CMIP5 Research Articles

Eastern Tropical Pacific atmospheric and oceanic projected changes based on CMIP6 models

Atmosphere and ocean dynamics and their projections for the 21st century are assessed in the Eastern Tropical Pacific, using an ensemble of 17 models from the Coupled Model Intercomparison Project – CMIP6, under two radiative scenarios. Projections in the Panama Bight (PB) and Equatorial Pacific cold tongue (CT) are studied in more detail. In the 2071–2100 period and SSP5-8.5 scenario, referenced to the 1985–2014 period, air temperature (sea surface temperature) is expected to rise ∼3.5 °C (∼3 °C). Precipitation is projected to increase > 3 mm day−1 in the mean position of the Intertropical Convergence Zone, and decrease toward the north. A similar meridional pattern is projected in sea level atmospheric pressure and sea surface salinity (SSS) with negative anomalies toward the south. Large seasonal variations, which dominate the region, are projected to remain similar for the rest of the century. However, in January-April a weakening in the Panama wind jet and intensification of surface wind in the CT is expected, while in the June-November season, a weakening of the Choco wind jet will affect both sub-regions. Mean sea surface height (SSH) is expected to decrease, probably dominated by barotropic wind effects over SSS reduction effect on SSH. However, sterodynamic sea level (SDSL) is projected to rise (∼21 cm) driven by the global mean thermosteric contribution. For the end of the century, a mean sea level rise of ∼69 cm is estimated in the ETP, with SDSL being about half the barystatic contribution. These projections should be used with caution, as climate models have shown limitation reproducing atmospheric and ocean observations in the tropical Pacific Ocean during the last decades, due to large internal variability and systematic biases.

Comparing the Atmospheric Responses to Reduced Arctic Sea Ice, a Warmer Ocean, and Increased CO2 and Their Contributions to Projected Change at 2°C Global Warming

Abstract We consider the combined and individual influences of Arctic sea ice loss, sea surface temperature (SST) warming, and the direct radiative effect of increased CO2 on the Northern Hemispheric climate. The surface climate (e.g., temperature and precipitation) and atmospheric circulation responses (e.g., sea level pressure and wind) to these drivers are quantified using simulations from the Polar Amplification Model Intercomparison Project (for sea ice loss and SST warming) and the Cloud Feedback Model Intercomparison Project (for increased CO2). We verify the linear additivity of the PAMIP-derived winter responses to sea ice loss and SST change. The responses to SST change are of greater magnitude than that due to sea ice loss or due to CO2 direct radiative forcing in most seasons and regions, excluding the Arctic. Notably, however, sea ice loss is at least as important as SST change for the winter atmospheric circulation response over the North Atlantic and Siberia. The dynamical responses to sea ice loss and SST change oppose each other in many regions in winter, while the responses to SST and CO2 direct radiative forcing are often opposing in summer. Such opposing responses are less evident for the thermodynamical response. The sum of all three responses reproduces well the spatial patterns of change at 2°C global warming in winter and autumn in phase 6 of the Coupled Model Intercomparison Project projections but overestimates their magnitude.

Cross-Seasonal Effect on Tropical Pacific Precipitation: Implication of South Pacific Quadrupole Simulation in CMIP6

Abstract The tropical Pacific convergence zone plays a crucial role in the global climate system. Previous research studies emphasized the cross-seasonal influence of the South Pacific quadrupole (SPQ) mode on the tropical Pacific climate. This study assesses the relationship between austral summer SPQ and austral winter tropical precipitation in phase 6 of the Coupled Model Intercomparison Project (CMIP6) models. The analysis emphasizes the historical experiments conducted within this time frame, spanning from 1979 to 2014. Our findings reveal that the SPQ is accurately represented in all CMIP6 models, but the connection between SPQ and precipitation is inadequately simulated in most models. To investigate the reasons behind these intermodel differences in reproducing SPQ-related processes, we categorize models into two groups. The comparisons demonstrate that the fidelity of model simulations in replicating the SPQ–tropical precipitation relationship hinges significantly on their capacity to reproduce the positive wind–evaporation–sea surface temperature (WES; SST) feedback over both the southwestern Pacific (25°–10°S; 150°E–160°W) and the southeastern Pacific (30°–10°S; 140°–80°W). This positive WES feedback propagates the SPQ signal into the tropics, intensifying the meridional gradient of SST anomaly in the tropical western-central Pacific, which consequently amplifies convection and rainfall in that area. In the group of models that failed to simulate this relationship accurately, the weakened WES feedback can be traced back to biases in wind speed and its variation. Furthermore, this WES feedback establishes a connection between SPQ and El Niño–Southern Oscillation (ENSO). A better rendition of the SPQ–tropical rainfall connection tends to result in a better simulation of the onset of SPQ-related ENSO events. As a result, this study advances our comprehension of extratropical impacts on the tropics, with the potential to enhance the accuracy of tropical climate simulation and prediction. Significance Statement Tropical rainfall plays an important role in the global climate system. Beyond the well-known influence of El Niño–Southern Oscillation (ENSO) on the tropical rainfall, the sea surface temperature (SST) anomaly in the South Pacific has a cross-seasonal impact on the precipitation over the tropical Pacific via air–sea coupled processes. Such SST anomaly pattern shows a quadrupole structure in the extratropical South Pacific, known as the South Pacific quadrupole (SPQ) mode. However, the relationship between SPQ and tropical precipitation remains poorly simulated in most state-of-the-art climate models. One primary reason for this gap between observed and simulated relationships is the underestimation of wind speed and its variation over the south tropical Pacific in these models. This limitation undermines their ability to accurately represent the air–sea interactions that drive tropical precipitation patterns, leading to inaccuracies in simulations. Our study aims to bridge this knowledge gap by enhancing our understanding of the extratropical effects on the tropical Pacific. By exploring the mechanisms underlying the SPQ–precipitation connection, we expect to improve the simulation and prediction capabilities of tropical climate models, thereby enhancing our ability to forecast and adapt to future climatic changes.

CMIP6 Models Underestimate Arctic Sea Ice Loss during the Early Twentieth-Century Warming, despite Simulating Large Low-Frequency Sea Ice Variability

Abstract The variability of Arctic sea ice extent (SIE) on interannual and multidecadal time scales is examined in 29 models with historical forcing participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6) and in twentieth-century sea ice reconstructions. Results show that during the historical period with low external forcing (1850–1919), CMIP6 models display relatively good agreement in their representation of interannual sea ice variability (IVSIE) but exhibit pronounced intermodel spread in multidecadal sea ice variability (MVSIE), which is overestimated with respect to sea ice reconstructions and is dominated by model uncertainty in sea ice simulation in the subpolar North Atlantic. We find that this is associated with differences in models’ sensitivity to Northern Hemispheric sea surface temperatures (SSTs). Additionally, we show that while CMIP6 models are generally capable of simulating multidecadal changes in Arctic sea ice from the mid-twentieth century to present day, they tend to underestimate the observed sea ice decline during the early twentieth-century warming (ETCW; 1915–45). These results suggest the need for an improved characterization of the sea ice response to multidecadal climate variability in order to address the sources of model bias and reduce the uncertainty in future projections arising from intermodel spread. Significance Statement The credibility of Arctic sea ice predictions depends on whether climate models are capable of reproducing changes in the past climate, including patterns of sea ice variability which can mask or amplify the response to global warming. This study aims to better understand how latest-generation global climate models simulate interannual and multidecadal variability of Arctic sea ice relative to available observations. We find that models differ in their representation of multidecadal sea ice variability, which is overall larger than in observations. Additionally, models underestimate the sea ice decline during the period of observed warming between 1915 and 1945. Our results suggest that, to achieve better predictions of Arctic sea ice, the realism of low-frequency sea ice variability in models should be improved.

Process-Based Evaluation of Intraseasonal Oceanic Kelvin Waves in the Pacific Ocean in CMIP6 Models

Abstract Oceanic intraseasonal Kelvin waves (KWs) help modulate upper-ocean thermal characteristics, providing feedbacks to important coupled air–sea phenomena in the tropics. The recent availability of daily thermocline depth fields from several phase 6 of the Coupled Model Intercomparison Project (CMIP6) models makes it possible to evaluate the performance of KWs and identify potential sources of bias. Most models fail to simulate a realistic spatial distribution of KW variability. Models simulate a large variability of KWs in the western or eastern Pacific rather than in the central Pacific as observed. The modeled KWs propagate slowly (about 1.5 m s−1) compared to observations (about 2.5 m s−1). This slow propagation is also identified in wavenumber–frequency spectra for KWs and meridional KW structures, which is more consistent with a second baroclinic mode structure in models compared to the first baroclinic mode structure in observations. An analysis of the relative contributions of the vertical wavenumber and background ocean stability to KW phase speeds indicates that the high vertical wavenumber bias in models contributes most to the slow propagation, in which the higher-than-observed vertical wavenumbers imply the biased incorporation of higher baroclinic modes in the model KW structure. This finding is further supported by the results of vertical mode decomposition that incorporates background density profiles. These results indicate that a realistic representation of the KW vertical structure is essential to produce realistic KW propagations in models. Significance Statement Oceanic intraseasonal Kelvin waves (KWs) play a significant role in regulating the heat content and temperature of the ocean, which provides feedbacks to coupled ocean–atmosphere phenomena in the tropics such as El Niño–Southern Oscillation (ENSO). Consequently, identifying the biases in KWs and the potential sources of those biases in state-of-the-art models is essential to improve simulations of ENSO and its diversity and advance forecasts of weather extremes around the globe induced by ENSO. We find that KWs in the models propagate slower than observations, mainly due to biases in their vertical structure, with secondary effects due to biases in model ocean stability. These issues may be potential sources for current imperfect ENSO model simulations and predictions.

Indian Ocean Intermediate Water Masses and Their Simulations by CMIP6 Models

Abstract Water masses are carriers of anthropogenic fingerprints in the ocean interior, with their property changes manifesting oceanic thermodynamic responses to climate change. Yet, delimiting ocean water masses remains challenging in either observational atlas or climate models. This study analyzes the distribution of Indian Ocean seawater in the density–spicity space and uses volumetric maxima and minima between σ = 27.1 and 27.4 kg m−3 to track the cores and boundaries of intermediate water masses, respectively. In addition to the well-known Antarctic Intermediate Water (AAIW) and Red Sea–Persian Gulf Intermediate Water (RS-PGIW), two other water masses are identified by the new approach. One is the Indian-AAIW (I-AAIW), as a mixture of the AAIW and the Indonesian Throughflow water, existing in the South Equatorial Current and the Agulhas Current system. The other [equatorial Indian Intermediate Water (EIIW)] exits in the equatorial Indian Ocean and Bay of Bengal, sourced from the RS-PGIW and overlying fresh waters. These waters are corroborated by nutrient and dissolved oxygen data. Around half (26 out of 51) of phase 6 of the Coupled Model Intercomparison Project (CMIP6) models can reasonably simulate these intermediate water masses. Compared with the observed water masses, the intermediate water masses in models are of a smaller thickness and the RS-PGIW is colder and fresher. The former arises from a warm bias in the thermocline, whereas the latter is likely linked to insufficient ventilation in the Red Sea and Persian Gulf in models owing to coarse grid resolution and a surface cold bias. Significance Statement Oceanic water masses are important for understanding climate change. Yet, the definition of water masses is controversial. Here, we use volumetric minima in the density–spicity space to track the boundaries of intermediate (27.1–27.4 kg m−3) water masses in the Indian Ocean. We identify four water masses: AAIW, I-AAIW, EIIW, and RS-PGIW. We delineate the geographical location of each water mass, including two newly identified water masses (I-AAIW and EIIW). We also examined the performance of 51 CMIP6 models in simulating these water masses and found that 26 models can reasonably simulate the distribution of these water masses. There are two systematic model biases emerging from these models. The intermediate water masses in models are of a thinner thickness arising from a warm bias in the thermocline, and the RS-PGIW is colder and fresher owing to insufficient ventilation in the Red Sea and Persian Gulf. These results provide a useful benchmark for understanding water masses in a changing climate and constraining climate models.

Reconstruction of Historical Site-Scale Dust Optical Depth (DOD) Time Series from Surface Dust Records and Satellite Retrievals in Northern China: Application to the Evaluation of DOD in CMIP6 Historical Simulations

Abstract The biases generated by state-of-the-art climate models in simulating dust optical depth (DOD) remain to be detailed. Here, a site-scale DOD dataset in March–August over northern China (NC) during 1980–2001 was reconstructed using the empirical relationship between MODIS-retrieved DOD and dust-event frequencies during 2001–21. Then, through the combined use of MODIS-based and reconstructed DOD, we evaluated the reproducibility of DOD from 10 models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6) for the historical period (1980–2001 and 2002–14) and under different Shared Socioeconomic Pathways (SSPs) during 2015–21. The results demonstrate that CMIP6 models and multimodel ensemble mean (MEM) are capable of capturing the spatial pattern of DOD, but with considerable uncertainty and intermodel variability in magnitude. Regionally averaged DOD is underestimated by 56.09% during 1980–2001 and overestimated by 30.97% during 2002–14 in MEM over NC. Simultaneously, the intermodel standard deviations are greater than MEM during 2002–14, suggesting large discrepancies among individual models. Very few models accurately capture the trends in DOD, which can mainly be attributed to the different trends in simulated wind speed (WS), soil moisture, and vegetation cover, and their contributions to dust evolution. Under four SSPs, despite the best correlation between SSP1-2.6-modeled and MODIS DOD over Gobi Desert (GD), overestimation of DOD is still observed. More models under SSP1-2.6 capture the positive DOD trend, mainly attributable to positive changes in simulated WS over GD.

Equatorial Pacific Cold Tongue Bias Degrades Simulation of ENSO Asymmetry due to Underestimation of Strong Eastern Pacific El Niños

Abstract El Niño–Southern Oscillation (ENSO) exhibits a considerable asymmetry in sea surface temperature anomalies (SSTa), as El Niño events tend to be stronger and centered further east than La Niña events. Here, we analyze ENSO asymmetry in observations and preindustrial control integrations of 32 models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Observations indicate a significant link between strong eastern Pacific (EP) El Niño events and the ENSO amplitude and asymmetry. The large CMIP6 database confirms this strong link. Most CMIP6 models suffer from an equatorial Pacific cold SST bias. This cold tongue bias hinders the southward migration of the ITCZ toward the equator over the eastern equatorial Pacific, which is characteristic of strong EP El Niño events in observations. Therefore, many models underestimate the eastern equatorial Pacific rainfall anomalies and simulate a wind stress feedback over the western Pacific that is too weak and too far west. As a result, the cold tongue bias exerts a strong control on the climate models’ ability to generate strong EP El Niño events and therefore on the ENSO overall amplitude and asymmetry. We discuss the relevance of these results in view of a potential increase of strong EP El Niño events under global warming. Significance Statement El Niño and La Niña are asymmetric, as El Niño events tend to be stronger and further east than La Niña events. Due to an equatorial Pacific cold tongue bias with too cold SSTs, the simulation of ENSO asymmetry is degraded in many climate models participating in CMIP6. Here, we show how the cold tongue bias influences ENSO asymmetry. The cold bias hampers the simulation of strong eastern Pacific El Niños by making it more difficult for SST to exceed the threshold for deep atmospheric convection over the eastern equatorial Pacific. Recent research indicates that climate models with a realistic ENSO asymmetry agree on the projected ENSO under global warming. The results of this study suggest that reducing the cold tongue bias has the potential to enhancing the reliability of future ENSO projections.

Quantifying the historical and future heat-related mortality above the heat alert thresholds of the inaugural Chinese national heat-health action plan

BackgroundChina published its inaugural national heat-health action plan (HHAP) in 2023, but the mortality burden associated with temperatures exceeding the heat alert thresholds specified by this HHAP (maximum temperatures >35, 37, or 40 °C) remains unknown. We aimed to estimate the historical and future mortality burden associated with temperatures above the heat alert thresholds of the Chinese national HHAP. MethodsWe conducted time-series analyses to estimate the mortality burden associated with temperatures exceeding the three heat alert thresholds from 2016-2019 in Jiangsu Province (including 13 cities, population ∼80.7 million), China. A quasi-Poisson regression in conjunction with a distributed lag non-linear model was used to estimate the dose-response association between maximum temperature and mortality risk from 2016-2019, adjusting for potential covariates. We then projected the future mortality burden associated with temperatures exceeding these thresholds under three distinct levels of greenhouse gas (GHG) emission scenarios via scenario shared socioeconomic pathways [SSP] 1-2.6 (low), SSP2-4.5 (intermediate), and SSP5-8.5 (high), respectively, by assuming that there will be no adaptation to heat. Climate scenarios derived from the General Circulation Model (GCM) under the Coupled Model Intercomparison Project Phase 6 (CMIP6) were used. ResultsFrom 2016-2019, temperatures above 35 °C were associated with 0.51% of mortality, including 0.40% associated with 35 °C – 37 °C and 0.11% associated with >37 °C. Heat-related mortality risk was most prominent in those who were single/divorced/widowed and had <10 years of education. Under SSP2-4.5, compared with the 2020s, the excess mortality associated with >37 °C would increase by 1.4 times in the 2050s and 1.7 times in the 2090s. Under SSP5-8.5, the annual number of days with maximum temperature >37 °C would approximately double every 20 years (67 days annually in the 2090s). Consequently, compared with the 2020s, the excess mortality associated with >37 °C would increase by 2.8 times in the 2050s and 18.4 times in the 2090s. ConclusionSignificant mortality risk is associated with temperatures above the lowest heat alert threshold of the Chinese national HHAP (35 °C). If the high GHG emission scenario occurred, the annual number of days and excess mortality associated with maximum temperatures >37 °C would largely increase in the coming decades.

Intercomparison of machine learning methods for statistical downscaling of daily temperature under CMIP6 scenarios: a case study from Iran

ABSTRACT General circulation models (GCM) represent a contemporary and advanced tool designed to simulate the response of climate system to alterations in greenhouse gas levels. Increasing spatial resolutions of the outputs of GCMs on a regional scale requires downscaling process. In this study, four machine learning (ML) models, namely, decision tree regression (DTR), support vector regression, artificial neural network, and K-nearest neighbors (KNN) were employed to downscale the outputs of four Coupled Model Intercomparison Project 6 climate models. The daily observations of temperature data at Nazmakan station in Kohgiluyeh and Boyer-Ahmad Province, Iran, were trained and tested for the periods 1995–2009 and 2009–2015, respectively. According to results of four metrics, DTR and KNN obtained the most valid evaluation in the training and test data, respectively. In addition, downscaling methods were used to predict future daily temperature for 2015–2045 under two Shared Socioeconomic Pathway (SSP2-4.5 and SSP5-8.5) scenarios. Mann–Kendall test results revealed an increasing trend of temperature in most of the coming years. The projected trend expects cooler summers and warmer winters in future. Furthermore, the predicted data under two climate scenarios were assessed in comparison with observed data for 2015–2022. Finally, this study demonstrates the strengths and weaknesses of nonlinear ML techniques in statistical downscaling.

Fractional change of scattering and absorbing aerosols contributes to Northern Hemisphere Hadley circulation expansion.

The relative amount of scattering and absorbing aerosols is essential in determining the aerosol radiative and climate effects. Using reanalysis datasets and climate simulations, here, we show that changes in the relative amount of scattering and absorbing aerosols in the Northern Hemisphere (NH) high latitudes, manifested as long-term decreasing trends in aerosol single-scattering albedo (SSA), have played an important role in driving the widening and weakening trends of the NH Hadley circulation (HC) since the early 1980s. Decreasing SSA in the NH middle and high latitudes can notably warm the troposphere there, thus reducing the equator-to-pole temperature gradient, increasing static stability in mid-latitude regions, and leading to the widening and weakening trends of NH HC. Further analysis of the Coupled Model Intercomparison Project Phase 6 (CMIP6) aerosol forcing-only simulations also supports the importance of SSA trends in perturbing NH HC through the above mechanism.

The Summer North Atlantic Oscillation, Arctic sea ice, and Arctic jet Rossby wave forcing.

We use Coupled Model Intercomparison Project Phase 6 (CMIP6) coupled and Atmospheric Model Intercomparison Project (AMIP) climate models, dynamical analyses, and observations to investigate interactions between summer Arctic sea ice concentration (SIC) variations and the Summer North Atlantic Oscillation (SNAO). Observations suggest that SIC-SNAO relationships mainly come from the East Siberian to Arctic Canada (ESAC) region where a weak atmospheric jet stream exists in summer. Twelve CMIP6 models with the most realistic atmospheric climatologies over the North Atlantic and Europe agree well with reanalyses on relationships between SIC and Northern Hemisphere atmospheric circulation. CMIP6 model data indicate that ESAC SIC influences the SNAO with a lead time of several weeks. However, AMIP simulations do not reproduce the observed atmospheric circulation when observed sea ice is prescribed. Rossby wave analyses do though support observed ESAC SIC influences on the SNAO. We conclude that ESAC Arctic SIC modestly influences the SNAO, and such investigations require the use of coupled models.

Future changes in rainfall variability for the mainland Indochina southwest monsoon

Researching future changes in rainfall variability is critical for mitigating the possible effects of global warming, especially in areas where vulnerability is high, such as Indochina. While changes in mean and extreme rainfall have received a great deal of attention, rainfall variability has been poorly researched, despite its importance. We endeavored to determine the anticipated changes in rainfall variability during the mainland Indochina southwest monsoon (MSWM) by utilizing data derived from 5 ensemble models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). Employing band pass filtering techniques on daily rainfall data, we discerned variability across an expansive spectrum of temporal scales. Our research indicates that, in the event of global warming, the variability in MSWM rainfall is expected to increase by approximately 10-25% throughout the whole region. Notably, this increased unpredictability appears uniformly throughout a wide range of time intervals. Changes in average rainfall significantly aid in explaining the majority of the intermodal variance in the predicted MSWM rainfall variability. To gain further insight into this phenomenon, we examined the effects of elevated atmospheric moisture content through the estimation of modifications resulting from idealized local thermodynamic enhancement. We showed that increased atmospheric moisture, as suggested by the Clausius-Clapeyron relation, accounts for most of the predicted changes in rainfall variability at all time scales.

Evaluating Wheat Cultivation Potential in Ethiopia Under the Current and Future Climate Change Scenarios

Land suitability analyses are crucial for identifying sustainable areas for agricultural crops and developing appropriate land use strategies. Thus, the present study aims to analyze the current and future land suitability for wheat (Triticum aestivum L.) cultivation in Ethiopia. Twelve variables including soil properties, climate variables, and topographic characteristics were used in the evaluation of land suitability. Statistical methods such as Rotated Empirical Orthogonal Functions (REOF), Coefficient of Variation (CV), correlation, and parametric and non-parametric trend analyses were used to analyze the spatiotemporal variability in current and future climate data and identified significant patterns of variability. For future projections of land suitability and climate, this study employed climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) framework, downscaled using regional climate model version 4.7 (RegCM4.7) under two different Shared Socioeconomic Pathway (SSP) climate scenarios: SSP1 (a lower emission scenario) and SSP5 (a higher emission scenario). Under the current condition, during March, April, and May (MAM), 53.4% of the country was suitable for wheat cultivation while 44.4% was not suitable. In 2050, non-suitable areas for wheat cultivation are expected to increase by 1% and 6.9% during MAM under SSP1 and SSP5 climate scenarios, respectively. Our findings highlight that areas currently suitable for wheat may face challenges in the future due to altered temperature and precipitation patterns, potentially leading to shifts in suitable areas or reduced productivity. This study also found that the suitability of land for wheat cultivation was determined by rainfall amount, temperature, soil type, soil pH, soil organic carbon content, soil nitrogen content, and elevation. This research underscores the critical importance of integrating spatiotemporal climate variability with future projections to comprehensively assess wheat suitability. By elucidating the implications of climate change on wheat cultivation, this study lays the groundwork for developing effective adaptation strategies and actionable recommendations to enhance management practices. The findings support the county’s commitment to refining agricultural land use strategies, increasing wheat production through suitability predictions, and advancing self-sufficiency in wheat production. Additionally, these insights can empower Ethiopia’s agricultural extension services to guide farmers in cultivating wheat in areas identified as highly and moderately suitable, thereby bolstering production in a changing climate.

Extremes of Temperature and Precipitation Under CMIP6 Scenarios Projections Over Central Hokkaido, Japan

ABSTRACTClimate extreme events are intensifying globally, posing increasing risks across various sectors. Understanding climate extremes' spatiotemporal patterns and responses to climate change is crucial for effective management, especially on a regional scale. This study examines temperature and precipitation extremes, as well as compound dry‐hot events (CDHEs), in the Ishikari River basin (IRB) of Northeastern Japan, an area of significant socioeconomic importance. We focus on spatiotemporal analysis under multiple scenarios of temperature/precipitation extremes and CDHEs based on statistical downscaled datasets from the Coupled Model Intercomparison Project Phase 6. Results indicate that IRB underwent increased trends of extreme hot periods, extreme droughts, and heavy rainfalls during 1985–2014, which are significantly affected by the North Pacific Oscillation and Southern Oscillation Index. Future projections show that warming temperatures and less rainfall shift asymmetrical impacts on temperature and precipitation extremes, expecting increased warm spells and CDHEs but increased wet durations and less heavy rainfalls. Emission scenarios analysis suggests low‐emission scenarios (SSP1‐2.6) could mitigate their exacerbations, especially for CDHEs (decreased by 139%). Moreover, spatial‐pattern analysis reveals regional heterogeneity in temperature and precipitation extremes, with northern mountainous regions more susceptible to thermal extremes and southern plain regions (e.g., Sapporo city) experiencing prolonged drought and CDHEs. This study provides valuable insights into climate risk management and adaptation strategies.

Observed Changes and Projected Risks of Hot–Dry/Hot–Wet Compound Events in China

Compound extreme events can cause serious impacts on both the natural environment and human beings. This work aimed to explore the changes in compound drought–heatwave and heatwave–extreme precipitation events (i.e., CDHEs and CHPEs) across China using daily-scale gauge-based meteorological observations, and to examine their future projections and potential risks using the Coupled Model Intercomparison Project (CMIP6) under the shared socioeconomic pathway (SSP) scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5). The results show the following: (1) The frequencies of CDHEs and CHPEs across China showed a significant increasing trend from 1961 to 2020, with contrasting trends between the first half and second half of the period (i.e., a decrease from 1961 to 1990 and an increase from 1991 to 2020). Similar trends were observed for four intensity levels (i.e., mild, moderate, severe, and extreme) of CDHEs and CHPEs. (2) All the frequencies under three SSP scenarios will show increasing trends, especially under higher emission scenarios. Moreover, the projected intensities of CDHEs and CHPEs will gradually increase, especially for higher levels. (3) The exposure of the population (POP) and Gross Domestic Product (GDP) will be concentrated mainly in China’s coastal areas. The GDP exposures to the CDHEs and CHPEs will reach their highest values for SSP5-8.5, while the POP exposure will peak for SSP2-4.5 and SSP5-8.5, respectively. Our findings can offer scientific and technological support to actively mitigate future climate change risks.

Improved simulation of compound drought and heat extremes in eastern China through CWRF downscaling

Abstract Given their profound socio-economic impact and increasing occurrence, compound drought and heat extremes (CDHEs) have become a focal point of widespread concern. Studies have attempted to reproduce and predict these extremes using general circulation models (GCMs); however, the performance of these models in capturing compound events remains controversial. This study presents an improved simulation of CDHE trends over eastern China by using the regional Climate-Weather Research and Forecasting model (CWRF) to downscale the projections of two GCMs that participated in the Coupled Model Intercomparison Project Phase 6. The results show that CWRF downscaling significantly improved the underestimation of CDHE trends in GCM historical simulations, aligning better with observed trends. Moreover, the improvements of CWRF downscaling in simulating CDHEs are more pronounced than those for univariate events, i.e. extreme drought and extreme heat events. This enhancement largely results from CWRF’s better representation of land-atmosphere interaction processes, as indicated by the more realistic spatial distributions and intensities of the land-atmosphere coupling strength index. Under the SSP245 and SSP585 scenario, CWRF downscaling again predicts a more rapid increase in regional mean CDHE frequency compared to GCMs, with values nearing or exceeding 0.4 by the mid-21st century, suggesting a significant future threat to the study region. This study highlights the important role of land-atmosphere interactions in shaping CDHEs and the efficacy of regional climate models to reduce uncertainty in compound event simulations.

Evaluating the impact of climate change on yield and water use efficiency of different dry-season rice varieties cultivated under conventional and alternate wetting and drying conditions.

This study is the first attempt to assess rice cultivation under alternate wetting and drying (AWD) and continuous flooding (CF) using the latest scenarios from the Intergovernmental Panel on Climate Change(IPCC), utilizing AquaCrop Model. Field experiments were conducted during the dry season 2023 to get the model calibration and validation input. We used two shared socioeconomic pathways scenarios (SSP3-7.0 and SSP5-8.5) developed within Coupled Model Intercomparison Project Phase 6 (CMIP6) and projected the rice growth during 2040-2070. The simulation results demonstrated the effectiveness of AquaCrop in capturing crop development across treatments and varieties. This model's accuracy in simulating canopy cover (nRMSE = 14-32.5%), time series biomass (nRMSE = 22-42.5%), grain yield (Pd = 4.36-24.38%), and total biomass (nRMSE = 0.39-18.98%) was generally acceptable. The analysis of future climate shows an increasing trend in the monthly average temperature by 0.8°C (Tmin) and 1.3°C (Tmax) in both scenarios. While ETo values were not anticipated, rainfall was expected to increase with average values of 5.62mm to 11.25mm. In addition, the study found that varieties with growing periods longer than 93days after transplanting (DAT), such as CAR15 and Sen Kra Ob, were most impacted by heat stress conditions, leading to reduced yield, harvest index (HI), and water use efficiency (WUE). In our case, CAR15 and Sen Kra Ob grain yields were reduced by 53% and 8%, respectively. AWD maintains superior WUE compared with CF regardless of the type of varieties, suggesting this technique is a drought-adaptive strategy.

Role of the warm Arctic cold Eurasian-like pattern on the near future warming rate of East Asian surface temperature

Abstract Internal climate variability (ICV) plays an important role in either accelerating or slowing down the rate of surface temperature warming in East Asia in the near future. To examine the influence of ICV on East Asian surface temperature in the near future, we mainly analyzed the data sets obtained from Max Planck Institute Grand Ensemble model simulations under the Representative Concentration Pathway 8.5 scenario. It is found that the ICV associated with the so-called Warm Arctic-Cold Eurasian (WACE)-like pattern contributes to the near-future warming rate of East Asian surface temperature. Similar results are also obtained from large ensemble model simulations participating in the Coupled Model Intercomparison Project Phase 6 under the Shared Socioeconomic Pathways 5-8.5 scenario. This implies that the near-term warming rate in East Asia could vary depending on how the climate model simulates the WACE-like pattern, indicating that the ability to accurately simulate ICV in climate models is crucial for future climate mitigation and adaptation policies.&amp;#xD;

Global record-breaking recurrence rates indicate more widespread and intense surface air temperature and precipitation extremes.

We analyzed the evolution of extreme annual surface air temperature and rainfall on Earth, based on the recurrence rate of record-breaking events, and found the highest recurrence rates for record-high annual temperatures in the tropics, as opposed to the polar regions with the fastest warming. Both recurrence rates and the global surface area fraction with daily mean surface air temperatures exceeding 30° and 40°C provide further evidence for extremely hot years becoming more common and widespread. A similar analysis for precipitation highlighted some regions with more record-high annual total precipitation and others with record-low annual precipitation typically associated with drought. A multimodel ensemble of 306 runs with global climate models [Coupled Model Intercomparison Project phase 6 (CMIP) Shared Socioeconomic Pathways 2-45 (SSP2-45)] reproduced the statistics of record-breaking high temperatures, but there were some differences for the reanalysis precipitation record-breaking recurrence rates. The global climate model simulations suggested a slightly altered geographical pattern for record-breaking annual precipitation recurrence rates.