Heterogeneous chemical cycles of pyrogenic nitrogen and halides influence tropospheric ozone and affect the stratosphere during extreme pyrocumulonimbus (PyroCB) events. We report field-derived N2O5 uptake coefficients, γ(N2O5), and ClNO2 yields, φ(ClNO2), from two aircraft campaigns observing fresh smoke in the lower and mid troposphere and processed/aged smoke in the upper troposphere and lower stratosphere (UTLS). Derived φ(ClNO2) varied across the full 0–1 range but was typically < 0.5 and smallest in a PyroCB (< 0.05). Derived γ(N2O5) was low in agricultural smoke (0.2–3.6 ×10-3), extremely low in mid-tropospheric wildfire smoke (0.1 × 10-3), but larger in PyroCB processed smoke (0.7–5.0 × 10–3). Aged BB aerosol in the UTLS had a higher median γ(N2O5) of 17 × 10–3 that increased with sulfate and liquid water, but that was nevertheless 1–2 orders of magnitude lower than values for aqueous sulfuric aerosol used in stratospheric models.

Canonical understanding based on general circulation models (GCMs) is that the atmospheric circulation response to midlatitude sea-surface temperature (SST) anomalies is weak compared to the larger influence of tropical SST anomalies. However, the horizontal resolution of modern GCMs, ranging from roughly 300 km to 25 km, is too coarse to fully resolve mesoscale atmospheric processes such as weather fronts. Here, we investigate the large-scale atmospheric circulation response to idealized Gulf Stream SST anomalies in Community Atmosphere Model (CAM6) simulations with 14-km regional grid refinement over the North Atlantic, and compare it to the response in simulations with 28-km regional refinement and uniform 111-km resolution. The highest resolution simulations show a large positive response of the wintertime North Atlantic Oscillation (NAO) to positive SST anomalies in the Gulf Stream, a 0.8-standard-deviation anomaly in the seasonal-mean NAO for 2°C SST anomalies. The lower-resolution simulations show a weaker response with a different spatial structure. The enhanced large-scale circulation response results from an increase in resolved vertical motions with resolution and an associated increase in the influence of SST anomalies on transient-eddy heat and momentum fluxes in the free troposphere. In response to positive SST anomalies, these processes lead to a stronger North Atlantic jet that varies less in latitude, as is characteristic of positive NAO anomalies. Our results suggest that the atmosphere responds differently to midlatitude SST anomalies in higher-resolution models and that regional refinement in key regions offers a potential pathway to improve multi-year regional climate predictions based on midlatitude SSTs.

Groundwater resource sustainability faces significant challenges due to groundwater overdraft and waterlogging. Here we propose a novel framework for evaluating the sustainability of groundwater resources. The framework incorporates a dynamic calculation of the ecological groundwater depth (EGWD) at the grid scale, considering multiple protective targets. To quantitatively evaluate the groundwater sustainability, we utilize reliability, resilience, and vulnerability, to measure the frequency, duration, and extent of unsatisfactory conditions. We apply this framework to the lower part of Tao’er River Basin in China. During the non-growth period and growth period, the upper thresholds of the EGWD range from 1.16 to 2.05 meters and 1.16 to 4.05 meters, respectively. The lower thresholds range from 6.28 to 33.54 meters and 4.87 to 30.72 meters, respectively. Future climate change improves reliability performances in regions with deep groundwater depths. Although the precipitation infiltration increases in future scenarios, prolonged duration and enhanced intensity of extreme climate events lead to decreased resilience and vulnerability performances under climate change. The proportion of areas with resilience values less than 1/12 expands to 2~3 times that of the historical scenario. Furthermore, we observe that more areas face the dual challenges of groundwater depletion and waterlogging under future climate change, particularly in high-emission scenarios. This study enhances understanding of groundwater resource sustainability by considering the spatial-temporal distribution of the EGWD, climate change impacts, and the identification of key regions for management. The insights can inform the development of effective strategies for sustainable groundwater resource management.

Comprehending the intricate interplay between atmospheric aerosols and water vapour in subsaturated regions is vital for accurate modelling of aerosol–cloud–radiation–climate dynamics. But the microphysical mechanisms governing these interactions with ambient aerosols remain inadequately understood. Here we report results from high-altitude, relatively pristine site in Western-Ghats of India during monsoon, serving as a baseline for climate processes in one of the world’s most polluted regions. Utilizing a novel quartz crystal microbalance (QCM) approach, we conducted size-resolved sampling to analyse humidity-dependent growth factors, hygroscopicity, deliquescence behaviour, and aerosol liquid water content (ALWC). Fine-mode aerosols (≤2.5 μm) exhibited size-dependent interactions with water vapour, contributing significantly to ALWC. Deliquescence was observed in larger aerosols (>180 nm), influenced by organic species, with deliquescence relative humidity (DRH) lower than that of pure inorganic salts. This research highlights the significance of understanding ambient aerosol-water interactions and hygroscopicity for refining climate models in subsaturated conditions.

The land sink of anthropogenic carbon emissions, a crucial component of mitigating climate change, is primarily attributed to the CO₂ fertilization effect on global gross primary productivity (GPP). However, direct observational evidence of this effect remains scarce, hampered by challenges in disentangling the CO₂ fertilization effect from other long-term drivers, particularly climatic changes. Here, we introduce a novel statistical approach to separate the CO₂ fertilization effect on GPP and daily maximum net ecosystem production (NEPmax) using eddy covariance records across 38 extratropical forest sites. We find the median stimulation rate of GPP and NEPmax to be 16.4 ± 4% and 17.2 ± 4% per 100 ppm increase in atmospheric CO₂ across these sites, respectively. To validate the robustness of our findings, we test our statistical method using factorial simulations of an ensemble of process-based land surface models. We acknowledge that additional factors, including nitrogen deposition and land management, may impact plant productivity, potentially confounding the attribution to the CO₂ fertilization effect. Assuming these site-specific effects offset to some extent across sites as random factors, the estimated median value still reflects the strength of the CO₂ fertilization effect. However, disentanglement of these long-term effects, often inseparable by timescale, requires further causal research. Our study provides direct evidence that the photosynthetic stimulation is maintained under long-term CO₂ fertilization across multiple eddy covariance sites. Such observation-based quantification is key to constraining the long-standing uncertainties in the land carbon cycle under rising CO₂ concentrations.

Unlike the radiative forcing linked to CO2 and its cumulative storage in oceans since the start of the industrial era around two centuries ago, the Sun has heated the Earth for billions of years without accumulation and dramatic temperature drift. To overcome this obviously illogical difference in evolution, we first analyze several reasons showing that the current universally adopted relationship between carbon dioxide and global warming does not respect the fundamentals of Chemistry, Physics, and Thermodynamics. A recently proposed alternative mechanism, based on these hard sciences, is briefly recalled. In this new mechanism, heat on Earth is managed by water and its solid-liquid and liquid-vapor interphases equilibria before radiative elimination in space. Today, anthropogenic heat is increasingly seen as a complement to the solar heating although it is neglected in the universally adopted consensus. Anthropogenic heat releases are generally estimated from global energy consumption. A broader list of sources is established that includes the capture of solar thermal infrared radiations by artificial installations, including those acting as greenhouses. Three qualitative scenarios are proposed in which climate change depends on whether the ratio of anthropogenic heat releases relative to solar thermal contributions remains negligible, is acceptable or becomes so large that it could shorten the time until the next ice age. Currently, global temperature and ocean level are still very low compared to those in distant past. On the other hand, ice disappearance is indisputable, particularly at the levels of glaciers, floating ice, and permafrost. These features fit the scenario in which temperature continued to fluctuate as it did during the last 8,000 years of the current Holocene interglacial plateau while local rains, winds, floodings, droughts, etc., worsen in magnitude and frequency to help ice melt and evaporation manage excess heat. Policymakers should not wait to discover that decreasing atmospheric carbon dioxide has little effect on the worsening of climate events to begin mitigating of anthropogenic heat with the help of hard sciences scientists to work on quantification. Key points • Carbon dioxide-based radiative forcing as source of global warming does not resist to critical analysis based on fundamentals of chemistry, physics and thermodynamics • Thermal properties of water, water interphase exchanges, formation of clouds and radiative elimination to space control heat supplies and climate changes since water is present on Earth • Anthropogenic heat releases should not affect much temperature and ocean levels provided they remain negligible relative to solar heat supplies, but heat-dispersing local climatic vents should increase in strength and frequency

Atmospheric rivers (ARs) significantly impact the hydrological cycle and associated extremes in western continental regions. Recent studies suggest ARs also influence water resources and extremes in continental interiors. AR detection tools indicate that AR conditions are relatively frequent in areas east of the Rocky Mountains. The origin of these ARs, whether from synoptic-scale waves or mesoscale processes, is unclear. This study uses meteorological composite maps and transects of AR conditions during the four seasons. The analysis reveals that ARs east of the Rockies are associated with a long-wave baroclinic Rossby wave. This result demonstrates that eastern and midwestern ARs are dynamically similar to their western coastal counterparts, though mechanisms for vertical moisture flux differ between the two. These findings provide a foundation for understanding future climate change and ARs in this region and offer new methods for evaluating climate model simulations.

Upscaling flux tower measurements based on machine learning (ML) algorithms is an essential approach for large-scale net ecosystem CO2 exchange (NEE) estimation, but existing ML upscaling methods face some challenges, particularly in capturing NEE interannual variations (IAVs) that may relate to lagged effects. With the capacity of characterizing temporal memory effects, the Long Short-Term Memory (LSTM) networks are expected to help solve this problem. Here we explored the potential of LSTM for predicting NEE across various ecosystems using flux tower data over 82 sites in North America. The LSTM model with differentiated plant function types (PFTs) demonstrates the capability to explain 79.19% (R2 = 0.79) of the monthly variations in NEE within the testing set, with RMSE and MAE values of 0.89 and 0.57 g C m-2 d-1 respectively (r = 0.89, p < 0.001). Moreover, the LSTM model performed robustly in predicting cross-site variability, with 67.19% of the sites that can be predicted by both LSTM models with and without distinguished PFTs showing improved predictive ability. Most importantly, the IAV of predicted NEE highly correlated with that in flux observations (r = 0.81, p < 0.001), clearly outperforming that by the random forest model (r = -0.21, p = 0.011). Among all nine PFTs, solar-induced chlorophyll fluorescence, downward shortwave radiation, and leaf area index are the most important variables for explaining NEE variations, collectively accounting for approximately 54.01% in total. This study highlights the great potential of LSTM for improving carbon flux upscaling with multi-source remote sensing data.

A solid understanding of the mechanisms behind the presently observed, rapid warming of the northwest North Atlantic Continental Shelf is lacking. We hypothesize that a weakening of the Labrador Current System (LCS), especially the shelfbreak jet along the Scotian Shelf, is contributing to these changes and that the future evolution of the LCS will be key to accurate projections. Here we analyze the response of a transient simulation of the high-resolution GFDL Climate Model 2.6 (CM2.6) which realistically simulates the regional circulation but includes only a highly simplified representation of ocean biogeochemistry. Then, we dynamically downscale CM2.6 using a medium-complexity regional biogeochemical ocean model to obtain projections of several ecosystem-relevant variables. In the simulation, the shelfbreak jet weakens throughout the century because of a reduction of the along-shelf pressure gradient caused by a buoyancy gain of the upper water column along the shelf edge. This buoyancy gain is the result of an increased presence of subtropical waters in the continental slope. Importantly, we find that the weakening of the shelfbreak jet is not in response to a northward shift of the Gulf Steam, as has been hypothesized by others, and that previous reports of a northward shift of the Gulf Stream North Wall (GSNW) are an artifact of the temperature-based GSNW criterion in common use. The projected weakening of the shelfbreak jet is likely to lead to a reduction in nutrient availability and a subsequent decline in productivity on the Scotian Shelf, Gulf of St. Lawrence, and Grand Banks.

The Mediterranean region is experiencing pronounced aridification and in certain areas higher occurrence of intense precipitation. In this work, we analyze the evolution of the rainfall probability distribution in terms of precipitating days (or “wet-days”) and all-days quantile trends, in Europe and the Mediterranean, using the ERA5 reanalysis. Looking at the form of wet-days quantile trends curves, we identify four regimes. Two are predominant: in most of Northern Europe the rainfall quantiles all intensify, while in the Mediterranean the low-medium quantiles are mostly decreasing as extremes intensify. The wet-days distribution is then modeled by a Weibull law with two parameters, whose changes capture the four regimes. Assessing the significance of the parameter changes over 1950–2020 shows that a signal on wet-days distribution has already emerged in Northern Europe (where the distribution shifts to more intense rainfall), but not yet in the Mediterranean, where the natural variability is stronger. We extend the results by describing the all-days distribution change as the wet-days’, plus a contribution from the dry-days frequency change, and study their relative contribution. In Northern Europe, the wet-days distribution change is the dominant driver, and the contribution of dry-days frequency change can be neglected for wet-days percentiles above about 50\%. In the Mediterranean, however, the contribution to all-days change of wet-days distribution change is much smaller than the one of dry-days frequency. Therefore, in the Mediterranean the increase of dry-days frequency is crucial for all-days trends, even when looking at heavy precipitations.

Snow droughts are a new way to understand changes in snowpack and subsequent runoff. Globally, we do not have a good understanding of the drivers of snow droughts or how those drivers have changed historically. Here, we identify what has been the dominant driver of global snow droughts in mountain ranges, how it shifted historically, and what similarities exist in similar snow types. We explore this in all global mountain ranges, ones that are highly dependent on winter precipitation for summer water, and two regional case studies in the Cascade Range and the Himalayan Mountains. We found that in both the northern and southern hemispheres, dry snow droughts (driven by precipitation) are the most common. In both the northern and southern hemisphere, more mountain ranges shifted to having temperature be the main driver of snow droughts in the historical record. In the northern hemisphere, tundra, boreal, prairie, and ice snow type areas had the most area with dry snow droughts. In the southern hemisphere, all snow types except for tundra had the most area with temperature as the main driver of snow droughts. With this global, multivariate methodology, we were able to identify common drivers and patterns of historical snow droughts that exist across similar geographical areas (i.e., northern and southern hemisphere and mountain ranges) and snow type areas. More research is needed to better understand snow droughts, their drivers, and the risk they pose regionally to food and water security.

Surface, aircraft, and satellite measurements indicate pervasive cold season CO2 emissions across Arctic regions, consistent with a hyperactive biosphere and increased metabolism in plants and soils. A key remaining question is whether cold season sources will become large enough to permanently shift the Arctic into a net carbon source. Polar orbiting GHG satellites provide robust estimation of regional carbon budgets but lack sufficient spatial coverage and repeat frequency to track sink-to-source transitions in the early cold season. Mission concepts such as the Arctic Observing Mission (AOM) advocate for flying imaging spectrometers in highly elliptical orbits (HEO) over the Arctic to address sampling limitations. We perform retrieval and flux inversion simulation experiments using the AURORA mission concept, leveraging a Panchromatic imaging Fourier Transform Spectrometer (PanFTS) in HEO. AURORA simulations demonstrate the benefits of increased CO2 sampling for detecting spatial gradients in cold season efflux and improved monitoring of rapid Arctic change.

A document by Carl-Friedrich Schleussner. Click on the document to view its contents.

Basal crevasses threaten the stability of ice shelves through the potential to form rifts and calve icebergs. Different existing fracture theories lead to distinct calving predictions. Furthermore, it is important to determine the dependence of crevasse stability on temperature due to large vertical temperature variations on ice shelves. In this work, we explore the transition from basal crevasses to full thickness fractures considering the vertical temperature structure. Nye’s Zero-Stress approximation violates Newton’s second law. By upholding horizontal force balance, it has been shown analytically that the threshold stress for rift initiation is that of a freely- floating unconfined ice tongue. We generalize the force balance argument to show that while temperature structure influences crack depths, the threshold rifting stress is insensitive to temperature. In the classical Nye’s theory, basal crevasses would develop into rifts at a stress twice of that in our Nye’s theory adhering to horizontal force balance.

A document by Yechul Shin. Click on the document to view its contents.

This study investigates the origin of the zonal asymmetry in stratospheric ozone trends at northern high latitudes, identified in satellite limb observations over the past two decades. We use a merged dataset consisting of ozone profiles retrieved at the University of Bremen from SCIAMACHY and OMPS-LP measurements to derive ozone trends. We also use TOMCAT chemical transport model (CTM) simulations, forced by ERA5 reanalyses, to investigate the factors which determine the asymmetry observed in the long-term changes. By studying seasonally and longitudinally resolved observation-based ozone trends, we find, especially during spring, a well-pronounced asymmetry at polar latitudes, with values up to +6 % per decade over Greenland and -5 % per decade over western Russia. The control CTM simulation agrees well with these observed trends, whereas sensitivity simulations indicate that chemical mechanisms, involved in the production and removal of ozone, or their changes, are unlikely to explain the observed behaviour. The decomposition of TOMCAT ozone time series and of ERA5 geopotential height into the first two wavenumber components shows a clear correlation between the two variables in the middle stratosphere and demonstrates a weakening and a shift in the wavenumber-1 planetary wave activity over the past two decades. Finally, the analysis of the polar vortex position and strength points to a decadal oscillation with a reversal pattern at the beginning of the century, also found in the ozone trend asymmetry. This further stresses the link between changes in the polar vortex position and the identified ozone trend pattern.

Climate projections suggest an increase in drought frequency and intensity in various places over the globe, one of them being Southern Europe, expected to become a hotspot. However, 2018 presented an anomaly with the emergence of a rare “water seesaw” phenomenon, leading to severe drought in Central and Northern Europe while Southern Europe experienced high humidity. This unexpected event resulted in significant agricultural disparities, emphasizing the influence of interannual variability. The commentary underscores the danger of overlooking short-term climate variability, vital for accurate adaptation planning, especially for vulnerable regions, when focusing solely on long-term trends. This case serves as a motivation for exploration of global atmospheric circulation changes, emphasizing the need for nuanced modeling approaches to grasp subtle complexities in climate predictions and considering short-term climate variability alongside long-term trends.

In the face of a changing climate, the understanding, predictions and projections of natural and human systems are increasingly crucial to prepare and cope with extremes and cascading hazards, determine unexpected feedbacks and potential tipping points, inform long-term adaptation strategies, and guide mitigation approaches. Increasingly complex socio-economic systems require enhanced predictive information to support advanced practices. Such new predictive challenges drive the need to fully capitalize on ambitious scientific and technological opportunities. These include the unrealized potential for very high-resolution modeling of global-to-local Earth system processes across timescales, a reduction of model biases, enhanced integration of human systems and the Earth Systems, better quantification of predictability and uncertainties; expedited science-to-service pathways and co-production of actionable information with stakeholders. Enabling technological opportunities include exascale computing, advanced data storage, novel observations and powerful data analytics, including artificial intelligence and machine learning. Looking to generate community discussions on how to accelerate progress on U.S. climate predictions and projections, representatives of Federally-funded U.S. modeling groups outline here perspectives on a six-pillar national approach grounded in climate science that builds on the strengths of the U.S. modeling community and agency goals. This calls for an unprecedented level of coordination to capitalize on transformative opportunities, augmenting and complementing current modeling center capabilities and plans to support agency missions. Tangible outcomes include projections with horizontal spatial resolutions finer than 10 km, representing extremes and associated risks in greater detail, reduced model errors, better predictability estimates, and more customized projections to support the next generation of climate services.

Initialized climate model simulations have proven skillful for near-term predictability of the key physical climate variables. By comparison, predictions of biogeochemical fields like ocean carbon flux, are still emerging. Initial studies indicate skillful predictions are possible for lead-times up to six years at global scale for some CMIP6 models. However, unlike core physical variables, biogeochemical variables are not directly initialized in existing decadal preciction systems, and extensive empirical parametrization of ocean-biogeochemistry in Earth System Models introduces a significant source of uncertainty. Here, we propose a new approach for improving the skill of decadal ocean carbon flux predictions using observationally-constrained statistical models, as alternatives to the ocean-biogeochemistry models. We use observations to train multi-linear and neural-network models to predict the ocean carbon flux. To account for observational uncertainties, we train using six different observational estimates of the flux. We then apply these trained statistical models using input predictors from the Canadian Earth System Model (CanESM5) decadal prediction system to produce new decadal predictions. Our hybrid GCM-statistical approach significantly improves prediction skill, relative to the raw CanESM5 hindcast predictions over 1990-2019. Our hybrid-model skill is also larger than that obtained by any available CMIP6 model. Using bias-corrected CanESM5 predictors, we make forecasts for ocean carbon flux over 2020-2029. Both statistical models predict increases in the ocean carbon flux larger than the changes predicted from CanESM5 forecasts. Our work highlights the ability to improve decadal ocean carbon flux predictions by using observationally-trained statistical models together with robust input predictors from GCM-based decadal predictions.

Monthly global sea-air CO2 flux estimates from 1998-2020 are produced by extrapolation of surface water fugacity of CO2 (fCO2w) observations using an Extra-trees (ET) machine learning technique. This new product (AOML_ET) is one of the eleven observation-based submissions to the second REgional Carbon Cycle Assessment and Processes (RECCAP2) effort. The target variable fCO2w is derived using the predictor variables including date, location, sea surface temperature, mixed layer depth, and chlorophyll-a. A monthly resolved sea-air CO2 flux product on a 1˚ by 1˚ grid is created from this fCO2w product using a bulk flux formulation. Average global sea-air CO2 fluxes from 1998-2020 are -1.7 Pg C yr-1 with a trend of 0.9 Pg C decade-1. The sensitivity to omitting mixed layer depth or chlorophyll-a as predictors is small but changing the target variable from fCO2w to air-water fCO2 difference has a large effect, yielding an average flux of -3.6 Pg C yr-1 and a trend of 0.5 Pg C decade-1. Substituting a spatially resolved marine air CO2 mole fraction product for the commonly used zonally invariant marine boundary layer CO2 product yield greater influx and less outgassing in the Eastern coastal regions of North America and Northern Asia but with no effect on the global fluxes. A comparison of AOML_ET for 2010 with an updated climatology following the methods of Takahashi et al. (2009), that extrapolates the surface CO2 values without predictors, shows overall agreement in global patterns and magnitude.

Circulation patterns linked to the East Asian winter monsoon (EAWM) affect precipitation, surface temperature, and air quality extremes over East Asia. These circulation patterns can in turn be influenced by aerosol radiative and microphysical effects through diabatic heating and its impacts on atmospheric vorticity. Using global model simulations, we investigate the effects of anthropogenic aerosol emissions and concentration changes on the intensity and variability of the EAWM. Comparison with reanalysis products indicates that the model captures the mean state of the EAWM well. The experiments indicate that anthropogenic aerosol emissions strengthen the Siberian High but weaken the East Asian jet stream, making the land areas of East Asia colder, drier, and snowier. Aerosols reduce mean surface air temperatures by approximately 1.5°C, comparable to about half of the difference between strong and weak EAWM episodes in the control simulation. The mechanisms behind these changes are evaluated by analyzing differences in the potential vorticity budget. Anthropogenic aerosol effects on diabatic heating strengthen anomalous subsidence over southern East Asia, establishing an anticyclonic circulation anomaly that suppresses deep convection and precipitation. Aerosol effects on cloud cover and cloud longwave radiative heating weaken stability over the eastern flank of the Tibetan Plateau, intensifying upslope flow along the western side of the anticyclone. Both circulation anomalies contribute to reducing surface air temperatures through regional impacts on thermal advection and the atmospheric radiative balance.

A document by Michal Ostrowski. Click on the document to view its contents.

A document by Yi Yang. Click on the document to view its contents.

Key Points: • CMIP6 models can capture the timing of annual cycle (particularly in February) and spatial patterns of SIT resembling the observations. • Compared to sea-ice area, CMIP6 models exhibit larger negative biases in thickness/volume, with a higher degree of variation among models. • Seasonal variations in sea-ice show positive (negative) relationships between sea ice area and thickness during September (February). Abstract This study assesses less-explored Southern Ocean sea-ice parameters, namely Sea-ice Thickness and Volume, through a comprehensive comparison of 26 CMIP6 models with reanalyses and satellite observations. Findings indicate that models replicate the mean seasonal cycle and spatial patterns of sea-ice thickness, particularly during its maxima in February. However, some models simulate implausible historical mean states compared to satellite observations, leading to large inter-model spread. September sea-ice thickness is consistently biased low across the models. Our results show a positive relationship between modeled mean sea-ice area and thickness in September (i.e., models with more area tend to have thicker ice); in February this relationship becomes negative. While CMIP6 models demonstrate proficiency in simulating Area, thickness accuracy remains a challenge. This study, therefore, highlights the need for improved representation of Antarctic sea-ice processes in models for accurate projections of thickness and volume changes. Plain Language Summary In this study, we investigated sea-ice thickness and volume in the Southern Ocean using data from 26 different climate models and observation datasets. Our findings show that the models generally capture the seasonal cycle and spatial patterns of sea-ice thickness well, with the highest average thickness occurring in February. We also found that the models tend to perform better in simulating sea-ice area compared to thickness. Furthermore, simulated sea-ice area and thickness tend to behave differently during different seasons-positively (negatively) covarying in September (February). The models that performed well in simulating sea-ice area faced challenges in accurately representing thickness and volume. This raises the question regarding the overall performance of such models or, more definitively, whether it's reliable to evaluate model accuracy or performance based solely on sea-ice area. Nevertheless, sea-ice thickness simulations in CMIP6 can offer a basis for initial analyses of absolute sea-ice changes in the Southern Ocean, despite the need for more reliable observational thickness.