarse outputs of GCMs were downscaled by delta method. We produced all maps for three time periods (one for the current and two for the future) according to Extended De Martonne's classification. Finally, for each climatic zone, changes between the current and the future were compared. As the main result, simulated changes indicate shifts to warmer and drier zones. For example, in the current, extra arid-cold ( A1.1m2) climate is covering the largest area of the country (21.4 %), whereas in both A1B and A2 scenarios in the 2050s and the 2080s, extra arid-moderate ( A1.1m3) and extra arid-warm ( A1.1m4) will be the climate and will occupy the largest area of the country, about 21 and 38 %, respectively. This analysis suggests that the global climate change will have a profound effect on the future distribution of severe aridity in Iran. [ABSTRACT FROM AUTHOR]

The regional climate model (RegCM3) from the Abdus Salam International Centre for Theoretical Physics has been used to simulate the Indian summer monsoon for three different monsoon seasons such as deficit (1987), excess (1988) and normal (1989). Sensitivity to various cumulus parameterization and closure schemes of RegCM3 driven by the National Centre for Medium Range Weather Forecasting global spectral model products has been tested. The model integration of the nested RegCM3 is conducted using 90 and 30-km horizontal resolutions for outer and inner domains, respectively. The India Meteorological Department gridded rainfall (1° × 1°) and National Centre for Environment Prediction (NCEP)-Department of Energy (DOE) reanalysis-2 of 2.5° × 2.5° horizontal resolution data has been used for verification. The RegCM3 forced by NCEP-DOE reanalysis-2 data simulates monsoon seasons of 1987 and 1988 reasonably well, but the monsoon season of 1989 is not represented well in the model simulations. The RegCM3 runs driven by the global model are able to bring out seasonal mean rainfall and circulations well with the use of the Grell and Anthes-Kuo cumulus scheme at 90-km resolution. While the rainfall intensity and distribution is brought out well with the Anthes-Kuo scheme, upper air circulation features are brought out better by the Grell scheme. The simulated rainfall distribution is better with RegCM3 using the MIT-Emanuel cumulus scheme for 30-km resolution. Several statistical analyses, such as correlation coefficient, root mean square error, equitable threat score, confirm that the performance of MIT-Emanuel scheme at 30-km resolution is better in simulating all-India summer monsoon rainfall. The RegCM3 simulated rainfall amount is more and closer to observations than that from the global model. The RegCM3 has corrected its driven GCM in terms of rainfall distribution and magnitude over some parts of India during extreme years. This study brings out several weaknesses of the RegCM model which are documented in this paper. [ABSTRACT FROM AUTHOR]

This paper describes the effects of convective adjustment time scale ( τ) on the simulation of tropical climate. The NCAR-Community Atmosphere Model version 3 (CAM3) has been used for this study. In the default configuration of the model, the prescribed value of τ, a characteristic time scale with which convective available potential energy (CAPE) is removed at an exponential rate by convection, is assumed to be 1 h. However, some recent observational findings suggest that, it is larger by around one order of magnitude. In order to investigate the dependence of tropical climate simulation to this time scale, we conducted two simulations, one with a time scale of 1 h (CTRL) and another with 8 h (EXPT), and examined the differences in simulated climate. For this, we analyzed both the mean as well as transient features, viz., seasonal mean quantities, equatorial waves, and meridional migration of convective disturbances. The spatial distributions of seasonal mean precipitation are found to be better in EXPT. The spatial correlation coefficients of CTRL and EXPT with the observations are 0.79 and 0.83, respectively, for northern hemisphere winter. Similarly, for northern hemisphere summer, the values are 0.67 and 0.79, respectively. In addition, there is also an improvement in the simulation of equatorial waves, specifically, the Kelvin waves, Madden-Julian oscillation, and n = 1 equatorial Rossby waves become more realistic in EXPT. The characteristics of meridional migration of convective activity over tropics also become more reasonable in EXPT. Thus, it is found that there is a clear improvement in some of the key aspects of the simulated tropical climate with the revised convective adjustment time scale. [ABSTRACT FROM AUTHOR]

Statistical downscaling is a technique widely used to overcome the spatial resolution problem of General Circulation Models (GCMs). Nevertheless, the evaluation of uncertainties linked with downscaled temperature and precipitation variables is essential to climate impact studies. This paper shows the potential of a statistical downscaling technique (in this case SDSM) using predictors from three different GCMs (GCGM3, GFDL and MRI) over a highly heterogeneous area in the central Andes. Biases in median and variance are estimated for downscaled temperature and precipitation using robust statistical tests, respectively Mann-Whitney and Brown-Forsythe's tests. In addition, the ability of the downscaled variables to reproduce extreme events is tested using a frequency analysis. Results show that uncertainties in downscaled precipitations are high and that simulated precipitation variables failed to reproduce extreme events accurately. Nevertheless, a greater confidence remains in downscaled temperatures variables for the area. GCMs performed differently for temperature and precipitation as well as for the different test. In general, this study shows that statistical downscaling is able to simulate with accuracy temperature variables. More inhomogeneities are detected for precipitation variables. This first attempt to test uncertainties of statistical downscaling techniques in the heterogeneous arid central Andes contributes therefore to an improvement of the quality of predictions of climate impact studies in this area. [ABSTRACT FROM AUTHOR]

Climate change information required for impact studies is of a much finer scale than that provided by Global circulation models (GCMs). This paper presents an application of partial least squares (PLS) regression for downscaling GCMs output. Statistical downscaling models were developed using PLS regression for simultaneous downscaling of mean monthly maximum and minimum temperatures ( T and T) as well as pan evaporation to lake-basin scale in an arid region in India. The data used for evaluation were extracted from the NCEP/NCAR reanalysis dataset for the period 1948-2000 and the simulations from the third-generation Canadian Coupled Global Climate Model (CGCM3) for emission scenarios A1B, A2, B1, and COMMIT for the period 2001-2100. A simple multiplicative shift was used for correcting predictand values. The results demonstrated that the downscaling method was able to capture the relationship between the premises and the response. The analysis of downscaling models reveals that (1) the correlation coefficient for downscaled versus observed mean maximum temperature, mean minimum temperature, and pan evaporation was 0.94, 0.96, and 0.89, respectively; (2) an increasing trend is observed for T and T for A1B, A2, and B1 scenarios, whereas no trend is discerned with the COMMIT scenario; and (3) there was no trend observed in pan evaporation. In COMMIT scenario, atmospheric CO concentrations are held at year 2000 levels. Furthermore, a comparison with neural network technique shows the efficiency of PLS regression method. [ABSTRACT FROM AUTHOR]

Based on the availability of hemispheric gridded data sets from observations, analysis and global climate models, objective cyclone identification methods were developed and applied to these data sets. Due to the large amount of investigation methods combined with the variety of different datasets, a multitude of results exist, not only for the recent climate period but also for the next century, assuming anthropogenic changed conditions. Different thresholds, different physical quantities, and considerations of different atmospheric vertical levels add to a picture that is difficult to combine into a common view of cyclones, their variability and trends, in the real world and in GCM studies. Thus, this paper will give a comprehensive review of the actual knowledge on climatologies of mid-latitude cyclones for the Northern and Southern Hemisphere for the present climate and for its possible changes under anthropogenic climate conditions. [ABSTRACT FROM AUTHOR]

The goal of the paper is to present and examine the method of classification of daily circulation patterns that allows (i) a fair comparison of groupings among different datasets (typically representing the observed climate and that simulated by a general circulation model, GCM) and (ii) huge data sets, common in GCM studies based on daily values, to be classified. The circulation classification method is shown to be a useful tool in GCM validation and analysis of climate change response, particularly in comparisons of (i) shapes of the mean type patterns, (ii) the frequency and persistence of the types, (iii) the probability of transitions from one type to another, and (iv) conditional surface temperature distributions. It is also shown that a simultaneous examination of multiple classifications is beneficial in eliminating subjectivity of any single classification and allowing a detailed inspection of differences between climates. The classification method is a modification of the T-mode principal component analysis (PCA). The T-mode refers to the input data matrix where gridpoint values are arranged in rows and daily patterns in columns. The classification procedure is applied to observed daily 500 hPa geopotential height patterns and those simulated by the control and 2 x CO2 ECHAM3 GCM runs. [ABSTRACT FROM AUTHOR]

General circulation models (GCMs) are limited in their representation of regional climates. Thus, the selection and downscaling of the most suitable models for regional/local studies are crucial prior to climate change impact studies. This study addressed the selection and downscaling of GCM models from 100 ensembles each from the Shared Socioeconomic Pathways (SSP4.5 and SSP8.5) emission scenarios from the CMIP6 archive using an advanced envelop-based selection approach for Northern Nigeria. We used 2021–2050 as the short-term and 2051–2080 as the long-term study periods. The selection approach revealed that CanESM5 models are more skilful in simulating the warm and wet season, while HadGEM3-GC31-LL in the warm and dry season, whereas MPI-ESM1-2-HR and MPI-ESM1-2-LR are skilful in the cold and dry season. Furthermore, we downscaled the three most skilled models from each season and calculated their spatial averages over Northern Nigeria to provide a more precise illustration of the temperature and precipitation patterns. Under the SSP4.5 emission scenario, the ensemble mean of the downscaled and the (raw) GCMs projected about 13% (8–17%) and 20% (11–35%) increase in average annual precipitation during the short-term and long-term periods, respectively. Similarly, for SSP8.5, the models projected about 23% (5–38%) and 41% (29–60%) increase in the average annual precipitation during short-term and long-term periods respectively. For the temperature, under SSP4.5, the GCMs projected a 1.1 °C (0.26–1.6 °C) and 2.5 °C (0.87–4.04 °C) increase in average annual temperature for short-term and long-term periods respectively. Similarly, an increase of 1.2 °C (0.01–1.78 °C) and 2.7 °C (0.01–4.3 °C) is projected for SSP8.5 during the short-term and long-term periods respectively. These findings can be used for climate impact studies in the region. [ABSTRACT FROM AUTHOR]

This study investigates how to properly downscale the coupled general circulation model (CGCM) ensemble prediction dynamically more efficiently than conventional method. Specifically, the ensemble seasonal prediction skill of dynamically downscaled precipitation over South Korea is evaluated by comparing two experiments. The first experiment (EXP1) involves conventional ensemble forecasts. Five ensemble members (EMs) are downscaled dynamically with initial and lateral boundary conditions obtained from the outputs of five CGCM EMs. The results of each EM are averaged for ensemble prediction utilizing a simple composite method. The second experiment (EXP2) is the same as EXP1, but the initial and lateral boundary conditions are obtained by arithmetically averaging the outputs of the five CGCM EMs. Therefore, five integrations are carried out for the EXP1, but only one integration is performed for the EXP2. The results show that EXP2 simulates closer to the observed precipitation than EXP1. This improvement is attributed to the strongly simulated upper zonal wind that can influence the vertically integrated moisture flux convergence. EXP2 shows comparable or better performance in simulating the interannual variability of summer precipitation than EXP1. Unlike conventional methods, such as EXP1, EXP2 provides a prediction in a single integration, and the prediction is similar to or even better than the one obtained conventionally. Hence, EXP2 can be a powerful means to drastically reduce the prediction time by reducing the number of ensemble integration to just one. [ABSTRACT FROM AUTHOR]

The goal of this simulation study was to explore how rice yield for different water supply levels will respond to climate change at a field scale in northern Iran. The CSM-CERES-Rice model was used in combination with downscaled outputs of a General Circulation Model. Three representative concentration pathways (RCP2.6, RCP4.5, RCP8.5) and seven irrigation treatments (FI (full irrigation), PRD10, PRD30, PRD60 (partial root drying in different rates), RDI10, RDI30, RDI60 (regulated deficit irrigation in different rates)) were used in this study. Moreover, three adaptation strategies were evaluated to mitigate the vulnerability of yield to climate change. The results showed that irrigated rice yield will decrease for climate change projections for 2026–2047, but the reduction was insignificant for all RCPs. Our findings confirm the hypothesis that adaptations can significantly increase the irrigated rice yield under climate change. Shifting transplanting date 2 weeks earlier with FI, RDI10, PRD10, RDI30, and PRD30 showed a higher average yield between 4.67 and 5.03 ton/ha relative to RDI60 and PRD60 reference irrigation treatments for all RCPs. Shifting nitrogen fertilizer application date 1 week earlier under RCP2.6 and RCP8.5 and 2 weeks earlier under RCP4.5 with FI resulted in the highest yield ranging from 3.13 and 4.33 ton/ha. By adjusting the amount of nitrogen fertilizer applied, the highest yield was obtained for 2.5 times the application of the current application amount with FI for all RCPs. The evaluation of these adaptation scenarios suggests that shifting transplanting date is the best strategy compared to the other two adaptations, which resulted in a higher yield with the same amount of water for all RCPs. [ABSTRACT FROM AUTHOR]

Tropical cyclones (TCs) are the most devastating weather phenomena that trigger massive loss of property and life in the coastal areas of the Bay of Bengal (BoB). Scientific understanding of TC occurrence can aid policy-makers and residents in coastal areas to take the necessary actions and do appropriate planning in advance. In this study, we aimed to examine the possible linkage of weather parameters with the deadly 22 TC events in the BoB from 1975 to 2014 using principal component analysis, K-mean clustering, and general circulation model (GCMs). Results showed that among 22 TCs, cluster 1 belongs to 12 TCs that occurred under the same atmospheric situation when the sea level pressure (SLP) was below 990 hPa, and the temperature ranged from 30 to 39 °C. A deep negative anomaly in SLP and temperature was observed up to 500 hPa levels. In contrast, a negative depression was found at 300 hPa geopotential height (GPH) over the study area. Cluster 2 consisted of 9 TCs when SLP was below 1000 hPa, and the average temperature was 33.5 °C. A strong negative anomaly was noticed at surface level up to 500 hPa GPH, but dramatically, this depression was completely absent at 300 hPa geopotential height over the BoB and entire coastal region. Cluster 3 contained only 1 TC when the atmospheric circumstances were completely diverse, and the SLP was above 1000 hPa. The results of the GCM model revealed that the SLP was lower, and the temperature was higher over BoB compared to the North Indian Ocean. We identified the larger depression of SLP and unpredictable temperature anomalies in the upper atmosphere that can trigger enormous unpredictability throughout the atmospheric level, leading to severe TCs. The outcomes of this study can improve our understanding of weather variables in the upper atmospheric column for forecasting the TC system more accurately in the future. [ABSTRACT FROM AUTHOR]

NASA MODIS data have been analyzed to quantify the relationship between the sea surface temperature (SST), water vapor content (WV), and cloud fraction (CF) for the time period from January 2000 to March 2017. SST and WV follow a Clausius–Claperyon type of a relationship with modified constants up to 300 K. However, beyond SST of 300 K, an alternate function prescribes the SST-WV correlation. A functional correlation between WV and CF has also been found for a limited range of CF, by introducing the atmospheric temperature at pressure altitude of 650 mbar as an additional parameter. The latter parameter is considered to be a modifier for the condensation process for WV to CF. An alternate correlation between SST, WV, and CF is found, where an inverted CF data tracks with WV up to SST of 300 K, and then becomes anti-correlated. These functional relationships can be used in global thermodynamic model or analyses of the Earth climate system, and also validate sub-models used in complex numerical approaches such as regional or global circulation models. [ABSTRACT FROM AUTHOR]

The present study proposes a multi-stage procedure for selecting general circulation models (GCMs) which forms the basis for regional-scale climate change impact assessment with downscaling. The procedure analyses representation of climatic processes within the GCMs in the first stage, assuming that an improved process representation in the models can ensure better projections of future climate. The poor-performing models are removed after each stage. In the second stage, the ability of the GCMs to simulate the historical regional climatic variables are analysed. The characteristics of historical simulations of the climate models are compared with the observed climatic variables in terms of temporal and spatial similarity. The final stage involves removing interdependent models based on the mutual information (MI) measure estimated between the best performing climate models. The proposed method is validated on the Western Coast of India and the GCMs CESM1-BGC, CMCC_ESM2, FGOALS-G2, FIO_ESM_2_0 and MIROC4h are identified to be the better performing climate models for analysing climate change over the Western Coast of India. The effectiveness of the proposed approach is quantified in terms of reduction of uncertainty in the historic climate simulations and it is represented by the width of the simulation band for the period 1996 to 2005. The selection procedure reduces the width of the simulation band for rainfall from 359 to 118 mm, for maximum temperature from 11.7 to 2.8 °C, and for minimum temperature from 10.1 to 2.7 °C. The predictive ability of the selected climate models is also analysed for the near future period 2021–2030 and the selection procedure reduces the width of the precipitation simulation band from 428 mm for the full set of GCMs to 184 mm for the final selected set. These climate projections need to be downscaled before they can be used in climate change impact assessment, which will further reduce the data uncertainty. The selected models are highly dependent on the methods, indices and data considered for the analysis. The climate models which are not selected will have their own capabilities in other aspects which did not account for in the current study. The criteria for climate model selection needs to be fixed based on the focus of the impact analysis study. [ABSTRACT FROM AUTHOR]

Adequate subsurface drainage is required to combat future climate change–induced waterlogging challenges in humid and sub-humid regions. A field experiment (2016–2018) was conducted with three conventional subsurface drainage systems to calibrate and validate the CROPGRO-canola model and project climate change impacts on winter canola growth, yield, and water use efficiency (WUE). The outputs of the general circulation model (CanESM2) were downscaled using SDSM software under three representative concentration pathways (RCP2.6, RCP4.5, and RCP8.5) for the 2021–2050 period. The DSSAT 4.7.5 crop model was adapted to simulate the responses of winter canola to the projected climate change under different planting dates. Measured phonological data, leaf area index (LAI), total dry matter (TDM), and grain yield were used to calibrate the crop model. The model showed good potential to simulate winter canola (Brassica napus cv. Hyola 401) growth and yield, with 1–2-day differences between observed and predicted phonological dates and nRMSE, d, and R2 of 12–29%, 0.89–0.99, and 0.84–0.99, respectively, across drainage systems and crop indices. Under the three RCP scenarios, climate change will reduce crop yield by 8–33% in different drainage systems at the common planting date. However, planting date management will improve canola yield and WUE by 20–64% and 19–58%, respectively, in different drainage systems, mainly due to an increased transpiration/precipitation ratio. Based on the results, proper drainage management and agronomic operations make it possible to control waterlogging and thus achieve optimal canola yield and acceptable WUE under climate change. [ABSTRACT FROM AUTHOR]

Occurrence of extreme climatic phenomena such as frost will cause significant risks and costs to many sectors, especially agriculture, horticulture, and forests. Frost will cause the worst damage when it occurs at the critical stages of crops, especially in spring. The frost phenomena are one of the important climatic and environmental hazards that cause a lot of damage to the agricultural sector of Iran every year. In this respect, the present study intends to highlight the projection of late spring rost by global circulation models (GCMs) from Coupled Model Intercomparison Project Phase 6 (CMIP6). For this purpose, minimum temperature data of 17 synoptic stations were used in the period 1985–2014 in cold regions of Iran. For projecting the changes of LSF, the ACCESS-ESM1-5 and Nor-ESM2-LM Models were used under three (Shared Socioeconomic Pathway (SSP)) scenarios SSP1-2.6, SSP2-4.5, and SSP5-8.5 for the next three periods (i.e., 2020–2049, 2050–2079, and 2080–2099). Then, the changes were compared to the historical period (1985–2014). The root mean square error (RMSE), mean bias error (MBE), correlation coefficient (CC), and Nash-Sutcliff efficiency (NSE) indices evaluated the models' performances. The results revealed that the latest and earliest dates of LSF during the base period occurred in the western and central parts of Iran, respectively. The model evaluation indicated that the performance of ACCESS-ESM1-5 (MBE = 0.3, CC = 0.87, and NSE = 0.68) exhibited a higher accuracy than the NorESM2-LM model. Based on both GCM under all three SSP scenarios, the projection of changes in future periods (compared to the base period) indicated that the date of occurrence of LSF would be earlier than the base period, with the highest and lowest changes projected based on NorESM2-SSP5-8.5 and ACCESS-ESM1-5-SSP1-2.6 in Arak, Isfahan, Khorramabad, Sabzevar, Shahrekord, and Shahroud stations. In general, depending on the model and climate scenario, the LSF phenomenon occurs earlier or later in cold regions of Iran, and its changes would be between − 76 and + 19 days in the future period. [ABSTRACT FROM AUTHOR]

This study investigated the effect of climate change on future precipitation and temperature from 2021 to 2050. Three general circulation models (GCMs), namely GFDL-ESM2M, HadGEM2-ES, and IPSL-CM5A-LR, and two greenhouse emission scenarios, RCP2.6 and RCP8.5, were analyzed for this purpose. The CCT model, precipitation data, and minimum and maximum daily temperatures (from 1986 to 2019) were used for downscaling and correcting precipitation and daily temperature bias. According to the results, the weighted annual precipitation recorded in rain-gauge stations was ascending in all scenarios, except for RCP8.5 in the IPSL-CM5A-LR model. The mean weighted precipitation rate of rain-gauge stations in winter did not descend under any climate change conditions, but the precipitation rate decreased or increased in other seasons. The highest increase of 23 mm in the weighted mean precipitation in winter was calculated under the RCP2.6 scenario in the GFDL-ESM2M model. The highest decrease of 10.5 mm in the weighted mean precipitation was observed in autumn. No temperature decline occurred in meteorological stations. The highest increase of 3.1 °C in the weighted mean temperature and the highest seasonal temperature rise of 8.5 °C were observed in summer under the RCP8.5 scenario in the HadGEM2-ES model. According to the standardized precipitation index (SPI), almost 70% of the future 30-year period are dry years, and drought occurs in almost all scenarios in all Iranian watersheds from 2030 to 2040. Given severe long droughts (14 years), Iran needs a comprehensive management plan and a long-term vision of managers and authorities for water resources. [ABSTRACT FROM AUTHOR]

Identifying general circulation models (GCMs) that represent the climate of a specific area is crucial for climate change studies. However, the uncertainties in GCMs caused by computational constraints, such as coarser resolution, physical parameterizations, initializations, and model structures, make it imperative to identify a representative individual or group of GCM for a climate impact study. An advanced envelope-based multi-criteria selection approach was used to identify a subset of the most appropriate future GCMs in the Upper Awash Basin (UAB). The skill accounting is based on (1) the range of projected mean changes of climate variables, (2) range of variability in climate extremes, and (3) model run performance to represent historical climate data. Statistical Downscaling and bias correction were made for the selected model runs. The downscaled and bias-corrected monthly values for precipitation are expected to increase from 0.42 to 2.82% in mid-century and 0.15 to 3.79% by the end century, considering the SSP4.5 scenario. For SSP8.5, it increases from 1.45 to 5.51% and 2.57 to 9.78% in the respective periods. Likewise, under the SSP4.5 forcing scenario, the monthly average air temperature projected to be warmer, which increased from 0.68 to 1.5 °C during mid-century and 0.09 to 1.92 °C end-of-century. Meanwhile, for SSP8.5, the projection indicates an increment of 0.19 to 1.98 °C under mid-century and 2.37 to 7.00 °C end-century. The projected change of future precipitation and temperature in the study basin increases the precipitation intensities, wet days and dry spells due to high-temperature increment. [ABSTRACT FROM AUTHOR]

Advancements in computational power have enabled general circulation models (GCMs) to simulate climate variables at a higher resolution. However, GCM outputs often deviate from the observed climatological data and therefore need bias correction (BC) before they are used for impact studies. While there are several BC methods, BCs considering frequency, intensity and distribution of rainfall are few. This study proposes a BC method which focuses on separately correcting the frequency, intensity and distribution of precipitation. This BC was performed on high-resolution daily precipitation simulations of Meteorological Research Institute-Atmospheric General Circulation Model Version 3.2 with a 20-km grid size (MRI-AGCM3-2-S) model which is part of Coupled Model Intercomparison Project Phase 6 (CMIP6) on Netravati basin, a tropical river basin in India. The historical rain gauge station data was considered for testing the effectiveness of the BC method applied. The quantile–quantile (Q–Q) plot, Taylor diagram, Nash–Sutcliffe efficiency (NSE), coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), percentage bias (PBIAS) and correlation coefficient (R) are employed for the evaluation of the BC method. Higher R and R2 and lower RMSE, MAE and PBIAS values were observed for the bias-corrected GCM data than raw simulation. The PBIAS reduced from 15.6 to 6% when BC was applied. The analysis suggested that the proposed method effectively corrects the bias in rainfall over the basin. Furthermore, an attempt has been made to analyse the trend of historical and future rainfall in the basin. The analysis revealed a declining trend of precipitation in monsoon months with the magnitude of 12.44 mm and 56.7 mm in the historical and future periods respectively. This study demonstrates that BC should be applied before the use of GCM simulated precipitation for any analysis or impact studies to improve the predictions. [ABSTRACT FROM AUTHOR]

In this study, statistical and soft-computing methods are compared in forecasting groundwater levels under Shared Socioeconomic Pathways (SSPs) SSP1-2.6, SSP2-4.5, and SSP5-8.5 from Coupled Model Intercomparison Project Phase 6 (CMIP6) in Tasuj Plain, Iran, for a near future period (2022–2027). A combination of general circulation models (GCMs) was used in the projection of precipitation and temperature in the future period. The estimation of climate variables for 2020–2044 period indicated that the temperature will increase, while the precipitation will decline. In simulation temporal groundwater level, wavelet-nonlinear autoregressive network with exogenous inputs (NARX), autoregressive integrated moving average (ARIMA), and wavelet-adaptive neuro-fuzzy inference system (ANFIS) models were used. The comparison of performance criteria for these models in the simulation of groundwater level demonstrated that the wavelet-NARX model with the R2 of 0.99 has shown better efficiency. Finally, the simulation of groundwater level through the wavelet-NARX model was carried out for different scenarios. The results indicated that future groundwater levels in Tasuj Plain would continue to decline by 3.12 m, 3.96 m, and 4.79 m, for SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. [ABSTRACT FROM AUTHOR]

South of Minas Gerais state, in Southeast Brazil, is known for the coffee crop production (more than 30% of country's production) and hydroelectricity generation (1216 MW installed power). Droughts are natural climate phenomena that may strongly affect a region during a certain period. In this study, the severity of the droughts that hit southern Minas Gerais state was analyzed in the period from 1970 to 2020 and was projected up to 2098/2099 using four global circulation models (HadGEM2-ES, MIROC5, BESM, CanESM2), downscaled by Eta model to 20-km resolution, under two Representative Concentration Pathways (RCP4.5 and RCP8.5). To access the severity of the droughts, the Standard Precipitation Index considering the hydrological year (SPI12) was investigated over time and space. The results demonstrated that the 2013–2014 hydrological year was the dryest in southern Minas Gerais, followed by 2014/2015, which led to water shortage, reduction of the hydroelectricity and reduction of coffee crop production. Future projections indicate that extreme droughts will continue occurring, but with similar rarity. However, the RCM downscaling pointed out the possible occurrence of several dry consecutive years, which can collapse the hydrology and put at risk the economy of the region. Except from the Eta-MIROC under RCP 8.5, that simulated most of the droughts in middle to the end of XXI century, the other RCMs projected recurrent droughts for the next two decades, supporting the detection drought anomalies and helping in adoption actions to anticipate and mitigate drought effects in the future. [ABSTRACT FROM AUTHOR]

Temperature and precipitation are among the most important climatic elements in the study of climate change due to significant temporal and spatial changes, and the projection of their changes is very important in environmental hazards and planning. Therefore, in this study, the future of temperature and precipitation changes in the Middle East and North Africa (MENA) region was projected. For this purpose, the data of 23 global circulation models (GCMs) from Coupled Model Intercomparison Project phase 6 (CMIP6) were used as networks under the influence of two scenarios SSP3–7.0 and SSP5–8.5 for temperature and precipitation changes in the two future periods (2020–2049 and 2050–2079) were investigated comparing to the base period (1985–2014). The results showed that the temperature will increase in both periods, which will be between 0.8 and 3.3 °C in the period 2020–2079 compared to the base period. The highest and lowest temperature changes are related to the eastern and northern regions of the study area, respectively. Projection of precipitation changes also showed that the precipitation in most of the study area will decrease in the next two periods compared to the base period, which will be between 5 and 133 mm on average. Most of its changes are related to the northern regions and in the form of a strip from Morocco to the northwest of Iran. In both studied periods, the SSP5–8.5 scenario shows the highest temperature and precipitation changes in the study area. [ABSTRACT FROM AUTHOR]

Improving the economic productivity of limited available freshwater through producing more rice with less water is essential to sustain paddy production systems in the future. The effectiveness of two current-successful water management strategies, i.e., mid-season drainage (MSD) and alternate wetting and drying (AWD), under future climate was investigated for the first time involving the AquaCrop model. The model was calibrated and validated using 4-year field data of an early-matured rice cultivar. Future climate data was downscaled for a 2041–2070 period under two Representative Concentration Pathways (RCP) of RCP2.6 and RCP8.5 by applying 20 different Global Circulation Models. The calibrated AquaCrop was then used to predict yield, water productivity (WP), and economic water productivity (EWP) for different cropping calendars. For the current planting date, global warming will reduce rice yield (70–170 kg ha−1), WP (10–15%), and EWP (16–27%) under MSD and increases yield (1040–1290 kg ha−1) and decreases WP (21–31%), and EWP (22–32%) under AWD compared with the base period. Delayed cropping could not be a suitable strategy for both MSD and AWD under both climate scenarios. Under MSD and AWD, 10 days earlier transplanting will decrease rice yield by 65–130 kg ha−1 and WP (and EWP) by 5–11% in RCP2.6, while increasing by 413–820 kg ha−1 and 8–13% in RCP8.5, respectively. Investigation revealed that sustaining or improving current land and water productivity in the future mainly relies on the severity of global warming. However, the AWD strategy will be a more effective climate change-adaptation strategy than MSD in viewpoints of crop yield, WP, and EWP. [ABSTRACT FROM AUTHOR]

The extreme temperature indices (ETI) are an essential indicator of climate change. The detection of their changes over the next years can play an essential role in the climate action plan (CAP). In this study, four temperature indices (mean of daily minimum temperature (TN), mean of daily maximum temperature (TX), cold-spell duration index (CSDI), and warm-spell duration index (WSDI)) were defined by ETCCDI and two new indices,the maximum number of consecutive frost days (CFD) and the maximum number of consecutive summer days (CSU), were used to examine ETIs in Iran under climate change conditions. We used minimum and maximum daily temperatures of five general circulation models (GCMs), including HadGEM2-ES, IPSL-CM5A-LR, GFDL-ESM2M, MIROC-ESM-CHEM, and NorESM1-M, from the set of CMIP5 bias-correction models. We investigated two representative concentration pathway (RCP) scenarios of RCP4.5 and RCP8.5 during the historical (1965–2005) and future (2021–2060 and 2061–2100) periods. The performance of each model evaluated using the Taylor diagram on a seasonal scale. Among models, GFDL-ESM2M and HadGEM2-ES showed the highest, and NorESM1-M and IPSL-CM5A-LR showed the lowest performance in Iran. Then, an ensemble model was generated using independence weighted mean (IWM) method. The results of multi-model ensembles (MME) showed a higher performance compared to individual CMIP5 models in all seasons. Also, the uncertainty value significantly reduced, and the correlation value of the MME model reached 0.95 in all seasons. Additionally, it is found that WSDI and CSU indices showed positive anomalies in future periods, and CSDI and CFD showed negative anomalies throughout Iran. Also, at the end of the twenty-first century, no cold spells are projected in almost every part of Iran. The CSU index showed that summer days are increasing sharply; according to the results of the RCP8.5 scenario in spring (MAM) and autumn (SON), the CSU will increase by 18.79 and 20.51 days, respectively, at the end of the twenty-first century. It projected that in the future, the spring and autumn seasons will be shorter and summers will be much longer than before. [ABSTRACT FROM AUTHOR]

Using an ensemble of 22 climate models from the 5th Annual Report of Intergovernmental Panel on Climate Change (IPCC AR5), the projected robustness and variability of temperature and precipitation for the data-sparse region of Pakistan is studied both on seasonal and annual time scales for the 21st century. The winter season in Pakistan is displaying ensemble-based spatially robust and progressively more relative warming in temperature under representative concentration pathway (RCP) 8.5 scenario as compared to RCP 4.5 scenario, both in the middle (2035−2064) and end (2070−2099) of 21st century projection periods. On the other hand, the ensemble-based relative changes in precipitation during the aforementioned two projected periods are spatially less robust. Most of the atmosphere–ocean general circulation models (AOGCMs) project a relative increase of 5−10% in annual precipitation in all the regions of Pakistan. On a seasonal time scale, most AOGCMs project a relative precipitation decrease (increase) during winter (summer) in central and in southern (central and northern) Pakistan. All the AOGCMs under both RCPs project an increase in temperature in all Pakistan, northern Pakistan, and southern Pakistan on annual, winter, and summer time scales. [ABSTRACT FROM AUTHOR]

This study represents a new strategy for assessing how climate change has impacted urban water demand per capita in Neyshabur, Iran. Future rainfall depths and temperature variations are projected using several general circulation models (GCMs) for two representative concentration pathway (RCP) (i.e., RCP45 and RCP85) scenarios using LARS-WG software. A simulator model is developed using the genetic programming (GP) model to predict future water demand based on projected climate variables of rainfall depth and maximum temperature. The period of 1996–2016 is selected as the base period. Three future periods, namely the near-future (2021–2040), middle future (2041–2060), and far future (2061–2080), are also employed to assess climate change impact on water demand. Results indicate significant increases in annual projected rainfall depth (14~53%), maximum temperature (0.04~4.21 °C), and minimum temperature (1.01~4.71 °C). The projected monthly patterns of rainfall depth and temperature are predicted to cause a 1-month shift in the water demand peak (i.e., it will occur in April instead of May) for all future periods. Furthermore, the annual water demand per capita is projected to increase by 0.5~1.2%, 1.5~3.2%, and (2.2~7.1%), during the near-, middle-, and far-future periods, respectively. The uncertainty associated with water demand is also projected to increase over time for RCP45. The mathematical expression of urban water demand based on climatic variables is vital to managing the water resources of Neyshabur. The methodology proposed in the present study represents a robust approach to assessing how climate change might affect urban water demand in cities other than Neyshabur and provides crucial information for decision-makers. [ABSTRACT FROM AUTHOR]

Using an initial manifold approach, an ensemble forecast methodology is shown to simultaneously increase lead and realizable skill in long-range forecasting of monsoon over continental India. Initial manifold approach distinguishes the initial states that have coherence from a collection of unrelated states. In this work, an optimized and validated variable resolution general circulation model is being adopted for long-range forecasting of monsoon using the multi-lead ensemble methodology. In terms of realizable skill (as against potential) at resolution (~60km) and lead (2–5 months) considered here, the present method performs very well. The skill of the improved methodology is significant, capturing 9 of the 12 extreme years of monsoon during 1980–2003 in seasonal (June–August) scale. Eight-member ensemble-average hindcasts carried out for realizable skill with lead of 2 (for June) to 5 (for August) months and an optimum ensemble is presented. [ABSTRACT FROM AUTHOR]

Ecuador is likely to experience significant impacts associated with future changes in climate, but future projections for this region are challenging due to the complex topography and a wide range of climatic conditions. Here we use the Weather Research and Forecasting (WRF) model run at 10 km horizontal resolution over a domain encompassing all of Ecuador to investigate future changes in temperature and precipitation for the middle of the twenty-first century (2041–2070) under a low (RCP4.5) and a high (RCP8.5) emission scenario. The model was validated by running 30-year control runs for the present climate, driven both by the Climate Forecast System Reanalysis (CFSR) and the CCSM4 General Circulation Model. Bias and different correlation coefficient metrics were employed to compare the present-day model results with gridded (CRU TS v 4.03 and CHIRPS v 2.0) and in situ meteorological observations. Detailed hydrometeorological analyses over the Andes in both space and time domains show that WRF accurately simulates temperature variability. The precipitation seasonal cycle and interannual variability are also adequately simulated, but the model shows a general dry bias over the lowlands and a significant wet bias along the eastern Andean slopes. Results from future projections show that Ecuador could warm by an additional 1–2 K by the middle of the century compared with the end of the twentieth century. This warming is highly elevation-dependent, subjecting the highest peaks of the Andes to the strongest future warming. Bias-corrected future precipitation changes document a drying trend along coastal areas in RCP4.5 and increased future precipitation along the eastern Andean slopes in both scenarios. [ABSTRACT FROM AUTHOR]

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). [ABSTRACT FROM AUTHOR]

We evaluated the performance of general atmosphere circulation model (GCM) from the European Center for Medium Range Weather Forecasts (ECMWF) for estimating surface air temperature (T) and precipitation (P) in 55 locations in the Brazilian Amazon. We compared data from surface meteorological stations obtained by the Brazilian Institute of Meteorology (INMET) and ECMWF by linear regression analysis (LRA) using R2 and Willmott et al. (J Geophys Res C5:8995–9005,1985) index (d) as measurement of precision and accuracy, respectively. We applied the Fourier series analysis by extracting the trend and frequency components of P events with noise reduction in the time series. We used the multivariate K-means method to separate weather stations by Groups of Similar Performances (GSPs). The northwest region is characterized as the area with the highest precipitation supply but the lowest performances for T and P, with R2 lower than 0.18. ECMWF tend to overestimate P in dry season and to underestimate in rainy season. The proposed methodology of calibration of P data by the Fourier series was a good tool to predict an extreme event every 5 to 7 months in the region. ECMWF presented high performance (R2 > 0.60) when estimating P in a monthly scale and medium performance (R2 < 0.60) when estimating T in a monthly and 10-day period. The highest concentrations of surface meteorological stations in the eastern/southeastern portion of the Amazon region were decisive in the ECMWF performance expression, indicating an increased meteorological predictability in the anthropic areas, precisely where the agricultural areas of grain were established in the region. [ABSTRACT FROM AUTHOR]

Drought has become one of the most serious meteorological disasters for agricultural production in many areas around the world, and the situation could be worse under the impact of climate change. To facilitate better adaptation planning, this study proposed a drought assessment framework integrating downscaling method, drought index, copula technique, and bivariate frequency analysis, and applied it to investigate the change of the drought characteristics and drought risks from the past to the future in Huang-Huai-Hai River basin (HRB), North China. Drought was firstly defined by standardized precipitation evapotranspiration index (SPEI) based on 1497 observed grid data from 1979 to 2004. Then, we constructed the joint distribution of drought duration and severity based on copulas to detect and quantify the drought risks. To address the effect of climate change, similar calculation process was applied to the future climate data, which was downscaled using delta change method from representative concentration pathway (RCP 8.5) of 12 general circulation models (GCMs). The study results suggested that, under climate change condition, most irrigation districts over HRB would generally experience lower frequency of drought events but with extended duration; some districts would have more serious drought, but majority would experience similar or even lower level of severity. In light of the mean joint occurrence probability, the irrigation district at the south part of Huai River basin would likely experience the highest increase of drought risks in near future (by 0.86%) and distant future (by 0.76%), while most of other districts over HRB would face low risk of serious drought risks. The obtained results offer useful information to agricultural managers or water resources authorities who are interested in the development of effective long-term adaptation strategies for drought management. [ABSTRACT FROM AUTHOR]

Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) is one of the longest-serving observation platforms which provides the cloud fraction data with higher spatial and vertical resolutions. Their simulators are also incorporated in the General Circulation Models (GCMs) under the Coupled Model Intercomparison Project Phase 5 (CMIP5). Here, we have examined the spatial and vertical cloud fraction (CF) distributions and corresponding biases (CFmodel−CFobservations; CF bias) from CALIPSO observations and their simulators for the period 2006–2015 over Indian land and oceanic regions for Indian summer monsoon season of June to September. It is reported that the CF biases over the whole Indian land and oceanic regions are largely negative (> 55%) except the northeastern land regions and southern Bay of Bengal (BOB) which show positive biases (~ 10–60%). Such CF biases are prominent and consistent in all CMIP5 models. The CF biases are further analyzed in the vertical dimension using contoured frequency by altitude diagrams (CFADs). The large CF biases exist at each vertical level especially at higher altitudes (> 12 km). CF biases over BOB based on spatial distributions give the false impression of better performance of simulators which are exhibited by the co-existence of both positive and negative biases at the same level, especially at higher altitude levels. As a result, both positive and negative CF biases cancel each other which lead to the minimum values of CF biases at respective altitude levels. This mutual ambiguity between spatial and vertical CF biases is found to be a permanent feature of the CALIPSO simulator. [ABSTRACT FROM AUTHOR]

Biases in coupled general circulation models represent important limits for climate prediction. Based on historical runs of 26 coupled climate models of the Fifth Phase of the Coupled Model Intercomparison Project, the error of the climatological mean of the western Pacific warm pool (WPWP) is investigated. The results show that simulation of the morphology of the WPWP is significantly influenced by ocean currents. The upper-ocean heat budget analysis also indicates that heat advection plays a key role in determining the shape of the WPWP during the simulation. For the shrinkage of the tropical region and south section of smaller WPWPs, both the zonal heat advection bias caused by zonal ocean currents bias and meridional heat advection bias caused by the meridional sea temperature gradient bias are the crucial factors, while for the extension of larger WPWPs, the zonal heat advection bias is more important. For the northern section of WPWPs, the horizontal heat advection biases are still responsible for the shrinkage and extension in the region from 4°N to 7°N, while in the north of 7°N, the biases of WPWP are related to that of short wave radiation. In addition, in the equator and south section of the WPWP, the advective feedback plays a key role in the development of biases. [ABSTRACT FROM AUTHOR]

Northeast China is the main crop production region in China, and future climate change will directly impact crop potential yields, so exploring crop potential yields under future climate scenarios in Northeast China is extremely critical for ensuring future food security. Here, this study projected the climate changes using 12 general circulation models (GCMs) under two moderate Representative Concentration Pathway (RCP) scenarios (RCP 4.5 and 6.0) from 2015 to 2050. Then, based on the Global Agro-ecological Zones (GAEZ) model, we explored the effect of climate change on the potential yields of maize and paddy rice in Northeast China during 2015–2050. The annual relative humidity increased almost throughout the Northeast China under two RCPs. The annual precipitation increased more than 400 mm in some west, east, and south areas under RCP 4.5, but decreased slightly in some areas under RCP 6.0. The annual wind speed increased over 2 m/s in the west region. The annual net solar radiation changes varied significantly with latitude, but the changes of annual maximum temperature and minimum temperature were closely related to the terrain. Under RCP 4.5, the average maize potential yield increased by 34.31% under the influence of climate changes from 2015 to 2050. The average rice potential yield increased by 16.82% from 2015 to 2050. Under RCP 6.0, the average maize and rice potential yields increased by 25.65% and 6.34% respectively. The changes of maize potential yields were positively correlated with the changes of precipitation, wind speed, and net solar radiation (the correlation coefficients were > 0.2), and negatively correlated with the changes of relative humidity, minimum and maximum temperature under two RCPs. The changes of rice potential yields were positively correlated with the changes of precipitation (correlation coefficient = 0.15) under RCP 4.5. Under RCP 6.0, it had a slight positive correlation with net solar radiation, relative humidity, and wind speed. [ABSTRACT FROM AUTHOR]