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As a dominant climate mode of the southeast Indian Ocean, Ningaloo Niño/Niña has paramount impacts on regional climate and marine ecosystems. Using outputs from the latest phase of Coupled Model Intercomparison Project (CMIP6), we have systematically evaluated the simulation of Ningaloo Niño/Niña in the state‐of‐the‐art coupled models. Eighteen out of 28 CMIP6 models well reproduce the spatial pattern and seasonality of Ningaloo Niño/Niña, whereas the simulated amplitudes show large spread across the models. Main processes of the oceanic and atmospheric El Niño‐Southern Oscillation teleconnections and the coastal Bjerknes feedback are successfully captured by most of the CMIP6 models, but their uncertainties in the simulation are responsible for the inter‐model difference in amplitude. The CMIP6 models are also skillful in reproducing the regional climate impacts of Ningaloo Niño/Niña. Compared to Coupled Model Intercomparison Project Phase 5, a larger fraction of models in CMIP6 well reproduce the Ningaloo Niño/Niña, which provides a good hope for projecting its future changes. Plain Language Summary: The Ningaloo Niño/Niña is a kind of coastal version of the El Niño‐Southern Oscillation phenomenon in the southeast Indian Ocean, which is characterized by the prominent sea surface temperature variability off the west coast of Australia. As a dominant climate mode of the southeast Indian Ocean, Ningaloo Niño/Niña is known to exert strong impacts on regional climate and ocean biodiversity. For example, the unprecedented marine heatwave event associated with the 2011 extreme Ningaloo Niño severely damaged local fishery and coral reef. More severe impacts are expected under global warming. Hence, the future projection of Ningaloo Niño/Niña is of great importance for mitigating devastating climate impacts. As a prerequisite, we need to study first whether the state‐of‐the‐art coupled general circulation models can successfully reproduce such a coastal climate mode. Using the outputs from the latest Coupled Model Intercomparison Project Phase 6 (CMIP6), we have systematically evaluated the performance in simulating the Ningaloo Niño/Niña, including its basic characteristics, generation mechanisms, and the climate impacts. The results show that most of the CMIP6 models can realistically simulate the Ningaloo Niño/Niña, which provides us with confidence in projecting its future changes under global warming. Key Points: Coupled Model Intercomparison Project Phase 6 (CMIP6) ensemble mean simulation of Ningaloo Niño/Niña captures the observations despite the substantial inter‐model spreadInter‐model magnitude spread is due to different performances in both the El Niño‐Southern Oscillation teleconnections and the coastal Bjerknes feedbackThe CMIP6 models are skillful in reproducing the regional climate impacts of Ningaloo Niño/Niña [ABSTRACT FROM AUTHOR]

The Arctic stratospheric polar vortex is a key component of the climate system, which has significant impacts on surface temperatures in the mid-latitudes and polar regions. Therefore, understanding polar vortex variability is helpful for extended-range weather forecasting. The present study evaluates long-term changes in the position and strength of the polar vortex in the Arctic lower stratosphere during the winters from 1980/81 to 2013/14. Simulations of the Coupled Model Intercomparison Project Phase 6 (CMIP6) models are compared with Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA2) reanalysis dataset. Overall, the CMIP6 models well capture the spatial characteristics of the polar vortex with spatial correlation coefficients between the potential vorticity (PV) in the lower stratosphere from simulations and MERRA2 products generally greater than 0.85 for all CMIP6 models during winter. There is a good agreement in the position and shape of the polar vortex between the CMIP6 multi-model mean and MERRA2, although there exist differences between simulations of individual CMIP6 models. However, most CMIP6 models underestimate the strength of polar vortex in the lower stratosphere, with the largest negative bias up to about −20%. The present study further reveals that there is an anticorrelation between the polar vortex strength bias and area bias simulated by CMIP6 models. In addition, there is a positive correlation between the trend of EP-flux divergence for wavenumber one accumulated in early winter and the trend in zonal mean zonal wind averaged in late winter. As for the long-term change in polar vortex position, CanESM5, IPSL-CM5A2-INCA, UKESM1-0-LL, and IPSL-CM6A-LR well capture the persistent shift of polar vortex towards the Eurasian continent and away from North America in February, which has been reported in observations. These models reproduce the positive trend of wavenumber-1 planetary waves since the 1980s seen in the MERRA2 dataset. This suggests that realistic wave activity processes in CMIP6 models play a key role not only in the simulation of the strength of the stratospheric polar vortex but also in the simulation of the polar vortex position shift. [ABSTRACT FROM AUTHOR]

In the context of global warming, the frequency and intensity of extreme weather and climate events are increasing. However, the impact of these changes that is directly felt by people is the day-to-day temperature change. Extreme temperature changes between neighboring days (ETCNs) carry substantial disease risks and socioeconomic impacts. Evaluation studies of ETCN events with global climate models (GCMs) remain unknown in China. This study quantitatively evaluates the performances of 35 GCMs and the multi-model ensemble (MME) of the Coupled Model Intercomparison Project 6 (CMIP6) in simulating the extreme cooling (EC) and extreme warming (EW) events of two consecutive days as defined by relative thresholds. The results showed that from 1981 to 2013, the annual average EW frequencies showed an increasing trend over China, but a decreasing trend for EC events, and the frequency of EW events was higher than that of EC events. EW events mostly occurred in spring, while EC events occurred in autumn. Additionally, the performances of the CMIP6 models were quite different between EC and EW events. The models could capture the annual cycle of EC and EW events well, and the simulations of EW events were generally more reliable than those of EC events. Furthermore, most CMIP6 models overestimated the frequency of EW events but underestimated the frequency of EC events in China. The CMIP6 models could capture the trends in EC events in China but fail to simulate them in EW events. The interannual variability of EW events exhibited relatively better performance than that of EC events. The CMIP6 MME effectively improved the capabilities of the models to simulate the climatology of ETCN events. Individual CMIP6 models exhibited better performances than the CMIP6 MME in terms of the trend and interannual variability. Finally, according to the overall ranking of the CMIP6 models, MPI-ESM-1–2-HAM and FGOALS-f3-L achieved the best performance in simulating EW and EC events, respectively. This study selected the optimal models in different regions at the seasonal and annual scales, providing theoretical support for the frequency projection and modeling improvement of ETCN events. [ABSTRACT FROM AUTHOR]

Understanding and elucidating future changes in El Niño teleconnection is crucial because of the influence of El Niño on the globe via teleconnection. Existing climate models have consistently projected an eastward and poleward shift in El Niño teleconnection in the future, but the causes and dynamical processes of this shift have been poorly explained. Particularly, no study has examined the characteristic changes in the barotropic Rossby waves that form the teleconnection. This study investigates dynamic processes by analyzing the wavelength of the Rossby waves through spectral analysis, measuring the distance between circulation anomalies, and exploring the dispersion relationship of the barotropic Rossby waves. The results revealed that the wavelength of waves forming the teleconnection is expected to increase in warmer climate with an increase in the proportion of zonal-wavenumber-2 wave. Because changes in the mean state, including the strengthening of the westerly in the high-emission scenario, alter the frequencies of waves according to their zonal wavenumbers, this process is more notably manifested in the Southern Hemisphere, where the inter-model spread of the mean state is smaller, than in the Northern Hemisphere. Consequently, this will result in a shift in the position of El Niño's influence on the high latitudes of both Hemispheres. Specifically, in the Southern Hemisphere, it is anticipated that ocean warming in the seas in front of West Antartica and moisture transport towards Antarctica, induced by El Niño, will shift eastward in the high-emission scenario compared to its conventional position. • The El Niño teleconnection changes in the future as the wavelength of the wave forming the teleconnection increases. • The mean state changes reduce the frequency of barotropic Rossby waves with smaller zonal wavenumbers, causing the increase. • This can lead to a shift in the location of El Niño's impact on high latitudes. [ABSTRACT FROM AUTHOR]

Precipitation fluctuations in the Pearl River Basin (PRB) have a significant impact on river runoff, causing huge economic losses and casualties. However, future precipitation variations in the PRB remain unclear. Therefore, we explored the projected changes in precipitation in the PRB based on the coupled model intercomparison project phase 6 (CMIP6) model via three shared socio-economic pathways scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5). In our study, the optimal ensemble of global climate models in the PRB was identified using the comprehensive rating index (CRI), which is based on climatology, spatial variation, and interannual variability, and it was used to analyze potential precipitation changes in the basin in the period 2025–2100. The results showed that the CMIP6 models underestimated precipitation in the PRB; the consistency between the observations and the multi-model ensemble mean of the four best models was higher than those of any other ensembles, and the CRI value was highest (0.92). The annual precipitation in the PRB shows a significant increasing trend under three scenarios from 2025 to 2100 (p < 0.01), with the highest rate of precipitation increase being seen under the high-emission scenario. By the end of the 21st century, the regional mean precipitation in the PRB will increase by 13%, 9.4%, and 20.1% under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, respectively. Spatially, the entire basin is projected to become wetter, except for a slight decrease of less than 6% in the central part of the basin and the Pearl River Delta in the near term in the 21st century, and the highest increases are projected to occur in the Xijiang River basin.

China is committed to achieving carbon neutrality before 2060. This study projected the changes in climate and runoff in the source region of the Yellow River Basin for 2021–2060 under lower carbon emission pathways (SSP1–2.6 and SSP2–4.5) using a statistically downscaled climate dataset and the SWAT hydrological model. Results showed that the climate will become warmer and wetter from 2021–2060. In comparison with the baseline period (1995–2014), in terms of the ensemble mean, annual mean air temperature, annual precipitation, and annual runoff will increase by 1.3 °C and 1.6 °C, by 11.1% and 11.2%, and by 12.8% and 11.9% under SSP1–2.6 and SSP2–4.5 scenarios, respectively. Moreover, the seasonal pattern of runoff was projected to change. The proportion of monthly runoff to the annual total will decrease by 0.6–1.0% in summer but increase by 0.1–1.0% during the period from January to April and September to December. The multimodel ensemble mean (MEM) of extremely high monthly flow (Q10) will increase by 3.5–13.4% in the flood season (June to August) and water storage season (September to December). The MEM of extremely low monthly flow (Q90) will increase by 19.4–26.2% from February to April but decrease by 5.0–8.9% in January, May, and December. Thus, the warmer and wetter climate from 2021–2060 will likely cause flatter seasonal distribution of runoff, lower risk of water scarcity at the annual scale and of drought from February to April, but higher risk both of flood in the flood season and of drought in December, January, and May. Generally, the flatter pattern of runoff would likely alleviate water scarcity in the dry and water storage seasons to some degree, and the increase in monthly runoff in the water storage season will benefit hydroelectric power generation and agriculture and animal husbandry production. However, in some years, the increase in Q10 in the flood season will likely increase flood prevention pressure, and the decrease in Q90 in May will likely obstruct grass revival.

Abstract Atmospheric warming results in an intensification of annual precipitation over the globe but large uncertainties remain regionally and at seasonal scales, especially for extremes. Using 29 models from the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6), we investigate future seasonal changes in extreme precipitation (under the scenario SSP5‐8.5) and how it compares to changes in mean precipitation. Over land, we find a strong intensification of the wettest day in all seasons over the mid and high latitudes of the Northern Hemisphere and over India during the monsoon. Extreme intensity decreases in the subtropics for some seasons, including over large regions around the Mediterranean basin and Southern Africa, and these drying patterns are not apparent in annual results. The key finding is that the CMIP6 multi‐model mean always shows that seasonal changes in mean and extreme precipitation align where there is high model agreement. That is, in all seasons by the end of the 21st century, extremes intensify in regions where mean precipitation increases and decline where mean precipitation decreases. This should not hide inherent uncertainties associated, namely the large range of changes intensity that can be found across the models, and an important modulation of the changes by internal variability. Yet, this study shows that the multi‐model mean shows broad consistency such that future seasonal changes in mean precipitation could be used to infer future changes in extremes (and vice versa), thus providing valuable information for risk planning and mitigation strategies.

Using on the CN05.1 monthly average temperature observation data set from 1961 to 2014 and the output data from 19 global climate models from Coupled Model Intercomparison Project Phase 6 (CMIP6), the simulation ability of CMIP6 models on the climatology spatial distribution and interannual variability of temperature in Southwestern China was systematically evaluated by means of Taylor diagram, Taylor index and interannual variability skill score. The variation characteristics of future temperature in this area were predicted under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 scenarios. The results showed that: (1) compared with other seasons, most CMIP6 models had the best performance in simulating the spatial distribution of autumn temperature climatology during 1961-2014; and CMIP6 models underestimated the interannual variability of seasonal and annual average temperature. Among the 19 models, the best models simulated the temperature in Southwestern China were ACCESS-CM2, CMCC-CM2-SR5 and CMCC-ESM5. (2) The multi-model ensemble mean(MME) of 3 best-fit models simulated the climatology spatial distribution and interannual variability of average temperature better than the MME of 19 models. (3) Compared with the multi-year average temperature observed in the same period during 1961-2014, the seasonal and annual average temperature in Southwestern China showed an upward trend in the future under the four climatic scenarios, seasonal and annual average temeprature increased by 0.94-3.48℃. Under the four scenarios, the increase of average temperature in summer was the largest(2.17-3.48℃) and the interannual fluctuation range was the smallest, the increase of temperature in winter was the smallest(0.94-2.24℃) and the interannual fluctuation range was the largest. (4) In the early of 21st century, there was little difference in the increase of seasonal and annual average temperature under 4 scenarios. During the middle of the 21st century, the upward trend of seasonal and annual average temperature in high radiation forcing scenarios was gradually larger than that in low radiation forcing scenarios. (5) Under the four scenarios, the anomaly values of multi-year average temperature at the early (2015-2034), middle (2045-2064) and end (2081-2100) period of 21st century and the historical(1961-2014) observed temperature showed the spatial distribution characteristics that the northwest was greater than southeast of this region, and the high latitude and high altitude areas were greater than the low latitude and low altitude areas. With the passage of time, at the end of 21st century, the temperature anomaly in the same region was significantly higher under high forcing scenarios than that in low forcing scenarios. [ABSTRACT FROM AUTHOR]

Scenario projections of climate changes in the XXI century in Crimea are presented based on the data of an ensemble of models of the recent generation CMIP6 for aggressive SSP585 and moderate SSP245 scenarios of anthropogenic impact. Variations of season-averaged values of temperature and precipitation for winter and summer, the number of days with extreme anomalies of temperature and precipitation, the rate of precipitation, and the contribution of heavy precipitation to the seasonal total are shown. For the current period, the data of the models are compared with reanalysis data ERA5 and interpolated grid observation data CRUTS4.05. On the average over the model ensemble, the summer temperature is expected to increase by ~6 and 2.5°C by the late XXI century according to scenarios SSP585 and SSP245, respectively. The number of days in summer with extremely high temperature (95% percentile of the distribution function for 1981–2010) will increase 5–6 times as soon as the mid-XXI century and will reach ~40 days by the end of the century, even for a moderate scenario. Strong year-to-year variations are typical of seasonal precipitation, at which statistically insignificant increase in winter can be seen in the models, while the summer precipitation is expected to decrease considerably by ~15 and 40% for scenarios SSP585 and SSP245, respectively. At the same time, no statistically significant decrease is expected in the intensity and extremeness of precipitation. In winter, the proportion of extreme precipitation in its total will increase considerably (from 25 to 35%). It should be noted that the changes forecasted for the nearest two decades are practically independent of the analyzed scenarios of anthropogenic impact. The decrease in precipitation at an increase in the temperature demonstrates the important role of atmospheric dynamics in the future changes in the characteristics of precipitation in the region under study. [ABSTRACT FROM AUTHOR]

This study investigates the future changes in dangerous precipitation extremes with multiyear return periods and the population exposure across China at the 1.5–4°C warming levels via the latest simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The results show that the simulations project more frequent dangerous precipitation extremes across China under the warmer climate regardless of the shared socioeconomic pathway (SSP), with more substantial occurrence increases at the high warming levels. Consequently, the population exposure to dangerous precipitation extremes is anticipated to increase persistently in most regions of China except for some parts of northwestern China and the Tibetan Plateau. For the events estimated to occur once every 10 years, the 1.5, 2.0, 3.0, and 4.0°C warming relative to the current state will result in approximately 29.9, 47.8, 72.9, and 84.3% increases in the aggregated population exposure over China under the SSP5-8.5 scenario, respectively. However, the exposure change is somewhat subject to the emission scenarios, with larger proportional increases under the regional-rivalry scenario of SSP3-7.0 compared to the fossil-fueled development scenario of SSP5-8.5. The increased exposure under all the scenarios is primarily attributed to the climate change factor, and the population change and their interaction component make a minor contribution. Furthermore, compared to the 2.0°C warmer climate, the 0.5°C less warming under the 1.5°C climate can trigger remarkable decreases of 16.5–20.8% for exposure to once-in-decade events over China. Additionally, the changes in the occurrence and exposure are much larger for the rarer events. Hence, our analyses indicate that limiting warming to 1.5°C is beneficial to reducing the impacts associated with precipitation extremes across China, particularly for the more extreme events.

Photovoltaic power potential (P PV) is part of the strategies in Brazil to satisfy the population's energy demand and contribute to reduction of global warming in the climate change context. This study assesses climate change's impact on P PV using a set of 47 state of the art Earth System Models from CMIP6 under two climate change (SSP2-4.5 and SSP5-8.5) scenarios. Changes in solar radiation, temperature and P PV are calculated for the near-term (2021–2050) and end-century (2071–2100) future, concerning the historical period (1981–2010). For the future, the sign of climate change is consistent in both scenarios, but with more intense magnitudes in the SSP5-8.5 for the end-century, and indicate a brightening in North region and warming in the whole country. For the near-term future, P PV projects the maximum increases (≈3%) in the North under both scenarios. The most notable decreases in P PV are noticed by the end-century (−5%) over the country, except for the extreme north. The sensitivity to the increase in temperature of current P PV systems' technology would not allow increases in the yield of P PV , canceling the positive effect of the brightening. • 47 state of the art climate models from CMIP6 were evaluated to determine P PV over Brazil. • P PV was projected for two future periods under two climate scenarios (SSP2-4.5 and SSP5-8.5). • For the 2021–2050 period, P PV projects the maximum increases (2.5%–3%) in the north of Brazil under both scenarios. • The largest decreases in P PV are noticed by the end-century (−5%) under SSP5-8.5 over the whole country. • P PV reductions are mainly due to the increase in TAS (warming). [ABSTRACT FROM AUTHOR]

Estimates of the effective radiative forcing from aerosol‐radiation interaction (ERFari) of anthropogenic Black Carbon (BC) have been disputable and require better constraints. Here we find a substantial decline in atmospheric absorption of −5.79Wm−2decade−1 over eastern central China (ECC) responding to recent anthropogenic BC emission reductions. By combining the observational finding with advances from Coupled Model Intercomparison Project phase6 (CMIP6), we identify an emergent constraint on the ERFari of anthropogenic BC. We show that across CMIP6 models the simulated trends correlate well with simulated annual mean shortwave atmospheric absorption by anthropogenic BC over China. Making use of this emergent relationship allows us to constrain the aerosol absorption optical depth of anthropogenic BC and further provide a constrained range of 2.4–3.0 Wm−2 for its top‐of‐atmosphere ERFari over China, higher than existing estimates. Our work supports a strong warming effect of BC over China, and highlights the need to improve BC simulations over source regions. Plain Language Summary: Black Carbon (BC) widely affects the Earth system as a warming agent. However, estimates of the effective radiative forcing from aerosol‐radiation interaction (ERFari) by anthropogenic BC remain disputable with recent studies arguing for a downward revision, and therefore require better constraints. Here we find that atmospheric absorption declines evidently over eastern central China since 2005 due to reduced anthropogenic BC emissions. When analyzing results from the latest Coupled Model Intercomparison Project phase 6 (CMIP6) models, we further find that though the majority of models underestimate the observed trends, the simulated trends across CMIP6 models correlate well with simulated annual mean shortwave atmospheric absorption by anthropogenic BC over China. Combining this relationship with the observational range of the trends allows us to identify an emergent constraint on the aerosol absorption optical depth of anthropogenic BC and further provide an observationally constrained range of 2.4–3.0Wm−2 for its top‐of‐atmosphere ERFari. This estimate is substantially higher than all existing estimates, providing independent evidence to support a strong warming effect of BC over China. Our work also points out a common low bias of current global models in simulating BC over China, and highlights the need to improve BC simulations over source regions. Key Points: Atmospheric absorption declines substantially over eastern central China in response to recent reductions of anthropogenic Black Carbon (BC) emissionsThe observational finding combined with the latest Coupled Model Intercomparison Project phase 6 (CMIP6) models enable an emergent constraint on the anthropogenic BC forcing over ChinaThe constrained effective radiative forcing from aerosol‐radiation interaction of anthropogenic BC is higher than existing estimates [ABSTRACT FROM AUTHOR]

Sudden stratospheric warmings (SSWs) are associated with episodes of extreme winter weather, often leading to significant societal impacts. Using observations alone, it is hard to quantify the SSW impact on surface extreme weather, because the observational record only covers ∼40 events. Climate models provide an effective way to increase this sample size, potentially into 1000s of events, but only if we trust that they capture SSW processes well. We evaluate the representation of stratosphere‐troposphere coupling associated with SSWs in 10 models from the sixth Coupled Model Intercomparison Project (CMIP6). In general, the models and the reanalysis have different patterns of tropospheric precursors, but similar stratospheric responses to precursors. The individual CMIP6 models capture the main spatial features of surface impact: A negative phase of the Northern Annular Mode (NAM), with distinctive alternating positive and negative temperature anomalies across the northern hemisphere. We assess the time evolution of SSW surface impacts for individual models over different regions and construct an overview of how well each model captures surface impacts. Models perform particularly well in simulating the surface impact evolution over the Baffin Bay and Siberia regions. Over the United States, Northwest Europe, and the Middle East, there is less agreement among models. Models underestimate the magnitude of temperature anomalies associated with SSWs over the Middle East and over the Eastern Seaboard of North America, but the magnitude of the circulation responses are comparable with reanalysis. Plain Language Summary: The stratosphere is the layer of the Earth's atmosphere between 10 and 50 km above the surface. In the winter hemisphere, air over the pole is cold, and surrounded by strong westerly winds, a structure known as the stratospheric polar vortex (SPV). Around six times a decade, the SPV breaks down very quickly in events known as sudden stratospheric warmings. Although this may seem remote from surface weather, these changes in the stratosphere are often followed by extreme winter weather at the Earth's surface, such as the extreme cold snap in 2018. These events can have significant societal impact, causing cold‐related mortality and disruption to transport and infrastructure. It is therefore important to gain a better understanding of these events. Only around 40 SSWs have been observed which is a limited sample size. Scientists therefore use models to obtain a larger number of such events. We assess several models here and find that models perform better in some regions than others at simulating the observed surface impacts. Models perform best over the Baffin Bay region and Siberia, although they underestimate the magnitude of the temperature anomalies associated with SSWs over the Middle East and the eastern Seaboard of North America. Key Points: Sixth Coupled Model Intercomparison Project models capture the main spatial features of surface impacts following Sudden stratospheric warmingsModels perform better at simulating the time evolution of surface impacts at higher latitudesThe magnitude of temperature anomalies over the Middle East and North America is reduced in models compared with reanalysis [ABSTRACT FROM AUTHOR]

With rapid warming since the mid‐20th century, China has experienced remarkable changes in the extreme temperatures. We use the updated observational data and the newest generation of climate models from Coupled Model Intercomparison Project Phase 6 (CMIP6) to investigate the relative contribution from different external forcing to the temperature extremes. We find that both intensity and frequency indices of extreme temperature experience continuous warming during 1951–2018. More intense and more frequent warm extremes and less intense and less frequent cold extremes are observed in most regions. An exception is a warming slowdown in the intensity of the coldest extremes since the late 1990s in northeast China. These observed changes are generally well reproduced by CMIP6 climate models, especially for the warm days and nights. Detection analyses based on an optimal fingerprinting method show that anthropogenic forcing (ANT) is the main driver for these changes, with cold extremes less detectability than warm extremes. Three‐signal detections show that both greenhouse gas (GHG) and anthropogenic aerosols (AA) influences can be detected and separated in most warm extreme indices but not in the cold extremes, while the natural forcing influence is negligible for most indices. GHG forcing plays a dominant role, accounting for about 1.6 (1.1–2) times of observed warming in changes of most indices, while the AA offset about 35% (10–60%) of GHG induced warming for warm extremes. Anthropogenic factors including land use and ozone may have a very small positive contribution to the extreme temperatures. [ABSTRACT FROM AUTHOR]

The recently released NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset is a high-resolution daily downscaled dataset derived from the Coupled Model Intercomparison Project Phase 6 (CMIP6) model simulations. We comprehensively evaluated the performance of the NEX-GDDP dataset in simulating the characteristics of the climatological precipitation and extreme precipitation events across China from 1960 to 2014. We present here the projected changes in precipitation at the end of the 21st century (2070–2099) with respect to a reference period (1985–2014). We compared the NEX-GDDP dataset with both a state-of-the-art gauge data analysis system and the CMIP6 global climate models. The NEX-GDDP dataset significantly outperformed the CMIP6 simulations in replicating the climatological patterns of precipitation. It showed a closer alignment with the observed data, as evidenced by notably increased correlation coefficients and reduced model-relative errors. The NEX-GDDP dataset exceled in accurately reproducing the spatial distribution of indices of extreme precipitation, surpassing the performance of the CMIP6 simulations. The projections show an increased frequency of heavy precipitation under higher emission scenarios in both datasets, with the CMIP6 simulations consistently estimating a higher probability of heavy rain than the NEX-GDDP dataset. • The NEX-GDDP dataset can well describe the climatology but not trend for observed precipitation. • The NEX-GDDP dataset is also represent the spatial distribution of precipitation extremes than the CMIP6 simulations. • The CMIP6 simulations project a higher probability of heavy rain than the NEX-GDDP dataset. [ABSTRACT FROM AUTHOR]

In the present study, we assess the Tropical Indian Ocean (TIO) circulation features from the available ocean reanalysis products and the latest version of Coupled Model Intercomparison Project (CMIP6) climate model simulations. We considered the following reanalysis products; Ocean Reanalysis System 5 (ORAS5), Estimating the Circulation and Climate of the Ocean (ECCO), Global Ocean Data Assimilation System (GODAS), Ensemble Coupled Data Assimilation (ECDA), the Bluelink Reanalysis (BRAN) and Simple Ocean Data Assimilation (SODA) and compared them against in-situ observations and the satellite-derived Ocean Surface Current Analyses Real-time (OSCAR). The reanalysis products underestimate the strength and location of the Wyrtki Jets. The BRAN reanalysis performed well compared to the other products in representing the TIO surface zonal currents, followed by ORAS5. The vertical extension of subsurface zonal currents in the equatorial Indian Ocean and their seasonal maxima are well captured in ORAS5. Thus, our analysis suggests that the ORAS5 is a qualitative product to estimate the TIO circulation. We further evaluated the TIO current patterns simulated by CMIP6 models with in-situ data/ ORAS5. The majority of the models show discrepancies in simulating equatorial and south equatorial current systems with a mean bias of 0.1cms−1 and 0.2cms−1, respectively. NorESM2-MM, NorESM2-LM, CanESM5, CESM2-WACCM-FV2, and E3SM-1-ECA models showed a superior skill in reproducing the TIO circulation compared to the rest of the models. Our analysis highlights the importance of assessing various reanalysis products and coupled climate models in representing the circulation of the TIO and, consequently, their role in depicting regional weather and climate. • The BRAN reanalysis performed well compared to the other products in representing the TIO surface circulation. • CMIP6 models are problematic in simulating equatorial zonal currents and south equatorial currents. • NorESM2, and CanESM5 models showed a superior skill in reproducing the TIO circulation compared to the rest of the models. [ABSTRACT FROM AUTHOR]

The interannual variability of the Northern Hemisphere winter climate is strongly linked to the El Niño-southern oscillation (ENSO). While North Pacific and North America often exhibit a robust response to ENSO, so-called the Pacific–North America pattern, the Arctic and Eurasian climate responses to ENSO remain elusive. This study examines 40 different climate models from the coupled model intercomparison project phase 6 (CMIP6) to find the distinct Arctic and Eurasian temperature response to two types of El Niño events. Specifically, Central Pacific El Niño events are accompanied by significant pan-Arctic warming, whereas Eastern Pacific (EP) El Niño events are accompanied by cooling over the Barents-Kara Seas and Eurasian continent. During the EP El Niño events, pan-Arctic sea-level pressure (SLP) effectively strengthens, leading to weaker westerlies and surface air cooling over the northern Eurasian continent. These distinct Arctic and Eurasian winter temperature responses to two types of El Niño do not appear clearly in reanalysis data, spanning 1979–2021, probably because of the small sample size of El Niño events since the satellite era.

This study evaluates the relationship between El Niño‐Southern Oscillation (ENSO) and the East Asian winter monsoon (EAWM) in 26 Coupled Model Intercomparison Project Phase 6 (CMIP6) models. Of particular concern is the plausible factors for the model's capability of simulating the ENSO‐EAWM relationship. Results show that the model's ability of simulating the ENSO‐EAWM relationship is more dependent upon the longitudinal extension of ENSO‐related equatorial Pacific sea surface temperature (SST) anomalies than the amplitude of the equatorial central‐eastern Pacific SST anomalies. The influence of the amplitude of ENSO on the simulation of the ENSO‐EAWM relationship depends on the westward extension of ENSO‐related equatorial Pacific SST anomalies. Another factor for the model's ability of simulating the ENSO‐EAWM relationship is the SST anomalies in the tropical western North Pacific (WNP). A westward extension of the equatorial Pacific SST anomalies shifts the west branch of anomalous Walker circulation too far westward, which causes westward displaced anomalous ascending (descending) motion around the Philippines through modulating regional meridional vertical circulation in El Niño (La Niña) years. The weak SST anomalies in the tropical WNP lead to the failure of inducing anomalous lower‐level anticyclone (cyclone) over the Philippine Sea through a Rossby wave response in El Niño (La Niña) years. The accompanying weak anomalous lower‐level southwesterly (northeasterly) winds along the west flank of the anomalous anticyclone (cyclone) account for the weak ENSO‐EAWM relationship. Key Points: The El Niño‐Southern Oscillation‐East Asian winter monsoon (ENSO‐EAWM) relationship varies largely among the CMIP6 modelsThe longitudinal location of equatorial Pacific sea surface temperature (SST) anomalies is more critical than the amplitude to simulating the ENSO‐EAWM relationshipThe tropical western North Pacific SST anomalies are another important factor in the simulation of the ENSO‐EAWM relationship [ABSTRACT FROM AUTHOR]

The dominant interannual SST variability in the eastern equatorial Atlantic is referred to as the Atlantic Zonal Mode (AZM), which peaks in boreal summer impacts global weather patterns. The cold (warm) phase of this ocean-atmospheric coupled phenomenon enhances (weakens) the intensity of the Indian Summer Monsoon Rainfall (ISMR). Observational studies show a strengthening relationship between AZM and ISMR in recent decades, providing a predictive signal for the ISMR. However, a suite of Coupled Model Intercomparison Project Phase 6 (CMIP6) model simulations in the highest emission scenario (SSP58.5) show a weakening relationship between ISMR and AZM in the future (2050–2099). The strengthening of atmospheric thermal stability over the tropical Atlantic in the warming scenario weakens the associated convection over the eastern equatorial Atlantic in response to the warm phase of AZM. This leads to weakening velocity potential response over the Indian subcontinent, resulting in a weak AZM–ISMR relationship. There is no convincing evidence to indicate that either the tropical Atlantic SST bias or the AZM–ISMR teleconnection bias plays a crucial role in the potential weakening of this relationship. These results imply that ISMR prediction will become more challenging in a warming scenario as one of the major external boundary forces that influence monsoon weakens. [ABSTRACT FROM AUTHOR]

The Arctic amplification is driven by several intertwined causes that are embedded in an overall changing energy balance of the atmosphere and ocean. We investigate the impact of quadrupled CO2 concentrations on the Arctic atmospheric energy budget in CMIP6 models. The decomposition of the energy budget accounts for the atmospheric radiation budgets, the sensible and latent heat flux at the surface, and the convergence of atmospheric energy transport. The CO2 response of these components is found to strongly depend on the Arctic season and underlying surface type. While the widespread Arctic radiative-advective equilibrium remains intact during boreal summer, profound changes are restricted to the winter season: Strongly increasing surface heat fluxes over areas of retreating sea ice are largely counteracted by dropping positive heat fluxes over open Arctic ocean. For retreating sea ice, the increase in the surface fluxes is stronger for a subset of climate models with weaker Arctic amplification. For these regions, we propose an intermediate transformation of the local radiativeadvective equilibrium to a radiative-convective equilibrium. The wintertime changes in the components of the atmospheric energy budget strongly relate to alterations at the surface, concerning the modification of sea ice extent, surface temperature and stability. We find robust linear correlations for the mediating effect during winter. The energy transport convergence is derived as residual in our energetic framework as main mechanism to ensure the local energy budget. On a large scale, we find an overall decreasing transport convergence to balance the surplus energy from the surface which outruns the intensification of the Arctic radiation deficit in a warmer climate. [ABSTRACT FROM AUTHOR]

The Arctic amplification is driven by several intertwined causes that are embedded in an overall changing energy balance of the atmosphere and ocean. We investigate the impact of quadrupled CO2 concentrations on the Arctic atmospheric energy budget in CMIP6 models. The decomposition of the energy budget accounts for the atmospheric radiation budgets, the sensible and latent heat flux at the surface, and the convergence of atmospheric energy transport. The CO2 response of these components is found to strongly depend on the Arctic season and underlying surface type. While the widespread Arctic radiative-advective equilibrium remains intact during boreal summer, profound changes are restricted to the winter season: Strongly increasing surface heat fluxes over areas of retreating sea ice are largely counteracted by dropping positive heat fluxes over open Arctic ocean. For retreating sea ice, the increase in the surface fluxes is stronger for a subset of climate models with weaker Arctic amplification. For these regions, we propose an intermediate transformation of the local radiative-advective equilibrium to a radiative-convective equilibrium. The wintertime changes in the components of the atmospheric energy budget strongly relate to alterations at the surface, concerning the modification of sea ice extent, surface temperature and stability. We find robust linear correlations for the mediating effect during winter. The energy transport convergence is derived as residual in our energetic framework as main mechanism to ensure the local energy budget. On a large scale, we find an overall decreasing transport convergence to balance the surplus energy from the surface which outruns the intensification of the Arctic radiation deficit in a warmer climate.

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We investigate the impacts of external forcings and internal climate variability (ICV) on global‐mean surface air temperature (GMST) variations (1850–2014) in observation and an ensemble of sixth Coupled Model Intercomparison Project (CMIP6) historical simulations. Using multiple regression models, we estimate that 83% of the observed GMST variance is explained by global‐scale anthropogenic forcing. Most CMIP6 models underestimate this anthropogenic contribution (median explained variance is 67%), but compare better with observation when the North Atlantic regional contribution is included in the anthropogenic forcing (median 77%). The ICV contributions to the GMST variance are ∼8% for observation and ∼7% for the models (median). The models' GMST responses to the external forcings and ICV vary widely, with the two responses being oppositely related. The North Atlantic predominantly drives the free GMST variability in observations, whereas the Pacific drives this in most CMIP6 models due to stronger than observed simulated GMST‐Pacific ICV relationships. Plain Language Summary: Global surface temperature has significantly warmed over the last one and a‐half centuries due to the combined effects of human produced greenhouse gases and aerosols and naturally occurring volcanic eruptions and solar output changes. These externally forced temperature changes are modulated by the climate system's internal variability. Most climate models simulate these different temperature changes, but with significant differences between the models' simulations. Here, we analyze data from observations and from simulations by 42 state‐of‐the‐art climate models to quantify the effects of external forcings and internal variability on surface temperature change. We find that most of the climate models underestimate the observed warming arising from global climate forcing but overestimate the multidecadal variations arising from North Atlantic aerosol forcing. The effect on temperature change from internal variability is much smaller than that from the combined external forcing, in both observations and climate models. Also, the models showing the largest temperature responses to external forcing show the smallest responses to internal variability, and vice‐versa. Understanding the reasons for these differences between climate simulations may help improve the confidence in climate change projections. Key Points: Most models underestimate (overestimate) the surface temperature response to global‐scale anthropogenic (North Atlantic regional) forcingThe models' diverse responses to external forcing have a strong negative relationship with the responses to internal variabilityThe North Atlantic (Pacific) internal variability dominates the unforced surface temperature variations in observations (most models) [ABSTRACT FROM AUTHOR]

The reliability of the projection of Indian Summer Monsoon Rainfall (ISMR) and associated wind circulation in the simulation of the Coupled Climate Models (CCMs) is based on their ability to reproduce themselves in the control experiment. The performances of CCMs, namely BCC-CSM2-MR and BCC-ESM1 of the Beijing Climate Center, China, and MPI-ESM1-2-HR and MPI-ESM1-2-LR of Max Planck Institute (MPI) Germany, are evaluated under control experiment of CMIP6, since they had better performed in Coupled Model Intercomparison Project phase 5 (CMIP5). Under the historical experiment (control experiment) in the period from 1979 to 2014, the simulated wind circulation, relative humidity, and rainfall are evaluated on the seasonal and sub-seasonal scales during the Indian summer monsoon season (i.e. June-July-August-September). The simulated wind at pressure levels of 1000, 850, 700 and 200 hPa and relative humidity at similar pressure levels are considered for the evaluation. The India Meteorological Department (IMD) observed rainfall (0.25° x 0.25°) is taken to validate the model's simulated rainfall. Further, to validate the zonal wind (u component), meridional wind (v component) and relative humidity, the reanalysed data (0.25° × 0.25°) at the pressure levels of 1000, 850, 700 and 200 hPa are taken from ERA5 of the European Centre for Medium-Range Weather Forecasting (ECMWF). The seasonal and monthly mean wind and relative humidity are vertically averaged from the levels of 1000 to 700 hPa, while monthly mean wind at the level of 200 hPa is considered for upper-level analysis. The models BCC-CSM2-MR, BCC-ESM1, MPI-ESM1-2-HR and MPI-ESM1-2-LR simulated wind, humidity and rainfall on the monthly and seasonal scales are validated against the respective observed/reanalysed data. The evaluations show that the CMIP6 model BCC-CSM2-MR performs well in reproducing relative humidity over the Arabian Sea and the Bay of Bengal. The model BCC-CSM2-MR and BCC-ESM1 perform well in simulating JJAS rainfall in comparison to observed rainfall of IMD. [ABSTRACT FROM AUTHOR]

The Arctic stratospheric polar vortex is a key component of the climate system, which has significant impacts on surface temperatures in the mid-latitudes and polar regions. Therefore, understanding polar vortex variability is helpful for extended-range weather forecasting. The present study evaluates long-term changes in the position and strength of the polar vortex in the Arctic lower stratosphere during the winters from 1980/81 to 2013/14. Simulations of the Coupled Model Intercomparison Project Phase 6 (CMIP6) models are compared with Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA2) reanalysis dataset. Overall, the CMIP6 models well capture the spatial characteristics of the polar vortex with spatial correlation coefficients between the potential vorticity (PV) in the lower stratosphere from simulations and MERRA2 products generally greater than 0.85 for all CMIP6 models during winter. There is a good agreement in the position and shape of the polar vortex between the CMIP6 multi-model mean and MERRA2, although there exist differences between simulations of individual CMIP6 models. However, most CMIP6 models underestimate the strength of polar vortex in the lower stratosphere, with the largest negative bias up to about −20%. The present study further reveals that there is an anticorrelation between the polar vortex strength bias and area bias simulated by CMIP6 models. In addition, there is a positive correlation between the trend of EP-flux divergence for wavenumber one accumulated in early winter and the trend in zonal mean zonal wind averaged in late winter. As for the long-term change in polar vortex position, CanESM5, IPSL-CM5A2-INCA, UKESM1-0-LL, and IPSL-CM6A-LR well capture the persistent shift of polar vortex towards the Eurasian continent and away from North America in February, which has been reported in observations. These models reproduce the positive trend of wavenumber-1 planetary waves since the 1980s seen in the MERRA2 dataset. This suggests that realistic wave activity processes in CMIP6 models play a key role not only in the simulation of the strength of the stratospheric polar vortex but also in the simulation of the polar vortex position shift.

The present study examines the ability of Coupled Model Inter‐comparison Project phase 6 (CMIP6) models in representing the dominant modes of tropical Indian Ocean (TIO) sea surface temperature (SST) variability on the interannual and decadal time scale. Historical simulations from 27 CMIP6 models are assessed against Extended Reconstructed SST over the period of 1854 to 2014. Spectrum analysis reveals that many models reproduce interannual and decadal variability of TIO SST but underestimate the amplitude of variability with some disparity in the periodicity. All models can reproduce the dominant basin‐wide mode of interannual and decadal variability of TIO SST reasonably well. Skill score analysis of TIO SST variability reveals that KACE‐1‐0‐G has highest skill, followed by FGOALS‐f3‐L, EC‐Earth3‐Veg‐LR, ACCESS‐ESM1‐5, CanESM5‐CanOE on the interannual timescale and FGOALS‐f3‐L, CanESM5‐CanOE, KACE‐1‐0‐G and CanESM5, respectively, showed highest skills for decadal variability. It is found that variations in radiation and latent heat flux are primarily responsible for interannual variability in TIO SST, the basin‐wide warming, in the observations. Taylor diagram analysis reveals that all the models exhibit better skill for the radiative flux; however, skill for the latent heat and momentum flux varies from model to model. It is important to note that the models in which the latent heat flux and zonal wind are better represented have produced better TIO SST variability compared to other models. A higher discrepancy in latent heat and zonal momentum flux leads to improper wind‐evaporation‐SST and wind‐circulation‐SST feedback, which in turn restricts the model skill. Besides, model that has realistic central and eastern Pacific SST variability show better skill for TIO SST variability in both interannual and decadal time scales. The present study advocates that better representation of latent heat flux and zonal wind in coupled models is important for the accurate simulation of interannual and decadal variability in TIO SST. [ABSTRACT FROM AUTHOR]

Climate change is a major challenge for human society and the natural environment. Evidence suggests that human activities play a role in increasing temperature at various temporal-spatial scales. The effects of climate change can be assessed by analyzing air temperature trends. According to the latest IPCC report, global average temperatures will increase by 1.5 degrees Celsius by the end of this century. The main purpose of this study is to assess CMIP6 projected temperature over different climate zones of Iran and its trend in the future. The result of this study can be useful for a wide range of management areas, especially the study of water resources, snow reserves, agriculture, and tourism. In this study, the mean annual temperature data of 43 synoptic stations of the Iran Meteorological Organization (IRIMO) were obtained from 1990 to 2009. To evaluate the anomaly and temperature trend in Iran until the end of the 21st century, the data of three models BCC-CSM2-MR, CAMS-CSM1-0, and MRI-ESM2-0 of CMIP6 models under two scenarios of SSP2.4-5 and SSP5.8-5 were used. We divided the period into four twenty-year periods, which are the first period (2020-2040), the second period (2041-2061), the third period (2061-2080), and the fourth period (2081-2100), respectively. To evaluate the air temperature which is the output of selected CMIP6 models, three statistical measures of RMSE, NSE and KGE were used. Using the Delta Change Factor (DCF) method, the bias of the models was corrected. Then, non-parametric Man-Kendall (MK) and Sen's Slope Estimator tests were used to analyze trend analysis and trend slope in long-term data series. The maximum temperature is seen on the northern coast of the Persian Gulf and the Sea of Oman and the minimum temperature is seen in the northwest following the heights of the Zagros and also in the north of Iran (Alborz Mountains). The minimum annual temperature of 10.80°C was calculated based on observed data for the period 1990-2009, and the maximum temperature was 27.90°C on the coasts of the Oman Sea in southeastern Iran and Khuzestan province on the shores of the Persian Gulf in southwestern Iran. The intensity of the increase in temperature in Iran in mountainous areas is mainly due to the increase in the minimum temperature rather than to the maximum one. Generally, the intensity of the warming in Iran is mostly projected in cold and temperate regions. There is also a tendency for the temperature to rise further at higher latitudes. The projected temperature of CMIP6 models based on the SSP2.4-5 and SSP8.5-8 scenarios in Iran over four 20-year periods from 2020 to 2100 showed that the average slope of the temperature trend in Iran will reach 0.05°C per year, which shows an increase by a factor of 0.01 throughout Iran. As a general result, the annual trend of air temperature in Iran, based on observational data and projected output of CMIP6 models, shows warmer climate conditions for Iran. Temperature anomaly was not negative in any of the scenarios and periods; whereas positive anomaly is seen throughout the country. This increase in anomaly can be a major threat to the country's water resources. [ABSTRACT FROM AUTHOR]

To assess the performances of state-of-the-art global climate models on simulating the Arctic clouds and surface radiation balance, the 2001–2014 Arctic Basin surface radiation budget, clouds, and the cloud radiative effects (CREs) in 22 coupled model intercomparison project 6 (CMIP6) models are evaluated against satellite observations. For the results from CMIP6 multi-model mean, cloud fraction (CF) peaks in autumn and is lowest in winter and spring, consistent with that from three satellite observation products (CloudSat-CALIPSO, CERES-MODIS, and APP-x). Simulated CF also shows consistent spatial patterns with those in observations. However, almost all models overestimate the CF amount throughout the year when compared to CERES-MODIS and APP-x. On average, clouds warm the surface of the Arctic Basin mainly via the longwave (LW) radiation cloud warming effect in winter. Simulated surface energy loss of LW is less than that in CERES-EBAF observation, while the net surface shortwave (SW) flux is underestimated. The biases may result from the stronger cloud LW warming effect and SW cooling effect from the overestimated CF by the models. These two biases compensate each other, yielding similar net surface radiation flux between model output (3.0 W/m2) and CERES-EBAF observation (6.1 W/m2). During 2001–2014, significant increasing trend of spring CF is found in the multi-model mean, consistent with previous studies based on surface and satellite observations. Although most of the 22 CMIP6 models show common seasonal cycles of CF and liquid water path/ice water path (LWP/IWP), large inter-model spreads exist in the amounts of CF and LWP/IWP throughout the year, indicating the influences of different cloud parameterization schemes used in different models. Cloud Feedback Model Intercomparison Project (CFMIP) observation simulator package (COSP) is a great tool to accurately assess the performance of climate models on simulating clouds. More intuitive and credible evaluation results can be obtained based on the COSP model output. In the future, with the release of more COSP output of CMIP6 models, it is expected that those inter-model spreads and the model-observation biases can be substantially reduced. Longer term active satellite observations are also necessary to evaluate models' cloud simulations and to further explore the role of clouds in the rapid Arctic climate changes. [ABSTRACT FROM AUTHOR]

Understanding historical and future drought patterns is crucial to acclimation and the mitigation of drought. The negative impact of China’s droughts on the social economy has attracted attention; however, there is still no comprehensive or long-term monitoring pattern for future droughts. Here we evaluated the precipitation and temperature simulation capability of Coupled Model Intercomparison Project Phase 6 (CMIP6) and evaluated the temporal and spatial pattern of droughts during 1961–2099 across China. The results show that the multi-model ensemble mean (MME) is more representative of the observed precipitation and temperatures across China than the single climate model. China experienced an overall drying trend in the historical period. After 1991, the drought frequency (DF), drought duration (DD), and drought intensity (DI) in the northwest of the Inland River Basin and in the Yangtze River Basin increased significantly. Compared with the historical period, China will suffer more frequent drought events, although the DD and DI will be weakened under SSP1-2.6 and SSP2-4.5, while China will experience longer DD and more serious drought events under SSP3-7.0 and SSP5-8.5. The Hai River Basin and Huai River Basin are expected to have more serious drought trends in summer. Compared with historical periods, the drought trend will increase by 2.9–5.7 times and 1.1–4.2 times, respectively. The results can be used for decision making regarding future drought control.

The planetary boundary layer height (PBLH) plays an essential role in affecting many meteorological parameters in climate change, and it is closely associated with the concentration of near surface air pollutants as well. Uncertainty of simulating PBLH is large partly due to the complexity in the modulation of surface energy, and the behaviors of the climate models in reproducing PBLH have not been fully evaluated, which also limits the understanding of future changes in PBLH. Here by utilizing a multi-model ensemble of Coupled Model Intercomparison Project Phase 6 (CMIP6), we first thoroughly evaluate the capability of CMIP6 models in reproducing the spatial distribution of PBLH over China during the period of 1995–2014, revealing large discrepancies among the CMIP6 models. Meanwhile, inaccuracy of simulated meteorological parameters can lead to large deviations of PBLH in models, among which sensible heat flux (SHFLX) and near surface relative humidity (RH) show stronger modulation effects. To enhance the confidence of future projections, we apply a method by selecting the five models with the smallest biases and a high signal-to-noise ratio, and the results indicate that changes in PBLH under shared socioeconomic pathways exhibit a dipole pattern in both summer and winter. Specifically, PBLH is projected to decrease over North China and increase over Central China and South China in summer, while in winter PBLH tends to increase stronger in southern China and weaker in the northern flank. To reveal the mechanism governing the PBLH, we find that changes of PBLH is closely associated with surface energy. SHFLX and near surface RH are the dominant factors shaping the changes in the spatial heterogeneity of PBLH in the future. This study has important implications for the improvement of surface energy and PBLH in climate models in order to effectively predict the future changes of air pollutants. [Display omitted] • CMIP6 models tend to overestimate historical PBLH in summer and winter over China. • Changes in PBLH exhibit quasi-dipole patterns in both winter and summer over China. • Future summer PBLH decrease in north China implies climate benefits reducing ozone. • Sensible heat and relative humidity are vital shaping future PBLH spatial changes. [ABSTRACT FROM AUTHOR]

Northeast China lies in the transition zone from the humid monsoonal to the arid continental climate, with diverse ecosystems and agricultural land highly susceptible to climate change. This region has experienced significant greening in the past three decades, but future trends remain uncertain. In this study, we provide a quantitative assessment of how vegetation, indicated by the leaf area index (LAI), will change in this region in response to future climate change. Based on the output of eleven CMIP6 global climates, Northeast China is likely to get warmer and wetter in the future, corresponding to an increase in regional LAI. Under the medium emissions scenario (SSP245), the average LAI is expected to increase by 0.27 for the mid-century (2041–2070) and 0.39 for the late century (2071–2100). Under the high emissions scenario (SSP585), the increase is 0.40 for the mid-century and 0.70 for the late century, respectively. Despite the increase in the regional mean, the LAI trend shows significant spatial heterogeneity, with likely decreases for the arid northwest and some sandy fields in this region. Therefore, climate change could pose additional challenges for long-term ecological and economic sustainability. Our findings could provide useful information to local decision makers for developing effective sustainable land management strategies in Northeast China.

• Study of the projected reliability of hydraulic infrastructure over the planning horizon. • Hydrological deep learning models driven by CMIP6 models are trained and validated. • Quantification of changes of the exceedance probability of the design flood. • The age at which projected equates to design reliability is reduced. • Infrastructure designed for smaller exceedance probability is more vulnerable. Extreme hydrological events have been recorded around the world, confirming the changing hydroclimate and therefore threatening the reliability over a planning horizon of the existing and projected hydraulic infrastructure. Unfortunately, there is a gap between climate science and engineering planning and design, which is partially filled in this study, whose aim is to evaluate the expected reliability over a planning horizon of a hydraulic infrastructure and its uncertainty for hydroclimatic projections. To achieve this goal, we implemented hydrological deep learning models that are driven by the Coupled Model Intercomparison Project Phase 6 (CMIP6). After validating and testing the hydrological deep learning models, sample series of the annual maximum flood (AMF) were generated for different mountain rivers of Chile and used for a standard frequency analysis that updates the exceedance probability of the flood events used for the hydraulic design. We found that the mean and the standard deviation of the sample series of the AMF are the key parameters that enable quantifying temporal changes in the exceedance probability of the design flow. The cumulative effects of these changes are summarized by the projected reliability over the planning horizon of the infrastructure, and the age of the infrastructure at which this projected reliability equates to the design reliability, quantifies the impact of climate change on the expected security of the hydraulic project. We show that the infrastructure that was designed for the smaller exceedance probability is more vulnerable to climate change, where the updated planning horizon can be as small as 45% of the planning horizon used for the design. [ABSTRACT FROM AUTHOR]

This study evaluates the historical simulation of surface air temperature (SAT) over the Greater Mekong Subregion (GMS) during the period 1981–2014 with 31 models from Coupled Model Intercomparison Project phase 6 (CMIP6). The CMIP6 multi-model ensemble shows a weaker SAT growth trend than the reanalysis data in all seasons. The SAT increase is underestimated mostly in winter, by around 0.2 °C/decade. The simulated SAT climatology shows positive bias over the Gaoligong Mountain in western Yunnan, the Rakhine Mountain in Myanmar, and the Annamite Range in southern Vietnam, and negative bias elsewhere. We make a best-model ensemble to improve the representability of the GMS SAT. The range of SAT interannual variability is underestimated over most parts of GMS in boreal spring, autumn, and winter. In summer, the SAT range bias shows a dipole pattern with negative bias over the northwestern GMS and positive bias over the southeastern GMS. Dynamical diagnosis suggests that the bias in vertical velocity perturbation, manifesting as weakened regional Hadley circulation, is a major contributor to the SAT range bias in summer via vertical advection of mean potential temperature. The bias-corrected projection of the summer SAT is presented for three shared socio-economic pathways (SSPs). The SAT rising relative to the reference period 1995–2014 tends to be more spatially homogeneous under higher emission scenarios. The range of SAT interannual variability would increase over the northern GMS but decrease over the southern GMS under three SSPs scenarios. This SAT dipole variation is associated with intensified regional Hadley circulation over the GMS. The results also reiterate the urgent need for net zero emission by 2050 to maintain a sustainable environment. • Most CMIP6 models underestimate the SAT increase over the Greater Mekong Subregion during the period 1981–2014. • The best-performing models show two opposing biases in the interannual variability of summer SAT between the southeastern and northwestern parts of the Greater Mekong Subregion • The vectical advection of mean potential temperature is the key factor that causes the range bias of the SAT interannual variation in summer [ABSTRACT FROM AUTHOR]

As a coupled large-scale oceanic and atmospheric pattern in the Southern Ocean, the Antarctic circumpolar wave (ACW) has substantial impacts on the global climate. In this study, using the European Centre for Medium-Range Weather Forecasts ERA5 dataset and historical experiment outputs from 24 models of the Coupled Model Intercomparison Project Phase 5 and Phase 6 (CMIP5/CMIP6) spanning the 1980s and 1990s, the simulation capability of models for sea-level pressure (SLP) and sea surface temperature (SST) variability of the ACW is evaluated. It is shown that most models can capture well the 50-month period of the ACW. However, many simulations show a weak amplitude, but with various phase differences. Selected models can simulate SLP better than SST, and CMIP6 models generally perform better than the CMIP5 models. The best model for SLP simulation is the CanESM5 model from CMIP6, whereas the best model for SST simulation is the ACCESS1.3 model from CMIP5. It seems that the SST simulation benefits from the inclusion of both a carbon cycle process and a chemistry module, while the SLP simulation benefits from only the chemistry module. When both SLP and SST are taken into consideration, the CanESM5 model performs the best among all the selected models.

Using data from the Integrated Global Radiosonde Archive Version 2 (IGRA2) and the Multi Model Ensemble (MME) of four global climate models (GCMs), named CanESM5, IPSL-CM6A-LR, MIROC6, and MRI-ESM2-0, within the framework of phase 6 of the Coupled Model Intercomparison Project (CMIP6), we analyzed the changes in atmospheric total column water vapor (TCWV) over Central Asia in the future (2021–2100) under SSP-RCPs scenarios: SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-3.4, SSP4-6.0, and SSP5-8.5, relative to baseline period (1986–2005). Results showed that the annual mean TCWV from IGRA2 was consistent with the model output from 1979 to 2014 in Central Asia. Besides, the spatial distribution of TCWV in Central Asia during the baseline period was consistent between the models. The regional average value of Central Asia was between 10.8 mm and 12.4 mm, and decreased with elevation. TCWV will increase under different SSP-RCPs from 2021 to 2040, but showed different trends after 2040. It will increase under SSP1-1.9 and SSP1-2.6 scenarios from 2021 to 2050, and decrease after that. It will grow from 2021 to 2055 under SSP4-3.4 scenario, and then stay essentially constant. Under SSP2-4.5 and SSP4-6.0 scenarios, TCWV will rise rapidly during 2021–2065, but the growth will decline from 2065 to 2100. TCWV will continue to increase under SSP3-7.0 and SSP5-8.5 scenarios, and the largest increase is projected under SSP5-8.5 scenario. Change in near-surface temperature (Ts) matched the change in TCWV, but changes in precipitation and evapotranspiration are not significant during 2021–2100. In spite of the large variations in TCWV under different SSP-RCPs, the dominant characteristic in all scenarios shows that a large TCWV increase is demonstrated over areas with small TCWV amounts during the baseline period. On the contrary, increases will be small where the TCWV amounts had been large during the baseline period. The change in TCWV is highly correlated to the increase in Ts in Central Asia. Under SSP2-4.5, SSP3-7.0, SSP4-3.4, SSP4-6.0, and SSP5-8.5 scenarios, the higher the temperature due to higher radiative forcing, the steeper the regression slope between TCWV and Ts change. It is closest to the theoretical value of the Clausius-Clapeyron equation under SSP3-7.0 and SSP5-8.5 scenarios, but not presented under other scenarios. Spatially, steeper regression slopes during 2021–2100 have been found around the Caspian Sea in the southwest and in the high-elevation areas in the southeast of Central Asia, which is likely related to the abundant local water supply for evaporation.

We investigate the influence of external forcings on the frequency of temperature extremes over land at the global and continental scales by comparing HadEX3 observations and simulations from the Coupled Model Intercomparison Programme Phase 6 (CMIP6) project. We consider four metrics including warm days and nights (TX90p and TN90p) and cold days and nights (TX10p and TN10p). The observational dataset during 1951–2018 shows continued increases in the warm days and nights and decreases in the cold days and nights in most land areas in the years after 2010. The area of the so-called ‘warming hole’ in North America is much reduced in 1951–2018 compared with that in 1951–2010. The comparison between observation and simulations based on an optimal fingerprinting method shows that the anthropogenic forcing, dominated by greenhouse gases, plays the most important role in the changes of the frequency indices. Changes in CMIP6 multi-model mean response to all forcing need to be scaled down to best match the observations, indicating that the multi-model ensemble mean may have overestimated the observed changes. Analyses that involve signals from anthropogenic and natural external forcings confirm that the anthropogenic signal can be detected over global land as a whole and for most continents in all temperature indices. Analyses that include signals from greenhouse gas (GHG), anthropogenic aerosol (AA) and natural external (NAT) forcings show that the GHG signal is detected in all indices over the globe and most continents while the AA signal can be detected mainly in the warm extremes but not the cold extremes over the globe and most continents. The effect of NAT is negligible in most land areas. GHG’s warming effect is offset partially by AA’s cooling effect. The combined effects from both explain most of the observed changes over the globe and continents.

Based on observations and reanalyses, the ability of 24 models from the Coupled Model Inter-comparison Project Phase 6 (CMIP6) in simulating the Hadley circulation's (HC) long-term variability was evaluated, and possible reasons for the simulation uncertainties were analyzed. The results showed that each model utilized in this study can reproduce the HC climatological pattern well, but the simulated HC variability shows large inter-model uncertainties. The first principle mode of the HC's long-term variability (EOF1) exhibited an equatorial asymmetric structure (i.e., asymmetric mode, AM) in observations. The CMIP6 models were divided into two groups according to their abilities to reproduce the AM: models that could simulate the AM well were classified as Type I, and models that could not simulate the AM and exhibited an approximately equatorial symmetric structure of the EOF1 were classified as Type II. The results showed that the EOF1 spatial structure is determined by variations in the meridional gradient of the tropical ocean sea surface temperature (SST). Type I models can significantly reproduce the relationship between the EOF1's variation and the meridional gradient of tropical SST, so as to reproduce the HC's long-term variability characteristics. In Type II models, the EOF1's variation is tightly related to SST distribution, similar to the El Niño-Southern Oscillation (ENSO). Type II models exaggerate the variation of ENSO, which is associated with equatorial symmetric structure of SST anomalies, resulting in enhanced variability in the HC of the equatorial symmetric part. This is the reason why EOF1 of the HC in Type II models exhibits an equatorial symmetric structure. The results point out the possible reasons for the different simulations of CMIP6 models in the HC variability and provide a reference for future climate models to be developed in this area. • CMIP6 models reproduce the HC climatological pattern well, but the HC variability shows large inter-model uncertainties. • A model reproduction of the tropical SST meridional gradient is a key factor in determining the HC variability simulation. • The unsatisfactory performance of the variability of the HC is due to the exaggeration of the ENSO. [ABSTRACT FROM AUTHOR]

Cold surge (CS) events are the most serious extreme cold events in winter in China, causing large economic losses and casualties. The occurrence of CS events has slightly increased since the 1990s. However, the possible future changes in these events remain unclear, and quantifying robust projected changes in CS events is important for developing adaptation and policy planning. Here, we project the occurrence of CS events and strong CS (SCS) events using the weighted multi-model ensemble (MME) of the Coupled Model Intercomparison Project 6 (CMIP6) through the application of the rank-based weighting (RBW) approach under three shared socioeconomic pathway (SSP126, SSP245, and SSP585) scenarios. The corresponding weights of each model were obtained depending on the comprehensive historical performance from three aspects: climatology, spatial variation, and interannual variability. The results show that the RBW approach can reduce the relative bias by approximately 50% compared to the unweighted MME. The occurrence of CS and SCS events shows a decreasing trend during 2015–2099 over northern China under the three SSP scenarios. There are also robust change projections during the long-term (2080–2099) and 2015–2099 periods under SSP245 and SSP585, especially in the NEC region, which exhibits a signal-to-noise ratio (SNR) that is >1. However, the occurrence of SCS events shows slight increases of 1.18% and 3.55% over northern China (notably western and eastern Northwest China) during the near-term (2020–2039) under SSP126 and SSP245, respectively. Obvious reductions in projected uncertainty are widespread throughout northern China after applying the RBW approach compared to the unweighted MME, which mainly depends on the scenario, region, and term variation. Then, a robust decreased frequency may contribute to projected changes in large-scale atmospheric circulation (a more positive AO and weaker SH and EAT) under SSP585. Our results emphasize that the weighted MME can be taken into account when projecting future extreme climate change in some areas to enhance reliability. • Projected changes in cold surges are more credible using the rank-based weighting method compared to the unweighted MME. • A slight increase in strong cold surges during the near-term (2020–2039) under the SSP126 and SSP245 scenarios is expected. • The significant reduction in the projected uncertainty is >50% under the SSP585 scenario. [ABSTRACT FROM AUTHOR]

Observations indicate that the Tibetan Plateau (TP) has an annual-mean ~9.3 W m−2 positive radiation budget (R T) at the top of the atmosphere, the largest at the same continental latitudes. This unique radiative heating is critical to the TP's thermal forcing and hydrological cycle. Here we use satellite and reanalyzed data to investigate the characteristics of R T over the TP in the observation and 28 CMIP6 models. The positive observational R T is mainly caused by low surface temperature associated with high elevation. Most models underestimate annual mean R T over the whole TP, with a multimodel average of 2.0 W m−2. This R T bias results mainly from weaker absorbed shortwave radiation (ASR) that exhibits substantial seasonal and regional differences. Serious R T , ASR, and surface albedo biases appear over the western TP. For instance, their wintertime multimodel-mean biases of −44.9 W m−2, −53.3 W m−2, and 0.23 are larger than the eastern TP's counterparts (−38.6 W m−2, −40.6 W m−2, and 0.15). Simulated R T also shows a large intermodel spread. In the models with worse R T 's performance, weaker ASR is primarily attributed to higher surface albedo that coincides well with lower surface temperature. In contrast, the models that reproduce well the R T have less surface albedo bias but somewhat overestimate surface temperature. Weaker simulated cloud radiative cooling reduces reflected shortwave radiation that alleviates the R T bias, especially over the springtime eastern TP. This study highlights the importance of land surface states and clouds in modeling the TP's climate. Observations indicate that the Tibetan Plateau (TP) has an annual-mean ~ 9.3 W m−2 positive radiation budget (R T) at the top of the atmosphere, the largest among land regions in the same latitudes. This study aims to investigate the characteristics of R T over the TP in the observation and CMIP6 models. Most models underestimate annual mean R T over the whole TP, with a multimodel average of 2.0 W m−2. This bias results mainly from weaker absorbed shortwave radiation, exhibiting substantial seasonal and regional differences. CMIP6 simulations show a large multimodel spread, especially over the wintertime and springtime western TP. This study highlights the importance of land surface states (e.g., surface temperature and albedo) and clouds in modeling the TP's climate. • The Tibetan Plateau (TP) has the largest annual mean positive top-of-atmosphere radiation budget (R T) at the same continental latitudes. • Most CMIP6 atmospheric models underestimate annual mean R T over the whole TP. • This R T bias results mainly from weaker absorbed shortwave radiation that exhibits substantial seasonal and regional differences over the western and eastern TP. [ABSTRACT FROM AUTHOR]

Historical runs of 30 Coupled Model Intercomparison Project Phase-6 (CMIP6) General circulation models (GCMs) are evaluated for the representation of Boreal Summer Intraseasonal Oscillations (BSISO). Several statistical metrics were developed to evaluate the characteristic features of BSISO, such as propagation, phase speed, and exchange of air-sea fluxes at the air-sea interface over the major regions of the Indian Summer Monsoon (ISM). The mean state of the monsoon precipitation in the CMIP6 models is evaluated by using seasonal mean bias, pattern correlation, and root mean square error. The majority of CMIP6 models underestimate the precipitation over central India and overestimate the precipitation over the eastern equatorial region. Multi-model mean (MME) of the models shows good agreement of precipitation pattern with the observations. In the observations, the precipitation anomalies propagate northward from the equatorial latitudes to the northern latitudes over the ISM region (60°E-100°E longitudes). However, the initiation of northward propagating convection shows a significant variation with time in the CMIP6 models. Most of the models well simulated the BSISO propagation over the Bay of Bengal (BoB) and the Indian subcontinent. The majority of the models underestimate the phase speed of BSISO over the Arabian Sea (AS), and easterlies from the western north Pacific, which led to the failure of models in representing the northwest-southeast tilt of convection. Surface turbulent fluxes and zonal winds lag the deep convection over the North Indian Ocean on intraseasonal timescales. However, misrepresentation of air-sea fluxes in the CMIP6 models leads to the significant biases of intraseasonal variances. This study examines the simulation characteristic features of BSISO by CMIP6 models and is mainly attributes them to the atmospheric internal dynamics and air-sea interactions. The present study further suggests that improving atmospheric-oceanic feedback mechanisms, specific humidity, and low-level winds in the CMIP6 models is necessary to accurately predict the ISM intraseasonal variability. • The majority of the CMIP6 models well capture the northward propagation of precipitation anomalies over North Indian Ocean. • In the observations, the phase-speed of BSISO over Bay of Bengal is less than that of the Arabian Sea. • The majority of the models underestimate the phase-speed of BSISO over Arabian Sea, leads to the failure of models in represent the northwest-southeast tilt of convection. • Maximum number of models capture the ocean to atmosphere response with SST lead of precipitation by ~10 (~7) days over Arabian Sea (Bay of Bengal). [ABSTRACT FROM AUTHOR]

Understanding historical and future drought patterns is crucial to acclimation and the mitigation of drought. The negative impact of China's droughts on the social economy has attracted attention; however, there is still no comprehensive or long-term monitoring pattern for future droughts. Here we evaluated the precipitation and temperature simulation capability of Coupled Model Intercomparison Project Phase 6 (CMIP6) and evaluated the temporal and spatial pattern of droughts during 1961–2099 across China. The results show that the multi-model ensemble mean (MME) is more representative of the observed precipitation and temperatures across China than the single climate model. China experienced an overall drying trend in the historical period. After 1991, the drought frequency (DF), drought duration (DD), and drought intensity (DI) in the northwest of the Inland River Basin and in the Yangtze River Basin increased significantly. Compared with the historical period, China will suffer more frequent drought events, although the DD and DI will be weakened under SSP1-2.6 and SSP2-4.5, while China will experience longer DD and more serious drought events under SSP3-7.0 and SSP5-8.5. The Hai River Basin and Huai River Basin are expected to have more serious drought trends in summer. Compared with historical periods, the drought trend will increase by 2.9–5.7 times and 1.1–4.2 times, respectively. The results can be used for decision making regarding future drought control. [ABSTRACT FROM AUTHOR]