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In this study, the performance of 24 Coupled Model Intercomparison Project Phase 6 (CMIP6) models in simulating the dynamic processes of Arctic sea ice concentration (SIC)‐ and El Niño‐Southern Oscillation (ENSO)‐ forced teleconnection during winter is subjectively and objectively evaluated. The Arctic SIC‐forced teleconnection is associated with a warm Arctic‐cold Eurasian pattern of surface temperature (T2m), a low Arctic‐high Eurasian pattern of sea level pressure (SLP), and a southeastward propagating wave‐train originating from Arctic in the upper troposphere. The ENSO‐forced teleconnection is associated with a poleward propagating wave‐train originating from tropical Pacific in the upper troposphere, a low North Pacific‐high Arctic pattern of SLP, and a cold North Pacific‐warm Greenland pattern of T2m. The metrics of Taylor skill scores and Distance between indices of simulation and observation (DISO) are used to objectively and quantitatively evaluate the performance of models. The results of subjective and objective evaluation are essentially consistent. The CanESM5, MPI‐ESM1‐2‐HR, EC‐Earth3, and MRI‐ESM2‐0 models have the best performance in simulating the Arctic SIC‐forced teleconnection. The CESM2, ACCESS‐CM2, NESM3, NorESM2‐MM, CAS‐ESM2‐0, MRI‐ESM2‐0 models have the best performance in simulating the ENSO‐forced teleconnection. The two best‐performing multi‐model ensembles well reproduce the dynamic processes of the Arctic SIC‐ and ENSO‐ forced teleconnection. The diversity of model performance is attributed to the different skills of different models in simulating the interannual variability of Arctic SIC, the anomalous deep warm high over the Barents‐Kara Seas, the interannual variability of tropical Pacific SSTs, and the wave number of poleward propagating Rossby waves. Plain Language Summary: The connection between Arctic and tropical climates has an important influence on the climate in Northern Hemisphere. The Arctic sea ice‐driven teleconnection may induce increased cold surges toward the low latitude regions of East Asia, while the El Niño‐Southern Oscillation (ENSO)‐driven teleconnection may induce increased temperatures over northern North America and Greenland. The Coupled Model Intercomparison Project Phase 6 (CMIP6) models consists of state‐of‐the‐art numerical models that are widely used in climate simulation and prediction. Hence, it is important to understand the performance of these models in simulating the dynamic processes of Arctic‐tropical climate connection and the potential reasons. In this study, the dynamic processes in ocean‐atmosphere associated with the Arctic sea ice‐ and ENSO‐ driven teleconnection are first analyzed using observed/re‐analysis data. The performance of 24 CMIP6 models in simulating the dynamic processes of Arctic sea ice‐ and ENSO‐ driven teleconnection is then subjectively and objectively evaluated. The results indicate a diversity of performance in these models. This diversity are mainly caused by the different skills of models in simulating the interannual variability of SIC and ENSO as well as the associated atmospheric circulation anomalies. Key Points: The performance of CMIP6 models has a diversity in simulating the dynamic processes of Arctic‐tropical climate connectionBest‐performing multi‐model ensembles with good skill in simulating dynamic processes of Arctic‐tropical climate connection are selectedThe diversity in performance of models are affected by the skill of models in simulating the interannual variability sea ice and SSTs [ABSTRACT FROM AUTHOR]

Observational and modeling studies have elucidated the influential role played by the southern and northern extratropical Pacific (SEP and NEP) forcing in shaping dynamics of tropical Pacific climate variability. However, the relative importance of the NEP and SEP and the timescale on which they impact the tropics remain unclear. Using a linear inverse model (LIM) that selectively incorporates or excludes tropical‐extratropical coupling, we find a reduction in tropical interannual variability (∼40%) and low‐frequency (sub‐decadal to decadal) variability in the southeastern tropical Pacific region (∼70%) in the absence of SEP. Conversely, the absence of NEP yields no significant impact on tropical interannual variability but markedly diminishes low‐frequency variability in the central tropical Pacific region (∼70%). LIM and statistic diagnostics on CMIP6 models show the low‐frequency to total variability ratio in the tropical Pacific depending on their NEP and SEP representation. Models with more (less) low‐frequency power tend to show stronger NEP (SEP) dynamics. Plain Language Summary: The tropical Pacific climate variability exerts a strong impact on global climate, regional weather, and marine ecosystems. The tropical and extratropical Pacific are closely coupled with each other through oceanic and atmospheric processes. Previous studies have shown that the southern and northern extratropical Pacific (SEP and NEP) forcing greatly impact the tropical Pacific climate variability. To understand and predict tropical Pacific variability, it is necessary to study the relative importance and the timescale on which the SEP and NEP exert their influence. In this study, we use an empirical dynamical model to exclude the impacts of SEP or NEP on the tropical Pacific based on the observational data. We find that the absence of SEP leads to a significant reduction of interannual variance (∼40%) and the low‐frequency (sub‐decadal to decadal) variance in the southeastern tropical Pacific region (∼70%), while the absence of the NEP does not change the interannual variance but significantly reduces the low‐frequency variance in the central tropical Pacific region (∼70%). In observations, the ratio of low‐frequency to total tropical variability is 0.36, while CMIP6 models exhibit a wider range, with enhanced low (high) frequency power associated with stronger NEP (SEP) dynamics. Key Points: The SEP dynamics contribute ∼40% of the tropical interannual variance and ∼70% of the southeastern tropical low‐frequency varianceThe NEP dynamics do not impact the tropical interannual variance but lead to ∼70% of the central tropical low‐frequency variabilityCMIP6 models show stronger NEP (SEP) dynamics tend to manifest ENSO with more (less) low‐frequency (>6 years) power [ABSTRACT FROM AUTHOR]

In light of the lack of ground-based observations, this study utilizes reanalysis data from the CHIRPS database and CMIP6 models under the SSP5-8.5 scenario to predict future rainfall in the Beni–Irumu region of eastern DR Congo. The use of reanalysis data offers a viable method for understanding historical and future climate trends in regions with limited ground data. Using a spatial resolution of 0.05°, selected general circulation models (GCMs) were downscaled to CHIRPS data. Analysis of historical rainfall data over 32 years reveals spatial disparities, with high-altitude regions like Mount Stanley experiencing higher annual mean rainfall (1767.87 ± 174.41 mm) compared to lower areas like Kasenyi (863.65 ± 81.85 mm), in line with orographic effects. Future projections under the SSP5-8.5 scenario indicate significant decreases in rainfall for areas such as Oicha (−565.55 mm) in the near term, while regions like Kasindi/Yihunga exhibit moderate decrease (−58.5 mm). In the mid-term, some areas show signs of recovery, with Bulongo experiencing a minor decrease (−21.67 mm), and Kasindi/Yihunga (+152.95 mm) and Kyavinyonge (+71.11 mm) showing increases. Long-term projections suggest overall improvements, with most areas experiencing positive rainfall differences; however, persistent challenges remain in Oicha (−313.82 mm). These findings highlight the dynamic impacts of climate change on rainfall distribution in the Beni–Irumu region, underscoring the need for targeted interventions to address the varied impacts, especially in vulnerable regions like Oicha. [ABSTRACT FROM AUTHOR]

El Niño–Southern Oscillation (ENSO) usually peaks in the boreal winter—November to January of the following year. This particular feature of ENSO is known as the seasonal phase locking of ENSO. In this study, based on 34 climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), the seasonal phase-locking characteristics of the model-simulated El Niño and La Niña events are evaluated in terms of the evolution of the SST anomalies associated with ENSO and the probability distribution of the peak month—the time at which ENSO peaks. It is found that CMIP6 models underestimate the phase-locking strength of ENSO for both El Niño and La Niña events. The ensemble mean peak month matches the observations, but the inter-model spread is large. The models simulate the phase locking of El Nino events better than that of La Niña events, and the large simulation bias of CMIP6 for La Niña phase-locking in the models may have an impact on the simulation of seasonal phase-locking in the ENSO. [ABSTRACT FROM AUTHOR]

Numerical models serve as an essential tool to investigate the causes and effects of Arctic sea ice changes. Evaluating the simulation capabilities of the most recent CMIP6 models in sea ice volume flux provides references for model applications and improvements. Meanwhile, reliable long-term simulation results of the ice volume flux contribute to a deeper understanding of the sea ice response to global climate change. In this study, the sea ice volume flux through six Arctic gateways over the past four decades (1979–2014) were estimated in combination of satellite observations of sea ice concentration (SIC) and sea ice motion (SIM) as well as the Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS) reanalysis sea ice thickness (SIT) data. The simulation capability of 17 CMIP6 historical models for the volume flux through Fram Strait were quantitatively assessed. Sea ice volume flux simulated from the ensemble mean of 17 CMIP6 models demonstrates better performance than that from the individual model, yet IPSL-CM6A-LR and EC-Earth3-Veg-LR outperform the ensemble mean in the annual volume flux, with Taylor scores of 0.86 and 0.50, respectively. CMIP6 models display relatively robust capability in simulating the seasonal variations of volume flux. Among them, CESM2-WACCM performs the best, with a correlation coefficient of 0.96 and a Taylor score of 0.88. Conversely, NESM3 demonstrates the largest deviation from the observation/reanalysis data, with the lowest Taylor score of 0.16. The variability of sea ice volume flux is primarily influenced by SIM and SIT, followed by SIC. The extreme large sea ice export through Fram Strait is linked to the occurrence of anomalously low air temperatures, which in turn promote increased SIC and SIT in the corresponding region. Moreover, the intensified activity of Arctic cyclones and Arctic dipole anomaly could boost the southward sea ice velocity through Fram Strait, which further enhance the sea ice outflow.

This study used 15 Coupled Model Intercomparison Project Phase 6 (CMIP6) models to analyze the mean and extreme projection precipitation during the Indian summer monsoon (ISM) season, i.e., June to September (JJAS). A continuous rise in precipitation and its associated unpredictability were predicted by global climate models using the CMIP6. The extreme values are estimated on the basis of generalized extreme value (GEV) distribution. Four socioeconomic paths are used as SSP126, SSP245, SSP370 and SSP585 to understand the impact of low-emission to high-emission scenarios. In all the scenarios, it is seen that all the models predict a significant rise in JJAS mean and extreme precipitation. Moreover, a strong correlation is obtained between the historical (1995–2014) and near-emission future simulation (2021–2040), mid-emission future simulation (2041–2060), high-emission future simulations (2081–2100) of air temperature and precipitation. In extreme emission scenarios, ensemble mean of CMIP6 models shows an increasing amplitude in surface air temperature and precipitation over India in the near future (2021–2040), mid future (2041–2060) and the end of the century (2081–2100). The CMIP6 simulations mainly support the results of all models, but they also demonstrate improved robustness across models. HIGHLIGHTS Projection of mean and extreme precipitation over India using CMIP6 models during JJAS season.; A generalized extreme values distribution is utilized to estimate the climate extreme.; Four socioeconomic paths are used as SSP126, SSP245, SSP370, and SSP585 to understand the impact of low-emission to high-emission scenarios over precipitation.; A strong association is observed between surface air temperature and precipitation over India.; Extreme precipitation may rise up to 80% over India in the SSP585 scenario.;

Data scarcity and unavailability of observed rainfalls in the northeastern states of India limit prediction of extreme hydro-climatological changes. To fill this gap, a data assimilation approach has been applied to re-construct accurate high-resolution gridded (5 km2) daily rainfall data (2001–2020), which include seasonality assessment, statistical evaluation, and bias correction. Random forest (RF) and support vector regression were used to predict rainfall time series, and a comparison between machine learning and data assimilation-based gridded rainfall data was performed. Five gridded rainfall datasets, namely, Indian Monsoon Data Assimilation and Analysis (IMDAA) (12 km2), APHRODITE (25 km2), India Meteorological Department (25 km2), PRINCETON (25 km2), and CHIRPS (25 and 5 km2), have been utilized. For re-constructed rainfall datasets (5 km2), the comparative seasonality and change assessment have been performed with respect to other rainfall datasets. CHIRPS and APHRODITE datasets have shown better similarities with IMDAA. The RF and assimilated rainfall (AR) have superiority based on bias and extremity, and AR data were recognized as the best accurate data (>0.8). Precipitation change analysis (2021–2100) performed utilizing the bias-corrected and downscaled CMIP6 datasets showed that the dry spells will be enhanced. Considering the CMIP6 moderate emission scenario, i.e., SSP245, the wet spell will be enhanced in future; however, when considering SSP585 (representing the extreme worst case), the wet spells will be decreased. HIGHLIGHTS A unique data assimilation approach is applied to construct an accurate high-resolution gridded (5 km2) daily rainfall time series.; Evaluation and bias correction of multisource gridded rainfall datasets were performed.; Random forest and support vector regression machine learning methods were applied for the prediction of rainfall.; Assessment of long-term rainfall changes was done in the wettest regions of the world.;

This study assesses the Hadley circulation (HC) anomalies' structure during the historical El Niño decaying stage, utilizing 29 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6). While all 29 models successfully capture the climatological HC's structure, they exhibit considerable variations in representing HC anomalies during this stage, highlighting noteworthy inter-model differences. The CMIP6 models are categorized into two distinct groups grounded on the correlation coefficient (r) between simulated anomalies of the HC and observed patterns: Type I models (r > 0.5) and Type II models (r < − 0.5). The findings elucidate that the pivotal determinant influencing the successful characterization of HC anomalies lies in the models' capacity to simulate tropical sea surface temperature (SST) variability and the asymmetric decay rates of SST on the equatorial flanks during this stage. Type I models proficiently replicate the asymmetric structure of zonal-mean SST anomalies, which subsequently results in an asymmetric structure of HC anomalies. Conversely, Type II models generate zonal-mean SST anomalies characterized by equatorial symmetry. Therefore, these results underscore the substantial influence of El Niño–Southern Oscillation (ENSO) variability on the connection between tropical atmospheric circulation and SST. Moreover, this research offers valuable insights for improving the models' capacity to simulate tropical air–sea coupling processes effectively. [ABSTRACT FROM AUTHOR]

Diurnal temperature range (DTR), as a core indicator of the Earth system, exhibits obvious temporal and spatial variations, which is not entirely consistent over global. The historical simulation capabilities of 19 Coupled Model Intercomparison Project 6 (CMIP6) models for DTR were firstly evaluated against CRU_TS v4.04 data. Future changes under three shared socioeconomic pathways (SSP1-2.6, SSP2-4.5, SSP5-8.5) in DTR were projected using a multi-model ensemble mean (MME), and its interannual variations were seasonally explored through empirical orthogonal function (EOF) analysis. The results indicated that CMIP6 models could reflect the decreasing trend of DTR during 1901–2014, with the global spatial correlation coefficient between models and observation ranging from 0.4 to 0.7. MME outperformed individual models in both spatial and temporal variations, indicating higher accuracy and reliability. The future changes of DTR exhibited significantly decrease across the northern hemisphere and increase in the South America, and change magnitude enlarged with time extension and emission intensity, especially by more than 0.4 °C under SSP5-8.5. The decreasing trend of global DTR was kept in SSP2-4.5 and SSP5-8.5, while SSP1-2.6 changed increasing trend during 2015–2100. DTR showed seasonal variations and was mainly influenced by colder months. The dominant modes of interannual DTR and their relationship with the 500 hPa geopotential height, the 200 hPa U wind, and the outgoing longwave radiation showed higher features in tropical regions. The highly positive correlation between the first mode of DTR and the Niño3.4 index in December/January/February (DJF) is 0.67, indicating significant influence of the El Niño-Southern Oscillation on DTR. [ABSTRACT FROM AUTHOR]

Determining the future climate situation by using climate models seems necessary to consider in the field of adaptation or reducing the adverse effects of climate change. In this research, the temporal trend of rainfall, minimum and maximum temperature in the four stations in the Qarasu basin and in addition to, investigated using Thiessen's interpolation method. Among the five models of the CMIP6, three models were selected as the best models and used for MME. Biass Correction was done with CMHyd software for scenarios SSP2-4.5 and SSP5- 8.5 in periods 2026-2050, 2051-2075 and 2076-2100. The trend of variables in the base period (1990-2014) and future were investigated with Mann-Kendall test and sens slope. The results of analysis significant trends annual average maximum and minimum temperature of all stations and in catchment area according to SSP2.4-5 scenario in two near and middle future periods and for SSP5-8.5 scenario in all three future periods have a significant trend at the 99% level. In analysis significant trend seasonal rainfall according to SSP2.4-5 scenario in the summer season distant future all stations and in near future of the station area of Gorgan regional water company at the 95% level and for the SSP5-8.5 scenario only in the winter season in the distant future Ghafarhaji station has a significant trend at the 99% level. The future monthly rainfall in the catchment area according to scenario of SSP2.4-5 in August at the 99% probability level and SSP5-8.5 in March at the 95% probability level have a significant trend. [ABSTRACT FROM AUTHOR]

The results of a piControl experiment investigating general circulation models participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) were examined. The global interannual variability in the monthly surface temperature (ST) and sea level pressure (SLP) anomalies was considered. The amplitudes of the fluctuations in the anomalies of these meteorological fields between opposite phases of the El Niño–Southern Oscillation (ENSO) were calculated. It was shown that most CMIP6 models reproduced fluctuations in the ST and SLP anomalies between El Niño and La Niña not only in the equatorial Pacific, but also throughout the tropics, as well as in the middle and high latitudes. Some of the CMIP6 models reproduced the global structures of the ST and SLP anomaly oscillations quite accurately between opposite phases of ENSO, as previously determined from observational data and reanalyses. It was found that the models AS-RCEC TaiESM1, CAMS CAMS-CSM1-0, CAS FGOALS-f3-L, CMCC CMCC-ESM2, KIOST KIOST-ESM, NASA GISS-E2-1-G, NCAR CESM2-WACCM-FV2, and NCC NorCPM1 reproduced strong ENSO teleconnections in regions beyond the tropical Pacific. [ABSTRACT FROM AUTHOR]

Abstract Observational and modeling studies have elucidated the influential role played by the southern and northern extratropical Pacific (SEP and NEP) forcing in shaping dynamics of tropical Pacific climate variability. However, the relative importance of the NEP and SEP and the timescale on which they impact the tropics remain unclear. Using a linear inverse model (LIM) that selectively incorporates or excludes tropical‐extratropical coupling, we find a reduction in tropical interannual variability (∼40%) and low‐frequency (sub‐decadal to decadal) variability in the southeastern tropical Pacific region (∼70%) in the absence of SEP. Conversely, the absence of NEP yields no significant impact on tropical interannual variability but markedly diminishes low‐frequency variability in the central tropical Pacific region (∼70%). LIM and statistic diagnostics on CMIP6 models show the low‐frequency to total variability ratio in the tropical Pacific depending on their NEP and SEP representation. Models with more (less) low‐frequency power tend to show stronger NEP (SEP) dynamics.

This study assesses the aerosol optical depth (AOD) from historical simulations (2003–2014) and future climate scenarios (2015–2100) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) over the Middle East and North Africa (MENA) region. Multi-model mean (MME) AOD statistics are generated as the average of those from the five best-performing CMIP6 models, which reproduce observational climate statistics. These models were selected based on the validation of various climate metrics, including strong pattern correlations with observations (>0.8). The resulting MME reproduces the observed AOD seasonal cycle well. The observed positive trends (summer and annual) over the Arabian Peninsula (AP) and negative trends (winter) over North Africa are well captured by MME, as regional meteorological drivers associated with observed AOD trends, with few discrepancies. Crucially, the MME fails to capture the AOD trends over North West Africa (NWA). For MENA and NWA regions, two high-emission scenarios, SSP370 and SSP585, project a continuous rise in the annual mean AOD until the end of the century. In contrast, the low-emission scenarios, SSP126 and SSP245, project a decreasing AOD trend. Interestingly, the projected future AOD area-averaged over the AP region varies significantly across all four scenarios in time. Notably, a substantial decrease of about 8–10% in the AOD is projected by the SSP126, SSP245, and SSP585 scenarios at the end of the century (2080–2100) relative to the current period. This projected decrease in annual-mean AOD, including the frequency of extreme AOD years under SSP585, is potentially associated with a concurrent increase in annual-mean rainfall over the AP.

This research investigates the impact of climate change on the irrigation water requirements for coffee plants in the fruit development stage in Gia Lai and Dak Lak provinces, Vietnam. Observation data were collected from four meteorological stations, namely, An Khe, Pleiku, Buon Ma Thuot, and Buon Ho. To simulate future scenarios, five CMIP6 models (BBC-CSM2-MR, CanESM5, GFDL-ESM4, MIROC6, and MPIESM1.2-HR) were used with the SSP24.5 scenario for the years 2030-2049 and 20802099. The irrigation water requirements were calculated by the Cropwat model version 8.0. For 2030-2049, the simulation results of BCC-CSM2-MR at An Khe showed the highest increase of approximately 90 mm, while CanESM5 displayed only a 4 mm rise. On the other hand, both MIROC6 and MPI-ESM1.2-HR indicated a slight reduction, ranging from 4 to 10 mm at all stations. For 20802099, the BBC-CSM2-MR model at An Khe recorded the highest increase of 100 mm, whereas the GFDL-ESM4 model showed a decline of 90 mm at Buon Ho. Other models showed a fluctuation increase from 40 to 60 mm. This means that climate change has a significant impact on the irrigation water requirements for coffee plants in the Central Highlands of Vietnam. HIGHLIGHTS Five CMIP6 global climate models and the bias correction and spatial disaggregation method have been used to improve the resolution to get future data (rainfall and temperature).; Finding the variations of irrigation water requirements for coffee plants in the fruit development stage (the major industrial crop of this region) in Dak Lak and Gia Lai Provinces, the Central Highlands of Vietnam was warranted in this study. This area is affected heavily by climate change.;

This work investigates the inter-model diversity of the Pacific Decadal Oscillation's (PDO) impact on tropical cyclone frequency (TCF) over the Western North Pacific (WNP) from the historical simulation of twenty-two Coupled Model Intercomparison Project Phase 6 (CMIP6) models. The impact of the PDO is expressed as the TCF difference between the positive and negative PDO phases. The comparison between the models with high PDO skill and low PDO skill shows that the PDO-related sea surface temperature (SST) gradient between the western and central tropical Pacific plays an important role in changing the large-scale atmospheric dynamic fields for TC genesis and, thus, the TCF over the WNP. This SST gradient also significantly contributes to the inter-model spread of PDO's impact on TCF across the 22 CMIP6 models. We, therefore, stress that the PDO-related eastward SST gradient between the western and central tropical Pacific triggers the lower troposphere westerly and eastward extending of the monsoon trough over the WNP. The moistening of the atmosphere and enhancing ascending motion in the mid-troposphere promote convection, leading to the easterly wind anomaly over the upper troposphere, which reduces the vertical wind shear. Those favorable dynamic conditions consistently promote the TC formation over the southeastern part of the Western North Pacific. Our results highlight that PDO could impact the WNP TCF through its associated tropical SST gradient. [ABSTRACT FROM AUTHOR]

Background and Objectives: Global warming cause changes in temperature variables and consequently increase in the occurrence of extreme events like floods and droughts, result from extreme weather in most cases; Therefore, it is important to study and investigate the future changes of extreme values of meteorological and hydrological parameters, including temperature, more than the average or median value. Therefore, the purpose of this research is to investigate the effects of climate change on the trend of seasonal changes in extreme temperatures (very high and very low values) in coastal stations of Mazandaran province based on CMIP6 models and quantile regression method. Materials and Methods: In this research, in order to investigate the trend of extreme values of temperature in Mazandaran province for the future periods, the output of the sixth generation of climate change models (Coupled Model Intercomparison Project phase 6, CMIP6) was used. For this purpose, minimum and maximum temperature of four main synoptic stations of the province, including Babolsar, Qarakhil, Ramsar and Nowshahr, and also the output of the NorESM2-MM climate model from the CMIP6 in near future (2026-2055) and far future (2071-2100) at three optimistic (SSP126), moderate (SSP245) and pessimistic (SSP585) scenarios was used. In order to downscale climate model data at the studied meteorological stations, various methods in the CMhyd downscaling software were used, and the temperature data output for the method that had higher accuracy, was selected for analysis of trend. In the next step, the quantile regression method was used to investigate the seasonal trend of temperature extreme values and the results were analyzed. Results: The results of downscaling using different methods have shown that the Variance Scaling method has the best performance among the available methods in CMhyd. In general, the results show a positive temperature anomaly (annual temperature increase compared to the base period in SSP126=1.3, SSP245=2.56, SSP585=3.2 °C) in Mazandaran province in all months of the year until the end of the 21st century. The intensity of the anomalies is higher in the warm months than in the cold ones. Under the optimistic scenario, the extreme values of the temperature variables in the spring and autumn seasons will decrease by a maximum of 1 degree per decade in the distant future. However, the average minimum temperature is increasing in all scenarios (including the optimistic scenario) and the decrease is only predicted for extreme values. Under the average and pessimistic scenarios, there will be an increase in the temperature extreme values in all seasons, so that its intensity will be greater for the pessimistic scenario and in the spring season (0.15 °C per year). Conclusion: According to the results, it concluded that the significant increase in extreme temperature during the day and night, especially in the warm months of the year, will increase evapotranspiration, and along with the decrease in rainfall in the hot months, it will cause a decrease in water resources in different parts of Mazandaran province and pressure on the groundwater. Therefore, it is very important to formulate and implement appropriate management programs according to the needs of each region, in order to adapt to extreme temperatures and their adverse consequences. [ABSTRACT FROM AUTHOR]

To better understand the contributions of various external factors to past and future changes in global and regional climate, this study investigates the impacts of natural and anthropogenic forcings on historical and future changes in global land surface air temperature (GLSAT) using model simulations from the Detection and Attribution Model Intercomparison Project (DAMIP) in the Coupled Model Intercomparison Project Phase 6 (CMIP6). Results show that the anthropogenic forcing (ANT) can be robustly detected and separated from the response to the natural external forcing (NAT) since the 1970s. The observed warming changes since the 1950s are primarily attributed to the GHG forcing. ANT contributes a robust warming trend of 0.1–0.2 °C per decade for global landmass during 1951–2020 and cumulative warming by 2011–2020 (relative to 1901–1930) of 1.0–1.6 °C. These attributable warmings largely encompass the observed warming trend of ~ 0.18 °C per decade in 1951–2012 and the observed warming of 1.59 °C by 2011–2020 (relative to 1850–1900) for global landmass reported in IPCC AR5 and AR6, respectively. The anthropogenic warming is projected to increase by 3–6 °C for most global landmass under the SSP2-4.5 scenario, especially in the high latitudes Northern Hemisphere by the late twenty-first century, along with an increase in the mean and widespread flattening of the probability distribution functions (PDFs). The anthropogenic aerosol (AA) cooling effect is projected to decrease only modestly, from 0.7 °C in 2011-20 to 0.6 °C by the late 21st century, for the SSP2-4.5 scenario. [ABSTRACT FROM AUTHOR]

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Tropical convection plays a key role in regional and global climate variability, and future changes in tropical precipitation under anthropogenic global warming are critical for projecting future changes in regional climate. In this study, by analyzing Coupled Model Intercomparison Project (CMIP) phase 6 models, we show that changes in projected tropical precipitation in CMIP6 models vary among models, but they are largely associated with the model's warm pool intensity in the present-day (PD) climate. Models with stronger warm pools in the PD simulation tend to simulate an increase in precipitation in the central Pacific (CP) and a decrease in precipitation in the Maritime Continent (MC) under greenhouse warming. Significant differences in precipitation between the CP and MC regions induce low-level westerly anomalies over the west-central Pacific, favoring sea surface temperature warming in the CP region. This suggests that the associated air–sea interactions result in a particular tropical pattern in response to anthropogenic forcing. [ABSTRACT FROM AUTHOR]

El Niño–Southern Oscillation (ENSO) usually peaks in the boreal winter—November to January of the following year. This particular feature of ENSO is known as the seasonal phase locking of ENSO. In this study, based on 34 climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), the seasonal phase-locking characteristics of the model-simulated El Niño and La Niña events are evaluated in terms of the evolution of the SST anomalies associated with ENSO and the probability distribution of the peak month—the time at which ENSO peaks. It is found that CMIP6 models underestimate the phase-locking strength of ENSO for both El Niño and La Niña events. The ensemble mean peak month matches the observations, but the inter-model spread is large. The models simulate the phase locking of El Nino events better than that of La Niña events, and the large simulation bias of CMIP6 for La Niña phase-locking in the models may have an impact on the simulation of seasonal phase-locking in the ENSO.

The potential impacts of climate change on water resources in the Upper Indus Basin of Pakistan, a region heavily reliant on these resources for irrigated agriculture. We employ state-of-the-art global climate models from the CMIP6 project under the SSP245 scenario to evaluate changes in river runoff using the Soil and Water Assessment Tool (SWAT). Our findings indicate that temperature fluctuations play a crucial role in streamflow dynamics, given that the primary sources of river runoff in the Upper Indus Basin are snow and glacier melting. We project a substantial increase of approximately 18% in both minimum and maximum temperatures, precipitation pattern increases of 13–17%, and a significant rise in streamflow by 19–30% in the future, driven by warmer temperatures. Importantly, our analysis reveals season-specific impacts of temperature, precipitation, and streamflow, with increasing variability in projected annual changes as we progress into the mid and late 21st century. To address these changes, our findings suggest the need for integrated strategies and action plans encompassing hydroelectricity generation, irrigation, flood prevention, and reservoir storage to ensure effective water resource management in the region. [ABSTRACT FROM AUTHOR]

A study has been made of changes in some characteristics of daily precipitation in Russia for the winter and summer seasons in the 20th and 21st centuries using Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models. In the modern period, model data are compared with data from meteorological stations and ERA5 reanalysis. For winter and summer, changes in mean seasonal precipitation, the number of days with precipitation, and the frequency of extreme precipitation are analyzed. For the modern period 1991–2020, according to empirical data, in winter on the territory of Russia, a significant increase in seasonal precipitation amounts and the frequency of days with extreme precipitation on the Far East coast and in the central part of European Russia (ER) are detected. A decrease in the frequency of days with precipitation at most meteorological stations in Russia by 4–6 days/10 years is also noted. In summer, an increase in precipitation amounts and the frequency of days with precipitation is found in Western Siberia and on the coasts of the Sea of Okhotsk and the Pacific Ocean. A decrease in the amount and frequency of precipitation is obtained for southern ER and the south of Eastern Siberia. Climate models, on average for the ensemble, show an increase in the relative amounts of precipitation and the extreme precipitation frequency over most of the Russia territory in winter, and these trends may intensify in the coming decades. In summer, on the contrary, for southern ER, as a whole, there is a slight decrease in the seasonal precipitation totals and the number of days with precipitation. However, strong intermodel differences, especially in the summer season, do not allow us to draw unambiguous conclusions about changes in precipitation characteristics in Russia in the next 30 years. By the end of the 21st century, changes will become more pronounced. For example, in ER and northern Siberia, a noticeable increase in winter precipitation amounts and the frequency of extreme precipitation may occur. By the end of the 21st century, a slight decrease in the precipitation totals and the number of days with precipitation is possible in summer in ER. [ABSTRACT FROM AUTHOR]

The results of a piControl experiment investigating general circulation models participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) were examined. The global interannual variability in the monthly surface temperature (ST) and sea level pressure (SLP) anomalies was considered. The amplitudes of the fluctuations in the anomalies of these meteorological fields between opposite phases of the El Niño–Southern Oscillation (ENSO) were calculated. It was shown that most CMIP6 models reproduced fluctuations in the ST and SLP anomalies between El Niño and La Niña not only in the equatorial Pacific, but also throughout the tropics, as well as in the middle and high latitudes. Some of the CMIP6 models reproduced the global structures of the ST and SLP anomaly oscillations quite accurately between opposite phases of ENSO, as previously determined from observational data and reanalyses. It was found that the models AS-RCEC TaiESM1, CAMS CAMS-CSM1-0, CAS FGOALS-f3-L, CMCC CMCC-ESM2, KIOST KIOST-ESM, NASA GISS-E2-1-G, NCAR CESM2-WACCM-FV2, and NCC NorCPM1 reproduced strong ENSO teleconnections in regions beyond the tropical Pacific.

This study examines the relationship between teleconnections, which represent quasiperiodic modes of large-scale atmospheric and oceanic variability, and extreme heatwaves in the south-central (SC) U.S. region, aiming to understand multiyear variability in the heat extremes. Observational data and model simulations reveal that soil water has a stronger influence on surface air temperature in the SC region than individual teleconnections, though not as strong as their combined influence. Particularly, the Pacific North America (PNA), North Atlantic Oscillation (NAO), and Pacific Decadal Oscillation (PDO) teleconnections have a substantial impact on surface air temperature in the region through anomalous anticyclonic circulation, during their negative phases. The wave-activity flux analysis shows that the wave train associated with the PNA teleconnection originates from the extratropical North Pacific Ocean, likely influenced by sea surface temperature (SST) forcing. Similarly, the wave packet linked to the PDO pattern originates from the central Pacific Ocean in the middle latitudes. In contrast, the wave train related to the NAO pattern exhibits little connection to SST forcing, originating from Greenland and propagating across Europe, Russia, the North Pacific, and North America. While climate models generally capture the PNA and PDO wave trains, they struggle to accurately simulate the NAO wave train across the North Pacific Ocean towards North America. This indicates the current generation of climate models face challenges in representing the complex dynamics underlying the NAO wave train.

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. [ABSTRACT FROM AUTHOR]

Understanding climate change impacts on Tropical Storm (TS) activity is crucial for effective adaptation planning and risk assessment, particularly in densely populated low‐lying delta rivers basins like the Ganges and Mekong. The change to TS characteristics with warming is uncertain due to limitations in global climate model resolution and process‐representation and storm tracking algorithms (trackers). Here, we used 13 HighResMIP models and two trackers to estimate the uncertainty in projections of TS characteristics. We found different trackers producing qualitatively similar but quantitatively different results. Our results show a decline (median ∼52%) in the frequency of TS but increase in the strongest TS and Available Cyclone Energy (ACE) of TS over both basins. The higher‐resolution models extract TS with much higher intensity and ACE values compared to the lower‐resolution models. These results have implications for adaptation planning and risk assessment for TS and suggest the need for further high‐resolution modeling studies. Plain Language Summary: Tropical Storms (TS) are one of the world's most damaging natural hazards which result in colossal socio‐economic losses to life, infrastructure, and property, especially in low‐lying delta rivers basins like the Ganges and Mekong. Knowledge of changes to TS activity under climate change can therefore be helpful in better disaster risk mitigation and climate adaptation. Previous modeling studies have used coarse‐resolution global climate models unable to capture key TS characteristics. In this study, we utilized finer resolution (up to ∼25 km at six hourly time‐steps) CMIP6 HighResMIP models and two different tracking algorithms (trackers) to resolve a part of this uncertainty. Our results project a decline to the frequency of future TS but an increase in the strength of TS (in terms of intensity and Available Cyclone Energy, qualitatively similar for both trackers). These findings can be used to assess the future resilience of existing infrastructure systems to Tropical Storms across these densely populated basins. Key Points: We used multiple CMIP6 HighResMIP models and two trackers to estimate the change in the Tropical Storms (TS) characteristics over the Ganges and Mekong basinsOur results show a decline in the future frequency of TS but increase in the future intensity and Available Cyclone Energy of TS over both basinsBoth tracking algorithms produce qualitatively similar but quantitatively different results [ABSTRACT FROM AUTHOR]

Confronted with the reality of climate change, the rigour of the science underpinning advice to policymakers worldwide is increasingly important. Although life scientists often rely on preprocessed data repositories – which provide data that more closely match the spatial scales of the questions that life scientists ask – technical problems with underlying models persist and are not always evident. Demystifying climate model outputs, and developing practical workflows for their use, empowers life scientists to use these data directly. Doing so makes caveats more explicit and places decisions regarding potential tradeoffs in the hands of the user. Cross-disciplinary collaboration between life scientists and expert users of the output of Earth system models greatly enhances the likelihood of developing the robust evidence necessary to address the challenges posed by climate change to human and natural systems. For each assessment cycle of the Intergovernmental Panel on Climate Change (IPCC), researchers in the life sciences are called upon to provide evidence to policymakers planning for a changing future. This research increasingly relies on highly technical and complex outputs from climate models. The strengths and weaknesses of these data may not be fully appreciated beyond the climate modelling community; therefore, uninformed use of raw or preprocessed climate data could lead to overconfident or spurious conclusions. We provide an accessible introduction to climate model outputs that is intended to empower the life science community to robustly address questions about human and natural systems in a changing world. [ABSTRACT FROM AUTHOR]

As a new generation of global climate models, monsoon simulation in Coupled Model Intercomparison Project (CMIP) Phase 6 models is of great concern to climate modeling community. Using 21 CMIP3 models, 28 CMIP5 models and 38 CMIP6 models, we show evidence that the long-standing dry biases in South Asia (SA) are resulted from less rainfall with both less frequency and intensity in a shortened monsoon season. By evaluating several key metrics, we identify that the monsoon rainfall simulation in CMIP6 models has improved in both of the multimodel ensemble mean (MME) and individual models, consistent with the improvements in monsoon annual cycle and rainfall characteristics. Further analyses and sensitivity experiments show that the cold SST biases all year round over the northern Indian Ocean (NIO) are important sources for the persistent dry biases in the CMIPs' models. The cooling effect of SST biases on the tropospheric temperature becomes increasingly prominent since the boreal spring, weakening the baroclinity of monsoon circulation via the thermal wind relationship and eventually resulting in insufficient monsoon rainfall. Comparison across the three generation CMIP models also confirms that the improvement of SA summer rainfall simulation in CMIP6 MME benefits from the reduction of NIO SST biases. This study highlights the importance of improving SST simulation in reducing the monsoon rainfall biases. [ABSTRACT FROM AUTHOR]

The tropical Pacific decadal variability (TPDV) is an important component of the global interdecadal variability. Previous studies have shown that the TPDV in the sea surface temperature (SST) has two clear patterns, the El Niño–Southern Oscillation (ENSO)‐like and ENSO‐induced patterns, but the pattern of TPDV in the ocean subsurface is still a matter of debate in observations and models. The present study analyses the subsurface TPDV in the simulations of 26 CMIP6 models. The ENSO‐like and ENSO‐induced TPDVs in the subsurface are defined by the regression of the interdecadal anomalies of the oceanic subsurface temperature (Tsub) onto the PCs of two leading EOF modes of the interdecadal SST anomalies in the tropical Pacific. The pattern of the ENSO‐like TPDV in the subsurface shows high model consistency, whereas the ENSO‐induced TPDV in the subsurface has two distinct modes among the models, one with a centre in the central Pacific and the other showing a zonal dipole in the equatorial Pacific. The zonal pattern of the ENSO‐induced TPDV in the subsurface is mainly induced by the SST skewness in the equatorial eastern Pacific, which is further related to the surface heat flux feedback during La Niña. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

This work investigates the inter-model diversity of the Pacific Decadal Oscillation’s (PDO) impact on tropical cyclone frequency (TCF) over the Western North Pacific (WNP) from the historical simulation of twenty-two Coupled Model Intercomparison Project Phase 6 (CMIP6) models. The impact of the PDO is expressed as the TCF difference between the positive and negative PDO phases. The comparison between the models with high PDO skill and low PDO skill shows that the PDO-related sea surface temperature (SST) gradient between the western and central tropical Pacific plays an important role in changing the large-scale atmospheric dynamic fields for TC genesis and, thus, the TCF over the WNP. This SST gradient also significantly contributes to the inter-model spread of PDO’s impact on TCF across the 22 CMIP6 models. We, therefore, stress that the PDO-related eastward SST gradient between the western and central tropical Pacific triggers the lower troposphere westerly and eastward extending of the monsoon trough over the WNP. The moistening of the atmosphere and enhancing ascending motion in the mid-troposphere promote convection, leading to the easterly wind anomaly over the upper troposphere, which reduces the vertical wind shear. Those favorable dynamic conditions consistently promote the TC formation over the southeastern part of the Western North Pacific. Our results highlight that PDO could impact the WNP TCF through its associated tropical SST gradient.

The South Atlantic Ocean Dipole (SAOD) exerts strong influence on climate variability in parts of Africa and South America. Here we assess the ability of an ensemble of 35 state-of-the-art coupled global climate models to simulate the SAOD impacts on regional rainfall for the historical period (1950–2014), and their future projections (2015–2079). For both periods we consider the peak phase of the dipole in austral winter. Observational analysis reveals four regions with spatially coherent SAOD impacts on rainfall; Northern Amazon, Guinea Coast, Central Africa, and Southeast Brazil. The observed rainfall response to the SAOD over Northern Amazon (0.31 mm d ^−1 ), Guinea Coast (0.38 mm d ^−1 ), and Southeast Brazil (0.12 mm d ^−1 ) are significantly underestimated by the modeled ensemble-mean response of 0.10 ± 0.15 mm d ^−1 , 0.05 ± 0.15 mm d ^−1 , −0.01 ± 0.04 mm d ^−1 , respectively. A too southerly rain belt in the ensemble, associated with warmer-than-observed Atlantic cold tongue, leads to better performance of models over Central Africa (46% simulate observations-consistent SAOD-rainfall correlations) and poor performance over the Guinea Coast (only 5.7% simulate observations-consistent SAOD-rainfall correlations). We also find divergent responses among the projections of ensemble members precluding a categorical statement on the future strength of the SAOD-rainfall relationship in a high-emissions scenario. Our results highlight key uncertainties that must be addressed to enhance the value of SAOD-rainfall projections for the affected African and South American countries.

Abstract Understanding climate change impacts on Tropical Storm (TS) activity is crucial for effective adaptation planning and risk assessment, particularly in densely populated low‐lying delta rivers basins like the Ganges and Mekong. The change to TS characteristics with warming is uncertain due to limitations in global climate model resolution and process‐representation and storm tracking algorithms (trackers). Here, we used 13 HighResMIP models and two trackers to estimate the uncertainty in projections of TS characteristics. We found different trackers producing qualitatively similar but quantitatively different results. Our results show a decline (median ∼52%) in the frequency of TS but increase in the strongest TS and Available Cyclone Energy (ACE) of TS over both basins. The higher‐resolution models extract TS with much higher intensity and ACE values compared to the lower‐resolution models. These results have implications for adaptation planning and risk assessment for TS and suggest the need for further high‐resolution modeling studies.

Compound dry and hot events, the combination of high temperature and scarce rainfall, are receiving attention in recent times due to their devastating stress on ecosystems, agricultural production, and public health worldwide. Knowing the risk of compound dry and hot events, particularly for the severe ones on record, is essential for developing effective measures to mitigate the negative impacts. However, most of the existing investigations only focus on the long-term trend of compound dry and hot events during the past decades, and future changes in those record-breaking events remain sparsely reported, especially in China. With a focus on the typical severe compound dry and hot event over Southwest China in summer 2006, this study quantifies the future probability of such compound extreme under various emission and societal development scenarios. Results show that the compound dry and hot event in 2006 is the most severe during 1901–2020, with widespread spatial extent in Southwest China. The observed change in temperature plays a dominant role in the variation of compound severity. Based on Coupled Model Intercomparison Project phase 6 (CMIP6) multi-model simulations, we further show that the maximum ratio of the increase in the area affected by compound dry and hot events to that of 2006 is projected to probably be about twofold under the Shared Socioeconomic Pathway (SSP) 1–2.6 scenario in about 2040, while it would likely climb to nearly triple for the other two scenarios SSP2-4.5 and SSP5-8.5 in about 2060 and 2050, respectively. These findings could provide a support for urgent adaptation and mitigation efforts against compound dry and hot events to mitigate the impact.

The 2021 IPCC report found that most studies show declining trends for the global diurnal temperature range (DTR) since the 1950s, decreasing mainly during 1960–1980. This issue is revisited here using an up‐to‐date in‐situ data set, Hadley Center Integrated Surface Database, constrained by rigorous station selection conditions. The global observed DTR trend was found to reverse during 1980–2021, increasing significantly at a rate of 0.091 ± 0.008°C decade−1. The trend was dominated by a faster rate of increasing daily maximum air temperature. This increasing observed trend in the past four decades was not fully captured in raw CMIP6 models, as models only partially capture the spatial patterns. With global CMIP6 outputs and regionally‐available observations, the global land DTR was then estimated, through emergent constraints, to be 0.063 ± 0.012°C decade−1. The study raises concern for risks of increasing DTR globally and provides new insights into global DTR assessment. Plain Language Summary: In 2021, the IPCC reported a decrease in the near‐surface diurnal air temperature range (DTR) since the 1950s. However, using the in‐situ surface air temperature observations, the global DTR trend was found to reverse after the 1980s, as daily maximum air temperature increased faster than the daily minimum air temperature did between 1980 and 2021. The observed results for 1980–2014 were used to assess the historical simulations within CMIP6. Models generally depicted similar spatial variability as observed results but high variation existed between models. Most of the models did not capture the reversal of the global DTR trend and underestimated regional results. To narrow down the uncertainty and produce a complete global land DTR estimation, we applied the emergent constraint approach by combining observation data and model results. The global DTR trend from 1980 to 2014 was 0.063 ± 0.012°C decade−1. The constraining data was also used at a regional scale. It was shown that DTR trends for North America retained high uncertainty (−0.011 ± 0.026°C decade−1), while Europe and Asia showed reduced uncertainty with increasing DTR. Key Points: The up‐to‐date Hadley Center Integrated Surface Database (HadISD) in‐situ data reveals a reversed global diurnal air temperature range (DTR) trend, increasing for 1980–2021The observed spatial patterns are partially captured but the reversal trends are not shown by CMIP6 modelsThe emergent constraint for CMIP6 models with HadISD estimates increasing DTR at the global scale [ABSTRACT FROM AUTHOR]

A comprehensive model assessment on a 6-hourly scale from multiple dimensions is critical for model selection of dynamic downscaling to reduce uncertainties. In this study, we evaluated the performance of 13 models involved in the Coupled Model Intercomparison Project-6 (CMIP6) by comparing the simulated meteorological variables at the pressure levels of 850, 500, and 250 hPa, including 6-hourly air temperature, specific humidity, zonal wind, meridional wind, and geopotential height, to those from the ERA5 reanalysis data for the 1979–2014 period. The results indicated that the CMIP6 models could mostly reproduce the spatial pattern of the climatology. Most models underestimated air temperature and geopotential height while overestimating specific humidity and zonal and meridional wind speeds in the upper troposphere. Additionally, the interannual variability of zonal and meridional wind exhibited a relatively better performance but limited ability for specific humidity at 850 hPa. Regarding the annual and diurnal cycles, CMIP6 models reasonably captured the annual cycle shape, while an overestimation was detected in simulating the diurnal amplitude, notably at 250 hPa. Based on a comprehensive rating index and overall rankings, our findings showed that no single model was identified as suitable for the simulation of any variables and regions. The models performed well for five variables, including MPI-ESM1-2-LR, MPI-ESM1-2-HR, TaiESM1, and UKESM1-0-LL, over East Asia. Then, according to the overall performance of the five variables and model accessibility, the optimal model varied by region and shared socioeconomic pathway (SSP) scenario. The multi-model ensemble mean outperformed individual models over almost all regions when it comes to comprehensive performance. The MPI-ESM1-2-LR and MRI-ESM2-0 models were the best two out of 13 models as lateral boundary conditions applied to seven climate scenarios over East Asia. This study provides valuable scientific references for selecting the optimal CMIP6 models for the projection of dynamic downscaling over East Asia and eight subregions. [ABSTRACT FROM AUTHOR]

This study estimates short-term climate feedbacks by using a new set of radiative kernels applied to observations and the Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations. The new kernels are generated based on multiyear satellite observations, and they can well reproduce the top-of-atmosphere (TOA) radiation budget. The choice of radiative kernels influences the feedback estimation, especially the surface albedo feedback and cloud feedback in the Arctic and the Southern Ocean. Observational estimates show that tropospheric water vapor feedback makes the largest contribution to global warming, while lapse rate feedback is the largest contributor to local warming over East Asia. Compared to the observations, biases occur but differ when simulating global and East Asian local climate feedbacks. CMIP6 models overestimate global mean Planck, lapse rate, stratospheric temperature and water vapor, and cloud feedbacks, but underestimate global mean tropospheric water vapor and surface albedo feedbacks. Over East Asia, local Planck and lapse rate feedbacks are underestimated, while tropospheric water vapor, stratospheric temperature, and cloud feedbacks are overestimated. The simulation biases in local longwave (LW) and shortwave (SW) cloud feedbacks over East Asia are considerable, probably due to the failure in simulating cloud fraction response of marine cirrostratus, deep convective cloud, and stratus. The intermodel spread of cloud feedback is the largest for both global and East Asian local feedback processes. Our results suggest that contemporary climate models are still difficult to accurately simulate global and local climate feedback processes. [ABSTRACT FROM AUTHOR]

This study uses the 30 General Circulation Models (GCMs) from the Coupled Model Intercomparison Project phase-6 (CMIP6) to examine the simulations of the surplus/deficit Indian summer monsoon rainfall (ISMR) and its associated air-sea interactions on intraseasonal to interannual timescales. The majority of the CMIP6 models simulate the seasonal mean state of ISMR over the Indian mainland with systematic biases. Best performing models (BPM; AWI-ESM-1-1-LR, BCC-CSM2-MR, BCC-ESM1, CNRM-CM6-1, CNRM-ESM2-1, GFDL-CM4, INM-CM5-0, MIROC-ES2L, MIROC6, TaiESM1) well simulated the seasonal mean precipitation with Taylor skill score > 0.75 and normalized root mean square error (NRMSE) is < 0.7. However, the models are failed to simulate precipitation over the orographic regions (Western Ghats). Improving the simulations of low-level winds and sea surface temperature (SST) with high spatial resolutions would provide better precipitation simulations. B-MME (multimodel ensemble mean of BPM) can capture the negative IOD-like (Indian Ocean Dipole) pattern during deficit monsoon years and fail to capture the positive IOD-like pattern during surplus monsoon years. Models overestimate the moisture transport from the West Indian Ocean to the sub-continent of India during deficit monsoons, which plays a crucial role in modulating the precipitation and its associated intraseasonal variability. The present analysis identified that during deficit monsoon years, the faster moving 20–100 days oscillations are evident; however, these oscillations are sluggish during surplus monsoon years, which affects the duration of convection activity and causes dry conditions over the regions. During surplus monsoon years, the Bay of Bengal (Arabian Sea) responds strongly (slowly) to the atmosphere than the deficit monsoon years. However, models are fail to represent the ocean's response to the atmosphere over the Bay of Bengal. The freshwater forcing improvement in the models simulates the ocean to atmosphere response over the Indian region. The present study further suggests that the improved simulation of the Indian summer monsoon (ISM) variability by the GCMs is possible by improving the ocean and atmosphere feedback mechanisms, sensitivities of the models among internal variables, and orographic features necessary for the accurate simulation of intraseasonal variability. [ABSTRACT FROM AUTHOR]

Abstract The 2021 IPCC report found that most studies show declining trends for the global diurnal temperature range (DTR) since the 1950s, decreasing mainly during 1960–1980. This issue is revisited here using an up‐to‐date in‐situ data set, Hadley Center Integrated Surface Database, constrained by rigorous station selection conditions. The global observed DTR trend was found to reverse during 1980–2021, increasing significantly at a rate of 0.091 ± 0.008°C decade−1. The trend was dominated by a faster rate of increasing daily maximum air temperature. This increasing observed trend in the past four decades was not fully captured in raw CMIP6 models, as models only partially capture the spatial patterns. With global CMIP6 outputs and regionally‐available observations, the global land DTR was then estimated, through emergent constraints, to be 0.063 ± 0.012°C decade−1. The study raises concern for risks of increasing DTR globally and provides new insights into global DTR assessment.

The variability of the Hadley Circulation (HC) is greatly impacted by the meridional structure of sea surface temperature (SST), which has varied effects depending on its symmetrical or asymmetrical pattern relative to the equator. By using the Coupled Comparison Program International Project Phase 6 (CMIP6) model outputs and reanalysis datasets, this study assesses the capacity of CMIP6 models to simulate the relationship between the HC and tropical SST under different meridional structures, as well as investigates the possible causes for simulation biases. It is shown that the CMIP6 models can successfully reproduce climatological HC, tropical SST, and their spatial patterns of first leading modes under different meridional structures, where the correlation coefficient between simulations and observations reaches 0.8 or above. By comparison, the CMIP6 model outputs exhibit substantial differences in simulating the HC to SST response over the different meridional structures, with obvious inter-model differences. Considering the capability in simulating the HC to tropical SST response, the CMIP6 models are divided into two types, Type I model and Type II model. Models of Type I are those whose simulation results are basically close to the reanalysis data, with the biases being less than 20%. The models of Type II are those whose simulated response ratios are much stronger than those of the reanalysis. It is found that the models of Type II overestimate the intensity of El Niño-Southern Oscillation (ENSO) events, and remarkably underestimate the HC and SST correlation in the equatorial symmetric part, resulting in the inability of the models of Type II to capture the connection of the HC and tropical SST. The results indicate that, the component of the CMIP6 models in reproducing the ENSO events has a considerable impact on the simulation of the HC and tropical SST relationship, which offers recommendations for enhancing the capability of models to simulate large-scale tropical air-sea interactions.

In this work we analyse the effects of climate change on the recharge and the groundwater flow of the Urucuia Aquifer System in western Bahia State, Brazil (UAS‐BA) in the period 2041–2060. We assessed possible changes and impacts on groundwater recharge with four climate models from the sixth phase of the Coupled Model Intercomparison Project Phase 6 (CMIP6): ACCESS‐ESM1‐5, CanESM5, EC‐Earth3 and MIROC6, under scenarios SSP1‐2.6 and SSP5‐8.5 (Shared Socioeconomic Pathways—SSP). Visual Balan 2.0 aquifer recharge assessment code, and the MODFLOW hydrogeological model were used to analyse the impact of future climate changes on the recharge. The projections of relative change in precipitation, under the SSP1‐2.6 scenario, show a precipitation reduction between 5% and 20%, using ACCESS‐ESM1‐5, CanESM5 and EC‐Earth3 models. Using SSP5‐8.5 scenario, these models behave differently, while the CanESM5 and EC‐Earth3 models indicate an even greater decrease in precipitation, with values up to 35%, the ACCESS‐ESM1‐5 projects an increase in precipitation of up to 25%. Using MIROC6, though, an increase of up to 10% in precipitation in the UAS‐BA is expected, under both scenarios. Regarding temperature, all the models agree with a warming in the future varying from 2 to 3°C. Recharge projections calculated using the Visual Balan 2.0 indicated in general a reduction between 8% and 43%. Using the MODFLOW model, the results show a reduction of recharge to the aquifer varying from 3% to 45% using CanESM5, EC‐Earth3 and MIROC6. The ACCESS‐ESM1‐5 has a 26% reduction (5% increase) under SSP1‐2.6 (SSP5‐8.5). Furthermore, projections indicate a downward trend in the water table in the northern portion of UAS‐BA, reaching a maximum drop of up to 12 m with the CanESM5, under SSP5‐8.5 scenario. [ABSTRACT FROM AUTHOR]

This study assesses the simulation of the Boreal Summer Intraseasonal Oscillation (BSISO), particularly the moist convective processes associated with the BSISO in 10 models from phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6). Most of the models show a slower northward propagation of the BSISO and cannot simulate the origin of positive rainfall anomalies over the equatorial Indian Ocean. The generation of barotropic vorticity to the north of the convection centre leads to the northward shift of moisture convergence resulting in the northward propagation of convection. This mechanism is also insufficient in the models. The models show a lack of consistent evolution of moisture with increasing rain rates. Some of the CMIP6 models such as MPI‐ESM1‐2‐LR, FGOALS‐f3‐L, and NorESM2‐MM exhibit better northward propagation and the associated moist processes. A random relationship between vertical velocity, specific humidity, and precipitation is seen in the observation. On the other hand, the models show a deterministic relationship among these parameters with a much higher fraction of convective rain than large‐scale rain. Only FGOALS‐f3‐L (from the CMIP6 family), which uses convection resolving precipitation parameterization, where the convective and stratiform precipitation is calculated explicitly, produces the variability seen in the observation to a large extent. [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: The rise in surface air temperature due to human influence results in an intensification of the most extreme events of precipitation over the globe. Although this is shown by both observations and Earth System Models at the global scale, more uncertainties remain regionally and at the seasonal scales. In this study, we use a large ensemble of state‐of‐the‐art Earth System Models and investigate future seasonal changes in extreme precipitation and how it compares to changes in total precipitation. Our key finding is that, 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 is found locally and in regions where there is high agreement between the models with projected changes of a same sign. Hence, when future seasonal changes in precipitation can be assessed with high confidence, we find that changes in totals and extremes align in the future. Our results demonstrate that there is potential added value from examining concomitant projected changes in totals and extremes of precipitation on seasonal and regional scales which could help better inform decision‐making in relevant climate‐sensitive sectors. Key Points: We present projected seasonal changes in Coupled Model Intercomparison Project (CMIP6) total and extreme precipitation and show that they scale into the futureCMIP6 multi‐model mean seasonal change is of same sign for total and extreme precipitation when there is high agreement between modelsThis is found locally (at the grid cell scale) and regionally and is generally supported by the 29 individual models [ABSTRACT FROM AUTHOR]