Coupled Model Intercomparison Project Phase 5 General Circulation Models Research Articles

Enhanced performance of CMIP6 climate models in simulating historical precipitation in the Florida Peninsula

AbstractAssessing the performance of General Circulation Models (GCMs) in simulating historical regional precipitation is essential for climate assessments. This study analysed 18 GCMs from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and 27 GCMs from Phase 6 (CMIP6) for the Florida Peninsula. Naïve combinations formed the CMIP5‐Ensemble and CMIP6‐Ensemble. The reference dataset consisted of global gridded precipitation data from National Oceanic and Atmospheric Administration. GCM‐simulated precipitation data were compared to the reference dataset across multiple time scales between 1951 and 2005. Most CMIP5 and CMIP6 GCMs exhibited a negative bias at the daily time scale, with an absolute value ranging between 0 and 2 mm day−1 at the median level, except for INM‐CM4 and MRI‐CGCM3 (CMIP5) and CNRM‐ESM2‐1 (CMIP6). Two performance metrics, that is, Pearson correlation and mean absolute error (MAE), were used to evaluate the GCMs. A correlation value above 0.3 is statistically significant at the significance level (α = 0.05). For the Pearson correlation between simulated and observed monthly precipitation, only 15% of CMIP5 GCMs (3 out of 19) had a median value above 0.3, whilst this increased to approximately 39% (11 out of 28) for CMIP6 GCMs. For monthly climatological precipitation, only 21% (4 out of 21) of GCMs in CMIP5 showed a correlation with a median value of 0.8 or higher. This percentage rose to 50% (14 out of 28) for CMIP6. A notable difference was observed in the MAE between CMIP5 and CMIP6 climate models. Lastly, GCM raw outputs were evaluated based on rainy season characteristics (onset, demise and duration). Significant improvements from the CMIP5 to the CMIP6 models were observed in capturing the rainy season in the Florida Peninsula. This study thoroughly compares CMIP5 and CMIP6 climate models in simulating historical precipitation at various time scales for the study region. Although the results are specific to the study area, the methods can be applied more broadly.

Evaluation of the CMIP5 general circulation models for simulating the precipitation and temperature of the Koshi River Basin in Nepal

AbstractThis study evaluates the performance of 12 different general circulation models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5) to simulate precipitation and temperature in the Koshi River Basin, Nepal. Four statistical performance indicators: correlation coefficient, normalised root-mean-square deviation (NMRSD), absolute NMRSD, and average absolute relative deviation are considered to evaluate the GCMs using historical observations. Seven different climate indices: consecutive dry days, consecutive wet days, cold spell duration index, warm spell duration index, frost days, very wet days, and simple daily intensity index are considered to identify the most suitable models for the basin and future climate impact assessment studies. Weights for each performance indicator are determined using the entropy method, with compromise programming applied to rank the GCMs based on the Euclidian distant technique. The results suggest that CanESM2 and CSIRO-MK3.6.0 are the most suitable for predicting extreme precipitation events, and BCC-CSM 1.1, CanESM2, NorESM1-M, and CNRM-CM5 for extreme temperature events in Himalayan river basins. Overall, IPSL-CM5A-MR, CanESM2, CNRM-CM5, BCC-CSM 1.1, NorESM1-M, and CSIRO-Mk3.6.0 are deemed suitable models for predicting precipitation and temperature in the Koshi River Basin, Nepal.

Impacts and uncertainties of climate change on stream flow of the Bilate River (Ethiopia), using a CMIP5 general circulation models ensemble

The impact and uncertainty of climate change on stream flow of the Bilate River Watershed was assessed. Ensemble of 20 Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCMs) under two Representative Concentration Pathways and six GCM structures were selected to form 24 future climate scenarios for the watershed. Soil and Water Assessment Tool (SWAT) model was selected to simulate stream flow of the watershed. The respective statistical results of the coefficient of determination (R2), Nash-Sutcliffe coefficient (NSE) and percent bias (PB) are 0.79, 0.78 and 0.56 for calibration period and 0.64, 0.60 and -21.7 for validation period which show that the model predicted the stream flow reasonably. The annual stream flow increased progressively throughout the century for all time periods. The increases under RCP 8.5 scenario are the larger compared to RCP 4.5 scenarios, approximately 42.42% during the 2080s period. The six GCMs selected to see the uncertainties related to GCMs suggest that the river flow will change by small amounts of -6.18 to 7.83% change compared with the baseline. The simulated runoff depended on the projected amount of rainfall embedded in the GCM structures selected to simulate the future climate and less dependent on the local temperature increment. Key words: Climate change, Bilate River watershed, stream flow, soil and water assessment tool (SWAT), uncertainty.

Assessment and ranking of CMIP5 GCMs performance based on observed statistics over Cauvery river basin – Peninsular India

Assessing information on climate change over a regional scale is made possible through general circulation models (GCMs). However, developers generally have a dilemma in selecting suitable GCM for regional scale downscaling to reduce the computational burden. Ranking of GCMs based on various conditions will help these purposes, and the present study evaluates the performance of GCMs using various performance evaluation parameters for ranking. Performance of twenty-six Coupled Model Intercomparison Project Phase 5 (CMIP5) GCMs was assessed in the present study to evaluate and rank the predictability of near-surface air temperature (tas) and precipitation (pr). The non-parametric trend existing in observed data from 35 stations is compared with GCM projected trends using Mann-Kendall trend analysis to assess the model reliability. Performance evaluation parameters such as percentage BIAS (PBIAS %), normalized root mean squared error (NRMSE %) and coefficient of determination (R2). Neither of the CMIP5 GCM performed consistently well throughout all four seasons. Also, models performing better in projecting temperature statistics are poor in capturing the precipitation trends and vice versa. The seasonal ranking of GCMs based on their ability to reproduce the regional weather condition would help in selecting suitable GCM for regional climate studies.

Future streamflow assessment in the Haihe River basin located in northern China using a regionalized variable infiltration capacity model based on 18 CMIP5 GCMs

Abstract The impact of future climate change on streamflow is assessed in the Haihe River basin (HRB) by the Variable Infiltration Capacity (VIC) model, using the outputs from 18 general circulation models (GCMs) of the Coupled Model Inter-comparison Project Phase 5 (CMIP5). Three Representative Concentration Pathway (RCP) scenarios have been used, including RCP2.6, RCP4.5, and RCP8.5. Based on the model parameters calibration in six catchments in the HRB and parameter regionalization, the hydrological simulation for the whole HRB denotes good performance of the VIC model. Taking the period 1961–1990 as a baseline period, the outputs from the GCMs indicate that the HRB will become warmer and wetter in the 21st century (2010–2099). There might be an increasing trend for the streamflow in the HRB under future climate change scenarios. For example, in the 2050s (2040–2069), the streamflow may increase by 12%, 28%, and 24% under the RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively. Monthly, the highest and lowest increase in streamflow is in dry and wet seasons, respectively. Spatially, the increasing trend for streamflow in the north HRB is higher than that in the south HRB. The uncertainty from the GCMs and climatic scenarios should be further focused.

A non-local model output statistics approach for the downscaling of CMIP5 GCMs for the projection of rainfall in Peninsular Malaysia

Abstract In this study, a non-local model output statistics (MOS) approach is proposed for the downscaling of daily rainfall of coupled model intercomparison project phase 5 (CMIP5) general circulation models (GCMs) for the projections of rainfall in Peninsular Malaysia for two representative concentration pathway (RCP) scenarios, RCP4.5 and RCP8.5. Projections of eight GCMs for both the mentioned RCPs were used for this purpose. The GCM simulations were downscaled at 19 observed stations distributed over Peninsular Malaysia. Random forest (RF) was used for the development of non-local regression-based MOS models. The results revealed a high accuracy of the models in downscaling rainfall at all the observed stations. The mean absolute error (MAE) of the models were found in the range of 0.8–0.39; normalized root mean square error (NRMSE) between 7.4 and 41.7, Percent Bias (PBIAS) between –0.3 and 10.1, Nash–Sutcliffe coefficient (NSE) between 0.81 and 0.99 and R2 between 0.89 and 0.99. The increase in annual rainfall was in the range of 7.3–29.5%. The increase was higher for RCP8.5 compared to RCP4.5. The maximum increase was observed in the northern part of Peninsular Malaysia in the range of 20.7–29.5%, while the minimum in the south-west region was in the range of 7.6–15.2%.

Climate change uncertainties in seasonal drought severity-area-frequency curves: Case of arid region of Pakistan

The uncertainty assessment of the changes in drought characteristics due to climate change has caught the attention of the scientific community. This study used gauge-based gridded precipitation data obtained from Global Precipitation Climatology Centre (GPCC) to reconstruct historical droughts and downscale future precipitation projected by seven general circulation models (GCMs) of Coupled Model Inter-comparison Project phase 5 (CMIP5) under four Representative Concentration Pathways (RCP) scenarios, namely, RCP2.6, RCP4.5, RCP6.0 and RCP8.5. Support vector machine (SVM) and quantile mapping were used for downscaling and GCM bias correction, respectively. The model performances were assessed based on statistical measures. The historical and future projected precipitation data were finally used to characterize the seasonal droughts using Standardized Precipitation Index (SPI) for different crop growing periods. The drought severity-area-frequency (SAF) curves for the historical (1961–2010) and three future periods (2010–2039, 2040–2069, and 2070–2099) were developed. The uncertainty band of future drought SAF curves was estimated using Bayesian bootstrap (BB) at a 95% confidence level. As a result, SVM was successful in downscaling the precipitation of all selected CMIP5 GCMs. The seasonal ensemble of GCMs projected an increase in precipitation ranging from 8% to 41% under all scenarios. The historical SAF curves revealed that for equal drought severity, larger areas are affected by droughts having higher return periods. Future projections of droughts revealed the increase in affected area for lower severity and return period droughts and the decrease for higher severity and return period droughts. The uncertainty bands of drought SAF curves with higher return periods were found much wider compared to those with lower return periods which indicates more uncertainty in the projection of higher severity and return period droughts.

Long-term cloud fraction biases in CMIP5 GCMs over India during monsoon season

Using 24 years of cloud fraction (CF) data from the International Satellite Cloud Climatology Project (ISCCP) observations and their corresponding simulators in general circulation models (GCMs) from the Coupled Model Intercomparison Project phase 5 (CMIP5), we have analyzed cloud biases and their role on radiation over the Indian region (65–100° E and 5–40° N) for the monsoon season of June to September. The present study reports the spatial patterns of CFs and their biases in GCMs compared to observations. It is found that the simulated CFs are highly underestimated up to ~ 40%. Mean of total CF from ISCCP observations is 75% with at least 10% difference with simulated CFs. For high-topped clouds, this difference is about 3–4%. Except for high-topped clouds, other cloud types are not simulated realistically by CMIP5 models used in this study. Further, we investigated the individual cloud types classified based on cloud optical depth and cloud top pressure. We found that, in general, individual cloud types are poorly simulated by models, although some (Max Planck Institute Earth System Model, Low Resolution and Hadley Centre Global Environmental Model, version 2, Earth System) models convincingly simulate high-topped thin clouds. To assess the impact of cloud biases on the simulated radiative forcings, we studied shortwave and longwave cloud radiative forcings from CERES (Clouds and the Earth’s Radiant Energy System) observations and CMIP5 GCMs. It is noticed that the spatial patterns of biases in radiative forcings are similar to the patterns of biases in CFs for high-topped clouds, specifically over the oceanic regions. We find that the biases in cloud radiative forcings could potentially be caused due to the inefficacy of CMIP5 models in simulating high-topped anvil clouds (high-topped cirrus/stratocirrus clouds). The present study confirms that the uncertainty in simulating cloud fractions over the Indian region is still a prominent issue to be addressed in general circulation models.

Ranking of CMIP5 GCM Skills in Simulating Observed Precipitation over the Lower Mekong Basin, Using an Improved Score-Based Method

This study assessed the performances of 34 Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCMs) in reproducing observed precipitation over the Lower Mekong Basin (LMB). Observations from gauge-based data of the Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) precipitation data were obtained from 1975 to 2004. An improved score-based method was used to rank the performance of the GCMs in reproducing the observed precipitation over the LMB. The results revealed that most GCMs effectively reproduced precipitation patterns for the mean annual cycle, but they generally overestimated the observed precipitation. The GCMs showed good ability in reproducing the time series characteristics of precipitation for the annual period compared to those for the wet and dry seasons. Meanwhile, the GCMs obviously reproduced the spatial characteristics of precipitation for the dry season better than those for annual time and the wet season. More than 50% of the GCMs failed to reproduce the positive trend of the observed precipitation for the wet season and the dry season (approximately 52.9% and 64.7%, respectively), and approximately 44.1% of the GCMs failed to reproduce positive trend for annual time over the LMB. Furthermore, it was also revealed that there existed different robust criteria for assessing the GCMs’ performances at a seasonal scale, and using multiple criteria was superior to a single criterion in assessing the GCMs’ performances. Overall, the better-performed GCMs were obtained, which can provide useful information for future precipitation projection and policy-making over the LMB.

Use of Satellite Soil Moisture to Diagnose Climate Model Representations of European Soil Moisture‐Air Temperature Coupling Strength

AbstractSoil moisture‐air temperature coupling (SMTC) strength affects European summer air temperature variability. However, due to a lack of spatially extensive ground‐based soil moisture observations, General Circulation Model predictions of European‐mean SMTC strength have not been adequately verified and contain substantial uncertainties. Here we utilize remotely sensed soil moisture to evaluate estimates of SMTC strength provided by seven Coupled Model Intercomparison Project Phase 5 (CMIP5) General Circulation Models (GCMs). Soil moisture retrievals provided by the Soil Moisture Active/Passive (SMAP) mission are shown to be uniquely suited for this purpose. Comparisons to SMAP‐based estimates suggest that CMIP5 GCMs generally underestimate SMTC strengths—particularly in central Europe. This result is consistent with previous modeling work and highlights the value of L‐band soil moisture remote sensing for land surface‐atmosphere coupling analyses.

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.

Non-Stationary Frequency Analysis of Future Extreme Rainfall using CMIP5 GCMs over the Korean Peninsula

In this study, we performed stationary and non-stationary frequency analysis of daily rainfall extreme series based on climate change scenarios by CMIP5 (Coupled Model Intercomparison Project Phase5) 8 GCMs (General Circulation Models). We extracted the annual maximum daily rainfall and it was conducted tendency test for the abnormality verification. The data consisted of 30 sets according to 30 years in units and sequential to year and to derive each set rank, the number of rainfall occurrences over 80 mm and the summer rainfall were calculated. For the stationary and non-stationary frequency analysis, Gumbel distribution was adopted and average precipitation, location and scale parameters were calculated for each set. As a result of the analysis, it is considered that the stationary frequency analysis in RCP 4.5 and the non-stationary frequency analysis in RCP 8.5 are valid, and it is expected that various scenarios will be reviewed in the near future. The results of this study are expected to be useful for the future climate change policies. Keywords: CMIP5, GCMs, Gumbel, Non-stationary Frequency Analysis, Probability Precipitation

Analysis of the variability of the North Atlantic eddy-driven jet stream in CMIP5

The North Atlantic eddy-driven jet is a dominant feature of extratropical climate and its variability is associated with the large-scale changes in the surface climate of midlatitudes. Variability of this jet is analysed in a set of General Circulation Models (GCMs) from the Coupled Model Inter-comparison Project phase-5 (CMIP5) over the North Atlantic region. The CMIP5 simulations for the 20th century climate (Historical) are compared with the ERA40 reanalysis data. The jet latitude index, wind speed and jet persistence are analysed in order to evaluate 11 CMIP5 GCMs and to compare them with those from CMIP3 integrations. The phase of mean seasonal cycle of jet latitude and wind speed from historical runs of CMIP5 GCMs are comparable to ERA40. The wind speed mean seasonal cycle by CMIP5 GCMs is overestimated in winter months. A positive (negative) jet latitude anomaly in historical simulations relative to ERA40 is observed in summer (winter). The ensemble mean of jet latitude biases in historical simulations of CMIP3 and CMIP5 with respect to ERA40 are -2.43^circ and -1.79^circ respectively. Thus indicating improvements in CMIP5 in comparison to the CMIP3 GCMs. The comparison of historical and future simulations of CMIP5 under RCP4.5 and RCP8.5 for the period 2076–2099, shows positive anomalies in the jet latitude implying a poleward shifted jet. The results from the analysed models offer no specific improvements in simulating the trimodality of the eddy-driven jet.

Simulation of extreme rainfall and projection of future changes using the GLIMCLIM model

In this study, the performance of the Generalized LInear Modelling of daily CLImate sequence (GLIMCLIM) statistical downscaling model was assessed to simulate extreme rainfall indices and annual maximum daily rainfall (AMDR) when downscaled daily rainfall from National Centers for Environmental Prediction (NCEP) reanalysis and Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCM) (four GCMs and two scenarios) output datasets and then their changes were estimated for the future period 2041–2060. The model was able to reproduce the monthly variations in the extreme rainfall indices reasonably well when forced by the NCEP reanalysis datasets. Frequency Adapted Quantile Mapping (FAQM) was used to remove bias in the simulated daily rainfall when forced by CMIP5 GCMs, which reduced the discrepancy between observed and simulated extreme rainfall indices. Although the observed AMDR were within the 2.5th and 97.5th percentiles of the simulated AMDR, the model consistently under-predicted the inter-annual variability of AMDR. A non-stationary model was developed using the generalized linear model for local, shape and scale to estimate the AMDR with an annual exceedance probability of 0.01. The study shows that in general, AMDR is likely to decrease in the future. The Onkaparinga catchment will also experience drier conditions due to an increase in consecutive dry days coinciding with decreases in heavy (>long term 90th percentile) rainfall days, empirical 90th quantile of rainfall and maximum 5-day consecutive total rainfall for the future period (2041–2060) compared to the base period (1961–2000).

Multi-model ensemble projections of future extreme temperature change using a statistical downscaling method in south eastern Australia

Projections of changes in temperature extremes are critical to assess the potential impacts of climate change on agricultural and ecological systems. Statistical downscaling can be used to efficiently downscale output from a large number of general circulation models (GCMs) to a fine temporal and spatial scale, providing the opportunity for future projections of extreme temperature events. This paper presents an analysis of extreme temperature data downscaled from 7 GCMs selected from the Coupled Model Intercomparison Project phase 5 (CMIP5) using a skill score based on spatial patterns of climatological means of daily maximum and minimum temperature. Data for scenarios RCP4.5 and RCP8.5 for the New South Wales (NSW) wheat belt, south eastern Australia, have been analysed. The results show that downscaled data from most of the GCMs reproduces the correct sign of recent trends in all the extreme temperature indices (except the index for cold days) for 1961–2000. An independence weighted mean method is used to calculate uncertainty estimates, which shows that multi-model ensemble projections produce a consistent trend compared to the observations in most extreme indices. Great warming occurs in the east and northeast of the NSW wheat belt by 2061–2100 and increases the risk of exposure to hot days around wheat flowering date, which might result in farmers needing to reconsider wheat varieties suited to maintain yield. This analysis provides a first overview of projected changes in climate extremes from an ensemble of 7 CMIP5 GCM models with statistical downscaling data in the NSW wheat belt.

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.

Projected precipitation and air temperature over Europe using a performance-based selection method of CMIP5 GCMs

An assessment of the plausible climate change in precipitation and surface air temperature (SAT) over the European region by the end of the 21st century is provided. The assessment is based on the results of output of the ocean–atmosphere models participating in the Coupled Model Intercomparison Project, phase 5 (CMIP5). Six climate models that best reproduce the historical behaviour of SAT over greater Europe were selected from the CMIP5 project using a performance-based selection method of CMIP5 general circulation models for further assessments. The analysis of historical simulations within the scope of the CMIP5 project reveals that six models (namely, CNRM-CM5, HadGEM2ES, GFDL-CM3, CanESM2, MIROC5 and MPI-ESM-LR) sufficiently reproduce historical tendencies and natural variability over the region of interest. The climate change in SAT and precipitation by the end of the 21st century (2070–2099) was examined within the scope of RCP4.5 and RCP8.5 scenarios for these selected models. Typical regional warming due to RCP4.5 (RCP8.5) scenario is assessed as 3–4.5 °C (as 4–8 °C) in summer and winter, while a significant reduction of precipitation (typically 20–40%) is obtained only in summer.

Parallel comparison of the northern winter stratospheric circulation in reanalysis and in CMIP5 models

A parallel comparison is made of the circulation climatology and the leading oscillation mode of the northern winter stratosphere among six reanalysis products and 24 CMIP5 (Coupled Model Intercomparison Project Phase 5) models. The results reveal that the NCEP/NCAR, NECP/DOE, ERA40, ERA-Interim and JRA25 reanalyses are quite consistent in describing the climatology and annual cycle of the stratospheric circulation. The 20CR reanalysis, however, exhibits a remarkable “cold pole” bias accompanied by a much stronger stratospheric polar jet, similar as in some CMIP5 models. Compared to the 1–2 month seasonal drift in most coupled general circulation models (GCMs), the seasonal cycle of the stratospheric zonal wind in most earth system models (ESMs) agrees very well with reanalysis. Similar to the climatology, the amplitude of Polar Vortex Oscillation (PVO) events also varies among CMIP5 models. The PVO amplitude in most GCMs is relatively weaker than in reanalysis, while that in most of the ESMs is more realistic. In relation to the “cold pole” bias and the weaker oscillation in some CMIP5 GCMs, the frequency of PVO events is significantly underestimated by CMIP5 GCMs; while in most ESMs, it is comparable to that in reanalysis. The PVO events in reanalysis (except in 20CR) mainly occur from mid-winter to early spring (January–March); but in some of the CMIP5 models, a 1–2 month delay exists, especially in most of the CMIP5 GCMs. The long-term trend of the PVO time series does not correspond to long-term changes in the frequency of PVO events in most of the CMIP5 models.

Evaluations of atmospheric downward longwave radiation from 44 coupled general circulation models of CMIP5

AbstractAtmospheric downward longwave radiation at the surface (Ld) quantifies the atmospheric greenhouse effect. This study evaluated Ld simulations from 44 general circulation models (GCMs) in the Coupled Model Intercomparison Project Phase 5 (CMIP5) with a comprehensive data set of Ld observations at 156 global‐distributed sites from 1992 to 2005. Compared with the Baseline Surface Radiation Network data that are of the highest quality among the available Ld data sets, CMIP5 GCM Ld has a negligible bias, much better than CMIP3 GCMs, likely because of the improvement of low cloud simulations in CMIP5 models. However, the selection of validation data has an important impact on the evaluation results. The global mean Ld inferred from different bias‐removing methods are nearly the same, approximately 341 W m−2 globally averaged from 1992 to 2005. CMIP5 GCMs showed that global Ld increased at a rate of 1.54 W m−2 per decade (p &lt; 0.01) from 1979 to 2005, which is consistent with available reanalyses. This good agreement in long‐term trends of Ld is likely because both reanalyses and CMIP5 models reproduced the observed warming and the associated increase of water vapor content in the lower atmosphere. However, CMIP5 GCMs are still poor in producing monthly anomalies of Ld.

Estimating the Greenland ice sheet surface mass balance contribution to future sea level rise using the regional atmospheric climate model MAR

Abstract. To estimate the sea level rise (SLR) originating from changes in surface mass balance (SMB) of the Greenland ice sheet (GrIS), we present 21st century climate projections obtained with the regional climate model MAR (Modèle Atmosphérique Régional), forced by output of three CMIP5 (Coupled Model Intercomparison Project Phase 5) general circulation models (GCMs). Our results indicate that in a warmer climate, mass gain from increased winter snowfall over the GrIS does not compensate mass loss through increased meltwater run-off in summer. Despite the large spread in the projected near-surface warming, all the MAR projections show similar non-linear increase of GrIS surface melt volume because no change is projected in the general atmospheric circulation over Greenland. By coarsely estimating the GrIS SMB changes from GCM output, we show that the uncertainty from the GCM-based forcing represents about half of the projected SMB changes. In 2100, the CMIP5 ensemble mean projects a GrIS SMB decrease equivalent to a mean SLR of &amp;amp;plus;4 &amp;amp;pm; 2 cm and &amp;amp;plus;9 &amp;amp;pm; 4 cm for the RCP (Representative Concentration Pathways) 4.5 and RCP 8.5 scenarios respectively. These estimates do not consider the positive melt–elevation feedback, although sensitivity experiments using perturbed ice sheet topographies consistent with the projected SMB changes demonstrate that this is a significant feedback, and highlight the importance of coupling regional climate models to an ice sheet model. Such a coupling will allow the assessment of future response of both surface processes and ice-dynamic changes to rising temperatures, as well as their mutual feedbacks.