Climate Models Research Articles

Subpolar North Atlantic Mean State Affects the Response of the Atlantic Meridional Overturning Circulation to the North Atlantic Oscillation in CMIP6 Models

Abstract The Atlantic meridional overturning circulation (AMOC) plays an important role in climate, transporting heat and salt to the subpolar North Atlantic. The AMOC’s variability is sensitive to atmospheric forcing, especially the North Atlantic Oscillation (NAO). Because AMOC observations are short, climate models are a valuable tool to study the AMOC’s variability. Yet, there are known issues with climate models, like uncertainties and systematic biases. To investigate this, preindustrial control experiments from models participating in the phase 6 of Coupled Model Intercomparison Project (CMIP6) are evaluated. There is a large, but correlated, spread in the models’ subpolar gyre mean surface temperature and salinity. By splitting models into groups of either a warm–salty or cold–fresh subpolar gyre, it is shown that warm–salty models have a lower sea ice cover in the Labrador Sea and, hence, enable a larger heat loss during a positive NAO. Stratification in the Labrador Sea is also weaker in warm–salty models, such that the larger NAO-related heat loss can also affect greater depths. As a result, subsurface density anomalies are much stronger in the warm–salty models than in those that tend to be cold and fresh. As these anomalies propagate southward along the western boundary, they establish a zonal density gradient anomaly that promotes a stronger delayed AMOC response to the NAO in the warm–salty models. These findings demonstrate how model mean state errors are linked across variables and affect variability, emphasizing the need for improvement of the subpolar North Atlantic mean states in models.

Anthropogenic Climate Change Will Intensify European Explosive Storms Analogous to Alex, Eunice, and Xynthia

Abstract Extratropical storms, particularly explosive storms or “weather bombs” with exceptionally high deepening rates, present substantial risks and are susceptible to climate change. Individual storms may exhibit a complex and hardly detectable response to human-driven climate change because of the atmosphere’s chaotic nature and variability at the regional level. It is thus essential to understand changes in specific storms for building local resilience and advancing our overall comprehension of storm trends. To address this challenge, this study compares analogs—storms with a similar backward track until making landfall—in two climates of three explosive storms impacting different European locations: Alex (October 2020), Eunice (January 2022), and Xynthia (February 2010). We use a large ensemble dataset of 105 members from the Community Earth System Model, version 1 (CESM1). These analogs are identified in two periods: the present-day climate (1991–2001) and a future climate scenario characterized by high anthropogenic greenhouse gas emissions [representative concentration pathway 8.5 (RCP8.5), 2091–2101]. We evaluate future changes in the frequency of occurrence of the storms and intensity, as well as in meteorological hazards and the underlying dynamics. For all storms, our analysis reveals an increase in precipitation and wind severity over land associated with the explosive analogs in the future climate. These findings underscore the potential consequences of explosive storms modified by climate change and their subsequent hazards in various regions of Europe, offering evidence that can be used to prepare and enhance adaptation processes. Significance Statement This study investigates the impact of climate change on explosive storms, or weather bombs, and their potential consequences for European regions. We project future scenarios of three specific storms, Alex, Eunice, and Xynthia, using a state-of-the-art climate model. Our findings reveal an increase in precipitation and wind over land associated with these storms, emphasizing the heightened risks associated with climate change. The significance lies in understanding the local implications of explosive storms, aiding in the development of resilient strategies and adaptation measures.

Future Changes in the Vertical Structure of Severe Convective Storm Environments over the U.S. Central Great Plains

Abstract The effect of warming on severe convective storm potential is commonly explained in terms of changes in vertically integrated (“bulk”) environmental parameters, such as CAPE and 0–6-km shear. However, such events are known to depend on the details of the vertical structure of the thermodynamic and kinematic environment that can change independently of these bulk parameters. This work examines how warming may affect the complete vertical structure of these environments for fixed ranges of values of high CAPE and bulk shear, using data over the central Great Plains from two high-performing climate models (CNRM and MPI). To first order, projected changes in the vertical sounding structure are consistent between the two models: the environment warms approximately uniformly with height at constant relative humidity, and the shear profile remains relatively constant. The boundary layer becomes slightly drier (−2% to 6% relative humidity) while the free troposphere becomes slightly moister (+1% to 3%), with a slight increase in moist static energy deficit aloft with stronger magnitude in CNRM. CNRM indicates enhanced low-level shear and storm-relative helicity associated with stronger hodograph curvature in the lowest 2 km, whereas MPI shows near-zero change. Both models strongly underestimate shear below 1 km compared to ERA5, indicating large uncertainty in projecting subtle changes in the low-level flow structure in climate models. The evaluation of the net effect of these modest thermodynamic and kinematic changes on severe convective storm outcomes cannot be ascertained here but could be explored in simulation experiments. Significance Statement Severe thunderstorms and tornadoes cause substantial damage and loss of life each year, which raise concerns about how they may change as the world warms. We typically use a small number of common atmospheric parameters to understand how these localized events may change with climate change. However, climate change may alter the weather patterns that produce these events in ways not captured by these parameters. This work examines how climate change may alter the complete vertical structure of temperature, moisture, and wind and discusses the potential implications of these changes for future severe thunderstorms and tornadoes.

Poleward Migration of the Latitude of Maximum Tropical Cyclone Intensity—Forced or Natural?

Abstract Past studies have shown a significant observed poleward trend in the latitude at which tropical cyclones reach their lifetime maximum intensity (LMI), especially in the northwest Pacific basin. Given the brevity of the historical record, it remains difficult to separate the forced trend from internal variability of the climate system. A recently developed tropical cyclone downscaling model is used to downscale the Community Earth System Model, version 2 (CESM2), preindustrial control simulation. It is found that the observed trend in the latitude at which tropical cyclones reach their LMI in the northwest Pacific is very unlikely to be caused by internal variability. The same downscaling model is then used to downscale CESM2 simulations under historical forcing. The resulting trend distribution shows a significant poleward migration of tropical cyclone LMI even after regressing out both natural variability and the part of the forced warming pattern that projects onto natural variability. The results indicate that the observed poleward migration of the latitude at which tropical cyclones reach their LMI in the northwest Pacific basin is likely to be, at least in part, forced. However, the magnitude of the projected poleward trend in climate models can be significantly modulated by the simulated spatial pattern of ocean warming. This highlights how discrepancies between models and observations, with regard to projected changes to the equatorial zonal sea surface temperature gradient under anthropogenic forcing, can lead to large uncertainties in projected changes to the LMI latitude of tropical cyclones. Significance Statement Observations in the northwest Pacific basin show that the latitude at which tropical cyclones are at their most intense has been trending northward in the recent half century. These changes are important since tropical cyclones could bring hazardous weather to coastal areas that are poorly equipped to handle them. Here, we show that natural variations in Earth’s climate are very unlikely to explain the observed poleward trend in the latitude that tropical cyclone reach their maximum intensity. We find that it is much more likely that the observed trend is forced by human-related emissions, though the spatial pattern of warming in response to greenhouse emissions can have significant impacts on the magnitude of the trend.

Changes in the SST Seasonal Cycle in a Warmer North Pacific without Ocean Dynamical Feedbacks

Abstract Climate models project a significant intensification of the sea surface temperature (SST) seasonal cycle over the subpolar North Pacific due to global warming, with the shallower mixed layer widely recognized as the dominant factor. However, employing slab ocean experiments with only ocean–atmosphere thermal coupling, we find a substantial contribution from changes in surface heat flux to this seasonal cycle intensification. In particular, the stronger Newtonian cooling effect in winter acts as a more potent damping than in summer. This differential damping inhibits the warming in colder seasons, significantly contributing to the intensified SST seasonal cycle in the subpolar North Pacific. In addition, consistent phase shifts in the North Pacific are identified across CMIP6 models. In the northwest North Pacific, a phase advance is associated with anomalous heating in early spring, driven by enhanced warm atmospheric advection from lower latitudes and sea ice melting in marginal seas. In contrast, the southeast North Pacific exhibits a phase delay attributed to the anomalous cooling in spring relative to autumn. This cooling is due to weakened trade winds and increased presence of high clouds. The former leads to stronger evaporative cooling in spring, while the latter impedes shortwave radiation from reaching the ocean.

Cross-Time-Scale Analysis of Year-Round Atmospheric Circulation Patterns and Their Impacts on Rainfall and Temperatures in the Iberian Peninsula

Abstract This study presents a framework to assess climate variability and change through atmospheric circulation patterns (CPs) and their link with regional processes across time scales. We evaluate the CP impacts on daily rainfall and maximum and minimum temperatures in the Iberian Peninsula using sea level pressure (SLP) during 1950–2022. Different sensitivity analyses are performed, employing multiple spatial domains and number of patterns. An optimal classification is found in midlatitudes, centered over the Mediterranean basin and covering part of the North Atlantic Ocean, which can identify atmospheric configurations significantly related to discriminated rainfall and temperature anomalies, with clear seasonal behavior. The temporal variability of CPs is studied across time scales showing, e.g., that transitions between patterns are faster in autumn and spring, and that CPs exhibit distinct temporal variability at intraseasonal, seasonal, interannual, and decadal scales, including significant long-term trends on their frequency. CPs influence temperature and precipitation variations throughout the year. The winter season exhibits the largest atmospheric circulation variability, while the summer is dominated by persistent high-pressure structures—the subtropical Azores high—leading to warm and dry conditions. Based on an interannual correlation analysis, some CPs are significantly associated with the North Atlantic Oscillation (NAO), stronger during winter, indicating the NAO modulation on the regional-to-local climatic features. Overall, this approach arises as a dynamic cross-time-scale framework that can be adapted to specific user needs and levels of regional detail, being useful to study climate drivers for climate change and to perform a process-based evaluation of climate models.

Artificial Intelligence in Climate Change Mitigation: A Review of Predictive Modeling and Data-Driven Solutions for Reducing Greenhouse Gas Emissions

Artificial Intelligence (AI) is increasingly recognized as a powerful tool for addressing the challenges of climate change. Its ability to process vast amounts of data and generate advanced predictive models positions AI as a key player in efforts to reduce greenhouse gas (GHG) emissions and develop sustainable solutions. This review delves into the multifaceted role of AI in climate change mitigation, highlighting its potential in several critical areas. Firstly, AI is revolutionizing predictive climate modeling by providing more accurate forecasts and simulations, enabling better-informed policy and decision-making. Secondly, it is optimizing energy systems through smart grid management, demand forecasting, and the integration of renewable energy sources, thereby enhancing energy efficiency and reducing reliance on fossil fuels. Furthermore, AI is advancing carbon capture and storage technologies by improving the identification of optimal sites and enhancing process efficiency. In environmental monitoring, AI-driven solutions are enabling real-time detection and analysis of environmental data, contributing to more effective conservation efforts. This review also presents case studies and data that demonstrate the tangible impact of AI applications in driving progress towards global emission reduction targets. However, the adoption of AI in this domain is not without challenges. Issues such as data privacy, algorithmic transparency, and the ethical implications of AI deployment need to be carefully addressed. The paper concludes by outlining future research directions and emphasizing the need for interdisciplinary collaboration to fully harness the potential of AI in combating climate change.

Endogenous Preference for Nonmarket Goods in Carbon Abatement Decisions

Long-term decisions, such as decisions about carbon abatement, are usually based on the implausible assumption of constant social preference. This paper focuses on a specific case of market and nonmarket goods, and it investigates the optimal climate policy when social preference for them is also changed by climate policy in a seminal climate-economy model: the Dynamic Integrated Model of Climate and the Economy (DICE) model with a single objective to maximize market goods consumption. We amend DICE to study an optimization problem that trades off the values of market and nonmarket goods, considering endogenous preferences. The relative price of non-market goods grows over time because of increases in both relative scarcity and the appreciation that represents the preference of it. As nonmarket goods are increasingly valuable relative to market ones, climate damages on nonmarket goods are increasingly expensive. The social cost of carbon, which aggregated climate impact on both goods, is consequently higher. Because abatement decision affects the valuation of nonmarket goods in the utility function, unlike previous climate-economy models, we solve the model iteratively by taking the obtained abatement rates from the last run as inputs in the current run. The results in baseline calibration advocate a more stringent climate policy, where endogenous social preference to climate policy raises the social cost of carbon further by roughly 12%–18% this century. Moreover, neglecting changing social preference leads to an underestimate of nonmarket goods damages by 15%. Climate policy is self-reinforced if it favors the more expensive consumption good. The proposed method can be applied to other long-term problems with endogenous preferences. Funding: H. Liao appreciates the funding from the National Natural Science Foundation of China [Grants 71925008, 72293603, and 72488101].

Future climate-driven escalation of Southeastern Siberia wildfires revealed by deep learning

The Southeastern Siberia (SES) region has recently experienced increasingly extensive wildfires in spring, which have threatened its large carbon sequestration capacity from vast forests and peatlands. However, the underlying mechanisms propelling the increased fires and their potential responses to future climate change remain unclear. Here, by using reanalysis data and climate model output together with a deep learning model, we explore the relationship between positive-phase North Atlantic Tripole (NAT) sea-surface temperature anomalies and SES wildfire increases and project the future trend in SES wildfire intensities under climate change. We found that the positive-phase April NAT enhances the Siberian anticyclone, causing increased temperatures and snowmelt via strengthened transport of warm-air advection into the SES region. The latter process heightens the exposure of local high-density peatlands to favorable conditions for fire ignition and leads to more intensive wildfire incidents. We further demonstrate that the projected NAT variations can drive interdecadal changes in future April SES wildfires. With future phase shifting of NAT modes under global warming, the regionally averaged burned area in SES could be increased by 47–62% under different warming scenarios from 1982–2014 to 2015–2100. Our findings reveal the climate-driven escalation of future wildfires in SES in the context of global warming and call for active and urgent fire management strategies to mitigate the fire risk.

Centennial‐Scale Variability of the Atlantic Meridional Overturning Circulation in CMIP6 Models Shaped by Arctic–North Atlantic Interactions and Sea Ice Biases

AbstractClimate variability on centennial timescales has often been linked to internal variability of the Atlantic Meridional Overturning Circulation (AMOC). However, due to the scarceness of suitable paleoclimate proxies and long climate model simulations, large uncertainties remain on the magnitude and physical mechanisms driving centennial‐scale AMOC variability. For these reasons, we perform a systematic multi‐model comparison of centennial‐scale AMOC variability in pre‐industrial control simulations of state‐of‐the‐art global climate models. Six out of nine models in this study exhibit a statistically significant mode of centennial‐scale AMOC variability. Our results show that freshwater exchanges between the Arctic Ocean and the North Atlantic provide a plausible driving mechanism in a subset of models, and that AMOC variability can be amplified by ocean–sea ice feedbacks in the Labrador Sea. The amplifying mechanism is linked to sea ice cover biases, which could provide an observational constraint for centennial‐scale AMOC variability.

Using rare event algorithms to understand the statistics and dynamics of extreme heatwave seasons in South Asia

Abstract Computing the return times of extreme events and assessing the impact of climate change on such return times is fundamental to extreme event attribution studies. However, the rarity of such events in the observational record makes this task a challenging one, even more so for ‘record-shattering’ events that have not been previously observed at all. While climate models could be used to simulate such extremely rare events, such an approach entails a huge computational cost: gathering robust statistics for events with return time of centuries would require a few thousand years of simulation. In this study, we use an innovative tool, rare event algorithm, that allows us to sample numerous extremely rare events at a much lower cost than direct simulations. We employ the algorithm to sample extreme heatwave seasons, corresponding to large anomalies of the seasonal average temperature, in a heatwave hotspot of South Asia using the global climate model Plasim. We show that the algorithm estimates the return levels of extremely rare events with much greater precision than traditional statistical fits. It also enables the computation of various composite statistics, whose accuracy is demonstrated through comparison with a very long control run. In particular, our results reveal that extreme heatwave seasons are associated with an anticyclonic anomaly embedded within a large-scale hemispheric quasi-stationary wave-pattern. Additionally, the algorithm accurately represents the intensity-duration-frequency statistics of sub-seasonal heatwaves, offering insights into both seasonal and sub-seasonal aspects of extreme heatwave seasons. This innovative approach could be used in extreme event attribution studies to better constrain the changes in an event’s probability and intensity with global warming, particularly for events with return times spanning centuries or millennia.

Cause and Characteristics of Changes in Mesoscale Convective Systems within a Convection-Permitting Regional Climate Model

Abstract Changes in the frequency and intensity of mesoscale convective systems (MCS) are assessed using convection-permitting regional climate model simulations. We present a novel classification method that relates MCSs to the magnitude of their large-scale forcing environments to better understand the changing nature of MCSs across different forcing environments in a possible future climate scenario. Overall, the annual frequency, intensity, and amount of precipitation associated with MCSs are projected to increase for broad portions of the eastern conterminous United States (CONUS). Furthermore, changes in the characteristics of MCSs show larger, longer-lived, and faster MCSs particularly over the Midwest and Southeast. Seasonal examination of this response reveals a robust intensification of March–May (MAM) MCSs. The higher frequency of MAM MCSs are found to occur in weakly forced synoptic environments. Increased rainfall amounts are explained by an intensification of MCSs due to changing thermodynamics and enhanced lower-tropospheric moisture transport into the central CONUS by the North Atlantic Subtropical High. Differences in mid-latitude storm tracks are favorable towards the enhanced development of MCSs associated with strong baroclinic forcing within the Midwest and Northern Plains, but are only realized in the presence of strong changes in lower-tropospheric moisture transport. Overall, these results suggest a shift in the behavior of MAM MCSs that more closely corresponds to the behavior of June–August (JJA) MCSs in the historical climate. The increased occurrence of MCSs in weakly forced environments, particularly in MAM, deserves further investigation.

Flood risk assessment of the Narmada Basin, India, under climate change scenarios

ABSTRACT Climate change has led to a significant rise in the occurrence of extreme events such as floods and droughts. It is essential to accurately assess the hazards linked to climate change to effectively tackle flood risks in the future. This study investigates the risk of flooding in the Narmada Basin under climate change scenarios. Future precipitation data of global climate models are used to create intensity–duration–frequency curves and peak flows are estimated using a hydrological model. These peak flows serve as inputs for the hydraulic model to simulate floods across various return periods. The fuzzy inference system (FIS) is employed to optimize releases and reduce peak flows to mitigate flood impacts. The findings indicate that the extent of flooding and maximum water depths increase with higher return periods and future scenarios, and optimizing the reservoir releases, decreases the extent of flooding. This decrease in inundation area ranges from approximately 9 to 10% in the Bargi-to-Indira Sagar section to 20–23% in the Indira Sagar-to-Omkareshwar Sagar segment. In the Omkareshwar Sagar-to-Sardar Sarovar segment, the flooded area diminishes by 17–18% under the Radioactive Concentration Pathway (RCP) 4.5 and by 12–14% under the RCP 8.5 scenarios.

Spatial Interpolation of Seasonal Precipitations Using Rain Gauge Data and Convection‐Permitting Regional Climate Model Simulations in a Complex Topographical Region

ABSTRACTIn mountainous areas, accurately estimating the long‐term climatology of seasonal precipitations is challenging due to the lack of high‐altitude rain gauges and the complexity of the topography. This study addresses these challenges by interpolating seasonal precipitation data from 3189 rain gauges across France over the 1982–2018 period, using geographical coordinates, and altitude. In this study, an additional predictor is provided from simulations of a Convection‐Permitting Regional Climate Model (CP‐RCM). The simulations are averaged to obtain seasonal precipitation climatology, which helps capture the relationship between topography and long‐term seasonal precipitation. Geostatistical and machine learning models are evaluated within a cross‐validation framework to determine the most appropriate approach to generate seasonal precipitation reference fields. Results indicate that the best model uses a machine learning approach to interpolate the ratio between long‐term seasonal precipitation from observations and CP‐RCM simulations. This method successfully reproduces both the mean and variance of observed data, and slightly outperforms the best geostatistical model. Moreover, incorporating the CP‐RCM outputs as an explanatory variable significantly improves interpolation accuracy and altitude extrapolation, especially when the rain gauge density is low. These results imply that the commonly used altitude‐precipitation relationship may be insufficient to derive seasonal precipitation fields. The CP‐RCM simulations, increasingly available worldwide, present an opportunity for improving precipitation interpolation, especially in sparse and complex topographical regions.

Climatology of Orographic Precipitation Gradients Over High Mountain Asia Derived From Dynamical Downscaling

AbstractWithin High Mountain Asia (HMA), the annual melting of glaciers and snowpack provides vital freshwater to populations living downstream. Precipitation over HMA can directly affect the freshwater availability in this region by altering the mass balance of glaciers and snowpack. However, available reanalyses and downscaling simulations lack the resolution required to understand important glacier‐scale variations in precipitation. This study aimed to determine the current characteristics of orographic precipitation gradients (OPG) by curve‐fitting daily precipitation as a function of elevation from a 15‐year, 4‐km grid spaced Weather Research and Forecasting (WRF) model simulation focused on the Himalayan, Karakoram, and Hindu‐Kush mountain ranges. To facilitate precipitation curve‐fitting, the WRF model grid points were separated into regions of similar orientation, referred to as facets. Akaike Information Criterion‐corrected values and an F‐test p‐value identified the need for a curvature term to account for a varying OPG with elevation. Regions with similar seasonal variability were found using ‐means clustering of the monthly mean OPG coefficients. The central Himalayan slope's intra‐seasonal variability of OPG depended on synoptic scale conditions, in which cyclonically‐forced heavy‐precipitation events produced strong sublinear increases in precipitation with elevation. Initial testing of precipitation estimates using monthly coefficients showed promising results in downscaling daily WRF precipitation; the daily mean absolute error at each grid point had a lower magnitude than the daily mean precipitation total, on average. Results provide a physically‐based context for machine learning algorithms being developed to predict OPG and downscale precipitation output from global climate models over HMA.

Adapting to Climate Change with Machine Learning: The Robustness of Downscaled Precipitation in Local Impact Analysis

The skill, assumptions, and uncertainty of machine learning techniques (MLTs) for downscaling global climate model’s precipitation to the local level in Bolivia were assessed. For that, an ensemble of 20 global climate models (GCMs) from CMIP6, with random forest (RF) and support vector machine (SVM) techniques, was used on four zones (highlands, Andean slopes, Amazon lowlands, and Chaco lowlands). The downscaled series’ skill was evaluated in terms of relative errors. The uncertainty was analyzed through variance decomposition. In most cases, MLTs’ skill was adequate, with relative errors less than 50%. Moreover, RF tended to outperform SVM. Robust (weak) stationary (perfect prognosis) assumptions were found in the highlands and Andean slopes. The weakness was attributed to topographical complexity. The downscaling methods were shown to be the dominant source of uncertainties. This analysis allowed the derivation of robust future projections, showing higher annual rainfall, shorter dry spell duration, and more frequent but less intense high rainfall events in the highlands. Apart from the dry spell’s duration, a similar pattern was found for the Andean slopes. A decrease in annual rainfall was projected in the Amazon lowlands and an increase in the Chaco lowlands.

Modelling Climate Change Impacts on Location Suitability for Cultivating Avocado and Blueberry in New Zealand

Regional suitability for growing avocados and blueberries may alter with climate change. Modelling can provide insights into potential climate change impacts, thereby informing industry and government policy decisions to ameliorate future risks and capitalise on future opportunities. We developed continuous/sliding-scale models that used soil, terrain and weather data to assess location suitability for cultivating avocado and blueberry, based on physiological and phenological considerations specific to each crop. Using geographical information system (GIS) data on soil, slope and weather, we mapped cultivation suitability for avocado and blueberry across New Zealand, and, for accuracy, “ground-truthed” these maps in an iterative process of expert validation and model recalibration. We modelled the incremental changes in location suitability that could occur through climate change using “future” GIS-based weather data from climate model simulations for different greenhouse gas (GHG) pathways that ranged from stringent GHG mitigation to unabated GHG emissions. Changes in maps over time showed where suitability would increase or decrease and to what extent. These results indicate where avocado and blueberry might replace other crops that become less suitable over time, and where avocado might displace blueberry. The approach and models can be applied to other countries or extended to other crops with similar growing requirements.

Seasonal amplification of subweekly temperature variability over extratropical Southern Hemisphere land masses

Temperature variability has substantial socioeconomic impacts through its association with the frequency and severity of heat extremes. Under anthropogenic influence, climate models project seasonally-dependent amplifications of near-surface temperature variability over some sectors of the Southern Hemisphere, and robust positive trends have already been observed in recent decades. Here we show that the amplification of subweekly temperature variability simulated by the multi-model ensemble mean of the sixth phase of the Coupled Model Intercomparison Project (CMIP6) over South Africa, Australia, and South America is often substantially smaller than in reanalyses in recent decades, reaching a similar amplification only at the end of the 21st century due to a weaker amplification of subweekly variance generation efficiency. Analysis of a large model ensemble indicates that this discrepancy may be due to internal climatic variability suggesting that the recent rapid amplification seen in reanalyses may slow down or even temporarily reverse in the near future.

Evaluation and fusion of multi-source sea ice thickness products with limited in-situ observations

Sea ice thickness (SIT) is a critical and sensitive parameter in the climate system, with its dynamic changes profoundly influencing global climate models, navigational routes, and the potential for Arctic resource development. Given the widespread application of current satellite remote sensing technology in monitoring SIT, significant uncertainties remain. This study first underscores the importance of in-situ observations as a direct measurement method for SIT. However, the limitations of in-situ data in terms of acquisition cost, spatiotemporal coverage continuity, and distribution uniformity significantly hinder the effective evaluation of multi-source SIT products. To address this, the study innovatively introduces the Triple Collocation (TC) method, which effectively mitigates the impact of errors from individual data sources on the overall evaluation results through a mutual validation mechanism among multiple satellite data sources. This allows for a scientific assessment of multi-source SIT products even in the context of scarce in-situ observations. The findings indicate that the TC method not only successfully resolves the challenges of multi-source data evaluation but also facilitates data integration among these products, significantly enhancing the overall accuracy and spatiotemporal consistency of SIT data.

Marine heatwaves suppress ocean circulation and large vortices in the Gulf of Alaska

Large-scale anticyclonic vortices forming along the Gulf of Alaska continental slope serve as fertile ecosystems for marine life, significantly shaping the distribution of primary productivity, with 40–80% of the gulf’s open ocean surface chlorophyll-a concentrated in their cores. Between 2013 and 2023, Alaska experienced some of the largest and longest marine heatwaves ever recorded in the world’s oceans, persistently altering its ecosystem and fisheries. Here, using 30 years of satellite and reanalysis data, we find that the coastal upwelling atmospheric forcing conditions associated with the heatwaves have also significantly suppressed the Gulf of Alaska’s ocean circulation and the formation of large anticyclones. Climate model simulations spanning from 1850 to 2100 suggest that future changes in the Aleutian Low pressure system will lead to a 60% increase in upwelling extremes (>2 standard deviations), further weakening the ocean anticyclones. However, large uncertainties remain in the mechanisms controlling the Aleutian Low’s response to climate forcing in the models.