Assessment of GCMs simulation performance for precipitation and temperature from CMIP5 to CMIP6 over the Tibetan Plateau

Precipitation variability has great economic, social, and environmental impacts across the globe, and in particular in China. This paper evaluates the historical precipitation variability based on 20 general circulation models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) archive over the 20th century relative to two observational data sets and quantifies CMIP5 improvements over CMIP3. Multimodel ensemble means and individual models are assessed. Three future emission scenarios are used (representative concentration pathways (RCP) 8.5, RCP 4.5, and RCP 2.6), and 21st century CMIP5 estimates are put into context based on the 20th century biases. We find that CMIP5 models can reproduce the spatial pattern of precipitation over China during the 20th century, which represents an improvement over CMIP3. However, the models overestimate the magnitude of seasonal and annual precipitation in most regions of China, especially along the eastern edge of the Tibetan Plateau, and underestimate summer precipitation over southeastern China. For China as a whole, CMIP5's overestimation of annual precipitation is greater than CMIP3, which can be traced back to a greater underestimation of summer precipitation in CMIP3. There is a large spread among individual models, with the greatest uncertainties in simulating summer precipitation. Trends and correlations also suggest a better agreement of CMIP5 with observations than CMIP3. Throughout the 20th century, both the observations and models show an increasing trend in precipitation over parts of northwestern China and a decreasing trend over the Tibetan Plateau. There is poor agreement in precipitation trends over the southeast and northeast regions. In general, multimodel means cannot capture the amplitude of observed multidecadal precipitation variability. In the 21st century, precipitation is generally projected to increase across all of China under all three scenarios. RCP 8.5 exhibits the largest significant trend at a rate of +1.5 mm/yr, corresponding to 16% precipitation increase by the end of the century. The RCP 2.6 scenario shows the smallest increases, at +0.5 mm/yr (6%) by 2100. The greatest increases are projected to occur over the Tibetan Plateau and eastern China in summer, suggesting an altered monsoonal circulation in the future. However, due to the uncertainties in CMIP5, future precipitation projections should be interpreted with caution.

In the context of global warming and the release of projection results from the Coupled Model Intercomparison Project Phase 6 (CMIP6), it is important to conduct research on climate change on the Tibetan Plateau (TP) which exhibits unique geographic characteristics and complex climatic conditions. Using 10 general circulation models (GCMs) from CMIP6 corrected for bias by the daily translation (DT) method, we investigated the projected changes in precipitation and air temperature for the mid‐term (2031–2050) and long‐term (2061–2080) of the 21st century on the TP after evaluating the reliability of the bias correction, for four scenarios based on different combinations of shared socioeconomic pathways and representative concentration pathways. The multimodel ensembles (MMEs) indicated that the projected annual precipitation shows an increasing trend for the mid‐term and long‐term periods, with the projected increase for the long‐term primarily concentrated in areas with less precipitation. Meanwhile, the future precipitation changes exhibit a significant elevation dependency. The rate of increase of precipitation firstly fluctuates and then gradually increases along with the elevation increasing from 1,500 to 5,800 m. The valley value of rate mainly appears in two elevation zones (2,200–2,600 m and 3,400–3,800 m). The projected temporal changes in annual air temperatures reveal obvious warming over the TP. The projected warming is overall more pronounced with increase in elevation; however, it reaches a peak at approximately 5,000–5,200 m and then slows down slightly, indicating that warming is not dependent on elevation in the higher elevation zones. The present results have important implications for investigating the impacts of climate change on the water cycle in alpine regions.

This paper evaluates Indo-Pacific warm pool (IPWP) sea surface temperature (SST) warming biases of Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6. The IPWP warming trend in the CMIP5 multi-model ensemble (MME) is closer to observation than in CMIP6 MME, but the IPWP expanding trend is the opposite. There is no qualitative improvement in the simulation of IPWP warming from CMIP5 to CMIP6. In addition, four metrics were used to investigate the performance of Indo-Pacific region warming trends in all models. CMIP6 models perform better than CMIP5 with smaller root mean square error and bias in MME and higher skill scores in MME and top models, which is tightly linked to their better performance in simulating associated physical processes in CMIP6 models. IPWP warming biases are mainly attributed to the combined effects of positive atmospheric process biases and negative ocean dynamics term biases. The positive atmospheric process biases are primarily related to the shortwave radiation and latent heat flux from atmospheric forcing, the latter of which can be attributed to the biases in surface wind fields. Compared with CMIP5 models, the IPWP warming simulated by CMIP6 models is weaker, related to the less robust atmospheric processes and the shallower thermocline anomalies simulated by 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.

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.

As the earth's third pole, Qinghai-Tibet Plateau belongs to one of the most sensitive regions to climate change in the world. Based on the observed and the simulated daily precipitation from the Coupled Model Intercomparison Project Phase 5 (CMIP5), we evaluated the simulation performance of daily precipitation from selected CMIP5 models from 1975 to 2005 over the Qinghai-Tibet Plateau. We found that daily precipitation exhibited obvious long-range correlation characteristics using the detrended fluctuation analysis method. The scaling exponents of daily precipitation in summer and autumn are significantly larger than those in spring and winter. MIROC4H with the best performance can reproduce long-range correlation characteristic of daily precipitation series probably because of the higher resolution, which can capture small scale cloud convections. Besides there are seasonal differences in the simulation results among different regions of the Qinghai-Tibet Plateau, simulation effects of all climate models in summer and winter are better than those in spring and autumn. The performance of MIROC4H model works the best in spring. Overall, the scaling exponents of daily precipitation from BCC-CSM1-1-M, CMCC-CM and MIROC4H are close to the observations. CCSM4 and MIROC4H climate models could reproduce the internal dynamics characteristic of daily precipitation in autumn. But for winter, all climate models have exaggerated the scaling value in southeastern Qinghai-Tibet Plateau compared with the observed values.

Выполнена оценка возможных изменений среднегодовой приповерхностной температуры воздуха (ПТВ) в Дальневосточном регионе, включающем территорию и окраинные моря России, а также северо-западную часть Тихого океана, до 2099 г., для чего используются осредненные по ансамблю данные 33 моделей проекта CMIP6 (Coupled Model Intercomparison Project Phase 6), полученные в рамках четырех сценариев, отвечающих разным уровням антропогенного радиационного форсинга (слабого, умеренного и значительного). Анализируются различия между осредненными за 30-летние периоды аномалиями ПТВ. Для верификации модельных результатов проанализировано потепление, произошедшее в регионе с 1940–1969 до 1994–2023 гг., для чего использованы данные реанализа ERA5 и эксперимента Historical CMIP6. По обоим видам данных средняя ПТВ в регионе выросла на 1.1 °С: с 1940–1969 к 1994–2023 гг.; это сходство обосновывает оценки будущих изменений ПТВ по моделям CMIP6. Все сценарии SSP (Shared Socio-economic Pathways) будущего радиационного форсинга показывают приблизительно одинаковое повышение ПТВ с 1994–2023 по 2024–2053 гг., оно составляет в среднем по региону 1.2–1.5 °С. К 2070–2099 гг. средняя ПТВ в рассматриваемом регионе возрастет соответственно темпу эмиссии парниковых газов – на 1.7, 2.7, 3.8 и 4.8 °С. Как показывают данные реанализа ERA5, от 1940–1969 к 1994–2023 гг. увеличение ПТВ над морскими акваториями региона происходило весьма неравномерно: наибольшие темпы наблюдались в северной части Охотского моря (до 2 °С и более) и в прибрежных районах северо-западной части Берингова моря (до 1.0–1.2 °С). Увеличение ПТВ ослабевало в направлении с северо-запада на юго-восток, т.е. с удалением от суши, и составило 0.2–0.6 °С в северо-западной части Тихого океана. Картина потепления над морскими акваториями по данным CMIP6 выражена сильнее, чем по данным реанализа ERA5, но при этом качественно им соответствует. An assessment of possible changes in the annual mean surface air temperature (SAT) in the Far East Region (35°–65° N, 130°–180° E) is made from the present to 2099, using ensemble-averaged data from 33 CMIP6 (Coupled Model Intercomparison Project Phase 6) models obtained within the framework of four scenarios corresponding to the weak, moderate, or significant anthropogenic radiative forcing resulting from СО2 emissions. To elucidate long-term climate change, SAT averaged for 30-year periods, namely, 1994–2023, 2024–2053 and 2070–2099 are analyzed. To verify the model results, the warming that occurred in the region from the mid-20th century (1940–1969) to the early 21st century (1994–2023) is analyzed, using ERA5 data with the fine spatial resolution of 0.25°, and CMIP6 data with the coarser resolution, mostly 1.0°–2.0°. According to both data types, the regional SAT increased, on average by 1.1 °C from 1940–1969 to 1994–2023, justifying the use of forecast estimates based on the CMIP6 models in this work. All scenarios of possible radiative forcing show the similar SAT increase from the 1994–2023 to 2024–2053, on average 1.2–1.5 °C. On the contrary, by the 2070–2099, the regional SAT will increase in accordance with the emission rates on average by 1.7, 2.7, 3.8 and 4.8 °C, respectively. As for the Russian Far East land area, ERA5 and CMIP6 show similar spatial warming patterns, with the warming, on average, of 1.2 °C from 1940–1969 to 1994–2023, i.e. higher than that for the entire considered region including marine areas. From 1940–1969 to 1994–2023 negative annual mean SAT changed to positive one in some areas of the Primorsky, Khabarovsky and Kamchatksky provinces, implying the permafrost melting. According to the CMIP6 models, the land warming of 2.0–2.1 °C, 3.0–3.5 °C, 4.7–5.3 °C, and 6.1–6.6 °C is expected by the end of the 21st century for the scenarios with the different levels of radiative forcing. As shown by the ERA5 data, the SAT increase from 1940–1969 to 1994–2023 was very uneven for the marine areas: the highest rates were observed in the northern Okhotsk Sea (up to 2 °C and more) and in the coastal northwestern Bering Sea (up to 1.0–1.2 °C), which can be explained by the ice cover decrease. The SAT increase weakened in the direction from the northwest to southeast, i.e. with the distance from the land, and amounted to only 0.2–0.6 °C in the northwestern Pacific, which can be attributed to the effect of Pacific Decadal Oscillation (PDO). The coastal Okhotsk Sea off the Sakhalin Island is the only area where SAT decreased by 0.2–0.6 °C from 1940–1969 to 1994–2023, which probably can be attributed to the changes in the East Sakhalin Current transporting Amur River water southward along the coast but this suggestion should be verified. The warming pattern over the marine areas according to CMIP6 data qualitatively corresponds to that one based on ERA5 data, keeping in mind the lower resolution of the modeled data. The warming in the Northwest Pacific from the modeled data exceeds that one from ERA5, which can be explained by elimination of the PDO effects when averaging CMIP6 multi-model data.

The surface air temperature (SAT) trend on the Tibetan Plateau (TP) was 3.45°C 100 years−1 from 1961 to 2014. The multi‐model ensemble (MME) of 33 coupled models participated in the Coupled Model Intercomparison Project phase six (CMIP6) was about 1°C 100 years−1 lower than the observation. Although MME generally shows better skill in reproducing the distribution of SAT trend over TP than most of the CMIP6 models, its performance is greatly degraded by a small group of models, about 12% on average, with large biases. In this paper, the constrained multi‐model ensemble (CMME) based on a certain observation‐based threshold is used to constrain future projections of the SAT trend over TP. Compared with the MME results, the improvements in CMME are mainly over the eastern plateau in historical simulation and are relative to the reduction of the model biases to carbon dioxide (CO2) forcing. Under the high‐emission SSP5‐8.5 scenario, SAT increases significantly over the entire TP. The constraint of CMME on the MME is mainly over the eastern plateau with a difference of 0.5°C 100 years−1, about 6% of the MME results. Under the intermediate‐emission scenario SSP2‐4.5, the effect of CMME is relatively smaller, but the corresponding spatial distribution is similar to that under the SSP5‐8.5 scenario. The CMIP6 models tend to underestimate the warming trend projections over the water source regions in the northeastern plateau and should be noticed.

The changes in the winter climatology and variability of the East Asian winter monsoon (EAWM) for the late 21st century (2070–2099) under the Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios are projected in terms of EAWM indices (EAWMIs). Firstly, the capability of the climate models participating in the Coupled Model Intercomparison Project phase 5 (CMIP5) in simulating the boreal winter climatology and the interannual variability of the EAWM for the late 20th century (1971–2000) is examined. Nine of twenty-three climate models are selected based on the pattern correlations with observation and a multi-model ensemble is applied to the nine model data. Three of twelve EAWMIs that show the most significant temporal correlations between the observation and CMIP5 surface air temperatures are utilized. The ensemble CMIP5 is capable of reproducing the overall features of the EAWM in spite of some biases in the region. The negative correlations between the EAWMIs and boreal winter temperature are well reproduced and 3–5 years of the major interannual variation observed in this region are also well simulated according to power spectral analyses of the simulated indices. The fields regressed onto the indices that resemble the composite strong winter monsoon pattern are simulated more or less weakly in CMIP5 compared to the observation. However, the regressed fields of sea level pressure, surface air temperature, 500-hPa geopotential height, and 300-hPa zonal wind are well established with pattern correlations above 0.83 between CMIP5 and observation data. The differences between RCPs and Historical indicate strong warming, which increases with latitude, ranging from 1 to 5 °C under RCP4.5 and from 3 to 7 °C under RCP8.5 in the East Asian region. The anomalous southerly winds generally become stronger, implying weaker EAWMs in both scenarios. These features are also identified with fields regressed onto the indices in RCPs. The future projections reveal that the interannual variability of the indices will be maintained with an intensity similar to that of the present. The correlation between monsoon indices and Arctic Oscillation increases over time. On the other hand, the correlation between monsoon indices and North Atlantic Oscillation decreases.

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.

<p>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 introduce new information into a multi-model ensemble by representing region-scale internal variability, but also introduce redundant information, by virtue of a single model being represented by 50–100 outcomes. To preserve the contribution of internal variability and ensure redundancy does not overwhelm uncertainty estimates, a weighting approach is used to incorporate 50-members of the Community Earth System Model (CESM1.2.2), 50-members of the Canadian Earth System Model (CanESM2), and 100-members of the MPI Grand Ensemble (MPI-GE) into an 88-member Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model ensemble. The weight assigned to each multi-model ensemble member is based on the member's ability to reproduce observed climate (performance) and scaled by a measure of historical redundancy (dependence). Surface air temperature (SAT) and sea level pressure (SLP) diagnostics are used to determine the weights, and relationships between present and future diagnostic behavior are discussed. A new diagnostic, estimated forced trend, is proposed to replace a diagnostic with no clear emergent relationship, 50-year regional SAT trend.</p><p>The influence of the weighting is assessed in estimates of Northern European winter and Mediterranean summer end-of-century warming in the CMIP5 and combined SMILE-CMIP5 multi-model ensembles. The weighting is shown to recover uncertainty obscured by SMILE redundancy, notably in Mediterranean summer. For each SMILE, the independence weight of each ensemble member as a function of the number of SMILE members included in the CMIP5 ensemble is assessed. The independence weight increases linearly with added members with a slope that depends on SMILE, region, and season. Finally, it is shown that the weighting method can be used to guide SMILE member selection if a subsetted ensemble with one member per model is sought. The weight a SMILE receives within a subsetted ensemble depends on which member is used to represent it, reinforcing the advantage of weighting and incorporating all initial condition ensemble members in multi-model ensembles.</p>

In this paper the model outputs from the Coupled Model Intercomparison Project (CMIP) phase 5 (CMIP5) are used to examine the climatology and interannual variability of the East Asian winter monsoon (EAWM). The multimodel ensemble (MME) is able to reproduce reasonably well the circulation features of the EAWM. The simulated surface air temperature still suffers from a cold bias over East Asia, but this bias is reduced compared with CMIP phase 3 models. The intermodel spread is relatively small for the large-scale circulations, but is large for the lower-tropospheric meridional wind and precipitation along the East Asian coast. The interannual variability of the EAWM-related circulations can be captured by most of the models. A general bias is that the simulated variability is slightly weaker than in the observations. Based on a selected dynamic EAWM index, the patterns of the EAWM-related anomalies are well reproduced in MME although the simulated anomalies are slightly weaker than the observations. One general bias is that the northeasterly anomalies over East Asia cannot be captured to the south of 30°N. This bias may arise both from the inadequacies of the EAWM index and from the ability of models to capture the EAWM-related tropical–extratropical interactions. The ENSO–EAWM relationship is then evaluated and about half of the models can successfully capture the observed ENSO–EAWM relationship, including the significant negative correlation between Niño-3.4 and EAWM indices and the anomalous anticyclone (or cyclone) over the northwestern Pacific. The success of these models is attributed to the reasonable simulation of both ENSO’s spatial structure and its strength of interannual variability.

The frequency and intensity of extreme thermal stress conditions during summer are expected to increase due to climate change. This study examines sixteen models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) that have been bias-adjusted using the quantile delta mapping method. These models provide Universal Thermal Climate Index (UTCI) for summer seasons between 1979 and 2010, which are regridded to a similar spatial grid as ERA5-HEAT (available at 0.25° × 0.25° spatial resolution) using bilinear interpolation. The evaluation compares the summertime climatology and trends of the CMIP6 multi-model ensemble (MME) mean UTCI with ERA5 data, focusing on a regional hotspot in northwest India (NWI). The Pattern Correlation Coefficient (between CMIP6 models and ERA5) values exceeding 0.9 were employed to derive the MME mean of UTCI, which was subsequently used to analyze the climatology and trends of UTCI in the CMIP6 models.The spatial climatological mean of CMIP6 MME UTCI demonstrates significant thermal stress over the NWI region, similar to ERA5. Both ERA5 and CMIP6 MME UTCI show a rising trend in thermal stress conditions over NWI. The temporal variation analysis reveals that NWI experiences higher thermal stress during the summer compared to the rest of India. The number of thermal stress days is also increasing in NWI and major Indian cities according to ERA5 and CMIP6 MME. Future climate projections under different scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) indicate an increasing trend in thermal discomfort conditions throughout the twenty-first century. The projected rates of increase are approximately 0.09 °C per decade, 0.26 °C per decade, and 0.56 °C per decade, respectively. Assessing the near (2022–2059) and far (2060–2100) future, all three scenarios suggest a rise in intense heat stress days (UTCI > 38 °C) in NWI. Notably, the CMIP6 models predict that NWI could reach deadly levels of heat stress under the high-emission (SSP5-8.5) scenario. The findings underscore the urgency of addressing climate change and its potential impacts on human well-being and socio-economic sectors.

An assessment is made of the modes of interannual variability in the seasonal mean summer and winter Southern Hemisphere (SH) 500-hPa geopotential height in the twentieth century in models from the Coupled Model Intercomparison Project (CMIP) phase 5 (CMIP5) dataset. Modes of variability of both the slow (signal) and intraseasonal (noise) components in the CMIP5 models are evaluated against those estimated from reanalysis data. There is general improvement in the leading modes of the slow (signal) component in CMIP5 models compared with the CMIP phase 3 (CMIP3) dataset. The largest improvement is in the spatial structures of the modes related to El Niño–Southern Oscillation variability in SH summer. An overall score metric is significantly higher for CMIP5 over CMIP3 in both seasons. The leading modes in the intraseasonal noise component are generally well reproduced in CMIP5 models, and there are few differences from CMIP3. A new total overall score metric is used to rank the CMIP5 models over both seasons. Weighting the seasons by the relative spread of overall scores is shown to be suitable for generating multimodel ensembles for further analysis of interannual variability. In multimodel ensembles, it is found that an ensemble of size 5 or 6 is sufficient in SH summer to reproduce well the dominant modes. In contrast, about 13 models are typically are required in SH winter. It is shown that it is necessary that the selected models individually reproduce well the leading modes of the slow component.

The reliability of the near-land-surface air temperature (LSAT) projections from the state-of-the-art climate-system models that participated in the Coupled Model Intercomparison Project phase six (CMIP6) is debatable, particularly on regional scales. Here we introduce a method of constructing a constrained multi-model-ensemble (CMME), based on rejecting models that fail to reproduce observed LSAT trends. We use the CMME to constrain future LSAT projections under the Shared Socioeconomic Pathways 5–8.5 (SSP5–8.5) and 2–4.5 (SSP2–4.5), representing the high and intermediate scenarios. In comparison with the “raw” (unconstrained) CMIP6 multi-model ensemble (MME) mean, the impact of the observation-based constraint is less than 0.05oC 100 years−1 at a global scale over the second half of 21st century. However, the regional results show a wider range of positive and negative adjustments, from -1.0oC 100 years−1 to 1oC 100 years−1 under the SSP5–8.5 scenario. Although amplitude under SSP2–4.5 is relatively smaller, the CMME adjustment is similar to that under SSP5–8.5, indicating the scenario independency of the CMME impact. The ideal 1pctCO2 experiment suggests that the response of LSAT to carbon dioxide (CO2) forcing on regional scales is responsible for the MME biases in the historical period, implying the high reliability of CMME in the 21st century projections. The advantage of CMME is that it goes beyond the idea of “model democracy” assumed in MME. The unconstrained CMIP6 MME may be overestimating the risks of future warming over North America, but underestimating the risks over Asia.