General Circulation Model Research Articles

Hot Jupiters Are Asynchronous Rotators

Abstract Hot Jupiters are typically assumed to be synchronously rotating, from tidal locking. Their thermally driven atmospheric winds experience Lorentz drag on the planetary magnetic field anchored at depth. We find that the magnetic torque does not integrate to zero over the entire atmosphere. The resulting angular momentum feedback on the bulk interior can thus drive the planet away from synchronous rotation. Using a toy tidal–ohmic model and atmospheric general circulation model outputs for HD189733b, HD209458b, and Kepler7b, we establish that off-synchronous rotation can be substantial at tidal–ohmic equilibrium for sufficiently hot and/or magnetized hot Jupiters. Potential consequences of asynchronous rotation for hot Jupiter phenomenology motivate follow-up work on the tidal–ohmic scenario with approaches that go beyond our toy model.

The very-high-resolution configuration of the EC-Earth global model for HighResMIP

Abstract. We present the very-high-resolution (VHR) version of the EC-Earth global climate model, EC-Earth3P-VHR, developed for HighResMIP. The model features an atmospheric resolution of ∼16 km and an oceanic resolution of 1/12° (∼8 km), which makes it one of the finest combined resolutions ever used to complete historical and scenario-like CMIP6 simulations. To evaluate the influence of numerical resolution on the simulated climate, EC-Earth3P-VHR is compared with two configurations of the same model at lower resolution: the ∼100 km grid EC-Earth3P-LR (LR) and the ∼25 km grid EC-Earth3P-HR (HR). Of the three configurations, VHR shows the smallest drift in the global mean ocean temperature and salinity at the end of a 100-year 1950s control simulation, which points to a faster equilibrating phase than in LR and HR. In terms of model biases, we compare the historical simulations against observations over the period 1980–2014. In contrast to LR and HR, VHR shows a reduced equatorial Pacific cold tongue bias, an improved Gulf Stream representation with a reduced coastal warm bias and a reduced subpolar North Atlantic cold bias, and more realistic orographic precipitation over mountain ranges. By contrast, VHR shows a larger warm bias and overly low sea ice extent over the Southern Ocean. Such biases in surface temperature have an impact on the atmospheric circulation aloft, connected with a more realistic storm track over the North Atlantic yet a less realistic storm track over the Southern Ocean compared to the lower-resolution model versions. Other biases persist or worsen with increased resolution from LR to VHR, such as the warm bias over the tropical upwelling region and the associated cloud cover underestimation, a precipitation excess over the tropical South Atlantic and North Pacific, and overly thick sea ice and an excess in oceanic mixing in the Arctic. VHR shows improved air–sea coupling over the tropical region, although it tends to overestimate the oceanic influence on the atmospheric variability at midlatitudes compared to observations and LR and HR. Together, these results highlight the potential for improved simulated climate in key regions, such as the Gulf Stream and the Equator, when the atmospheric and oceanic resolutions are finer than 25 km in both the ocean and atmosphere. Thanks to its unprecedented resolution, EC-Earth3P-VHR offers a new opportunity to study climate variability and change of such areas on regional and local spatial scales, in line with regional climate models.

Comparing the outputs of general circulation and mesoscale models in the flood forecasts of mountainous basins

Comparing the outputs of general circulation and mesoscale models in the flood forecasts of mountainous basins

A combined wavelet analysis–quantile mapping (WA-QM) method for bias correction: capturing the intra-annual temporal patterns in climate model precipitation simulations and projections

Abstract Despite recent improvements, global climate models (GCMs) still have biases that prevent their direct application. Quantile mapping (QM) has been widely used in bias correction due to its effectiveness in aligning the cumulative density function of climate model data with observations. However, QM has a significant limitation: it fails to consider the temporal correspondence between the model data and observations, which restricts its ability to represent intra-annual temporal patterns. To address this issue, this study integrated wavelet analysis (WA) into QM to develop a combined method termed WA-QM, which aimed to preserve QM's efficacy in overall bias correction and preservation of climate change signals, while capturing the intra-annual temporal patterns. The effectiveness of WA-QM was investigated using monthly precipitation data from five CMIP6 models in the Pan Third Pole region, which includes central Asia (CA), southeast Asia (SEA), and the Tibetan Plateau (TP). Quantile delta mapping (QDM) and scaled distribution mapping (SDM) served as the benchmark methods for assessment. The results indicated that integrating WA into QDM or SDM did not compromise the ability of QDM or SDM to correct overall biases and preserve the model’s climate change signal. Furthermore, WA could be an effective tool to overcome QM’s limitation in capturing intra-annual temporal patterns. The WA approach employed discrete wavelet transformation to decompose GCM data into various frequency bands and then adjusted their standard deviations and signs to ensure that their relative relationships were consistent with those in observations. Compared to the standalone QM approach, WA-QM showed greater accuracy in monthly statistical metrics. In the CA region, WA-SDM reduced the monthly root mean square error of the mean by 25.2%, the standard deviation by 17.6%, and the 90th percentile by 26.7% compared to SDM. The effectiveness of WA-QM was demonstrated across different data periods, spatial areas, and GCMs.

Modeling of historical and future changes in temperature and precipitation in the Panj River Basin in Central Asia under the CMIP5 RCP and CMIP6 SSP scenarios

This study examines the complexities of climate modeling, specifically in the Panj River Basin (PRB) in Central Asia, to evaluate the transition from CMIP5 to CMIP6 models. The research aimed to identify differences in historical simulations and future predictions of rainfall and temperature, examining the accuracy of eight General Circulation Models (GCMs) used in both CMIP5 (RCP4.5 and 8.5) and CMIP6 (SSP2–4.5 and 5–8.5). The evaluation metrics demonstrated that the GCMs have a high level of accuracy in reproducing maximum temperature (Tmax) with a correlation coefficient of 0.96. The models also performed well in replicating minimum temperature (Tmin) with a correlation coefficient of 0.94. This suggests that the models have improved modeling capabilities in both CMIPs. The performance of Max Plank Institute (MPI) across all variables in CMIP6 models was exceptional. Within the CMIP5 domain, Geophysical Fluid Dynamics (GFDL) demonstrated outstanding skill in reproducing maximum temperature (Tmax) and precipitation (KGE 0.58 and 0.34, respectively), while (Institute for Numerical Mathematics) INMCM excelled in replicating minimum temperature (Tmin) (KGE 0.28). The uncertainty analysis revealed a significant improvement in the CMIP6 precipitation bias bands, resulting in a more precise depiction of diverse climate zones compared to CMIP5. Both CMIPs consistently tended to underestimate Tmax in the Csa zone and overestimate it in the Bwk zone throughout all months. Nevertheless, the CMIP6 models demonstrated a significant decrease in uncertainty, especially in ensemble simulations, suggesting improvements in forecasting PRB climate dynamics. The projections revealed a complex story, as the CMIP6 models predict a relatively small increase in temperature and a simultaneous drop in precipitation. This indicates a trend towards more uniform temperature patterns across different areas. Nevertheless, the precipitation forecasts exhibited increased variability, highlighting the intricate interaction of climate dynamics in the PRB area under the impact of global warming scenarios. Hydrological components in global climate models can be further improved and developed with the theoretical reference provided by this study.

South Atlantic subtropical anticyclone responses to stratospheric aerosol injection

Abstract The South Atlantic Subtropical Anticyclone (SASA) is a key component of large-scale atmospheric circulation and is responsible for driving the climate in eastern Brazil and western Africa. Climate projections under warming scenarios suggest a strengthening, as well as a westward and southward expansion of this system. However, little is known about how the combination of global warming and climate intervention affects this system. To address this, SASA was identified from 2015 to 2099 in a set of projections with and without stratospheric aerosol injection (SAI). Projections were obtained from different initiatives: the Assessing Responses and Impacts of Solar Climate Intervention on the Earth System with SAI using Community Earth System Model version 2 (CESM2) global climate model, the Stratospheric Aerosol Geoengineering Large Ensemble (GLENS) using CESM1, and the Geoengineering Model Intercomparison Project (GeoMIP/G6sulfur) using Max Planck Institute Earth System Model. As each project has its own specific model, scenario, SAI location, etc, no intercomparison was carried out among them. Instead, there is an indication of what occurs in each project when comparing the near (2040–2059) and the far future (2080–2099) projections under SAI and no-SAI scenarios. SASA under no-SAI scenarios, compared to the reference period (2015–2024), follows the pattern described in the literature, i.e. a tendency to be stronger and wider. However, these features are more evident in the GLENS project. This same project suggests that SAI scenarios contribute to reducing the impact of global warming on the SASA climatology, as SASA in the future acquires characteristics similar to those of the reference period. One of the possibilities for it is that GLENS has the largest SAI forcing, given that the goal was to cancel out the strong greenhouse gas-induced warming in Representative Concentration Pathway 8.5.

Seasonal lake‐to‐air temperature transfer functions derived from an analysis of 1395 modern lakes: A tool for reconstructing air temperature from proxy‐derived lake water temperature

AbstractLacustrine palaeotemperature reconstructions are important for characterising past temperature and hydroclimate change, validating multi‐proxy reconstructions and evaluating global climate models. In particular, lake water temperature is often derived from geochemical proxies—including clumped isotopes (Δ47), oxygen isotopes (δ18O), alkenone lipids (Uk’37) and GDGT compounds (TEX86). However, global climate models, constrained by resolution, computational demand and cost, are designed to simulate large‐scale processes, often at the expense of resolving lakes and simulating lake temperature. Consequently, this limitation complicates the comparison of climate model‐simulated variables such as air temperature, with lake water temperature or with other proxy variables (e.g. pollen‐derived air temperature), and requires the use of a transfer function to relate lake temperature to air temperature. Previous work developed transfer functions to translate proxy‐derived seasonal lake water temperature to mean annual air temperature using ground‐based measurements from 88 lakes. This study reports new lake‐to‐air temperature transfer functions (for annual, spring through summer, spring, summer and warmest month) that incorporate lake surface water temperature, and new variables including latitude and elevation, by analysing climate reanalysis data and long‐term satellite observations of surface temperatures for 1395 modern lakes via regression‐based inverse modelling. With the use of multiple regression models and a dataset roughly 10 times larger, the error in predictions of mean annual air temperature is reduced by up to 48% compared to previous work. To demonstrate the potential of the new transfer functions for integrating and comparing proxy data with model output, Pliocene and Pleistocene mean annual air temperature was reconstructed from Δ47‐derived lake temperatures and compared with model simulations for the Last Glacial Maximum and mid‐Piacenzian warm period. The new transfer functions, with reduced error, should enable more accurate palaeotemperature reconstructions from proxy‐derived lake water temperature and allow for more comprehensive assessments of climate model skill.

Short distances dominate connectivity patterns of coral communities in the North-West Arabian Sea

Larval connectivity relies on the ability of coral larvae to disperse into the environment following ocean currents. At short timescales, larval connectivity plays a key role in the resilience of coral reefs, as it determines their capacity to regain structure and function after major disturbances. At longer time scales, larval connectivity controls the distribution and, ultimately, the biogeography of species. We used a Lagrangian stochastic model to simulate the transport routes of coral larvae released from the major reef communities of the Arabian Sea and Gulf of Oman. The model used surface currents from two independent global circulation models, and we simulated 120 scenarios, covering four years and three larval competency models. Additionally we determine mean flow fields and LCS structures based on 20 years of reanalysis data from a third model. Connectivity values—the proportion of larvae successfully transported from their natal reef to another reef—varied significantly across reefs and years due to mesoscale variability in ocean currents, yet both circulation models produced similar overall patterns of connectivity. The general flow of larvae was from northwest to southeast in the Gulf of Oman, and from southwest to northeast in the Arabian Sea. The exchange of larvae across Ras Al-Hadd between the coral communities of the Arabian Sea and those of the Gulf of Oman is very low. Local retention (self-seeding) was the most important larval source for most reefs (mean = 32.3% for spawning corals and 70.8% for brooding corals). All reefs received larvae from at least one other reef and several received larvae from as many as five other reefs. ANOVA indicated significant differences between brooding and spawning coral larvae, and between reefs. Differences between years depended on the reef or reproduction type. Some reefs (Daymaniyat Islands in the Gulf of Oman and Mirbat in the Arabian Sea) could be considered sources of larvae, as they proportionally produced more larvae that later settled successfully than the other reefs. The limited connectivity between the Gulf of Oman and Arabian Sea supports their biogeographic distinction based on species distribution.

Impact of Grid Resolution on Wave‐Mean Flow Interactions With High Resolution Mars Global Climate Model Simulations

AbstractWe have executed a series of simulations with the NASA Ames Mars Global Climate Model by systematically increasing horizontal and vertical resolution with the goal of resolving small‐scale waves that significantly affect the mean thermal and momentum structure of the atmosphere. Our reference high‐resolution simulation is 1/4° horizontal resolution and ∼1.5 km vertical resolution, and we compare results to a low‐resolution simulation that is ∼4° in the horizontal and ∼4 km in the vertical. We show that the main biases in the zonal mean temperature and momentum fields between the low‐ and high‐resolution simulations can be alleviated by including parameterized orographic and non‐orographic gravity waves at low resolution.

Precipitation variability in CMIP6 climate models across the North Atlantic–European region and their Links to Atmospheric Circulation

Long-term changes in climate variability represent an important aspect of climate change, with various impacts on society and environment. In this study, we analyze outputs from 13 CMIP6 global climate models (GCMs) across the North Atlantic–European domain, focusing on their simulations of precipitation probability and short-term variability in both historical and future climates. Precipitation probability denotes the probability of a wet day (> 1 mm), and precipitation variability reflects the tendency to cluster wet days into sequences. By comparing against the ERA5 reanalysis, we found that the GCMs tend to overestimate precipitation probability across Europe in winter, whereas in summer, they have a tendency to underestimate it around 50°N. Precipitation variability is, on average, underestimated by the GCMs in summer, while overestimated in several regions in winter. Projections for the end of the twenty-first century indicate significant changes in both precipitation probability and variability which are more pronounced under the more pessimistic emission scenario compared to the moderate one. We found that the changes in probability and variability are mutually independent: the former being more latitudinal-dependent while the latter differs between the west and east. After identifying atmospheric circulation conducive and non-conducive to precipitation occurrence, we found that GCMs overestimating the frequency of conducive circulation tend to overestimate precipitation probability, and vice versa. Furthermore, increased precipitation variability is associated with higher circulation variability. Finally, our analysis reveals that projected changes in precipitation probability and variability are often linked to projected changes in atmospheric circulation, especially in winter.

Revisiting the Last Ice Area projections from a high-resolution Global Earth System Model

The Last Ice Area—located to the north of Greenland and the northern Canadian Arctic Archipelago—is expected to persist as the central Arctic Ocean becomes seasonally ice-free within a few decades. Projections of the Last Ice Area, however, have come from relatively low resolution Global Climate Models that do not resolve sea ice export through the waterways of the Canadian Arctic Archipelago and Nares Strait. Here we revisit Last Ice Area projections using high-resolution numerical simulations from the Community Earth System Model, which resolves these narrow waterways. Under a high-end forcing scenario, the sea ice of the Last Ice Area thins and becomes more mobile, resulting in a large export southward. Under this potentially worst-case scenario, sea ice of the Last Ice Area could disappear a little more than one decade after the central Arctic Ocean has reached seasonally ice-free conditions. This loss would have profound impacts on ice-obligate species.

Elucidating diverse population exposure to compound drought and heatwave events from two meteorological drought indices (SPI and SPEI)

Abstract Anthropogenic climate change has significantly exacerbated the frequency and severity of Compound Drought and Heatwave (CDHW) events, increasing risks such as water shortages, wildfires, and heat-related mortality. Previous studies often use singular drought indices, such as the Standardized Precipitation Index (SPI) or the Standardized Precipitation Evapotranspiration Index (SPEI). This study quantifies population exposure to CDHW events using both SPI and SPEI, based on data from six General Circulation Models (GCMs) under four future Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5). Exposure is defined as the product of CDHW Magnitude (℃) and the population in each region (million people), providing a quantitative measure of how CDHW events affect populations. The role of potential evapotranspiration (PET) in CDHW population exposure projections is examined by comparing SPI, which considers only precipitation, with SPEI, which accounts for both precipitation and PET in drought measurements. Results show that after 2050, CDHW Magnitude population exposure diverges significantly across scenarios, with SSP3-7.0 exhibiting the largest increase, reaching 0.72 (SPI) and 1.78 (SPEI) million person-℃ by the end of the century. Regions such as Western Africa (WAF), Southeastern Africa (SEAF), and South Asia (SAS) experience the largest increases in population exposure under SSP3-7.0 with SPEI, reaching 6.93, 6.77, and 5.56 million person-℃, respectively. Additionally, regions like Western & Central Europe (WCE), the Mediterranean (MED), WAF, Western Central Africa (WCA), Eastern Asia (EAS), and SAS display heightened sensitivity to PET, with discrepancies between SPEI and SPI projections exceeding 1 million person-℃. Attribution analysis reveals that climate change, particularly when drought is calculated using PET by SPEI, is the primary factor, followed by interaction change and population change. These findings emphasize the critical role of PET in CDHW projections and the need for region-specific adaptation strategies to manage escalating risks in highly vulnerable areas.

Revisiting sunspot number as an extreme ultraviolet (EUV) proxy for ionospheric F2 critical frequency

Abstract. This study reconsiders sunspot number (Sn) as a solar extreme ultraviolet (EUV) proxy for modeling the ionospheric F2 layer's critical frequency (foF2) over the period 1960–2023. We compare the performance of Sn with F10.7 and F30 solar radio fluxes, focusing on their ability to model the Ionospheric Global index (IG). Our results reveal that while F30 has shown a better correlation in recent solar cycles, Sn is the most stable and reliable over the entire dataset, obtaining the highest correlation. In addition, if we remove the saturation effects from considering a maximum value of Sn, the correlation increases, outperforming all other proxies and correctly predicting the long-term trend estimated by general circulation models.

Non-linear internal waves breaking in stellar-radiation zones. Parametrisation for the transport of angular momentum: Bridging geophysical-to-stellar fluid dynamics

Internal gravity waves (IGWs) are one of the mechanisms that can play a key role in efficiently redistributing angular momentum in stars along their evolution. The study of IGWs is thus of major importance since space-based asteroseismology reveals a transport of angular momentum in stars, which is stronger by two orders of magnitude than the one predicted by stellar models ignoring their action or those of magnetic fields. IGWs trigger angular momentum transport when they are damped by heat or viscous diffusion; at this point, they meet a critical layer where their phase velocity in the azimuthal direction equals the zonal wind or when they break. Theoretical prescriptions have been derived for the transport of angular momentum induced by IGWs because of their radiative and viscous dampings and of the critical layers they encounter along their propagation. However, none have been proposed for the transport of angular momentum triggered by their non-linear breaking. In this work, we aim to derive such a physical and robust prescription, which can be implemented in stellar structure and evolution codes. We adapted an analytical saturation model -- which has been developed for IGWs' nonlinear convective breaking in the Earth's atmosphere and has been successfully compared to in situ measurements in the stratosphere -- to the case of deep spherical stellar interiors. This allowed us to derive the saturated amplitude of the velocity of IGWs breaking in stellar radiation zones through convective overturning of the stable stratification or the instability of the vertical shear of IGWs motion and of the angular momentum transport they trigger. In a first step, we neglected the modification of IGWs by the Coriolis acceleration and the Lorentz force, which are discussed and taken into account in a second step. We derive a complete semi-analytical prescription for the transport of angular momentum by IGWs that takes into account both their radiative damping and their potential nonlinear breaking because of their convective and vertical shear instabilities. We show that the deposit of angular momentum by breaking waves increases with their latitudinal degree, the ratio of the Brunt-Va"is"al"a frequency and the wave frequency; and when the density decreases or the Doppler-shifted frequency vanishes. This allows us to bring the physical prescription for the interactions between IGWs and the differential rotation to the same level of realism as the one used in global circulation models for the atmosphere.

Excessive equatorial light rain causes modeling dry bias of Indian summer monsoon rainfall

Simulating accurately the South Asian summer monsoon is crucial for food security of several South Asian countries yet challenging for global climate models (GCMs). The GCMs suffer from some systematic biases including dry bias in mean monsoon rainfall over the India subcontinent and excessive equatorial light rain between which the relationship was rarely discussed. Numerical experiments are conducted for one month during active monsoon with global quasi-uniform resolution of 60 km (U60 km) and 3 km (U3 km) separately. Evaluation with observations shows that U3 km reduces the dry bias over northern India and excessive light rain over the equatorial Indian Ocean (EIO) that are both prominent in U60 km. Excessive light rain in U60 km contributes critically to stronger rainfall and latent heating over the EIO. A Hadley-type anomalous circulation is thus induced, whose subsidence branch suppresses updrafts and reduces moisture transport into northern India, contributing to the dry bias. The findings highlight the importance of constraining excessive light rain for regional climate projection in GCMs.

Efficiency metrics for ocean alkalinity enhancements under responsive and prescribed atmospheric pCO2 conditions

Abstract. Ocean alkalinity enhancement (OAE) and direct ocean removal (DOR) are emerging as promising technologies for enacting negative emissions. The long equilibration timescales, potential for premature subduction of surface water parcels, and extensive horizontal transport and dilution of added alkalinity make direct experimental measurement of induced CO2 uptake challenging. Therefore, the challenge of measurement, reporting, and verification (MRV) will rely to a great extent on general circulation models, parameterized and constrained by experimental measurements. A number of recent studies have assessed the efficiency of OAE using different model setups and different metrics. Some models use prescribed atmospheric CO2 levels, while others use fully coupled Earth system models. The former ignores atmospheric feedback effects, while the latter explicitly models them. In this paper it is shown that, even for very small OAE deployments, which do not substantially change atmospheric pCO2, the change in oceanic CO2 inventories differs significantly between these methods due to atmospheric feedback causing some ocean CO2 off-gassing. An analogous off-gassing occurs during direct air capture (DAC). Due to these feedback effects, care must be taken to compute the correct metrics when assessing OAE efficiency with respect to determining negative emissions credits, as opposed to determining the effect on global temperatures. This paper examines the commonly used metrics of OAE efficiency, their exact physical meanings, the assumptions inherent in their use, and the relationship between them. It is shown that the efficiency metric η(t), used in prescribed pCO2atm simulations, equals the equivalent schedule of a gradual DAC removal and storage in a fully coupled system.

Modeling tritium release to the atmosphere during the Fukushima Daiichi Nuclear Power Plant accident and application to estimating post-accident water system transit times.

During the Fukushima Daiichi Nuclear Power Plant (FDNPP) accident on March 11, 2011, radionuclides such as tritium were released into the environment across Japan, obscuring the natural background signal of tritium in precipitation. This anthropogenic component was rapidly washed out by precipitation according to measurements in Japan. However, the impact of the accident on the natural tritium-based estimation of water system transit times in Fukushima and other prefectures in Japan remains uncertain. For the first time, anthropogenic tritium from the FDNPP accident together with natural tritium were simulated in an atmospheric general circulation model with a good ability to represent tritium variations in daily and monthly precipitation. For the FDNPP accident, we estimate the maximum tritium atmospheric emission of 0.815 PBq with a tritium in precipitation peak of 68.7 Bq/L (582 tritium units) on March 2011 at Fukushima, which are consistent with previous estimations. Using our modeled outputs with tritium measurements, we improve tritium-tracer application for estimating mean transit times of Fukushima surface and groundwater systems impacted by the anthropogenic tritium from the FDNPP accident. While the anthropogenic impact of the FDNPP accident was limited compared to the tritium peak due to the thermonuclear testing, globally modeled tritium in precipitation is useful to apply for other areas of tritium-tracer studies.

Climate model downscaling in central Asia: a dynamical and a neural network approach

Abstract. High-resolution climate projections are essential for estimating future climate change impacts. Statistical and dynamical downscaling methods, or a hybrid of both, are commonly employed to generate input datasets for impact modelling. In this study, we employ COSMO-CLM (CCLM) version 6.0, a regional climate model, to explore the benefits of dynamically downscaling a general circulation model (GCM) from the Coupled Model Intercomparison Project Phase 6 (CMIP6), focusing on climate change projections for central Asia (CA). The CCLM, at 0.22° horizontal resolution, is driven by the MPI-ESM1-2-HR GCM (at 1° spatial resolution) for the historical period of 1985–2014 and the projection period of 2019–2100 under three Shared Socioeconomic Pathways (SSPs), namely the SSP1-2.6, SSP3-7.0, and SSP5-8.5 scenarios. Using the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) gridded observation dataset as a reference, we evaluate the performance of CCLM driven by ERA-Interim reanalysis over the historical period. The added value of CCLM, compared to its driving GCM, is evident over mountainous areas in CA, which are at a higher risk of extreme precipitation events. The mean absolute error and bias of climatological precipitation (mm d−1) are reduced by 5 mm d−1 for summer and 3 mm d−1 for annual values. For winter, there was no error reduction achieved. However, the frequency of extreme precipitation values improved in the CCLM simulations. Additionally, we employ CCLM to refine future climate projections. We present high-resolution maps of heavy precipitation changes based on CCLM and compare them with the CMIP6 GCM ensemble. Our analysis indicates an increase in the intensity and frequency of heavy precipitation events over CA areas already at risk of extreme climatic events by the end of the century. The number of days with precipitation exceeding 20 mm increases by more than 90 by the end of the century, compared to the historical reference period, under the SSP3-7.0 and SSP5-8.5 scenarios. The annual 99th percentile of total precipitation increases by more than 9 mm d−1 over mountainous areas of central Asia by the end of the century, relative to the 1985–2014 reference period, under the SSP3-7.0 and SSP5-8.5 scenarios. Finally, we train a convolutional neural network (CNN) to map a GCM simulation to its dynamically downscaled CCLM counterpart. The CNN successfully emulates the GCM–CCLM model chain over large areas of CA but shows reduced skill when applied to a different GCM–CCLM model chain. The scientific community interested in downscaling CMIP6 models could use our downscaling data, and the CNN architecture offers an alternative to traditional dynamical and statistical methods.

Tropical High Cloud Feedback Relationships to Climate Sensitivity

Abstract Clouds constitute a large portion of uncertainty in predictions of equilibrium climate sensitivity (ECS). While low cloud feedbacks have been the focus of intermodel studies due to their high variability among global climate models, tropical high cloud feedbacks also exhibit considerable uncertainty. Here, we apply the cloud radiative kernel technique of Zelinka et al. to 22 models across the CMIP5 and CMIP6 ensembles to survey tropical high cloud feedbacks and analyze their relationship to ECS. We find that the net high cloud feedback and its altitude and optical depth feedback components are significantly positively correlated with ECS in the tropical mean. On the other hand, the tropical mean high cloud amount feedback is not correlated with ECS. These relationships are most pronounced outside of areas of strong climatological ascent, suggesting the importance of thin cirrus feedbacks. Finally, we explore connections between high cloud feedbacks, climate sensitivity, and mean state high cloud properties. In general, high ECS models are cloudier in the upper troposphere but have a thinner high cloud population. Moreover, we find that having more thin cirrus in the mean state relates to more positive high cloud altitude and optical depth feedbacks, and it either amplifies or dampens the high cloud amount feedback depending on the large-scale dynamical regime (amplifying in descent and dampening in ascent). In summary, our analysis highlights the importance of tropical high cloud feedbacks for driving intermodel spread in ECS and suggests that mean state high cloud characteristics might provide a unique opportunity for observationally constraining high cloud feedbacks. Significance Statement Clouds play an important role in modulating the effects of climate change through feedback processes involving changes to their amount, altitude, and opacity. In this study, we seek to understand how changes to tropical high clouds under warming are related to the magnitude of warming that global climate models simulate. We find that tropical high cloud feedbacks robustly relate to the amount of warming a model predicts and that warmer models tend to have a thinner tropical high cloud climatology. Our results highlight a potential opportunity to form a new constraint using these relationships in order to narrow the spread of warming estimates among global climate models.

Mechanisms and Modeling of Sea Level Rise in the Context of Global Warming

Sea level rise due to global warming is an important topic in current climate change research. In this study, we explore how complex natural processes triggered by global warming drive sea level change by analyzing climate models. We focus on the main drivers of sea level rise, including ice sheet melting and ocean thermal expansion. In addition, the article discusses in detail how positive feedback mechanisms and negative feedback mechanisms work together to influence the climate change process. In order to predict the future trend of sea level rise, this article models and simulates the global temperature, glacier changes and ocean dynamics based on the General Circulation Model (GCM). Although the model has some uncertainties, especially in cloud feedback and data completeness, it still provides a valuable predictive tool for understanding future sea level changes. The conclusions of this study point to the possibility of accelerated sea level rise as global temperatures continue to rise, with wide-ranging and far-reaching impacts on coastal ecosystems and human societies around the globe.