Coupled Model Intercomparison Project Phase 5 Climate 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.

Estimation of future extreme rainfall in Barcelona (Spain) under monofractal hypothesis

AbstractClimate change effects on subdaily rainfall (from 5 min to a few hours) can hardly be measured in mid‐latitude climates due to the high natural variability of the precipitation patterns and their effects on local topography. The goal of this study was to obtain change projections of intensity–duration–frequency (IDF) curves, for up to 2‐h precipitation events, comparing two approaches that use the daily outputs of the downscaled Coupled Model Intercomparison Project Phase 5 (CMIP5) multi‐model projections: (a) direct scaling of the expected probable precipitation, from 2‐year to 500‐year return periods of daily rainfall and (b) a new semi‐stochastic approach, built by combining the physically forced outputs of climate models (on a daily scale) and stochastic simulation given by the probability distribution of a concentration index (n‐index) for individual rainfall events (on a subdaily scale). The approaches were applied to a set of 27 stations located around Barcelona, Spain, including a long reference series (with 5‐min rainfall records since 1927), representing the highly variable Mediterranean climate. The validation process showed a systematic error (bias) generally smaller than 10%, especially for rainfall extreme events with durations of less than 2 h. The concentration n‐index and IDF curves were projected by 10 downscaled CMIP5 climate models under 2 emission scenarios (RCP4.5 and RCP8.5), obtaining a consensual increase in both relative concentration and absolute intensities in Barcelona. Ensemble projection of rainfall concentration (n‐index) showed an increase up to 10% by 2071–2100 and about 20% (15%–30% range) for maximum intensities of 2‐year to 500‐year return periods. Results provide robustness in decision‐making regarding the design of stormwater management infrastructure at a local scale.

Assessment of the oceanic channel dynamics responsible for the IOD-ENSO precursory teleconnection in CMIP5 climate models

Existed studies have suggested a precursory relation between Indian Ocean Dipole (IOD) and El Niño and the Southern Oscillations (ENSO) with 1-year time lag. The underlying mechanisms were attributed to atmospheric bridge and/or oceanic channel processes. In this study, the oceanic channel dynamics in 23 climate models of the Coupled Model Intercomparison Project phase 5 (CMIP5) are assessed by correlation analyses in comparison with observations. The results show that the lag correlations between the IOD and ENSO anomalies associated with oceanic channel are significant, suggesting important role of oceanic channel dynamics in the cross-basin teleconnection in the analyzed CMIP5 models, consistent with observational analyses. In comparison, the correlations associated with atmospheric bridge are highly dispersive among the models and generally inconsistent with the observational analyses, suggesting model deficiencies. In a single climate model, the lag correlations associated with oceanic channel dynamics are consistent among different ensemble experiments, whereas those associated with atmospheric bridge processes are dispersive.

Attribution Analysis of Annual Precipitation Simulation Differences and Its Correction of CMIP5 Climate Models on the Chinese Mainland

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

Surface temperature variability in climate models with large and small internal climate variability

AbstractBy analyzing large ensemble simulations using the Community Earth System Model (CESM_LE), the Max Planck Institute Earth System Model Grand Ensemble (MPI_GE), and Coupled Model Intercomparison Project phase 5 (CMIP5) climate models, we quantified internal climate variability (ICV) of surface temperature in each model based on the spread of simulated global mean surface temperature from the ensemble mean. Then, we examined the characteristics of simulated surface temperature variability in climate models with large and small ICV in the present climate and in a future climate. Both the CESM_LE and MPI_GE members with large ICVs tended to simulate larger surface temperature variability at low latitudes, including El Niño and Southern Oscillation (ENSO) variability, and larger cooling and warming trends of the global mean surface temperatures than those with small ICVs in the present climate. Similar characteristics were observed in CMIP5 climate models with large and small ICVs in the present climate. This implies that surface temperature variability including extreme climate events should be cautiously examined in climate models with large and small ICVs. On the other hand, the characteristics of surface temperature variability simulated in the CMIP5 climate models with large or small ICVs were similar from the present climate to future climate with magnitude of ICVs. This was in contrast to that simulated in the CESM_LE and MPI_GE, in which the magnitude of ICV changes between the present climate and the future climate. We inferred that these differences between CMIP5 climate models and large ensemble simulations could primarily be attributed to intermodel differences in the CMIP5 climate models, including model physics and parameterizations.

Simulating adaptation strategies to offset potential impacts of climate variability and change on maize yields in Embu County, Kenya.

In this study, we assessed the possible impacts of climate variability and change on growth and performance of maize using multi-climate, multi-crop model approaches built on Agricultural Model Intercomparison and Improvement Project (AgMIP) protocols in five different agro-ecological zones (AEZs) of Embu County in Kenya and under different management systems. Adaptation strategies were developed that are locally relevant by identifying a set of technologies that help to offset potential impacts of climate change on maize yields. Impacts and adaptation options were evaluated using projections by 20 Coupled Model Intercomparison Project-Phase 5 (CMIP5) climate models under two representative concentration pathways (RCPs) 4.5 and 8.5. Two widely used crop simulation models, Agricultural Production Systems Simulator (APSIM) and Decision Support System for Agrotechnology Transfer (DSSAT) was used to simulate the potential impacts of climate change on maize. Results showed that 20 CMIP5 models are consistent in their projections of increased surface temperatures with different magnitude. Projections by HadGEM2-CC, HadGEM2-ES, and MIROC-ESM tend to be higher than the rest of 17 CMIP5 climate models under both emission scenarios. The projected increase in minimum temperature (Tmin) which ranged between 2.7 and 5.8°C is higher than the increase in maximum temperature (Tmax) that varied between 2.2 and 4.8°C by end century under RCP 8.5. Future projections in rainfall are less certain with high variability projections by GFDL-ESM2G, MIROC5, and NorESM1-M suggest 8 to 25% decline in rainfall, while CanESM2, IPSL-CM5A-MR and BNU-ESM suggested more than 85% increase in rainfall under RCP 8.5 by end of 21st century. Impacts of current and future climatic conditions on maize yields varied depending on the AEZs, soil type, crop management and climate change scenario. Impacts are largely negative in the low potential AEZs such as Lower Midlands (LM4 and LM5) compared with the high potential AEZs Upper Midlands (UM2 and UM3). However, impacts of climate change are largely positive across all AEZs and management conditions when CO2 fertilization is included. Using the differential impacts of climate change, a strategy to adapt maize cultivation to climate change in all the five AEZs was identified by consolidating those practices that contributed to increased yields under climate change. We consider this approach as more appropriate to identify operational adaptation strategies using readily available technologies that contribute positively under both current and future climatic conditions. This approach when adopted in strategic manner will also contribute to further strengthen the development of adaptation strategies at national and local levels. The methods and tools validated and applied in this assessment allowed estimating possible impacts of climate change and adaptation strategies which can provide valuable insights and guidance for adaptation planning.

Understanding Intermodel Diversity When Simulating the Time of Emergence in CMIP5 Climate Models

AbstractUsing a statistical method, we examined the time of emergence (ToE), which was defined as the year when the global mean surface temperature (GMST) trend exceeds the trend explained by internal variability, using 33 Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models for 1861–2100. We found that the GMST trend explained by internal variability showed minimal changes even though the GMST trend rapidly increased under the Representative Climate Pathway 8.5 scenario. However, there was large intermodel diversity for the ToEs in CMIP5 climate models. The intensity of Arctic amplification was closely associated with intermodel diversity of the ToEs. A significant difference in the amount of Arctic sea ice extent during the reference period (1861‐1910) played a role in determining whether there was slow or fast simulation of the ToE in CMIP5 climate models. We argue that a slow or fast ToE is influenced by how the climate models initially simulate the sea ice extent during the reference period.

Projections of Extreme Ocean Waves in the Arctic and Potential Implications for Coastal Inundation and Erosion

AbstractThe Arctic Ocean wave climate is undergoing a dramatic change due to the sea ice retreat. This study presents simulations of the Arctic regional wave climate corresponding to the surface winds and sea ice concentrations as simulated by five CMIP5 (Coupled Model Intercomparison Project Phase 5) climate models for the historical (1975–2005) and RCP8.5 scenario future (2081–2100) periods. The annual maximum significant wave height is projected to increase up to 6 m offshore and up to two to three times greater than the corresponding 1979–2005 value along some coastlines, as waves become more exposed to the fall storms there. The connection between the Atlantic Ocean and the Arctic wave climates is projected to strengthen due to increase of swell influence. Changes in the wave direction also seem to indicate a weakening of the Beaufort High illustrated by a counterclockwise rotation of the mean wave direction for extreme conditions in the Western Arctic. The projected changes in wave conditions lead to a general increase of the wave‐driven erosion and inundation potential along the Arctic coastlines. Potentially, hazardous extreme wave events are projected to become significantly more frequent and more intense. For example, in the Beaufort coastlines a once‐in‐20‐year event under the historical (1979–2005) climate is projected to occur, on average, once every 2–5 years during 2081–2100. This is a pressing issue, as it affects many Arctic coastal communities, as well as existing and emerging Arctic infrastructure and activities, with some of them having already suffered severe wave‐induced damage in the past years.

The linkage between CMIP5 climate models’ abilities to simulate precipitation and vector winds

Precipitation is one of the major challenges in climate modeling. Among various factors, the large-scale atmospheric circulation plays an important role in modulating regional precipitation through dynamic processes that has been widely discussed in previous studies. However, few efforts have been made to investigate the relationship of model abilities to simulate precipitation and vector winds. Such an investigation may help to understand the source of uncertainty of precipitation simulation. Here, we examined the relationship between model performances in simulating precipitation with that in simulating vector winds by using the Coupled Model Intercomparison Project Phase 5 (CMIP5) models. Our results suggest that the model biases/uncertainties in simulating climatological mean precipitation often accompanied by the biases/uncertainties in vector wind fields. Model ability to simulate precipitation is closely related to the ability to simulate vector winds, especially over monsoon regions and the regions with warm and moist advection or high terrains, such as the South Asian and East Asian summer monsoon region, the Alaskan region, the Rocky Mountain, etc. Over these regions, the models with higher horizontal resolution tends to generate improved simulations in both the vector winds and precipitation relative to the models with coarse horizontal resolution. Besides, the model’s ability to simulate vector winds, compared to simulate the zonal wind, meridional wind, and skin temperature, is more closely related to the ability to simulate precipitation. This indicates that it is more meaningful to evaluate the vector winds than the zonal or meridional wind from the perspective of improving regional precipitation simulation.

Climate variability from annual to multi-decadal timescales in a two-layer stochastic energy balance model: analytic solutions and implications for general circulation models

A low-order stochastically forced two-layer global energy balance model (EBM) admitting an analytical solution is developed for studying natural inter-annual, decadal and multi-decadal climate variability, and ultimately to better understand forced climate change. The EBM comprises upper and lower oceanic layers with a diffusive coupling, a radiative damping term including feedbacks and stochastic atmospheric forcing. The EBM is used to analyse the influence of radiative forcing, feedbacks and climate system inertia on the global mean surface temperature variance (climate variability) and to understand why Coupled Model Intercomparison Project, Phase 5 (CMIP5) models exhibit such a wide range in the level of variability in globally averaged surface air temperature. We examine the influence of the model parameters on the climate variability on different timescales. To this end, we derive the Fokker–Planck equation for the EBM and then obtain the analytical expression that quantifies the sensitivity coefficients for all model parameters. For all timescales, the most influential factors are as follows: (1) the magnitude of the stochastic forcing, (2) the feedback mechanisms, (3) the upper layer depth, (4) the diffusion parameter and (5) the lower ocean depth. Results from the EBM imply that the range of stochastic forcing in the CMIP5 climate models is around twice as important as the strength of radiative feedback or upper layer depth in causing the model-to-model spread in the magnitude of globally averaged climate model variability.

Projections of future changes in solar radiation in China based on CMIP5 climate models

Surface solar radiation research is important for understanding future climate change and the application of large-scale photovoltaic systems. We used the coupled model intercomparison project phase 5 (CMIP5) under the RCP8.5 scenario to project potential changes in surface solar radiation, surface temperatures, and cloud fractions between 2006 and 2049 in China, as well as how these changes may affect photovoltaic power generation. The results show that the following. (1) For surface temperatures, the median trends of all considered models show warming in China of 0.05 K/year. The maximum positive trends for all-sky radiation appear in the southeast of China, reaching 0.4 W/m2/year. Cloud cover exhibits a mainly decreasing trend in the overall region of China. (2) The all-sky radiation of most selected regions shows a decreasing trend. The maximum negative value (−0.08 W/m2/year) appears in Qinghai. (3) Compared with the average photovoltaic power output from 2006 to 2015, the photovoltaic power output in western China will decrease by −0.04 %/ year, while photovoltaic power output in southeastern China will increase by 0.06–0.1%/year.

Assessment of Precipitation Simulations in Central Asia by CMIP5 Climate Models

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

Climate Change Impact Assessment on Blue and Green Water by Coupling of Representative CMIP5 Climate Models with Physical Based Hydrological Model

Climatic changes have altered hydrological and climatic parameters worldwide, and climate projections suggest that such alterations will continue. In order to maintain the sustainable development and acquire the knowledge of water availability, climatic projection must be coupled with hydrological models. In this study, Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models output were integrated with a calibrated hydrological model, Soil and Water Assessment Tools (SWAT) to evaluate the potential effect of climate change on green and blue water over Upper Narmada river Basin (UNB). Therefore, top three representative climate models (MIROC5, CNRM-CM5 and MPI-ESM-LR) from 24 CMIP5 climate models were selected for hydrological modelling. Selected representative climate model outputs were bias corrected by distribution mapping to remove systematic bias correction. Multi-site model calibration approaches indicated Nash Sutcliffe Efficiency (NSE) and Coefficient of Determination (R2) as 0.77 and 0.76 for calibration (1978–1995), and 0.73 and 0.70 for validation (1996–2005), respectively. Calibrated model was run for baseline period (1970–2000) and three futuristic period P1 (2011–2040), P2 (2041–2070) and P3 (2071–2100) under Representative Concentration Pathways (RCPs) 4.5 and 8.5 scenarios. Results indicated annual precipitation decreasing under RCP4.5 and RCP8.5 scenarios changes in green and blue water varying from 16.22 to −14.10% (CNRM,P3) under RCP4.5 and from 38.25 to −22.57% under RCP8.5 with reference to baseline scenario. This study established the sensitivity of UNB to future climatic changes employing projections from CMIP5 climate models and exhibited an approach that applied multiple climate model outputs to estimate potential change over the river basin.

Long‐Term Changes in Global Socioeconomic Benefits of Flood Defenses and Residual Risk Based on CMIP5 Climate Models

A warmer climate is expected to accelerate the global hydrological cycle, causing more intense precipitation and floods. Despite recent progress in global flood risk assessment, the socioeconomic benefits of flood defenses (i.e., reduction in population/economic exposure) and the residual risk (i.e., residual population/economic exposure) are poorly understood globally and regionally. To address these knowledge gaps, we use the runoff data from a baseline and 11 Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models to drive the Catchment‐based Macro‐scale Floodplain model incorporating the latest satellite river width information. From the simulated annual maxima, we use a Gumbel distribution to estimate the river water depth‐flood return period relationship. We independently evaluate flood impacts on population and economy (i.e., gross domestic product) for a range of flood return periods. We estimate the socioeconomic benefits and the corresponding residual risk for the globe and 26 subcontinental regions. The global population (gross domestic product) exposed to flooding is ∼8% (∼7%) per year lower when implementing existing flood protection infrastructure extracted from the FLOod PROtection Standards database. If the current flood defenses were to be unchanged in the future (Representative Concentration Pathway 4.5 [RCP4.5] and RCP8.5, i.e., ∼2 to ∼4.3°C above the preindustrial levels), the globe and most of the regions (particularly where developing countries are concentrated) would experience an increase in residual risk. This increase is especially obvious when the gap of climate forcing between RCP8.5 and RCP4.5 widens by the end of the 21st century. We finally evaluate the impact of changed flood defense levels on the socioeconomic benefits and the corresponding residual risk.

CMIP5-based global wave climate projections including the entire Arctic Ocean

This study presents simulations of the global ocean wave climate corresponding to the surface winds and sea ice concentrations as simulated by five CMIP5 (Coupled Model Intercomparison Project Phase 5) climate models for the historical (1979–2005) and RCP8.5 scenario future (2081–2100) periods. To tackle the numerical complexities associated with the inclusion of the North Pole, the WAVEWATCH III (WW3) wave model was used with a customized unstructured Spherical Multi-Cell grid of ∼100 km offshore and ∼50 km along coastlines. The climate model simulated wind and sea ice data, and the corresponding WW3 simulated wave data, were evaluated against reanalysis and hindcast data. The results show that all the five sets of wave simulations projected lower waves in the North Atlantic, corresponding to decreased surface wind speeds there in the warmer climate. The selected CMIP5 models also consistently projected an increase in the surface wind speed in the Southern Hemisphere (SH) mid-high latitudes, which translates in an increase in the WW3 simulated significant wave height (Hs) there. The higher waves are accompanied with increased peak wave period and increased wave age in the East Pacific and Indian Oceans, and a significant counterclockwise rotation in the mean wave direction in the Southern Oceans. The latter is caused by more intense waves from the SH traveling equatorward and developing into swells. Future wave climate in the Arctic Ocean in summer is projected to be predominantly of mixed sea states, with the climatological mean of September maximum Hs ranging mostly 3–4 m. The new waves approaching Arctic coasts will be less fetch-limited as ice retreats since a predominantly southwards mean wave direction is projected in the surrounding seas.

Assessment of CMIP5 climate models over South Asia and climate change projections over Pakistan under representative concentration pathways

Future climate change projections from global climate models (GCMs) are the primary drivers of regional downscaling and impacts research. Climate projections are also in increased demand from disaster management agencies, policymakers, and other stakeholders. A set of 36 global climate models from the Coupled Model Inter-Comparison Project Phase 5 (CMIP5) is assessed for their performance over South Asia region for making future climate projection with a focus on Pakistan and its sub-regions. A suite of statistics was calculated to assess the credibility of GCMs with the observed statistics. The results show that GCMs have the ability to approximately capture the spatial patterns of temperature; however, the accuracy of precipitation simulation is relatively low. The future projection generated by the GCMs shows that temperature in Pakistan will increase in the 21st century by around 6.7°C under the RCP 8.5 scenarios and with increasing latitude, this warming is getting accelerated.

Assessment of CMIP5 climate models over South Asia and climate change projections over Pakistan under representative concentration pathways

Future climate change projections from global climate models (GCMs) are the primary drivers of regional downscaling and impacts research. Climate projections are also in increased demand from disaster management agencies, policymakers, and other stakeholders. A set of 36 global climate models from the Coupled Model Inter-Comparison Project Phase 5 (CMIP5) is assessed for their performance over South Asia region for making future climate projection with a focus on Pakistan and its sub-regions. A suite of statistics was calculated to assess the credibility of GCMs with the observed statistics. The results show that GCMs have the ability to approximately capture the spatial patterns of temperature; however, the accuracy of precipitation simulation is relatively low. The future projection generated by the GCMs shows that temperature in Pakistan will increase in the 21st century by around 6.7°C under the RCP 8.5 scenarios and with increasing latitude, this warming is getting accelerated.

A comparison of the structure and dynamics of Global atmospheric oscillation in reality and in the CMIP5 climate models

In this paper, global fields of mean anomalies of the sea-level atmospheric pressure and the surface air temperature are constructed for positive and negative phases of a recently discovered by Russian scientists Global atmospheric oscillation (GAO) with El Niño and La Niña as its elements. This is done using observations and their re-analyses, as well as the results of experiments with climate models. A GAO index is proposed, and its spectra, as well as spectra of El Niño – Southern Oscillation (ENSO), are calculated. It is shown that some of the modern models of ocean-atmosphere general circulation of the international Coupled Model Intercomparison Project Phase 5 (CMIP5) reproduce well the GAO spatial structure. The model temporal energy spectra, however, differ from the real ones in the general variation of the energy spectra of interannual-decadal oscillations, as well as in the periods of specific peaks in this range. A comparison of CMIP5 experiments called Historical and piControl shows that the climate models with the 11-year solar activity cycle forcing reproduce the GAO periodicity more accurately. It is concluded that the differences between the model spectra and the real ones cause major errors in the predictions of the El Niño onset for more than half a year.

Variability and teleconnections of South and East Asian summer monsoons in present and future projections of CMIP5 climate models

Coupled Model Inter-comparison Project Phase 5 (CMIP5) model outputs of the South and East Asian summer monsoon variability and their tele-connections are investigated using historical simulations (1861-2005) and future projections under the RCP4.5 scenario (2006-2100). Detailed analyses are performed using nine models having better representation of the recent monsoon teleconnections for the interactive Asian monsoon sub-systems. However, these models underestimate rainfall mainly over South Asia and Korea-Japan sector, the regions of heavy rainfall, along with a bias in location of rainfall maxima. Indeed, the simulation biases, underestimations of monsoon variability and teleconnections suggest further improvements for better representation of Asian monsoon in the climate models. Interestingly, the performance of Australian Community Climate and Earth System Simulator version 1.0 (ACCESS1.0) in simulating the annual cycle, spatial pattern of rainfall and multi-decadal variations of summer monsoon rainfall over South and East Asia appears to more realistic. In spite of large spread among the CMIP5 models, historical simulations as well as future projections of summer monsoon rainfall indicate multi-decadal variability. These rainfall variations, displaying certain epochs of more rainfall over South Asia than over East Asia and vice versa, suggest an oscillatory behaviour. Teleconnections between South and East Asian monsoon rainfall also exhibit a multi-decadal variation with alternate epochs of strengthening and weakening relationship. Furthermore, large-scale circulation features such as South Asian monsoon trough and north Pacific subtropical high depict zonal oscillatory behaviour with east-west-east shifts. Periods with eastward or westward extension of the Mascarene High, intensification and expansion of the upper tropospheric South Asian High are also projected by the CMIP5 models.

MJO simulation in CMIP5 climate models: MJO skill metrics and process-oriented diagnosis

The Madden-Julian Oscillation (MJO) simulation diagnostics developed by MJO Working Group and the process-oriented MJO simulation diagnostics developed by MJO Task Force are applied to 37 Coupled Model Intercomparison Project phase 5 (CMIP5) models in order to assess model skill in representing amplitude, period, and coherent eastward propagation of the MJO, and to establish a link between MJO simulation skill and parameterized physical processes. Process-oriented diagnostics include the Relative Humidity Composite based on Precipitation (RHCP), Normalized Gross Moist Stability (NGMS), and the Greenhouse Enhancement Factor (GEF). Numerous scalar metrics are developed to quantify the results. Most CMIP5 models underestimate MJO amplitude, especially when outgoing longwave radiation (OLR) is used in the evaluation, and exhibit too fast phase speed while lacking coherence between eastward propagation of precipitation/convection and the wind field. The RHCP-metric, indicative of the sensitivity of simulated convection to low-level environmental moisture, and the NGMS-metric, indicative of the efficiency of a convective atmosphere for exporting moist static energy out of the column, show robust correlations with a large number of MJO skill metrics. The GEF-metric, indicative of the strength of the column-integrated longwave radiative heating due to cloud-radiation interaction, is also correlated with the MJO skill metrics, but shows relatively lower correlations compared to the RHCP- and NGMS-metrics. Our results suggest that modifications to processes associated with moisture-convection coupling and the gross moist stability might be the most fruitful for improving simulations of the MJO. Though the GEF-metric exhibits lower correlations with the MJO skill metrics, the longwave radiation feedback is highly relevant for simulating the weak precipitation anomaly regime that may be important for the establishment of shallow convection and the transition to deep convection.