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In the recent past, Andhra Pradesh (AP) has experienced increasing trends in surface air mean temperature (SAT at a height of 2 m) because of climate change. In this paper, we attempt to estimate the SAT using the GFDL-ESM2G (Geophysical Fluid Dynamics Laboratory Earth System Model version 2G), available from the Coupled Model Intercomparison Project Phase-5 (CMIP5). This model has a mismatch with the India Meteorological Department (IMD)'s observations during April and May, which are the most heat-prone months in the state. Hence, in addition to the SAT from the model, the present paper considers other parameters, such as mean sea level pressure, surface winds, surface relative humidity, and surface solar radiation downwards, that have influenced the SAT. Since all five meteorological parameters from the GFDL-ESM2G model influence the IMD's SAT, an artificial neural network (ANN) technique has been used to predict the SAT using the above five meteorological parameters as predictors (input) and the IMD's SAT as the predictand (output). The model was developed using 1981–2020 data with different time lags, and results were tested for 2021 and 2022 in addition to the random testing conducted for 1981–2020. The statistical parameters between the IMD observations and the ANN estimations using GFDL-ESM2G predictions as input confirm that the SAT can be estimated accurately as described in the analysis section. The analysis conducted for different regions of AP reveals that the diurnal variations of SAT in the IMD observations and the ANN predictions over three regions (North, Central, and South AP) and overall AP compare well, with root mean square error varying between 0.97 °C and 1.33 °C. Thus, the SAT predictions provided in the GFDL-ESM2G model simulations could be improved statistically by using the ANN technique over the AP region. [ABSTRACT FROM AUTHOR]

The impact of climate change on surface runoff and soil moisture in the source region of the Yellow River is analyzed, which will provide a scientific basis for the rational use and protection of water resources in the source area. In this paper, the SWAT hydrological model was coupled with the Coupled Model Intercomparison Project (CMIP) to predict future changes in surface runoff and soil moisture in the source region of the Yellow River. The prediction of surface runoff and soil moisture in the Yellow River Basin was analyzed by a linear regression model. The SWAT model rate had a calibration period R2 of 0.876 and a validation period R2 of 0.972. The trend of surface runoff and annual mean temperature in the source region of the Yellow River from 2011 to 2022 showed an overall increasing trend, and soil moisture showed a general decreasing trend. 2011–2022 trends between surface runoff and annual mean temperature in the source region of the Yellow River showed a highly significant difference, indicating that surface runoff flow was significantly influenced by temperature. The difference between the trends in soil moisture and the annual mean temperature was highly significant. The surface runoff fluctuated greatly in different years, and the surface runoff changed greatly in different scenarios of CMIP5 (RCP2.6, RCP4.5, and RCP8.5). For all three climate change scenarios, the surface runoff displayed a downward trend. The surface runoff showed a similar uneven distribution for all scenarios on a yearly cycle. Under the three climate scenarios, the runoff was highest between May and August, with a slowly increasing trend from January to April and a slightly decreasing trend from September to December. The interannual and interannual distribution of soil water was basically consistent with the distribution of surface runoff, and there was an overall trend in the length of all soil water reduction scenarios. Surface runoff and soil moisture are and will be greatly affected by climate change (mainly temperature and precipitation). Under the three climate scenarios, the precipitation increases to some extent, but the surface runoff and soil moisture will both decrease, which may be attributed to the greater evaporation than the precipitation. [ABSTRACT FROM AUTHOR]

Existing research has proven the increase in sea surface temperature (SST) due to global warming. However, the sea bottom temperature (SBT) may exhibit different characteristics in various regional seas. The East China Shelf Seas (ECSSs), which are important shelf seas in the Western Pacific, hold ecological significance when analyzing their SBT variations in a warming future. This article investigates both the interannual and interdecadal SBT variations from 2006 to 2100, utilizing the projection results from phase 5 of the Climate Model Intercomparison Project (CMIP5) sponsored by the Intergovernmental Panel on Climate Change (IPCC). We conducted an analysis of the interdecadal variation by comparing the SBTs from the 2030s, 2060s, and 2090s to the SBT observed in the 2010s. Our findings reveal a significant increase in SBT in the ECSSs. By 2100, the region is projected to experience enhanced warming of 1.18 °C. The springtime warming intensity of the Bohai Sea, reaching 1.92 °C, can be twice the rate of global ocean warming. The outer shelf of the ECSSs also exhibits significant increases in SBT. Through an analysis of the correlation between SBT and ocean currents, we investigate the potential mechanisms behind these observations. This paper provides insights into future SBT variations from both an interannual and interdecadal perspective, explaining the causes and the projected increase in environmental stresses on the benthic ecosystem over the next eighty years. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of the annual precipitation observed by a network of 30 rain gauges in Iraq over a 65-year period (1941–2005). The simulated precipitation from 18 climate models in the CMIP5 project is investigated over the same area and time window. The Mann–Kendall test is used to assess the strength and the significance of the trends (if any) in both the simulations and the observations. Several exploratory techniques are used to identify the similarity (or disagreement) in the probability distributions that are fitted to both datasets. While the results show that large biases exist in the projected rainfall data compared with the observation, a clear agreement is also observed between the observed and modelled annual precipitation time series with respect to the direction of the trends of annual precipitation over the period. [ABSTRACT FROM AUTHOR]

The East China Shelf Seas, comprising the Bohai Sea, the Yellow Sea, and the shelf region of East China Sea, play significant roles among the shelf seas of the Western North Pacific Ocean. The projection of sea surface temperature (SST) changes in these regions is a hot research topic in marine science. However, this is a very difficult task due to the lack of available long-term projection data. Recently, with the high development of simulation technology based on numerical models, the model intercomparison projects, e.g., Phase 5 of the Climate Model Intercomparison Project (CMIP5), have become important ways of understanding climate changes. CMIP5 provides multiple models that can be used to estimate SST changes by 2100 under different representative concentration pathways (RCPs). This paper developed a CMIP5-based SST investigation framework for the projection of decadal and seasonal variation of SST in East China Shelf Seas by 2100. Since the simulation results of CMIP5 models may have degrees of errors, this paper uses hydrological observation data from World Ocean Atlas 2018 (WOA18) for model validation and correction. This paper selects seven representative ones including ACCESS1.3, CCSM4, FIO-ESM, CESM1-CAM5, CMCC-CMS, NorESM1-ME, and Max Planck Institute Earth System Model of medium resolution (MPI-ESM-MR). The decadal and seasonal SST changes in the next 100 years (2030, 2060, 2090) are investigated by comparing with the present analysis in 2010. The experimental results demonstrate that SST will increase significantly by 2100: the decadal SST will increase by about 1.55 °C, while the seasonal SST will increase by 1.03–1.95 °C. [ABSTRACT FROM AUTHOR]

Cloud dynamics and their response to future climate change continue to present a significant source of uncertainty in climate predictions. Besides the average cloud properties, the diurnal cloud cycle (DCC) exerts a substantial influence on Earth's energy balance by reflecting solar radiation during the daytime and continuously absorbing and reemitting longwave radiation throughout the whole day. Previous studies have demonstrated that climate models exhibit certain discrepancies in simulating the DCC; however, less research attention has been paid to the patterns of these DCC biases and their impacts on modeling the Earth's energy balance. Here, we employ satellite data to compare DCC patterns in Coupled Model Intercomparison Project Phase 5 (CMIP5) and their latest versions in CMIP6 at both regional and global scales. We found that some of the latest climate models tend to have larger DCC biases when using satellite observations as the references, and the radiative effects due to DCC changes account for nearly 50% of the changes in total cloud radiative effects (CREs), suggesting that the DCC biases play a significant role in modelingthe global energy budget. We therefore call for improving cloud parameterization schemes with particular attention to their diurnal cycle to reduce their impacts on future climate projections. [ABSTRACT FROM AUTHOR]

The fidelity of the decadal experiment in Coupled Model Intercomparison Project Phase-5 (CMIP5) has been examined, over different climate variables for multiple temporal and spatial scales, in many previous studies. However, most of the studies were for the temperature and temperature-based climate indices. A quite limited study was conducted on precipitation of decadal experiment, and no attention was paid to the catchment level. This study evaluates the performances of eight GCMs (MIROC4h, EC-EARTH, MRI-CGCM3, MPI-ESM-MR, MPI-ESM-LR, MIROC5, CMCC-CM, and CanCM4) for the monthly hindcast precipitation of decadal experiment over the Brisbane River catchment in Queensland, Australia. First, the GCMs datasets were spatially interpolated onto a spatial resolution of 0.05 × 0.05° (5 × 5 km) matching with the grids of observed data and then were cut for the catchment. Next, model outputs were evaluated for temporal skills, dry and wet periods, and total precipitation (over time and space) based on the observed values. Skill test results revealed that model performances varied over the initialization years and showed comparatively higher scores from the initialization year 1990 and onward. Models with finer spatial resolutions showed comparatively better performances as opposed to the models of coarse spatial resolutions, where MIROC4h outperformed followed by EC-EARTH and MRI-CGCM3. Based on the performances, models were grouped into three categories, where models (MIROC4h, EC-EARTH, and MRI-CGCM3) with high performances fell in the first category, and middle (MPI-ESM-LR and MPI-ESM-MR) and comparatively low-performing models (MIROC5, CanCM4, and CMCC-CM) fell in the second and third categories, respectively. To compare the performances of multi-model ensembles' mean (MMEMs), three MMEMs were formed. The arithmetic mean of the first category formed MMEM1, the second and third categories formed MMEM2, and all eight models formed MMEM3. The performances of MMEMs were also assessed using the same skill tests, and MMEM2 performed best, which suggests that evaluation of models' performances is highly important before the formation of MMEM. [ABSTRACT FROM AUTHOR]

Wheat offers winter forage for cattle grazing and is one of the most valuable cash crops in Texas. In this study, we evaluate the impacts of climate change projections on winter wheat grain yields in five major wheat producing counties in Texas (Deaf Smith, Ochiltree, Hansford, Moore, and Parmer). For this purpose, extant soil and climate data were utilized in conjunction with Agricultural Policy Environmental eXtender (APEX) and Coupled Model Intercomparison Project—Phase 5 (CMIP 5) climate projections to determine the most reasonable future trajectory of Texas winter wheat yields. The results indicate that Deaf Smith and Parmer counties are projected to experience the greatest yield decrease, 33.33%, about 696 kg/ha under the CMIP5 RCP4.5 (Texas projected temperature increase between 2.2 and 3.3 °C) 2046–2070 scenario compared to a 1981–2017 baseline. The maximum percentage yield increase was noticed in Ochiltree County under the CMIP5 RCP8.5 2071–2095 scenario, with an 84.2% (about 1857 kg/ha) yield increase compared to the 1981–2017 baseline. Parmer County is projected to experience the greatest yield decrease of 20%, about 348 kg/ha, under the RCP4.5 2046–2070 scenario when compared to the 1981–2005 baseline. The maximum percentage yield increase is projected for Ochiltree County—a 105.9% increase, about 2089 kg/ha—under the RCP8.5 2071–2095 scenario when compared to the 1981–2005 baseline. In general, with few exceptions, winter wheat yields are projected to rise under the projected climate scenarios. [ABSTRACT FROM AUTHOR]

After being repeatedly struck by droughts in the last few decades, water managers and stakeholders in the Southeast U.S. dread the future extremes that climate change might cause. In this study, the length of future dry periods is assessed using a sub-ensemble of downscaled CMIP5 climate models, which are proven to perform well in precipitation estimations. The length of a dry spell with a twenty-year return period is estimated for the cold and warm seasons for two time periods; 2020–2059 and 2060–2099, and considering two emission scenarios: RCP 4.5 and 8.5. The estimates are then compared with historical dry spells and differences in length and geospatial distribution analyzed. Based on the findings of this paper, little change can be expected in dry spell length during the warm season. Greater changes are to be expected in the cold season in the southern half of Florida, where dry spells are expected to be up to twenty days shorter, while dry spells in Alabama, Mississippi and Tennessee are predicted to be up to twenty days longer. The changes predicted by the models are positively associated with emission trajectory and future time period. [ABSTRACT FROM AUTHOR]

The evaluation of general circulation models (GCM) is a fundamental step in climate research in terms of both quality assurance/quality control and realistic representation of the dynamics of the atmospheric flows in the future projections. In this paper, a statistical method is introduced to evaluate GCMs with respect to teleconnection patterns in the winter 500 hPa geopotential height field over the Northern Hemisphere (NH). The procedure uses the combination of negative extrema method and receiver operating characteristic (ROC) curve analysis. The proposed method is demonstrated using selected general circulation models (GCMs) disseminated by the CMIP5 project. The ERA-20C reanalysis was used as a reference, supported by the NCEP/NCAR R1 reanalysis. The proposed method enables us to track changes in the geographical positions of the action centers (ACs); therefore, to detect improvement/deterioration in the GCM performance with time. It was found that the majority of the GCMs reproduce prominent teleconnections of the NH but fail to capture the eastward shift of the ACs over the Pacific Ocean in the last decades of the 20th century. The GCMs reproduce teleconnections with stronger correlations over the north-western part of the Atlantic Ocean compared to the reanalyses. The construction of mobile teleconnection indices is proposed to gain further insight into the performance of the models and to support a regional-scale analysis. The method can be easily applied to the recent CMIP6 simulations. [ABSTRACT FROM AUTHOR]

The effects of climate change have been observed in the Murrumbidgee River basin, which is one of the main river basins in the southeast region of Australia. The study area is the largest and most important agricultural production area within the Murray Darling Basin (MDB). It produces more than AUD 1.9 billion of agricultural products annually and accounts for about 46% of Australia's total agricultural production. Since Australia's economy largely depends on its natural resources, climate change adversely impacts the economy in various ways. According to the Intergovernmental Panel on Climate Change's fifth assessment report (IPCC, AR5), the adaptive capacity and adaptation processes have increased in Australia. The country has implemented policies and management changes in both rural and urban water systems to adapt to future drought, unexpected floods, and other climatic changes. In this study, future catchment runoff has been estimated using the hydrological model, Simplified Hydrolog (SIMHYD), which is integrated with data from three different General Circulation Models (GCMs) and future emission scenarios. Two different representative concentration pathway (RCP) emission scenarios, RCP 4.5 and RCP 8.5, have been used to obtain downscaled future precipitation and evapotranspiration data for the period of 2016 to 2100. Modeling results from the two emission scenarios showed an anticipated warmer and drier climate for the Murrumbidgee River catchment. Runoff in the Murrumbidgee catchment is affected by various dams and weirs, which yields positive results in runoff even when the monthly rainfall trend decreases. The overall runoff simulation result indicated that the impact of climate change is short and intense. The result of the Simplified Hydrolog (SIMHYD) modeling tool used in this study under the RCP 4.5 scenario for the period 2016 to 2045 indicates a significant future impact from climate change on the volumes of runoff in the Murrumbidgee River catchment. For the same period, the climate change prediction showed a decrease in total annual rainfall within the range of 2% to 62%. This reduction in rainfall is projected to decrease river runoff in the upper catchments (e.g., Tharwa, and Yass) by 17% to 58% over the projected periods. However, the runoff trends in the lower sub-catchments (e.g., Borambola) have increased by 137% to 87% under RCP 4.5 and RCP 8.5, respectively. This increasing potential runoff trend in the lower Murrumbidgee catchments gives an indication to build irrigation dams for dry season irrigation management. [ABSTRACT FROM AUTHOR]

Land resources are under relentless pressure from metropolitan regions, pollution, and climate shifts. The urge to monitor Land Use/Land Cover (LULC) and climate changes based on technology and sustainable management are addressed. This study analyzes the historical land cover maps to calculate growth patterns for the years 1985–2022 and uses Logistic Regression (LR) and Artificial Neural Networks (ANN) to project future dynamics forecasts for the years 2030–2040 in the Amman-Zarqa Basin (AZB). The state of the climate and the extreme indices projections of CMIP5 under RCP8.5 are linked to the corrected historical LULC maps and assessed. Given greater dry covering of large surface runoff, little rainfall, and high evapotranspiration rates, the state of the climate across the AZB notably showed instability in key climatic indices and a major exacerbation of warmth and drier soil in the basin. Both climate change and land use are contributing dynamics, but land-use alterations are much more dramatic changes than climate changes. Since the effects of climate alterations are mostly identifiable through land cover forms, land use practices put the phase that may be influenced by climate change. The results revealed that the daily extremes in 1992 are aligned with the corresponding increase of barren lands and diminished the half area of forest, cultivated, rainfed, and pasture lands in 1995. Rainfed regions were converted to agriculture or shrubland with an accuracy of 0.87, and urban encroachment caused the acreage of woodland, cultivated, rainfed, and grazing fields to decrease by almost half. Predicted land cover maps were created using LR in 2030 (Kappa = 0.99) and 2040 (Kappa = 0.90), in contrast to the ANN approach (Kappa = 0.99 for 2030 and 0.90 for 2040). By combining ANN and LR, decreasing bare soil was anticipated between 325 km2 and 344 km2. As a result, 20% of the total area of the major AZB cities' urban areas will be doubled. More subjective analysis is required to study and predict drought in the future to improve the resilience of various LULC types. [ABSTRACT FROM AUTHOR]

Nonpoint source pollution from cultivated croplands has often been associated with downstream water quality impairment in various watersheds. Given projected changes in global climate patterns, this study contributes to the existing literature by elucidating the impacts of climate projections on edge-of-field surface runoff and sediment and nutrient losses. We apply a well-tested ecohydrological model, Agricultural Policy Environmental eXtender (APEX), to continuous corn and corn–soybean fields in Buchanan County, Iowa, using climate scenarios developed from three well-known representative concentration pathway (RCP) climate projections: RCP 2.6, RCP 4.5, and RCP 8.5. Our results indicate that there will be a moderate to substantial increase in surface runoff, sediment, and nutrient losses depending upon the reference point of comparison (baseline scenario) and upon which climate scenario actually materializes. However, regardless of which climate scenario materializes and regardless of the baseline for comparison, soluble nitrogen losses are bound to increase, the magnitude depending upon the climate scenario. We find also that nutrient losses will be higher from continuous corn fields than from corn–soybean fields, given the tillage practices implemented on corn versus soybeans in the study area. Similarly, we find that nutrient losses may be higher from fields that receive manure than fields that receive only inorganic fertilizer, though this latter finding may be predicated upon the specific nutrient application rates utilized. [ABSTRACT FROM AUTHOR]

This paper investigates the temperature and precipitation extremes over the Arabian Peninsula using data from the regional climate model RegCM4 forced by three Coupled Model Intercomparison Project Phase 5 (CMIP5) models and ERA–Interim reanalysis data. Indices of extremes are calculated using daily temperature and precipitation data at 27 meteorological stations located across Saudi Arabia in line with the suggested procedure from the Expert Team on Climate Change Detection and Indices (ETCCDI) for the present climate (1986–2005) using 1981–2000 as the reference period. The results show that RegCM4 accurately captures the main features of temperature extremes found in surface observations. The results also show that RegCM4 with the CLM land–surface scheme performs better in the simulation of precipitation and minimum temperature, while the BATS scheme is better than CLM in simulating maximum temperature. Among the three CMIP5 models, the two best performing models are found to accurately reproduce the observations in calculating the extreme indices, while the other is not so successful. The reason for the good performance by these two models is that they successfully capture the circulation patterns and the humidity fields, which in turn influence the temperature and precipitation patterns that determine the extremes over the study region. [ABSTRACT FROM AUTHOR]

With the disturbance of human factors, the groundwater resources in the source region of the Yellow River have gradually depleted and the water quality has become worse, which has seriously affected the development of high-altitude areas. The groundwater quality of the source region of the Yellow River from 2016 to 2020 was evaluated using single-component and comprehensive evaluation methods, following by a prediction of the groundwater quality from 2021 to 2100 based on the RCPS (RCP 2.6, RCP 4.5, and RCP 8.5) scenarios coupled with the SWAT hydrological model under the CMIP5 global climate model. The results indicated that the groundwater temperature had an increasing trend, pH showed an obvious decreasing trend, and total hardness (Th), sulfate, and ammonia nitrogen (NH4+-N) contents exhibited no obvious increasing or decreasing trend in the source region of the Yellow River during 2016–2020. The increase rate of total nitrogen (TN) and total phosphorus (TP) in the future climate scenario followed the order of RCP 8.5 > RCP 4.5 > RCP 2.6, and the groundwater contents of TN and TP in the source region of the Yellow River gradually increased. This result is of great significance, as it can help clarify the current situation of groundwater in high-altitude and cold regions, showing the influence of groundwater on global climate change. It provides a reference for the development and utilization planning of groundwater resources in the source region of the Yellow River in the future. [ABSTRACT FROM AUTHOR]

Terrestrial ecosystems in China are threatened by land use and future climate change. Understanding the effects of these changes on vegetation and the climate-vegetation interactions is critical for vegetation preservation and mitigation. However, land-use impacts on vegetation are neglected in terrestrial ecosystems exploration, and a deep understanding of land-use impacts on vegetation dynamics is lacking. Additionally, few studies have examined the contribution of vegetation succession to changes in vegetation dynamics. To fill the above gaps in the field, the spatiotemporal distribution of terrestrial ecosystems under the current land use and climate baseline (1970–2000) was examined in this study using the Comprehensive Sequential Classification System (CSCS) model. Moreover, the spatiotemporal variations of ecosystems and their succession under future climate scenarios (the 2030s–2080s) were quantitatively projected and compared. The results demonstrated that under the current situation, vegetation without human disturbance was mainly distributed in high elevation regions and less than 10% of the national area. For future vegetation dynamics, more than 58% of tundra and alpine steppe would shrink. Semidesert would respond to climate change with an expansion of 39.49 × 104 km2, including the succession of the steppe to semidesert. Although some advancement of the temperate forest at the expense of substantial dieback of tundra and alpine steppe is expected to occur, this century would witness a considerable shrinkage of them, especially in RCP8.5, at approximately 55.06 × 104 km2. Overall, a warmer and wetter climate would be conducive to the occurrence and development of the CSCS ecosystems. These results offer new insights on the potential ecosystem response to land use and climate change over the Chinese domain, and on creating targeted policies for effective adaptation to these changes and implementation of ecosystem protection measures. [ABSTRACT FROM AUTHOR]

Using the self-organizing maps (SOM) method, we ranked and compared the simulation results of atmospheric circulation and precipitation for 32 global climate models (GCMs) in the Coupled Model Intercomparison Project Phase 5 (CMIP5) over China, and found that the ranking of the GCM's ability to simulate the frequency of sea level pressure (SLP) weather patterns (WPs) was not correlated with the ranking of its ability to simulate annual precipitation WPs. Then, we attributed the precipitation simulation differences and identified three main components for the differences in the multi-model simulation results: internal variability, frequency differences, and the combined term of the two, with internal variability being the largest of the three components. These three deviations depend ultimately on two factors: the ability to simulate the frequency of WPs and the ability to simulate the corresponding average daily precipitation generated by these WPs, with the second factor playing a decisive role. Then, to address the drawback that the model ensemble results cannot be effectively improved when each single model that makes up the ensemble model is dry or wet, a solution was proposed to correct for the simulation differences: the nodal precipitation differences of each WP were corrected. After the correction of the simulation differences, the simulation capability of all the individual models was greatly improved, which increases our confidence in using the CMIP5 models for future weather patterns and precipitation simulation and forecasting. [ABSTRACT FROM AUTHOR]

To develop measures to reduce the vulnerability of forests to drought, it is necessary to estimate specific water balances in sites and to estimate their development with climate change scenarios. We quantified the water balance of seven forest monitoring sites in northeast Germany for the historical time period 1961–2019, and for climate change projections for the time period 2010–2100. We used the LWF-BROOK90 hydrological model forced with historical data, and bias-adjusted data from two models of the fifth phase of the Coupled Model Intercomparison Project (CMIP5) downscaled with regional climate models under the representative concentration pathways (RCPs) 2.6 and 8.5. Site-specific monitoring data were used to give a realistic model input and to calibrate and validate the model. The results revealed significant trends (evapotranspiration, dry days (actual/potential transpiration < 0.7)) toward drier conditions within the historical time period and demonstrate the extreme conditions of 2018 and 2019. Under RCP8.5, both models simulate an increase in evapotranspiration and dry days. The response of precipitation to climate change is ambiguous, with increasing precipitation with one model. Under RCP2.6, both models do not reveal an increase in drought in 2071–2100 compared to 1990–2019. The current temperature increase fits RCP8.5 simulations, suggesting that this scenario is more realistic than RCP2.6. [ABSTRACT FROM AUTHOR]

Permafrost degradation caused by contemporary climate change significantly affects arctic regions. Active layer thickening combined with the thaw subsidence of ice-rich sediments leads to irreversible transformation of permafrost conditions and activation of exogenous processes, such as active layer detachment, thermokarst and thermal erosion. Climatic and permafrost models combined with a field monitoring dataset enable the provision of predicted estimations of the active layer and permafrost characteristics. In this paper, we present the projections of active layer thickness and thaw subsidence values for two Circumpolar Active Layer Monitoring (CALM) sites of Eastern Chukotka coastal plains. The calculated parameters were used for estimation of permafrost degradation rates in this region for the 21st century under various IPCC climate change scenarios. According to the studies, by the end of the century, the active layer will be 6–13% thicker than current values under the RCP (Representative Concentration Pathway) 2.6 climate scenario and 43–87% under RCP 8.5. This process will be accompanied by thaw subsidence with the rates of 0.4–3.7 cm∙a−1. Summarized surface level lowering will have reached up to 5 times more than current active layer thickness. Total permafrost table lowering by the end of the century will be from 150 to 310 cm; however, it will not lead to non-merging permafrost formation. [ABSTRACT FROM AUTHOR]

We examine the capability of thirteen Coupled Model Intercomparison Project (CMIP) phase 5 (CMIP5) models in simulating climatology and interannual variability ofWinter North Pacific Storm Track (WNPST). It is found that nearly half of the selected models can reproduce the spatial pattern of WNPST climatology. However, the strength and spatial variation of WNPST climatology are weak in most of the models. Most differences among the models are in the northeast of the simulated multi-model ensemble (MME) climatology, while it is more consistent in the south. The MME can reflect not only the center position, but also the strength and spatial distribution of interannual variation of the WNPST amplitude. Except for CNRM-CM5, the interannual standard deviations of simulated WNPST strength and spatial variation in all other models are weak. ACCESS1-3 and CanESM2 have a better capability in simulating the spatial modes of WNPST, while the simulated second and third modes in some models are in opposite order with those in NCEP (National Centers for Environmental Prediction) reanalysis. Only five models and MME can capture "midwinter suppression" feature in their simulations. Compared with NCEP reanalysis, the winter longitude index is larger and latitude index is smaller in most of the models, indicating the simulated storm track is further east and south. CNRM-CM5, MME and CMCC-CM could be used to evaluate interannual variation of strength index, longitude index and latitude index respectively. Nevertheless, only INM-CM4 and CNRM-CM5 can simulate southward drift of WNPST. [ABSTRACT FROM AUTHOR]

Using the CMIP5 model outputs, a few characteristics of Indian Summer Monsoon (ISM) rainfall and Niño 3.4 temperature are analysed during June-July-August-September (JJAS). Focusing on specified regions around central-northeast India, some general characteristic features of ISM precipitation are studied, which are shown to be varying among models. The trend of decreasing rainfall in that region as noticed in observations suggests an inconsistency among models. The ENSO also shows variation, and its phasing indicates disagreement. Unlike other models, FGOALS-g2 is identified as not suggesting any trend in Niño 3.4 temperature and needs attention for model evaluation purposes. ISM and ENSO correlation in either historical or the RCP 8.5 scenario confirm a negative signature, agreeing with the usual ISM, ENSO connection. Precipitation over the globe shows a rising trend in an ensemble of CMIP5 model outputs for the RCP 8.5 scenario, though no consensus is reached for the Indian region. Precipitation time series around the Indian subcontinent vary widely among models. Analyses with various future scenarios indicate that the Indian subcontinent shows much larger uncertainty, in terms of precipitation, compared to that from the whole world. This study identifies a few areas where CMIP5 models are in agreement or disagreement with each other. Such an analysis could be useful for understanding various processes in CMIP5 models that involve ISM precipitation and can lead to improving the representation of processes in models. [ABSTRACT FROM AUTHOR]

This study aims to evaluate the climate- and human-induced impacts on two contrasting river basins in India, specifically, the Ganges and the Godavari. Monthly discharge simulations from global hydrological models (GHMs), run with and without human influence using CMIP5 projections under the framework of the Inter-Sectoral Impact Model Intercomparison Project, are utilized to address the scientific questions related to the quantification of the future impacts of climate change and the historical impacts of human activities on these river basins. The five state-of-the-art GHMs were considered and subsequently used to evaluate the human and climate change impacts on river discharges (seasonal mean discharge and extreme flows) during the pre-monsoon, monsoon, and post-monsoon seasons under the RCP2.6 and RCP8.5 emission scenarios. Results showed that human impacts during the baseline period on long-term seasonal discharge in the Ganges and Godavari River basins for the pre-monsoon season are around 40% and 23%, respectively, and these impacts are stronger than the future climate change impact in the pre-monsoon season for the Ganges basin, whereas, for the Godavari basin, the same pattern is observed with some exceptions. The human impact in the course of the historical period on the pre-monsoon flows of both the Ganges and the Godavari are more significant than on the monsoon and post-monsoon flows. In the near future (2010–39) time slice, the impact of climate change on the streamflow of the Ganges is highest for the post-monsoon season (13.4%) under RCP 8.5 as compared to other seasons. For Godavari, in the near-future period, this impact is highest for the pre-monsoon season (18.2%) under RCP 2.6. Climate-induced changes in both of the basins during both the monsoon and post-monsoon seasons is observed to have a higher impact on future flows than direct human impact-induced changes to flow during the current period. High flows (31.4% and 19.9%) and low flows (51.2% and 36.8%) gain greater influence due to anthropogenic actions in the time of the pre-monsoon season compared to other times of year for the Ganges and Godavari basins, respectively. High flows for the Ganges during the near future time slice are most affected in the monsoon season (15.8%) under RCP 8.5 and, in the case of the Godavari, in the pre-monsoon season (18.4%) under the RCP 2.6 scenario. Low flows of the Ganges during the near-future period are most affected during the monsoon season (22.3%) and for the Godavari, low flows are affected most for the post-monsoon season (22.1%) under RCP 2.6. Uncertainty in the streamflow estimates is more pronounced for the Godavari basin compared to the Ganges basin. The findings of this study enhance our understanding of the natural and human-influenced flow regimes in these river basins, which helps the formation of future strategies, especially for inter-state and transboundary river management. [ABSTRACT FROM AUTHOR]

Future projection of drought vulnerability is vital for northern provinces of Iran, including North Khorasan, Khorasan-Razavi, and South Khorasan, due to the highly dependent of their economy on agriculture. The study is motivated by the fact that no research has been conducted to project the future Drought Vulnerability Index (DVI). DVI consist of three components of exposure, sensitivity, and adaptation capacity. More exposure levels of drought, higher sensitivity value, and lower adaptation capacity lead to a higher amount of vulnerability. Combined ERA-Interim-observation meteorological data, CMIP5 models under RCP4.5 and RCP8.5 scenarios, and national census data are used to estimate DVI in the past and future periods. CanESM2, GFDL-ESM2M, and CNRM-CM5 General Circulation Model (GCM) are selected from CMIP5 based on Taylor diagram results. The delta-change technique was selected for statistical downscaling of GCM outputs because it is most widely used. The study period is regarded as 1986–2005 as observation and four future 20-years periods during 2021–2100. Results indicated that the dissipation of the class of "very low" vulnerability is eminent in the near future period of 2021–2040 under the RCP4.5 scenario, and all provinces would experience a new worse class of "very high" vulnerability at 2081–2100, both under RCP4.5 and RCP8.5 scenarios. [ABSTRACT FROM AUTHOR]

The aim of this study is to investigate whether different Representative Concentration Pathways (RCPs), as they are determined in the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC), lead to different regimes in the energetics components of the Lorenz energy cycle. The four energy forms on which this investigation is based are the zonal and eddy components of the available potential and kinetic energies. The corresponding transformations between these forms of energy are also studied. RCPs are time-dependent, consistent scenarios of concentrations of radiatively active gases and particles. In the present study, four RCPs are explored, namely, rcp26, rcp45, rcp60, rcp85; these represent projections (for the future period 2006–2100) that result in radiative forcing of approximately 2.6, 4.5, 6.0 and 8.5 Wm−2 at year 2100, respectively, relative to pre-industrial conditions. The results are presented in terms of time projections of the energetics components from 2020 to 2100 and show that the different RCPs yield diverse energetics regimes, consequently impacting the Lorenz energy cycle. In this respect, projections under different RCPs of the Lorenz energy cycle are presented. [ABSTRACT FROM AUTHOR]

Using historical data compiled by the Climate Research Unit, spatial and temporal analysis, trend analysis, empirical orthogonal function (EOF) analysis, and Taylor diagram analysis were applied to test the ability of 24 Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models to accurately simulate the annual mean surface air temperature in central Asia from the perspective of the average climate state and climate variability. Results show that each model can reasonably capture the spatial distribution characteristics of the surface air temperature in central Asia but cannot accurately describe the regional details of climate change impacts. Some of the studied models, including CNRM-CM5, GFDL-CM3, and GISS-E2-H, could better simulate the high- and low-value centers and the contour distribution of the surface air temperature. Taylor diagram analysis showed that the root mean square errors of all models were less than 3, the standard deviations were between 8.36 and 13.45, and the spatial correlation coefficients were greater than 0.96. EOF analysis showed that the multi-model ensemble can accurately reproduce the surface air temperature characteristics in central Asia from 1901 to 2005, including the rising periods and the fluctuations of the north and south inversion phases. Overall, this study provides a valuable reference for future climate prediction studies in central Asia. [ABSTRACT FROM AUTHOR]

In the context of climate change, the impact of hydro-meteorological extremes, such as floods and droughts, has become one of the most severe issues for the governors of mega-cities. The main purpose of this study is to assess the spatiotemporal changes in extreme precipitation indices over Ho Chi Minh City, Vietnam, between the near (2021–2050) and intermediate (2051–2080) future periods with respect to the baseline period (1980–2009). The historical extreme indices were calculated through observed daily rainfall data at 11 selected meteorological stations across the study area. The future extreme indices were projected based on a stochastic weather generator, the Long Ashton Research Station Weather Generator (LARS-WG), which incorporates climate projections from the Coupled Model Intercomparison Project 5 (CMIP5) ensemble. Eight extreme precipitation indices, such as the consecutive dry days (CDDs), consecutive wet days (CWDs), number of very heavy precipitation days (R20mm), number of extremely heavy precipitation days (R25mm), maximum 1 d precipitation amount (RX1day), maximum 5 d precipitation amount (RX5day), very wet days (R95p), and simple daily intensity index (SDII) were selected to evaluate the multi-model ensemble mean changes of extreme indices in terms of intensity, duration, and frequency. The statistical significance, stability, and averaged magnitude of trends in these changes, thereby, were computed by the Mann-Kendall statistical techniques and Sen's estimator, and applied to each extreme index. The results indicated a general increasing trend in most extreme indices for the future periods. In comparison with the near future period (2021–2050), the extreme intensity and frequency indices in the intermediate future period (2051–2080) present more statistically significant trends and higher growing rates. Furthermore, an increase in most extreme indices mainly occurs in some parts of the central and southern regions, while a decrease in those indices is often projected in the north of the study area. [ABSTRACT FROM AUTHOR]

The freezing level in the free troposphere often intercepts the terrain of the world's major mountain ranges, creating a rain–snow limit. In this work, we use the free tropospheric height of the 0 ° C isotherm ( H 0 ) as a proxy of both levels and study its distribution along the western slope of the subtropical Andes (30 ° –38 ° S) in present climate and during the rest of the 21st century. This portion of the Andes corresponds to central Chile, a highly populated region where warm winter storms have produced devastating landslides and widespread flooding in the recent past. Our analysis is based on the frequency distribution of H 0 derived from radiosonde and surface observations, atmospheric reanalysis and climate simulations. The future projections primarily employ a scenario of heavy greenhouse gasses emissions (RCP8.5), but we also examine the more benign RCP4.5 scenario. The current H 0 distribution along the central Chile coast shows a gradual decrease southward, with mean heights close to 2600 m ASL (above sea level) at 30 ° C S to 2000 m ASL at 38 ° S for days with precipitation, about 800 m lower than during dry days. The mean value under wet conditions toward the end of the century (under RCP8.5) is close to, or higher than, the upper quartile of the H 0 distribution in the current climate. More worrisome, H 0 values that currently occur only 5% of the time will be exceeded in about a quarter of the rainy days by the end of the century. Under RCP8.5, even moderate daily precipitation can increase river flow to levels that are considered hazardous for central Chile. [ABSTRACT FROM AUTHOR]

The increasing number of extreme climate events is having a great impact on the terrestrial ecosystem. In this study, we applied a Taylor diagram to evaluate the 7 extreme temperature indices (ETI) of 12 models and the multi-model ensemble (MME) mean from phase 5 of the Coupled Model Intercomparison Project (CMIP5) during 1961–2005, and found that the MME has the best simulation effect. Warm indices and warm duration indices increase slowly, rapidly, and extremely under the representative concentration pathway 2.6 (RCP2.6), RCP4.5, and RCP8.5 scenarios, respectively. In contrast, the decrease in cold indices and cold duration indices are slow, rapid and extreme, respectively. The ETI from 2021–2100 under the RCP2.6 and RCP4.5 scenarios have primary periods ranging from 1–16 years. Under the RCP2.6 and RCP4.5 scenarios, the changes of warm indices are relatively largest in the basin of the central, and southeastern, while, under the RCP8.5 scenario, the changes are relatively significant, except for basin of northeast. The cold indices have the most significant decreasing trend in the Tibetan Plateau and its surrounding areas, under the three RCP scenarios. The findings from this study can provide reference for the risk management and prevention of climate disasters in the context of climate change in mainland China. [ABSTRACT FROM AUTHOR]

Water resources from rivers are essential to humans. The discharge of rivers is demonstrated to be significantly affected by climate change in the literature, particularly in the boreal and subboreal climate zones. The Da Hinggan Mountains in subboreal northeast China form the headwaters of the Heilongjiang River and the Nenjiang River, which are important water resources for irrigation of downstream agriculture and wetlands. In this study, long-term (44 years) hydrologic, climate and forest dynamics data from the Tahe were analyzed using the soil and water assessment tool (SWAT) model to quantify the effects of climate and forest change on runoff depth. Meanwhile, downscaled precipitation and temperature predictions that arose from global climate models (GCMs) under four representative concentration pathways (RCP 2.6, RCP 4.5, RCP 6.0 and RCP 8.5) were forced using the SWAT model to investigate the climate change impacts on the Tahe River flows in the future. The results indicated that compared with the 1972–1982 period, the forest biomass in the 1984–1994 period was reduced by 17.6%, resulting in an increase of 16.6% in mean annual runoff depth. On the contrary, with reforestation from the 1995–2005 period to the 2006–2016 period, the mean forest biomass was increased by 9.8%, resulting in the mean runoff depth reduction of 11.9%. The tree species composition shift reduced mean annual runoff depth of 13.3% between the 1984–1994 period and the 2006–2016 period. Compared with base years (2006–2016), projections of GCM in the middle of the 21st century indicated that both mean annual temperature and precipitation were expected to increase by −0.50 °C and 43 mm under RCP 2.6, 0.38 °C and 23 mm under RCP 4.5, 0.67 °C and 36 mm under RCP 6.0 and 1.00 °C and 10 mm under RCP 8.5. Simulated results of the SWAT model showed that annual runoff depth would increase by 18.1% (RCP 2.6), 11.8% (RCP 4.5), 23.6% (RCP 6.0), and 11.5% (RCP 8.5), compared to the base years. Such increased runoff was mainly attributed to the increase in April, July, August, September and October, which were consistent with the precipitation prediction. We concluded that the future climate change will increase the water resources from the river, thereby offsetting the possible decline in runoff caused by the forest recovery. The findings of this study might be useful for understanding the impacts of climate and forest change on runoff and provide a reasonable strategy for managers and planners to mitigate the impact of future climate change on water resources in the subboreal forested watersheds. [ABSTRACT FROM AUTHOR]

The meridional temperature gradient between mid and high latitudes decreases by Arctic amplification. Following this decrease, the circulation in the mid latitudes may change and, therefore, the meridional flux of heat and moisture increases. This might increase the Arctic temperatures even further. A proxy for the vertically integrated atmospheric horizontal energy flux was analyzed using the self-organizing-map (SOM) method. Climate Model Intercomparison Project Phase 5 (CMIP5) model data of the historical and Representative Concentration Pathway 8.5 (RCP8.5) experiments were analyzed to extract horizontal flux patterns. These patterns were analyzed for changes between and within the respective experiments. It was found that the general horizontal flux patterns are reproduced by all models and in all experiments in comparison with reanalyses. By comparing the reanalysis time frame with the respective historical experiments, we found that the general occurrence frequencies of the patterns differ substantially. The results show that the general structure of the flux patterns is not changed when comparing the historical and RCP8.5 results. However, the amplitudes of the fluxes are decreasing. It is suggested that the amplitudes are smaller in the RCP8.5 results compared to the historical results, following a greater meandering of the jet stream, which yields smaller flux amplitudes of the cluster mean. [ABSTRACT FROM AUTHOR]

The Gravity Recovery and Climate Experiment (GRACE) observations have provided global observations of total water storage (TWS) changes at monthly intervals for over 15 years, which can be useful for estimating changes in GWS after extracting other water storage components. In this study, we analyzed the TWS and groundwater storage (GWS) variations of the main Polish basins, the Vistula and the Odra, using GRACE observations, in-situ data, GLDAS (Global Land Data Assimilation System) hydrological models, and CMIP5 (the World Climate Research Programme's Coupled Model Intercomparison Project Phase 5) climate data. The research was conducted for the period between September 2006 and October 2015. The TWS data were taken directly from GRACE measurements and also computed from four GLDAS (VIC, CLM, MOSAIC, and NOAH) and six CMIP5 (FGOALS-g2, GFDL-ESM2G, GISS-E2-H, inmcm4, MIROC5, and MPI-ESM-LR) models. The GWS data were obtained by subtracting the model TWS from the GRACE TWS. The resulting GWS values were compared with in-situ well measurements calibrated using porosity coefficients. For each time series, the trends, spectra, amplitudes, and seasonal components were computed and analyzed. The results suggest that in Poland there has been generally no major TWS or GWS depletion. Our results indicate that when comparing TWS values, better compliance with GRACE data was obtained for GLDAS than for CMIP5 models. However, the GWS analysis showed better consistency of climate models with the well results. The results can contribute toward selection of an appropriate model that, in combination with global GRACE observations, would provide information on groundwater changes in regions with limited or inaccurate ground measurements. [ABSTRACT FROM AUTHOR]

This study evaluates 32 climate models from CMIP5 compared with a daily gridded observation dataset of extreme precipitation indices including total extreme precipitation (R95p), maximum consecutive five days of precipitation (RX5day) and wet days larger than 10 mm of precipitation (R10mm) over Northern China during the historical period (1986–2005). Results show the majority models have good performance on spatial distribution but overestimate the amplitude of precipitation over Northern China. Most models can also capture interannual variation of R95p and RX5d, but with poor simulations on R10mm. Considering both spatial and temporal factors, the best multi-model ensemble (Group 1) has been selected and improved by 42%, 34%, and 37% for R95p, RX5d, and R10mm, respectively. Projection of extreme precipitation indicates that the fastest-rising region is in Northwest China due to the enhanced rainfall intensity. However, the uncertainty analysis shows the increase of extreme rainfall over Northwest China has a low confidence level. The projection of increasing extreme rainfall over Northeast China from Group 1 due to the longer extreme rainfall days is more credible. The weak subtropical high and southwest winds from Arabian Sea lead to the low wet biases from Group 1 and the cyclonic anomalies over Northeast China, which result in more extreme precipitation. [ABSTRACT FROM AUTHOR]

In this study, the impact of the Pacific Decadal Oscillation (PDO) on the China winter temperature (CWT) was assessed on an interdecadal timescale, and the capacities of the 35 models of the fifth Coupled Model Intercomparison Project (CMIP5) were assessed by simulating the PDO-CWT teleconnection. The Met Office Hadley Centre's sea ice and sea surface temperature (HadISST) were used as the observational data, and Climatic Research Unit (CRU) datasets provided long-term temperature data for the 1901–2005 period. By calculating the spatial correlation coefficient between the PDO index and winter temperature in China, thirteen CMIP5 models close to the HadISST datasets were selected for this study. These models were averaged as the good multi-model ensemble (GOODMME), and the PDO-CWT spatial correlation between the GOODMME and the observations was 0.80. Overall, the correlation coefficient between the PDO index and atmospheric circulation suggests that the GOODMME produces the same excellent results as do the observations. The results also verify the GOODMME's superiority in simulating the impact of the PDO on winter temperatures in China. The possible mechanisms underlying the impact of the different phases of the PDO on the CWT are also described. [ABSTRACT FROM AUTHOR]

Huai River Basin (HRB) is an important food and industrial production area and a frequently drought-affected basin in eastern China. It is necessary to consider the future drought development for reducing the impact of drought disasters. Three global circulation models (GCMs) from Coupled Model Intercomparison Project phase 5 (CMIP5), such as CNRM-CM5 (CNR), HadGEM2-ES (Had) and MIROC5 (MIR), were used to assessment the future drought conditions under two Representative Concentration Pathways (RCPs) scenarios, namely, RCP4.5 and RCP8.5. The standardized precipitation evapotranspiration index (SPEI), statistical method, Mann-Kendall test, and run theory were carried out to study the variations of drought tendency, frequency, and characteristics and their responses to climate change. The research showed that the three CMIP5 models differ in describing the future seasonal and annual variations of precipitation and temperature in the basin and thus lead to the differences in describing drought trends, frequency, and drought characteristics, such as drought severity, drought duration, and drought intensity. However, the drought trend, frequency, and characteristics in the future are more serious than the history. The drought frequency and characteristics tend to be strengthened under the scenario of high concentration of RCP8.5, and the drought trend is larger than that of low concentration of RCP4.5. The lower precipitation and the higher temperature are the main factors affecting the occurrence of drought. All three CMIP5 models show that precipitation would increase in the future, but it could not offset the evapotranspiration loss caused by significant temperature rise. The serious risk of drought in the future is still higher. Considering the uncertainty of climate models for simulation and prediction, attention should be paid to distinguish the effects of different models in the future drought assessment. [ABSTRACT FROM AUTHOR]

Surface incident shortwave radiation (SSR) is crucial for understanding the Earth's climate change issues. Simulations from general circulation models (GCMs) are one of the most practical ways to produce long-term global SSR products. Although previous studies have comprehensively assessed the performance of the GCMs in simulating SSR globally or regionally, studies assessing the performance of these models over high-latitude areas are sparse. This study evaluated and intercompared the SSR simulations of 48 GCMs participating in the fifth phase of the Coupled Model Intercomparison Project (CMIP5) using quality-controlled SSR surface measurements at 44 radiation sites from three observation networks (GC-NET, BSRN, and GEBA) and the SSR retrievals from the Clouds and the Earth's Radiant Energy System, Energy Balanced and Filled (CERES EBAF) data set over high-latitude areas from 2000 to 2005. Furthermore, this study evaluated the performance of the SSR estimations of two multimodel ensemble methods, i.e., the simple model averaging (SMA) and the Bayesian model averaging (BMA) methods. The seasonal performance of the SSR estimations of individual GCMs, the SMA method, and the BMA method were also intercompared. The evaluation results indicated that there were large deficiencies in the performance of the individual GCMs in simulating SSR, and these GCM SSR simulations did not show a tendency to overestimate the SSR over high-latitude areas. Moreover, the ensemble SSR estimations generated by the SMA and BMA methods were superior to all individual GCM SSR simulations over high-latitude areas, and the estimations of the BMA method were the best compared to individual GCM simulations and the SMA method-based estimations. Compared to the CERES EBAF SSR retrievals, the uncertainties of the SSR estimations of the GCMs, the SMA method, and the BMA method are relatively large during summer. [ABSTRACT FROM AUTHOR]

We analyse and quantify the recurrences of European temperature extremes using 32 historical simulations (1900–1999) of the fifth Coupled Model Intercomparison Project (CMIP5) and 8 historical simulations (1971–2005) from the EUROCORDEX experiment. We compare the former simulations to the 20th Century Reanalysis (20CRv2c) dataset to compute recurrence spectra of temperature in Europe. We find that, (1) the spectra obtained by the model ensemble mean are generally consistent with those of 20CR; (2) spectra biases have a strong regional dependence; (3) the resolution does not change the order of magnitude of spectral biases between models and reanalysis, (4) the spread in recurrence biases is larger for cold extremes. Our analysis of biases provides a new way of selecting a subset of the CMIP5 ensemble to obtain an optimal estimate of temperature recurrences for a range of time-scales. [ABSTRACT FROM AUTHOR]

We qualitatively and quantitatively assessed the factors related to vegetation growth using Earth system models and corroborated the results with historical climate observations. The Earth system models showed a systematic greening by the late 21st century, including increases of up to 100% in Gross Primary Production (GPP) and 60% in Leaf Area Index (LAI). A subset of models revealed that the radiative effects of CO2 largely control changes in climate, but that the CO2 fertilization effect dominates the greening. The ensemble of Earth system model experiments revealed that the feedback of surface temperature contributed to 17% of GPP increase in temperature-limited regions, and radiation increase accounted for a 7% increase of GPP in radiation-limited areas. These effects are corroborated by historical observations. For example, observations confirm that cloud cover has decreased over most land areas in the last three decades, consistent with a CO2-induced reduction in transpiration. Our results suggest that vegetation may thrive in the starkly different climate expected over the coming decades, but only if plants harvest the sort of hypothesized physiological benefits of higher CO2 depicted by current Earth system models. [ABSTRACT FROM AUTHOR]

This study focusses on identifying a set of representative climate model projections for the Upper Indus Basin (UIB). Although a large number of General Circulation Models (GCM) predictor sets are available nowadays in the CMIP5 archive, the issue of their reliability for specific regions must still be confronted. This situation makes it imperative to sort out the most appropriate single or small-ensemble set of GCMs for the assessment of climate change impacts in a region. Here a set of different approaches is adopted and applied for the step-wise shortlisting and selection of appropriate climate models for the UIB under two RCPs: RCP 4.5 and RCP 8.5, based on: (a) range of projected mean changes, (b) range of projected extreme changes, and (c) skill in reproducing the past climate. Furthermore, because of higher uncertainties in climate projection for high mountainous regions like the UIB, a wider range of future GCM climate projections is considered by using all possible extreme future scenarios (wet-warm, wet-cold, dry-warm, dry-cold). Based on this two-fold procedure, a limited number of climate models is pre-selected, from of which the final selection is done by assigning ranks to the weighted score for each of the mentioned selection criteria. The dynamically downscaled climate projections from the Coordinated Regional Downscaling Experiment (CORDEX) available for the top-ranked GCMs are further statistically downscaled (bias-corrected) over the UIB. The downscaled projections up to the year 2100 indicate temperature increases ranging between 2.3 °C and 9.0 °C and precipitation changes that range from a slight annual increase of 2.2% under the drier scenarios to as high as 15.9% in the wet scenarios. Moreover, for all scenarios, future precipitation will be more extreme, as the probability of wet days will decrease, while, at the same time, precipitation intensities will increase. The spatial distribution of the downscaled predictors across the UIB also shows similar patterns for all scenarios, with a distinct precipitation decrease over the south-eastern parts of the basin, but an increase in the northeastern parts. These two features are particularly intense for the "Dry-Warm" and the "Median" scenarios over the late 21st century. [ABSTRACT FROM AUTHOR]

Frequent flooding events in recent years have been linked with the changing climate. Comprehending flooding events and their risks is the first step in flood defense and can help to mitigate flood risk. Floodplain mapping is the first step towards flood risk analysis and management. Additionally, understanding the changing pattern of flooding events would help us to develop flood mitigation strategies for the future. This study analyzes the change in streamflow under different future carbon emission scenarios and evaluates the spatial extent of floodplain for future streamflow. The study will help facility managers, design engineers, and stakeholders to mitigate future flood risks. Variable Infiltration Capacity (VIC) forcing-generated Coupled Model Intercomparison Project phase 5 (CMIP5) streamflow data were utilized for the future streamflow analysis. The study was done on the Carson River near Carson City, an agricultural area in the desert of Nevada. Kolmogorov–Smirnov and Pearson Chi-square tests were utilized to obtain the best statistical distribution that represents the routed streamflow of the Carson River near Carson City. Altogether, 97 projections from 31 models with four emission scenarios were used to predict the future flood flow over 100 years using a best fit distribution. A delta change factor was used to predict future flows, and the flow routing was done with the Hydrologic Engineering Center's River Analysis System (HEC-RAS) model to obtain a flood inundation map. A majority of the climate projections indicated an increase in the flood level 100 years into the future. The developed floodplain map for the future streamflow indicated a larger inundation area compared with the current Federal Emergency Management Agency's flood inundation map, highlighting the importance of climate data in floodplain management studies. [ABSTRACT FROM AUTHOR]

The Coupled Model Intercomparison Project Phase 5 (CMIP5) provides data, which is widely used to assess global and regional climate change. In this study, we evaluated the ability of 37 global climate models (GCMs) of CMIP5 to simulate historical precipitation in Central Asia (CA). The relative root mean square error (RRMSE), spatial correlation coefficient, and Kling-Gupta efficiency (KGE) were used as criteria for evaluation. The precipitation simulation results of GCMs were compared with the Climatic Research Unit (CRU) precipitation in 1986–2005. Most models show a variety of precipitation simulation capabilities both spatially and temporally, whereas the top six models were identified as having good performance in CA, including HadCM3, MIROC5, MPI-ESM-LR, MPI-ESM-P, CMCC-CM, and CMCC-CMS. As the GCMs have large uncertainties in the prediction of future precipitation, it is difficult to find the best model to predict future precipitation in CA. Multi-Model Ensemble (MME) results can give a good simulation of precipitation, and are superior to individual models. [ABSTRACT FROM AUTHOR]

The present study developed a novel approach to study the climate change impact on the water resources and generation of hydropower optimally using forecasted stream flows for the Xin'anjiang water shed in China. Future flows were projected using six large-scale Global circulation models (GCMs) with RCP4.5 and RCP8.5 scenarios. A newly developed mathematical modeling using particle swarm optimization was incorporated to work out the projected optimal electricity generation from the Xin'anjiang hydropower station. The results reveal that watershed will be warmer by the end of the 21st century with a maximum increase of up to 4.9 °C for mean maximum, and 4.8 °C for mean minimum temperature. Six GCMs under Representative Concentration pathways (RCPs) showed that future precipitation is complex to predict with certainty and significant differences were observed among the different GCMs. The overall mean monthly and seasonal precipitation increase for most scenarios with the maximum increase during the 2020s and 2080s, whereas 2050s exhibited the lesser increase. Resultantly, there would be an increase in the stream flows during these periods, which was used for electricity production up to 31.41 × 108 kW·h. [ABSTRACT FROM AUTHOR]

This study aims to quantitatively assess the impacts of climate change on the flood-prone risk areas in Davao Oriental, Philippines for the years 2030, 2050, and 2100 in comparison with the present situation by identifying flood risk zones based on multisource data, including rainfall, slope, elevation, drainage density, soil type, distance to the main channel, and population density. The future temperatures and rainfall projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5) predictions of the Intergovernmental Panel on Climate Change (IPCC) were used. The future temperatures from the CMIP5 predictions showed that Davao Oriental should experience approximately 1 °C and 3 °C increases under the Representative Concentration Pathway (RCP)4.5 and RCP8.5 scenarios, respectively, while the rainfall should slightly increase in the coming years. Among the 39 general circulation models (GCMs) available from CMIP5, the GFDL-ESM2M model showed good agreement with the observed rainfall dataset at the local stations. The intensity of rainfall should increase approximately 69% in the future, resulting in an increase in the magnitude of the floods. The resulting flood risk map shows that 95.91% of Davao Oriental is presently under the low and moderate flood risk categories, and those categories should slightly decrease to 95.75% in the future. The high and very high flood risk areas cover approximately 3% of the province at present and show no dramatic change in the future. Presently, 28 out of the 183 barangays (towns) are at high and very high risks of floods, whereas in the coming years, only one barangay will be added to the very high risk of floods. These barangays under the high and very high categories of flood risk are primarily situated on riversides and coastal areas. Thus, immediate actions from decision-makers are needed to develop a community-based disaster risk plan under the future conditions. [ABSTRACT FROM AUTHOR]

Assessment of actual evapotranspiration (ET) is essential as it controls the exchange of water and heat energy between the atmosphere and land surface. ET also influences the available water resources and assists in the crop water assessment in agricultural areas. This study involves the assessment of spatial distribution of seasonal and annual ET using Surface Energy Balance Algorithm for Land (SEBAL) and provides an estimation of future changes in ET due to land use and climate change for a portion of the Narmada river basin in Central India. Climate change effects on future ET are assessed using the ACCESS1-0 model of CMIP5. A Markov Chain model estimated future land use based on the probability of changes in the past. The ET analysis is carried out for the years 2009–2011. The results indicate variation in the seasonal ET with the changed land use. High ET is observed over forest areas and crop lands, but ET decreases over crop lands after harvest. The overall annual ET is high over water bodies and forest areas. ET is high in the premonsoon season over the water bodies and decreases in the winter. Future ET in the 2020s, 2030s, 2040s, and 2050s is shown with respect to land use and climate changes that project a gradual decrease due to the constant removal of the forest areas. The lowest ET is projected in 2050. Individual impact of land use change projects decreases in ET from 1990 to 2050, while climate change effect projects increases in ET in the future due to rises in temperature. However, the combined impacts of land use and climate changes indicate a decrease in ET in the future. [ABSTRACT FROM AUTHOR]