Coupled Model Intercomparison Project Phase 5 Multi-model Ensemble Research Articles

Effects of Excessive Equatorial Cold Tongue Bias on the Projections of Tropical Pacific Climate Change. Part II: The Extreme El Niño Frequency in CMIP5 Multi-Model Ensemble

Under increased greenhouse gas (GHG) forcing, climate models tend to project a warmer sea surface temperature in the eastern equatorial Pacific than in the western equatorial Pacific. This El Niño-like warming pattern may induce an increase in the projected occurrence frequency of extreme El Niño events. The current models, however, commonly suffer from an excessive westward extension of the equatorial Pacific cold tongue accompanied by insufficient equatorial western Pacific precipitation. By comparing the Representative Concentration Pathway (RCP) 8.5 experiments with the historical simulations based on the Coupled Model Intercomparison Project phase 5 (CMIP5), a “present–future” relationship among climate models was identified: models with insufficient equatorial western Pacific precipitation error would have a weaker mean El Niño-like warming pattern as well as a lower increase in the frequency of extreme El Niño events under increased GHG forcing. Using this “present–future” relationship and the observed precipitation in the equatorial western Pacific, this study calibrated the climate projections in the tropical Pacific. The corrected projections showed a stronger El Niño-like pattern of mean changes in the future, consistent with our previous study. In particular, the projected increased occurrence of extreme El Niño events under RCP 8.5 forcing are underestimated by 30–35% in the CMIP5 multi-model ensemble before the corrections. This implies an increased risk of the El Niño-related weather and climate disasters in the future.

Comparison of regional characteristics of land precipitation climatology projected by an MRI-AGCM multi-cumulus scheme and multi-SST ensemble with CMIP5 multi-model ensemble projections

Ensembles of climate change projections created by general circulation models (GCMs) with high resolution are increasingly needed to develop adaptation strategies for regional climate change. The Meteorological Research Institute atmospheric GCM version 3.2 (MRI-AGCM3.2), which is listed in the Coupled Model Intercomparison Project phase 5 (CMIP5), has been typically run with resolutions of 60 km and 20 km. Ensembles of MRI-AGCM3.2 consist of members with multiple cumulus convection schemes and different patterns of future sea surface temperature, and are utilized together with their downscaled data; however, the limited size of the high-resolution ensemble may lead to undesirable biases and uncertainty in future climate projections that will limit its appropriateness and effectiveness for studies on climate change and impact assessments. In this study, to develop a comprehensive understanding of the regional precipitation simulated with MRI-AGCM3.2, we investigate how well MRI-AGCM3.2 simulates the present-day regional precipitation around the globe and compare the uncertainty in future precipitation changes and the change projection itself between MRI-AGCM3.2 and the CMIP5 multiple atmosphere–ocean coupled GCM (AOGCM) ensemble. MRI-AGCM3.2 reduces the bias of the regional mean precipitation obtained with the high-performing CMIP5 models, with a reduction of approximately 20% in the bias over the Tibetan Plateau through East Asia and Australia. When 26 global land regions are considered, MRI-AGCM3.2 simulates the spatial pattern and the regional mean realistically in more regions than the individual CMIP5 models. As for the future projections, in 20 of the 26 regions, the sign of annual precipitation change is identical between the 50th percentiles of the MRI-AGCM3.2 ensemble and the CMIP5 multi-model ensemble. In the other six regions around the tropical South Pacific, the differences in modeling with and without atmosphere–ocean coupling may affect the projections. The uncertainty in future changes in annual precipitation from MRI-AGCM3.2 partially overlaps the maximum–minimum uncertainty range from the full ensemble of the CMIP5 models in all regions. Moreover, on average over individual regions, the projections from MRI-AGCM3.2 spread over roughly 0.8 of the uncertainty range from the high-performing CMIP5 models compared to 0.4 of the range of the full ensemble.

Global pattern of historical and future changes in rapid temperature variability

Day-to-day (DTD) temperature variation reflects a rapid weather variability, which significantly affects human health and ecosystems. However, while a few of studies have addressed certain regional variations, no global pattern of rapid temperature variability has yet been investigated. Here, using global daily temperature observation data, we present a study of the worldwide spatial heterogeneity of rapid temperature variability and its long-term trends over the past 60 years. We found a significant decline in northern mid and high latitudes in boreal winter but a significant increase in the Arctic coast, South China and Australia in boreal summer during the study period. Using observational data and Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model ensemble simulations, we further demonstrate that the human-caused increase in greenhouse gases (GHGs) concentration leads to a significant change in meridional temperature gradient, which in turn results in the observed decline of rapid temperature variability in the mid and high latitudes and the increase in rapid temperature variability in Arctic Coast in summer. In contrast, human-induced increase in GHGs and aerosol accounts for approximately one third and two third of the decline of rapid temperature variability in North China in boreal summer, respectively. However, the increase in summer rapid temperature variability in southern China appears to be primarily associated with the long-term internal climate variability. It is further shown that, based on the CMIP5 multi-model ensemble simulations, the projected rapid temperature variability shows a significant decrease in the high latitudes in winter but a slight increase in tropical zones by the end of this century. These findings clearly reveal an important role of human activities on the historical and future rapid temperature variability.

An investigation of weighting schemes suitable for incorporating large ensembles into multi-model ensembles

Abstract. Multi-model ensembles can be used to estimate uncertainty in projections of regional climate, but this uncertainty often depends on the constituents of the ensemble. The dependence of uncertainty on ensemble composition is clear when single-model initial condition large ensembles (SMILEs) are included within a multi-model ensemble. SMILEs allow for the quantification of internal variability, a non-negligible component of uncertainty on regional scales, but may also serve to inappropriately narrow uncertainty by giving a single model many additional votes. In advance of the mixed multi-model, the SMILE Coupled Model Intercomparison version 6 (CMIP6) ensemble, we investigate weighting approaches to incorporate 50 members of the Community Earth System Model (CESM1.2.2-LE), 50 members of the Canadian Earth System Model (CanESM2-LE), and 100 members of the MPI Grand Ensemble (MPI-GE) into an 88-member Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble. The weights assigned are based on ability to reproduce observed climate (performance) and scaled by a measure of redundancy (dependence). Surface air temperature (SAT) and sea level pressure (SLP) predictors are used to determine the weights, and relationships between present and future predictor behavior are discussed. The estimated residual thermodynamic trend is proposed as an alternative predictor to replace 50-year regional SAT trends, which are more susceptible to internal variability. Uncertainty in estimates of northern European winter and Mediterranean summer end-of-century warming is assessed in a CMIP5 and a combined SMILE–CMIP5 multi-model ensemble. Five different weighting strategies to account for the mix of initial condition (IC) ensemble members and individually represented models within the multi-model ensemble are considered. Allowing all multi-model ensemble members to receive either equal weight or solely a performance weight (based on the root mean square error (RMSE) between members and observations over nine predictors) is shown to lead to uncertainty estimates that are dominated by the presence of SMILEs. A more suitable approach includes a dependence assumption, scaling either by 1∕N, the number of constituents representing a “model”, or by the same RMSE distance metric used to define model performance. SMILE contributions to the weighted ensemble are smallest (<10 %) when a model is defined as an IC ensemble and increase slightly (<20 %) when the definition of a model expands to include members from the same institution and/or development stream. SMILE contributions increase further when dependence is defined by RMSE (over nine predictors) amongst members because RMSEs between SMILE members can be as large as RMSEs between SMILE members and other models. We find that an alternative RMSE distance metric, derived from global SAT and hemispheric SLP climatology, is able to better identify IC members in general and SMILE members in particular as members of the same model. Further, more subtle dependencies associated with resolution differences and component similarities are also identified by the global predictor set.

Representation of the boreal summer tropical Atlantic–western North Pacific teleconnection in AGCMs: comparison of CMIP5 and CMIP6

Previous studies have revealed that warm (cold) sea surface temperature (SST) anomalies in the northern tropical Atlantic (NTA) can enhance (weaken) the anomalous low-level anticyclone over the western North Pacific (WNP) during boreal summer. This study assesses the ability of current atmospheric general circulation models (AGCMs) to simulate such an NTA–WNP connection by using Atmospheric Model Intercomparison Project experiments from 23 Coupled Model Intercomparison Project Phase 5 (CMIP5) and 35 CMIP6 climate models. It is shown that both the CMIP5 and CMIP6 multimodel ensemble (MME) averages and the majority of the individual AGCMs can reasonably reproduce the observed pattern of the NTA-related anomalous anticyclone over the WNP during boreal summer. Overall, the performance of the CMIP6 AGCMs in representing the NTA–WNP connection is similar to that of the CMIP5 AGCMs, except that the former tends to have a smaller spread than the latter among models. Additionally, both the CMIP5 and CMIP6 MME averages as well as the individual models can reasonably represent the mechanism responsible for the boreal summer NTA–WNP connection, which involves a zonally westward-extending overturning circulation over the Pacific–Atlantic Oceans. Furthermore, the intensity of the NTA-related WNP anomalous anticyclone is positively correlated with that of the WNP local climatological convection activity for both the CMIP5 and CMIP6 AGCMs, implying that better representation of the WNP climatological convection activity may be crucial for improving the skill of AGCMs to simulate the boreal summer NTA–WNP connection. However, model bias in the simulation of climatological convection activity over the WNP remains large for the current CMIP6 AGCMs, although the bias is reduced over most of the tropical and subtropical Pacific–Atlantic regions compared to that for the CMIP5 AGCMs during boreal summer.

Projecting meteorological, hydrological and agricultural droughts for the Yangtze River basin

Drought is a multifaceted natural hazard that occurs in virtually any component of the hydrological cycle. Drought monitoring and prediction from multiple viewpoints are essential for reliable risk planning and management. This study presents a joint prognosis of meteorological (M-drought), hydrological (H-drought) and agricultural (A-drought) droughts for the period 2021–2100 over the Yangtze River basin (YRB). The prognosis uses an ensemble of 10 models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) for two future emission scenarios (RCP4.5 and RCP8.5). Precipitation, runoff, and soil moisture are used to quantify M-drought, H-drought, and A-drought, respectively. The results indicate that the raw CMIP5 multimodel ensemble for the YRB generally overestimates precipitation while underestimating temperature. The precipitation, runoff, and soil moisture are all projected to increase in the coming decades at the spatial scale of the entire YRB. Moreover, the magnitudes of drought shift from moderate and severe in the past (1954–2013) to extreme and exceptional in the future. The durations of drought are anticipated to prolong in the future, especially for the A-droughts. A-droughts are projected to be more severe than M- and H-droughts. Furthermore, the headwater areas and the areas surrounding the intersection of Sichuan, Guizhou and Chongqing are anticipated to increase in A-drought severity. These findings provide insight to inform drought planning and management in the YRB, and improve our understanding of the ability of precipitation, runoff, and soil moisture to describe droughts under global warming scenarios.

Selection of CMIP5 multi-model ensemble for the projection of spatial and temporal variability of rainfall in peninsular Malaysia

This study uses a multi-model ensemble (MME) for the assessment of the spatial and temporal variations of rainfall in peninsular Malaysia under climate change scenarios. The past performance approach was used for the selection of GCM ensemble from a pool of Coupled Model Intercomparison Project Phase 5 (CMIP5) GCMs. The performances of four bias correction methods, namely, scaling, gamma quantile mapping, generalized quantile mapping, and power transformation were assessed to select the most suitable method for the downscaling of daily rainfall of selected GCMs based on APHRODITE rainfall at a spatial resolution of 0.25° × 0.25°. The downscaling model was used for the projections of daily rainfall for the period 2010–2099 for four representative concentration pathways (RCP) scenarios, namely, RCP2.6, RCP4.5, RCP6.0, and RCP8.5. Random forest regression algorithm was used to develop the multi-model ensemble (MME) mean of GCM-projected rainfall for different RCPs in order to show the changes in rainfall for three future periods, 2010–2039, 2040–2069, and 2070–2099. The results revealed four GCMs, BCC-CSM1.1(M), CCSM4, CSIRO-Mk3.6.0, and HadGEM2-ES as the most suitable GCMs for the projection of daily rainfall of peninsular Malaysia. The power transformation was found as the most suitable method for the correction of biases in GCM daily rainfall. The MME mean of projected rainfall showed the increase in rainfall in peninsular Malaysia for all the scenarios and future periods. The maximum increase in annual rainfall was projected by 15.72% during 2070–2099 for RCP8.5. The variability of future rainfall was also found to increase along with mean rainfall. The increase in rainfall variability was projected by 26.15% for RCP8.5 during 2070–2099. The spatial pattern of rainfall changes revealed more variability in future rainfall in the northeast where frequency of hydro-climatic disasters is higher compared to other regions. The results indicate the possible increase in hydro-climatic disaster in peninsular Malaysia due to climate change.

State-of-the-art climate modeling of extreme precipitation over Africa: analysis of CORDEX added-value over CMIP5

The ability of the state-of-the-art Global and Regional Climate Models (GCMs and RCMs) to reproduce the mean and spatial characteristics of extreme precipitation indices over Africa is evaluated. In particular, the extent to which CORDEX (COordinated Regional Downscaling Experiment) adds useful details on the performance of CMIP5 (Coupled Model Intercomparison Project Phase 5) multimodel ensemble is investigated. Comparison of the present day simulation was performed with two precipitation observation datasets, the high-resolution TRMM (Tropical Rainfall Measuring Mission) and coarse resolution GPCP (Global Precipitation Climatology Project), to evaluate models strengths and weaknesses. Eight indices generated from absolute (1 mm) and percentile (95th) based thresholds as defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) are computed for seventeen CMIP5 GCMs and six CORDEX RCMs (for twelve downscaling experiments) for each year during the historical period (1975–2004). Our results suggest a good consistency between GPCP and TRMM in producing annual mean and frequency of extreme precipitation events over Africa despite few inconsistencies. However, their associated intensities largely differed from one another with GPCP data displaying drier bias. Overall, multimodel ensembles simulations overestimate the frequency of extreme precipitation events and underestimate their intensities. The results further show that CMIP5 exhibits wet bias of precipitation events and drier bias of precipitation intensity than CORDEX, and that CORDEX produces precipitation magnitude within the range of the observations and more in line with the higher-resolution TRMM data. This illustrates the added value achieved with the higher-resolution CORDEX multimodel ensemble for the simulation of such events, and points toward the use of these RCMs to study extreme precipitation for a better assessment of climate change over Africa.

Time of emergence in regional precipitation changes: an updated assessment using the CMIP5 multi-model ensemble

This study conducted an updated time of emergence (ToE) analysis of regional precipitation changes over land regions across the globe using multiple climate models from the Coupled Model Intercomparison Project phase 5 (CMIP5). ToEs were estimated for 14 selected hotspots over two seasons of April to September (AS) and October to March (OM) from three RCP scenarios representing low (RCP2.6), medium (RCP4.5), and high (RCP8.5) emissions. Results from the RCP8.5 scenario indicate that ToEs would occur before 2040 over seven hotspots including three northern high-latitude regions (OM wettening), East Africa (OM wettening), South Asia (AS wettening), East Asia (AS wettening) and South Africa (AS drying). The Mediterranean (both OM and AS drying) is expected to experience ToEs in the mid-twenty-first century (2040-2080). In order to measure possible benefits from taking low-emission scenarios, ToE differences were examined between the RCP2.6 scenario and the RCP4.5 and RCP8.5 scenarios. Significant ToE delays from 26 years to longer than 67 years were identified over East Africa (OM wettening), the Mediterranean (both AS and OM drying), South Asia (AS wettening), and South Africa (AS drying). Further, we investigated ToE differences between CMIP3-based and CMIP5-based models using the same number of models for the comparable scenario pairs (SRESA2 vs. RCP8.5, and SRESB1 vs. RCP4.5). Results were largely consistent between two model groups, indicating the robustness of ToE results. Considerable differences in ToEs (larger than 20 years) between two model groups appeared over East Asia and South Asia (AS wettening) and South Africa (AS drying), which were found due to stronger signals in CMIP5 models. Our results provide useful information on the timing of emerging signals in regional and seasonal hydrological changes, having important implications for associated adaptation and mitigation plans.

Changes in record-breaking temperature events in China and projections for the future

As global warming intensifies, more record-breaking (RB) temperature events are reported in many places around the world where temperatures are higher than ever before . The RB temperatures have caused severe impacts on ecosystems and human society. Here, we address changes in RB temperature events occurring over China in the past (1961–2014) as well as future projections (2006–2100) using observational data and the newly available simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5). The number of RB events has a significant multi-decadal variability in China, and the intensity expresses a strong decrease from 1961 to 2014. However, more frequent RB events occurred in mid-eastern and northeastern China over last 30 years (1981–2010). Comparisons with observational data indicate multi-model ensemble (MME) simulations from the CMIP5 model perform well in simulating RB events for the historical run period (1961–2005). CMIP5 MME shows a relatively larger uncertainty for the change in intensity. From 2051 to 2100, fewer RB events are projected to occur in most parts of China according to RCP 2.6 scenarios. Over the longer period from 2006 to 2100, a remarkable increase is expected for the entire country according to RCP 8.5 scenarios and the maximum numbers of RB events increase by approximately 600 per year at end of twenty-first century.

Maritime Continent seasonal climate biases in AMIP experiments of the CMIP5 multimodel ensemble

The fidelity of 28 Coupled Model Intercomparison Project phase 5 (CMIP5) models in simulating mean climate over the Maritime Continent in the Atmospheric Model Intercomparison Project (AMIP) experiment is evaluated in this study. The performance of AMIP models varies greatly in reproducing seasonal mean climate and the seasonal cycle. The multi-model mean has better skill at reproducing the observed mean climate than the individual models. The spatial pattern of 850 hPa wind is better simulated than the precipitation in all four seasons. We found that model horizontal resolution is not a good indicator of model performance. Instead, a model’s local Maritime Continent biases are somewhat related to its biases in the local Hadley circulation and global monsoon. The comparison with coupled models in CMIP5 shows that AMIP models generally performed better than coupled models in the simulation of the global monsoon and local Hadley circulation but less well at simulating the Maritime Continent annual cycle of precipitation. To characterize model systematic biases in the AMIP runs, we performed cluster analysis on Maritime Continent annual cycle precipitation. Our analysis resulted in two distinct clusters. Cluster I models are able to capture both the winter monsoon and summer monsoon shift, but they overestimate the precipitation; especially during the JJA and SON seasons. Cluster II models simulate weaker seasonal migration than observed, and the maximum rainfall position stays closer to the equator throughout the year. The tropics-wide properties of these clusters suggest a connection between the skill of simulating global properties of the monsoon circulation and the skill of simulating the regional scale of Maritime Continent precipitation.

Multidecadal Trends in Large-Scale Annual Mean SATa Based on CMIP5 Historical Simulations and Future Projections

Abstract Based on observations and Coupled Model Intercomparison Project Phase 5 (CMIP5) results, multidecadal variations and trends in annual mean surface air temperature anomalies (SATa) at global, hemispheric, and hemispheric land and ocean scales in the past and under the future scenarios of two representative concentration pathways (RCPs) are analyzed. Fifteen models are selected based on their performances in capturing the temporal variability, long-term trend, multidecadal variations, and trends in global annual mean SATa. Observational data analysis shows that the multidecadal variations in annual mean SATa of the land and ocean in the northern hemisphere (NH) and of the ocean in the southern hemisphere (SH) are similar to those of the global mean, showing an increase during the 1900–1944 and 1971–2000 periods, and flattening or even cooling during the 1945–1970 and 2001–2013 periods. These observed characteristics are basically reproduced by the models. However, SATa over SH land show an increase during the 1945–1970 period, which differs from the other hemispheric scales, and this feature is not captured well by the models. For the recent hiatus period (2001–2013), the projected trends of BCC-CSM1-1-m, CMCC-CM, GFDL-ESM2M, and NorESM1-ME at the global and hemispheric scales are closest to the observations based on RCP4.5 and RCP8.5 scenarios, suggesting that these four models have better projection capability in SATa. Because these four models are better at simulating and projecting the multidecadal trends of SATa, they are selected to analyze future SATa variations at the global and hemispheric scales during the 2006–2099 period. The selected multi-model ensemble (MME) projected trends in annual mean SATa for the globe, NH, and SH under RCP4.5 (RCP8.5) are 0.17 (0.29) °C, 0.22 (0.36) °C, and 0.11 (0.23) °C·decade −1 in the 21st century, respectively. These values are significantly lower than the projections of CMIP5 MME without model selection.

Predictability of Precipitation Over the Conterminous U.S. Based on the CMIP5 Multi-Model Ensemble.

Characterizing precipitation seasonality and variability in the face of future uncertainty is important for a well-informed climate change adaptation strategy. Using the Colwell index of predictability and monthly normalized precipitation data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model ensembles, this study identifies spatial hotspots of changes in precipitation predictability in the United States under various climate scenarios. Over the historic period (1950–2005), the recurrent pattern of precipitation is highly predictable in the East and along the coastal Northwest, and is less so in the arid Southwest. Comparing the future (2040–2095) to the historic period, larger changes in precipitation predictability are observed under Representative Concentration Pathways (RCP) 8.5 than those under RCP 4.5. Finally, there are region-specific hotspots of future changes in precipitation predictability, and these hotspots often coincide with regions of little projected change in total precipitation, with exceptions along the wetter East and parts of the drier central West. Therefore, decision-makers are advised to not rely on future total precipitation as an indicator of water resources. Changes in precipitation predictability and the subsequent changes on seasonality and variability are equally, if not more, important factors to be included in future regional environmental assessment.

Statistical downscaling of CMIP5 multi-model ensemble for projected changes of climate in the Indus River Basin

The simulation results of CMIP5 (Coupled Model Inter-comparison Project phase 5) multi-model ensemble in the Indus River Basin (IRB) are compared with the CRU (Climatic Research Unit) and APHRODITE (Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation) datasets. The systematic bias between simulations and observations is corrected by applying the equidistant Cumulative Distribution Functions matching method (EDCDFm) and high-resolution simulations are statistically downscaled. Then precipitation and temperature are projected for the IRB for the mid-21st century (2046–2065) and late 21st century (2081–2100).The results show that the CMIP5 ensemble captures the dominant features of annual and monthly mean temperature and precipitation in the IRB. Based on the downscaling results, it is projected that the annual mean temperature will increase over the entire basin, relative to the 1986–2005 reference period, with greatest changes in the Upper Indus Basin (UIB). Heat waves are more likely to occur. An increase in summer temperature is projected, particularly for regions of higher altitudes in the UIB. The persistent increase of summer temperature might accelerate the melting of glaciers, and has negative impact on the local freshwater availability. Projections under all RCP scenarios show an increase in monsoon precipitation, which will increase the possibility of flood disaster. A decreasing trend in winter and spring precipitation in the IRB is projected except for the RCP2.6 scenario which will cause a lower contribution of winter and spring precipitation to water resources in the mid and high altitude areas of the IRB.

Effects of excessive equatorial cold tongue bias on the projections of tropical Pacific climate change. Part I: the warming pattern in CMIP5 multi-model ensemble

The excessive cold tongue error in the equatorial Pacific has persisted in several generations of climate models. Based on the historical simulations and Representative Concentration Pathway (RCP) 8.5 experiments in the Coupled Model Intercomparison Project phase 5 (CMIP5) multi-model ensemble (MME), this study finds that models with an excessive westward extension of cold tongue and insufficient equatorial western Pacific precipitation tend to project a weaker east-minus-west gradient of sea surface temperature (SST) warming along the equatorial Pacific under increased greenhouse gas (GHG) forcing. This La Nina-like error of tropical Pacific SST warming is consistent with our understanding of negative SST-convective feedback over the western Pacific warm pool. Based on this relationship between the present simulations and future projections, the present study applies an “observational constraint” of equatorial western Pacific precipitation to calibrate the projections of tropical Pacific climate change. After the corrections, CMIP5 models robustly project an El Nino-like warming pattern, with a MME mean increase by a factor of 2.3 in east-minus-west gradient of equatorial Pacific SST warming and reduced inter-model uncertainty. Corrections in projected changes in tropical precipitation and atmospheric circulation are physically consistent. This study suggests that a realistic cold tongue simulation would lead to a more reliable tropical Pacific climate projection.

21st century drought outlook for major climate divisions of Texas based on CMIP5 multimodel ensemble: Implications for water resource management

Summary Management of water resources in Texas (United States) is a challenging endeavor due to rapid population growth in the recent past coupled with significant spatiotemporal variations in climate. While climate conditions impact the availability of water, over-usage and lack of efficient management further complicate the dynamics of supply availability. In this paper, we provide the first look at the impact of climate change projections from an ensemble of Coupled Model Intercomparison Project Phase 5 (CMIP5) on 21st century drought characteristics under three future emission trajectories: Representative Concentration Pathway (RCP) 2.6, RCP 4.5 and RCP 8.5, using the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI). In addition, we evaluate the performance of the ensemble in simulating historical (1950–1999) observations from multiple climate divisions in Texas. Overall, the ensemble performs better in simulating historical temperature than precipitation. In semi-arid locations such as El Paso and Laredo, decreasing precipitation trends are projected even under the influence of climate policies represented by the RCP 4.5. There is little variability in the SPI across climate divisions and across RCPs. The SPEI, on the other hand, generally shows a decreasing trend toward the latter half of the 21st century, with multi-year droughts becoming the norm under the RCP 8.5, particularly in regions that are already dry, such as El Paso. Less severe droughts are projected for the sub-humid eastern edge of the state. Considering that state water planning agencies are already forecasting increased water shortages over the next 50 years, we recommend proactive approaches to risk management such as adjusting the planning tools for potential recurrence of multi-year droughts in regions that are already water-stressed.

Comparison of dryland climate change in observations and CMIP5 simulations

A comparison of observations with 20 climate model simulations from the Coupled Model Intercomparison Project, Phase 5 (CMIP5) revealed that observed dryland expansion amounted to 2.61×106 km2 during the 58 years from 1948 to 2005, which was four times higher than that in the simulations (0.55×106 km2). Dryland expansion was accompanied by a decline in aridity index (AI) (drying trend) as a result of decreased precipitation and increased potential evapotranspiration across all dryland subtype areas in the observations, especially in the semi-arid and dry subhumid regions. However, the CMIP5 multi-model ensemble (MME) average performed poorly with regard to the decreasing trends of AI and precipitation. By analyzing the factors controlling AI, we found that the overall bias of AI in the simulations, compared with observations, was largely due to limitations in the simulation of precipitation. The simulated precipitation over global drylands was substantially overestimated compared with observations across all subtype areas, and the spatial distribution of precipitation in the MME was largely inconsistent in the African Sahel, East Asia, and eastern Australia, where the semi-arid and dry subhumid regions were mainly located.

Implications of freshwater flux data from the CMIP5 multimodel output across a set of Northern Hemisphere drainage basins

AbstractThe multimodel ensemble of the Coupled Model Intercomparison Project, Phase 5 (CMIP5) synthesizes the latest research in global climate modeling. The freshwater system on land, particularly runoff, has so far been of relatively low priority in global climate models, despite the societal and ecosystem importance of freshwater changes, and the science and policy needs for such model output on drainage basin scales. Here we investigate the implications of CMIP5 multimodel ensemble output data for the freshwater system across a set of drainage basins in the Northern Hemisphere. Results of individual models vary widely, with even ensemble mean results differing greatly from observations and implying unrealistic long‐term systematic changes in water storage and level within entire basins. The CMIP5 projections of basin‐scale freshwater fluxes differ considerably more from observations and among models for the warm temperate study basins than for the Arctic and cold temperate study basins. In general, the results call for concerted research efforts and model developments for improving the understanding and modeling of the freshwater system and its change drivers. Specifically, more attention to basin‐scale water flux analyses should be a priority for climate model development, and an important focus for relevant model‐based advice for adaptation to climate change.

Systematic land climate and evapotranspiration biases in CMIP5 simulations.

[1] Land climate is important for human population since it affects inhabited areas. Here we evaluate the realism of simulated evapotranspiration (ET), precipitation, and temperature in the CMIP5 multimodel ensemble on continental areas. For ET, a newly compiled synthesis data set prepared within the Global Energy and Water Cycle Experiment-sponsored LandFlux-EVAL project is used. The results reveal systematic ET biases in the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations, with an overestimation in most regions, especially in Europe, Africa, China, Australia, Western North America, and part of the Amazon region. The global average overestimation amounts to 0.17 mm/d. This bias is more pronounced than in the previous CMIP3 ensemble (overestimation of 0.09 mm/d). Consistent with the ET overestimation, precipitation is also overestimated relative to existing reference data sets. We suggest that the identified biases in ET can explain respective systematic biases in temperature in many of the considered regions. The biases additionally display a seasonal dependence and are generally of opposite sign (ET underestimation and temperature overestimation) in boreal summer (June–August).

Climate extremes indices in the CMIP5 multimodel ensemble: Part 1. Model evaluation in the present climate

This paper provides a first overview of the performance of state‐of‐the‐art global climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) in simulating climate extremes indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI), and compares it to that in the previous model generation (CMIP3). For the first time, the indices based on daily temperature and precipitation are calculated with a consistent methodology across multimodel simulations and four reanalysis data sets (ERA40, ERA‐Interim, NCEP/NCAR, and NCEP‐DOE) and are made available at the ETCCDI indices archive website. Our analyses show that the CMIP5 models are generally able to simulate climate extremes and their trend patterns as represented by the indices in comparison to a gridded observational indices data set (HadEX2). The spread amongst CMIP5 models for several temperature indices is reduced compared to CMIP3 models, despite the larger number of models participating in CMIP5. Some improvements in the CMIP5 ensemble relative to CMIP3 are also found in the representation of the magnitude of precipitation indices. We find substantial discrepancies between the reanalyses, indicating considerable uncertainties regarding their simulation of extremes. The overall performance of individual models is summarized by a “portrait” diagram based on root‐mean‐square errors of model climatologies for each index and model relative to four reanalyses. This metric analysis shows that the median model climatology outperforms individual models for all indices, but the uncertainties related to the underlying reference data sets are reflected in the individual model performance metrics.