Individual General Circulation Models Research Articles

Calibrated multi‐model ensemble seasonal prediction of Bangladesh summer monsoon rainfall

AbstractBangladesh summer monsoon rainfall (BSMR), typically from June through September (JJAS), represents the main source of water for multiple sectors. However, its high spatial and interannual variability makes the seasonal prediction of BSMR crucial for building resilience to natural disasters and for food security in a climate‐risk‐prone country. This study describes the development and implementation of an objective system for the seasonal forecasting of BSMR, recently adopted by the Bangladesh Meteorological Department (BMD). The approach is based on the use of a calibrated multi‐model ensemble (CMME) of seven state‐of‐the‐art general circulation models (GCMs) from the North American Multi‐Model Ensemble project. The lead‐1 (initial conditions of May for forecasting JJAS total rainfall) hindcasts (spanning 1982–2010) and forecasts (spanning 2011–2018) of seasonal total rainfall for the JJAS season from these seven GCMs were used. A canonical correlation analysis (CCA) regression is used to calibrate the raw GCMs outputs against observations, which are then combined with equal weight to generate final CMME predictions. Results show, compared to individual calibrated GCMs and uncalibrated MME, that the CCA‐based calibration generates significant improvements over individual raw GCM in terms of the magnitude of systematic errors, Spearman's correlation coefficients, and generalised discrimination scores over most of Bangladesh areas, especially in the northern part of the country. Since October 2019, the BMD has been issuing real‐time seasonal rainfall forecasts using this new forecast system.

Evaluation of bias correction techniques for generating high-resolution daily temperature projections from CMIP6 models

Due to the considerable biases in general circulation models (GCMs) simulation, bias correction methods are required and widely applied to reduce the model biases for impact studies. This study evaluated the performance of two bias correction methods, quantile delta mapping (QDM) and scaled distribution mapping (SDM), for generating high-resolution daily maximum temperature (Tmax) and minimum temperature (Tmin) projections for Canada using the latest GCMs from the Coupled Model Intercomparison Project phase 6 (CMIP6). CMIP6 GCMs show overall consistency with observations before and after bias correction, with better performance on Tmax compared to Tmin. QDM shows better performance relative to observations while SDM shows superior skill in preserving the raw climate signals. QDM and SDM methods are effective in reducing the biases of Tmax and Tmin for all GCMs. Both methods show similar skills in reproducing monthly probability distribution and capturing seasonal spatial patterns over Canada. Multi-model ensemble means have good performance in simulating the monthly mean of Tmax and Tmin but poor performance for high and low quantiles as well as standard deviation. QDM and SDM corrected ensemble means have the best performance. This study presents a comprehensive assessment of bias correction methods applications for individual CMIP6 GCMs and their multi-model ensemble means for high-resolution daily temperature predictions for Canada, providing a reference significance for bias correction studies as well as technical support for further impact assessment and adaptation planning around the world.

Uncertainties in estimating the effect of climate change on 100-year return value for significant wave height

The process of estimating the effect of a changing climate on the severity of future ocean storms is plagued by large uncertainties; for safe design and operation of offshore structures, it is nevertheless important that best possible estimates of climate effects is made given the available data. We explore the variability in estimates of 100-year return value of significant wave height (HS), and changes in estimates over a period of time, for output of WAVEWATCH-III models from 7 representative Coupled Model Intercomparison Project (CMIP) Phase 5 General Circulation Models (GCMs), and the FIO-ESM v2.0 CMIP Phase 6 GCM. Non-stationary extreme value analysis of peaks-over-threshold and block maxima using Bayesian inference provide posterior estimates of return values as a function of time; MATLAB software for the extreme value analysis is provided. Best overall estimates for return values, and changes in return value over the period 1979-2100, are calculated by averaging estimates for individual GCMs. We focus attention on neighbourhoods of locations east of Madagascar and south of Australia where a previous study of CMIP5-derived output reported significant decrease and increase in HS respectively, under Representative Concentration Pathway (RCP) scenarios RCP4.5 and RCP8.5. There is large variation between return value estimates from different GCMs, and with longitude and latitude within each neighbourhood for estimates based on samples corresponding to ≤ 165 years of model output; these sources of uncertainty tend to be larger than those due to typical modelling choices (such as choice of threshold for peaks over threshold, or block length for block maxima). However, we also find that careful threshold choice and block length are critical east of Madagascar, because of the presence of a mixed population of storms there. Nevertheless, there is general evidence supporting the trends reported by others, but these findings are conditional on the choice of 8 GCMs being representative of climate evolution. We use simple randomisation testing to identify “significant” departures from steady climate. The long 700-year pre-industrial control (piControl) output of the CMIP6 GCM offers an excellent opportunity to quantify the apparent inherent variability in return value as a function of time, estimated using a subsample of output corresponding to a continuous time interval of between 20 and 160 years in length, where no climate forcing is present. We find large variation in return value estimates of approximately ±15% made from samples corresponding to periods of time of around 50 years drawn from piControl data.

Observed and CMIP6 simulated occurrence and intensity of compound agroclimatic extremes over maize harvested areas in China

Understanding the changes in the frequency and intensity of compound agroclimatic extremes is important for studying the resilience of the food system under anthropogenic warming. However, the spatiotemporal variation of compound agroclimatic extremes for specific crops, and the performance of the Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations in reproducing them, have largely been under-addressed. Herein, we have investigated the spatiotemporal variation of the occurrences and intensities of four compound agroclimatic extremes (hot-wet, hot-dry, cold-wet, cold-dry) for maize harvested areas in China during 1990–2014 and examined the capability of the CMIP6 to capture these variations. The results did not reveal any significant trends but there was a pronounced interannual volatility in both the occurrence and intensity of compound agroclimatic extremes. Seventy one percent of the maize harvested areas in China experienced at least one compound extreme event during the study period. Hotspots for high occurrence and high intensity included the major plains in the Huang-Huai-Hai and southern China maize zones. In general, five general circulation models (GCMs) from the CMIP6 relatively poorly captured the variation of the occurrence and intensity, and reported lower interannual volatility and less regional disparity compared to the observations, and showed weakness in capturing time-specific events. At the national scale, the five GCM ensemble mean outperformed any single model, and no single GCM outperformed any other individual GCMs. The best model differed according to the specific region of interest and the type of event. Our results highlight the need to improve field management decisions and adopt adaptation strategies in accord with local conditions to reduce the potential impacts on food systems.

Comparison of river basin-scale hydrologic projections from a clustering based ensemble and model democracy approach using SHETRAN

ABSTRACT The high computational requirements of physically based fully distributed hydrological models (PDHM) constrain the use of all available general circulation models (GCMs) to assess climate change impacts. Here, an approach of ensembling GCMs using clustering based on future climatological variables was compared with model democracy while using a PDHM, SHETRAN, forced with six GCMs. The methodology is applied to hydrological projections in Netravathi River basin from the present to the near (2021–2050) and far (2071–2100) future. The results demonstrate that some GCMs project increase (50%, 30%) while others show decrease (10%, 11%) in the far future relative to the historical period (1980–2005) for streamflow and sediment load, respectively. The spread in the projection of climatological and hydrological variables from ensembled GCMs was retained as in model democracy whereas actual evapotranspiration showed overestimation relative to individual GCMs in the far future due to limitations of the clustering approach. Hence, we suggest employing individual GCMs for hydrological impact studies.

Assessment of surface downward longwave radiation in CMIP6 with comparison to observations and CMIP5

Surface downward longwave radiation (SDLR) plays an important role in understanding the greenhouse effect and global warming. The simulated SDLR from 47 coupled models in the Coupled Model Intercomparison Project (CMIP6) general circulation models (GCMs) was evaluated by comparing them with ground measurements and CMIP5 results. The estimated SDLR using all CMIP6 GCMs based on the multimodel ensemble (MME) methods was validated as well. The bias values of the SDLR simulations from individual CMIP6 GCMs averaged over the selected 183 sites around the world varied from −10 to 10 W m−2, while the root mean squared error (RMSE) values ranged from 20 to 26 W m−2. Compared to CMIP5 models, the CMIP6 GCMs did not show a significant tendency to underestimate SDLR. However, the SDLR from CMIP6 GCMs exhibited the relatively better precision at low altitude and low latitude sites compared to that at high altitude and high latitude sites. Moreover, the Bayesian model averaging (BMA) method increased the correlation coefficient (R) by approximately 0.02 and reduced the RMSE by approximately 5 W m−2 on average compared to the individual CMIP6 GCMs. The trend in SDLR was also investigated in this study, which has been related to the changes in air temperature (SAT), and water vapor pressure (WVP).

Evaluation of Surface Upward Longwave Radiation in the CMIP6 Models with Ground and Satellite Observations

Surface upward longwave radiation (SULR) is an indicator of thermal conditions over the Earth’s surface. In this study, we validated the simulated SULR from 51 Coupled Model Intercomparison Project (CMIP6) general circulation models (GCMs) through a comparison with ground measurements and satellite-retrieved SULR from the Clouds and the Earth’s Radiant Energy System, Energy Balanced and Filled (CERES EBAF). Moreover, we improved the SULR estimations by a fusion of multiple CMIP6 GCMs using multimodel ensemble (MME) methods. Large variations were found in the monthly mean SULR among the 51 CMIP6 GCMs; the bias and root mean squared error (RMSE) of the individual CMIP6 GCMs at 133 sites ranged from −3 to 24 W m−2 and 22 to 38 W m−2, respectively, which were higher than those found between the CERES EBAF and GCMs. The CMIP6 GCMs did not improve the overestimation of SULR compared to the CMIP5 GCMs. The Bayesian model averaging (BMA) method showed better performance in simulating SULR than the individual GCMs and simple model averaging (SMA) method, with a bias of 0 W m−2 and an RMSE of 19.29 W m−2 for the 133 sites. In terms of the global annual mean SULR, our best estimation for the CMIP6 GCMs using the BMA method was 392 W m−2 during 2000–2014. We found that the SULR varied between 386 and 393 W m−2 from 1850 to 2014, exhibiting an increasing tendency of 0.2 W m−2 per decade (p < 0.05).

A flow-weighted ensemble strategy to assess the impacts of climate change on watershed hydrology

Evaluation of hydrological response to future climate change is essential for water quality risk assessment and adaptive management implementation within a watershed context. In this study, we present a modeling framework that integrates a hydrological model with projections of General Circulation Models (GCMs). Temperature and precipitation projections of six GCMs with two downscaling methods are used to force the Soil and Water Assessment Tool (SWAT) model in the Hamilton Harbour watershed in Ontario, Canada. A flow-based weighting strategy was developed to integrate the projections of multiple GCMs based on their ability to recreate empirical flow frequency distributions in multiple monitoring sites. Our study renders support to the ability of the weighted model ensemble to draw meaningful hydrological forecasts. Nonetheless, we also note that the ensemble strategy understates the frequency and magnitude of flow extremes, and therefore the domain that is collectively delineated by individual GCMs can still provide complementary planning information. Climate change is projected to trigger a distinct increase in air temperature and precipitation during the late winter-early spring period, which in turn will likely result in an earlier snowmelt and changes in the magnitude and timing of peak flow events. Analysis of the water cycle shows that the sensitivity of the individual hydrological components to climate change may vary along the urban-to-agriculture gradient. The projected declining soil–water content in agricultural catchments highlights the likelihood of more intensified drought conditions in the croplands. Evapotranspiration rates will likely increase across the entire watershed, whereas surface runoff could be reduced but less so in urbanized locations. Overall, our findings suggest that any future planning exercise to ameliorate the prevailing water quality conditions will only be insightful if we consider the interplay between climate change and urbanization processes in the area.

Projection of Future Heat Waves in the United States. Part I: Selecting a Climate Model Subset

The widespread increase in global temperature is driving more frequent and more severe local heatwaves within the contiguous United States (CONUS). General circulation models (GCMs) show increasing, but spatially uneven trends in heatwave properties. However, the wide range of model outputs raises the question of the suitability of this method for indicating the future impacts of heatwaves on human health and well-being. This work examines the fitness of 32 models from CMIP5 and their ensemble median to predict a set of heatwave descriptors across the CONUS, by analyzing their capabilities in the simulation of historical heatwaves during 1950–2005. Then, we use a multi-criteria decision-making tool and rank the overall performance of each model for 10 locations with different climates. We found GCMs have different capabilities in the simulation of historical heatwave characteristics. In addition, we observed similar performances for GCMs over the areas with a partially similar climate. The ensemble model showed better performance in simulation of historical heatwave intensity in some locations, while other individual GCMs represented heatwave time-related components more similar to observations. These results are a step towards the use of contemporary weather models to guide heatwave impact predictions.

Evaluation of Bayesian Multimodel Estimation in Surface Incident Shortwave Radiation Simulation over High Latitude Areas

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.

Climate change is expected to increase yield and water use efficiency of wheat in the North China Plain

Climate change impacts on winter wheat yield and water-use were assessed with the AquaCrop model for different representative concentration pathway (RCP) scenarios (RCP 2.6, RCP 4.5, RCP 6.0 and RCP 8.5) and time slices (2040s, 2060s, 2080s) using ensemble projections from 10 general circulation models (GCM) for a site in the North China Plain. To test the role of crop and soil management practices under climate change, simulations with deficit irrigation (DI) and mulching were also performed. DI was defined as a practice where soil water content was restored to field capacity after depleting 150% of readily available water (RAW). The chosen mulching practice had a fixed capacity to reduce evaporation by 60%. Simulated outputs were compared with the baseline simulations (baseline period: 1984–2015). The results indicate that yield and water use efficiency (WUE) of wheat will increase under all RCP scenarios and time slices. Analysis revealed that winter wheat in the NCP would benefit from both CO2 fertilization and warming. Warming will shorten the growing cycle but largely due to curbing of the overwintering period. Due to accelerated growth/development, flowering and maturity will be advanced compared to the baseline period. Warming will also ease the low-temperature stress on biomass production. The seasonal evapotranspiration will reduce mainly due to the shorter growing cycle and the CO2-induced reduction in transpiration. Increase in yield and WUE is expected even under DI, while mulching can further increase WUE. Projections using individual GCMs indicated that the variability in crop production would be higher for the high-end scenario (RCP 8.5) than for RCP 2.6. These findings imply that in general, wheat in the NCP is less vulnerable to climate change than in other parts of the world. In addition, the expected increase in the yield and WUE of wheat may positively affect current depletion rates of groundwater for irrigation; however, more work is needed to quantify this. In future, it will be possible to introduce short duration wheat and long duration maize (second crop in rotation) cultivars.

Observed and model simulated twenty-first century hydro-climatic change of Northern Ethiopia

Study regionThis study focuses on Tekeze river basin of northern Ethiopia, and it is characterized by a typical dry biogeophysical environment. Study focusIn recent years, recurrent droughts are having an adverse impact on agricultural production and water resources in northern Ethiopia. Climate change through changes on temperature, precipitation and streamflow, may further strain this critical situation. This study has investigated the observed (1961–2014) and potential (2006–2099) hydro-climatic changes in Tekeze river basin of northern Ethiopia. Artificial Neural Networks (ANNs) are used to downscale temperature and precipitation predicated by 30 General Circulation Models (GCMs) as well as the projected streamflow changes for two Representative Concentration Pathways (RCP4.5 and RCP8.5) scenario. New hydrological insights for the regionResults indicate that the variability of climatic factors as temperature and precipitation was observed to be both spatially and temporally diverse for the considered Tekeze river basin. Accordingly, the response of streamflow was also spatiotemporally complex. GCMs were evaluated with several performance indictors regarding patterns in hydro-climatic variables. The analysis showed the superiority of the multimodel ensemble means compared with individual GCM output. GCM projections for the 21century indicate a gradual reductions in streamflow attributed to the combined effect of increasing temperature and decreasing precipitation. The persistent increase of temperature and decrease of precipitation will have negative impacts on water availability and agriculture, hence site specific adaptation strategies are necessary.

Temperature and precipitation change in Malawi: Evaluation of CORDEX-Africa climate simulations for climate change impact assessments and adaptation planning

Malawi is highlighted as one of the most vulnerable countries in the world to the effects of climate change. The large uncertainty around future climate change in the region remains a barrier to adaptation planning. Despite this high potential vulnerability, relatively little research has gone into determining how well available models represent this country's climate. This work therefore evaluates the ability of existing General Circulation Models (GCMs) and Regional Climate Models (RCMs) to hindcast climatic variables in Malawi at a resolution appropriate for climate change impact assessment and adaptation planning. We focus on monthly precipitation rate, and mean, maximum and minimum surface air temperature. This assessment compares available observed datasets against the outputs of six ERA-interim driven RCMs and 21 GCM-driven RCMs from the Coordinated Regional Climate Downscaling Experiment (CORDEX) initiative, and the 11 GCMs which form their boundary conditions. It was found that the performance of the RCMs is highly influenced by their boundary conditions. None of the individual or ensemble RCMs or GCMs assessed in this paper correlate well with the observed datasets for any of the assessed climatic variables. While, they do simulate the trending change in temperature variables well, the simulated outputs for precipitation are highly divergent. Based on these findings we suggest that either the ensemble RCMs or ensemble GCMs would be suitable for understanding projected temperature trends, with the RCMs providing better spatial resolution. However, none of the assessed models provide certainty over future precipitation trends in Malawi. As such we suggest that impact assessments and adaptation plans in Malawi will need to be designed and tested against a range of future precipitation scenarios. To improve modelling for Malawi it is recommended that regional climate models be improved for higher spatial resolution and inclusion of the impacts from large water bodies, including Lake Malawi.

Developing climate model ensembles: A comparative case study

Projections of future changes in hydroclimatic variables are available through various general circulation models (GCMs) that are part of the Coupled Model Intercomparison Project Phase 5 (CMIP5). Assessment of the performance of these models in simulating past climate, both individually and as an ensemble, has received much attention and is commonly the first step in assessing their suitability of application. In this study, we have evaluated the ability of various ensemble models to simulate past temperature and precipitation in the Gulf Basin region of North America, chosen as an illustrative case study. We have developed ensembles from 34 CMIP5 GCMs using six diverse approaches, including random forest, support vector regression, neural networks, linear regression, and weighted k-nearest neighbors, and compared the performance of the ensembles with each other and the individual GCMs using a robust set of metrics and nonparametric tests. For temperature, random forest outperforms all other ensembles and the best-performing GCM by a statistically significant margin and is able to simulate temporal and spatial patterns in temperature well. None of the ensembles are able to adequately simulate observed precipitation patterns in the study area, likely due to spatial differences in precipitation drivers in the region, as well as the coarseness of the dataset itself. However, the random forest, support vector regression, neural network, and linear regression ensembles achieved statistically significant improvements to precipitation simulation, as compared to the individual GCMs and the simple arithmetic mean ensemble, which has been used in several studies.

Modelling seasonal flows alteration in the Vietnamese Mekong Delta under upstream discharge changes, rainfall changes and sea level rise

ABSTRACTThe Vietnamese Mekong Delta (VMD) is one of the world’s most vulnerable deltas to climate change and sea level rise. Adequate understandings of future hydrological changes are crucial for effective water management and risk-proofing, however, this knowledge body is currently very limited. This study quantifies the responses of the VMD’s river flow regime to multiple stimuli, namely future upstream inflow variation, local climate change, and sea level rise. The one-dimensional hydrodynamic model MIKE 11 was used to simulate discharges and water levels across the delta. We developed four scenarios to represent changes in the upstream discharges, precipitation changes and sea level rise, covering the 2036–2065 period. We downscaled climate data and applied three bias-correction methods for five General Circulation Models (GCM), and two Representative Concentration Pathways (RCPs). The climate change projections show similar trends of increasing wet season precipitation and decreasing dry season precipitation. However, cross-scenario variations are sometimes large, depending on the individual GCMs, the RCPs and specific locations. The hydraulic simulation results indicate that, under discharge changes between −20% and +10%, combined with in-delta precipitation variations during the dry season, river discharges at the four representative stations could reduce substantially from −2.5% to −100.2%. During the wet season, the calculated river discharges show increase between 7.3% and 46.7% under four considered scenarios. Substantial changes in the VMD’s river flow regime could have potentially serious implications for water management, especially saltwater intrusion, and therefore calling for timely adaptation measures.

Evaluation of performance of seasonal precipitation prediction at regional scale over India

The seasonal scale precipitation amount is an important ingredient in planning most of the agricultural practices (such as a type of crops, and showing and harvesting schedules). India being an agroeconomic country, the seasonal scale prediction of precipitation is directly linked to the socioeconomic growth of the nation. At present, seasonal precipitation prediction at regional scale is a challenging task for the scientific community. In the present study, an attempt is made to develop multi-model dynamical-statistical approach for seasonal precipitation prediction at the regional scale (meteorological subdivisions) over India for four prominent seasons which are winter (from December to February; DJF), pre-monsoon (from March to May; MAM), summer monsoon (from June to September; JJAS), and post-monsoon (from October to December; OND). The present prediction approach is referred as extended range forecast system (ERFS). For this purpose, precipitation predictions from ten general circulation models (GCMs) are used along with the India Meteorological Department (IMD) rainfall analysis data from 1982 to 2008 for evaluation of the performance of the GCMs, bias correction of the model results, and development of the ERFS. An extensive evaluation of the performance of the ERFS is carried out with dependent data (1982–2008) as well as independent predictions for the period 2009–2014. In general, the skill of the ERFS is reasonably better and consistent for all the seasons and different regions over India as compared to the GCMs and their simple mean. The GCM products failed to explain the extreme precipitation years, whereas the bias-corrected GCM mean and the ERFS improved the prediction and well represented the extremes in the hindcast period. The peak intensity, as well as regions of maximum precipitation, is better represented by the ERFS than the individual GCMs. The study highlights the improvement of forecast skill of the ERFS over 34 meteorological subdivisions as well as India as a whole during all the four seasons.

Multi-model ensemble projections of future extreme heat stress on rice across southern China

Extreme heat events have become more frequent and intense with climate warming, and these heatwaves are a threat to rice production in southern China. Projected changes in heat stress in rice provide an assessment of the potential impact on crop production and can direct measures for adaptation to climate change. In this study, we calculated heat stress indices using statistical scaling techniques, which can efficiently downscale output from general circulation models (GCMs). Data across the rice belt in southern China were obtained from 28 GCMs in the Coupled Model Intercomparison Project phase 5 (CMIP5) with two emissions scenarios (RCP4.5 for current emissions and RCP8.5 for increasing emissions). Multi-model ensemble projections over the historical period (1960–2010) reproduced the trend of observations in heat stress indices (root-mean-square error RMSE = 6.5 days) better than multi-model arithmetic mean (RMSE 8.9 days) and any individual GCM (RMSE 11.4 days). The frequency of heat stress events was projected to increase by 2061–2100 in both scenarios (up to 185 and 319% for RCP4.5 and RCP8.5, respectively), especially in the middle and lower reaches of the Yangtze River. This increasing risk of exposure to heat stress above 30 °C during flowering and grain filling is predicted to impact rice production. The results of our study suggest the importance of specific adaption or mitigation strategies, such as selection of heat-tolerant cultivars and adjustment of planting date in a warmer future world.

Assessment and simulation of global terrestrial latent heat flux by synthesis of CMIP5 climate models and surface eddy covariance observations

The latent heat flux (LE) between the terrestrial biosphere and atmosphere is a major driver of the global hydrological cycle. In this study, we evaluated LE simulations by 45 general circulation models (GCMs) in the Coupled Model Intercomparison Project Phase 5 (CMIP5) by a comparison with eddy covariance (EC) observations from 240 globally distributed sites from 2000 to 2009. In addition, we improved global terrestrial LE estimates for different land cover types by synthesis of seven best CMIP5 models and EC observations based on a Bayesian model averaging (BMA) method. The comparison results showed substantial differences in monthly LE among all GCMs. The model CESM1-CAM5 has the best performance with the highest predictive skill and a Taylor skill score (S) from 0.51–0.75 for different land cover types. The cross-validation results illustrate that the BMA method has improved the accuracy of the CMIP5 GCM’s LE simulation with a decrease in the averaged root-mean-square error (RMSE) by more than 3 W/m2 when compared to the simple model averaging (SMA) method and individual GCMs. We found an increasing trend in the BMA-based global terrestrial LE (slope of 0.018 W/m2 yr−1, p < 0.05) during the period 1970–2005. This variation may be attributed directly to the inter-annual variations in air temperature (Ta), surface incident solar radiation (Rs) and precipitation (P). However, our study highlights a large difference from previous studies in a continuous increasing trend after 1998, which may be caused by the combined effects of the variations of Rs, Ta, and P on LE for different models on these time scales. This study provides corrected-modeling evidence for an accelerated global water cycle with climate change.

Analysis of temperature projections in the Koshi River Basin, Nepal

This study analyzes temperature projections in the Koshi River Basin in Nepal using data obtained from ten General Circulation Models (GCMs) for three IPCC Special Range of Emission Scenarios (SRES): B1, A1B and A2. Low resolution data of minimum and maximum temperature obtained from the selected GCMs was downscaled using the statistical downscaling model Long Ashton Research Station Weather Generator (LARS-WG) for ten stations located in two physiographic regions of the study area: the Middle Mountains (1500–2700 m) and the Siwalik Hills (700–1500 m). The projected temperature and differences in projections among individual GCM projections for changes in the mean value of seasonal and annual Tmin and Tmax are presented for three future periods: 2011–2030 (2020s), 2046–2065 (2055s) and 2080–2099 (2090s). We also analyzed the baseline period and future Tmin and Tmax data through seven indices, as recommended by the Expert Team on Climate Change Detection, Monitoring and Indices (ETCCDMI). Results show that the LARS-WG model performs well when downscaling Tmin and Tmax. An increase in seasonal as well as mean annual minimum and maximum temperature is projected for all three future periods. Projected warming, as well as the differences among projections from different GCMs, increases with time for each of the three scenarios. The cold years during the 2055s and 2090s are expected to be hotter than the hot years during the baseline period. The increase in temperature, as well as the range of uncertainty, is expected to be higher in the Mountains than in the Hills. The number of summer days and tropical nights is expected to increase during all three future periods. The temperature of the coldest day, coldest night, warmest day and warmest night is also expected to increase in both the regions during all three future periods.

Analysis of future precipitation in the Koshi river basin, Nepal

We analyzed precipitation projections for the Koshi river basin in Nepal using outputs from 10 General Circulation Models (GCMs) under three emission scenarios (B1, A1B and A2). The low resolution future precipitation data obtained from the GCMs was downscaled using the statistical downscaling model LARS-WG. The data was downscaled for 48 stations located in the six physiographic regions in the Koshi basin. The precipitation projections for three future periods, i.e. 2020s, 2055s and 2090s, are presented using empirical Probability Density Functions (PDFs) for each physiographic region. The differences between the mean values of individual GCM projections and the mean value of the multi-model for the three scenarios allow for the estimation of uncertainty in the projections. We also analyzed the precipitation of the baseline and future periods using six indices that are recommended by the Expert Team on Climate Change Detection, Monitoring and Indices (ETCCDMI). Results indicate that not all GCMs agree on weather changes in precipitation will be positive or negative. A majority of the GCMs and the average values of all the GCMs for each scenario, indicate a positive change in summer, autumn and annual precipitation but a negative change in spring precipitation. Differences in the GCM projections exist for all the three future periods and the differences increase with time. The estimated uncertainty is higher for scenario A1B compared to B1 and A2. Differences among scenarios are small during the 2020s, which become significant during the 2055s and 2090s. The length of the wet spell is expected to increase, whereas the length of the dry spell is expected to decrease in all three future periods. There is a large scatter in the values of the indices: number of days with precipitation above 20 mm, 1-day maximum precipitation, 5-day maximum precipitation, and amount of precipitation on the days with precipitation above 95th percentile, both in direction and magnitude of the change.