Explore the vast range of titles available.

Saltwater intrusion is a critical concern for coastal communities due to its impacts on fresh ecosystems and civil infrastructure. Declining recharge and rising sea level are the two dominant drivers of saltwater intrusion along the land‐ocean continuum, but there are currently no global estimates of future saltwater intrusion that synthesize these two spatially variable processes. Here, for the first time, we provide a novel assessment of global saltwater intrusion risk by integrating future recharge and sea level rise while considering the unique geology and topography of coastal regions. We show that nearly 77% of global coastal areas below 60° north will undergo saltwater intrusion by 2100, with different dominant drivers. Climate‐driven changes in subsurface water replenishment (recharge) is responsible for the high‐magnitude cases of saltwater intrusion, whereas sea level rise and coastline migration are responsible for the global pervasiveness of saltwater intrusion and have a greater effect on low‐lying areas. Plain Language Summary Coastal watersheds around the globe are facing perilous changes to their freshwater systems. Driven by climatic changes in recharge and sea level working in tandem, sea water encroaches into coastal groundwater aquifers and consequently salinizes fresh groundwater, in a process called saltwater intrusion. To assess the vulnerability of coastal watersheds to future saltwater intrusion, we applied projections of sea level and groundwater recharge to a global analytical modeling framework. Nearly 77% of the global coast is expected to undergo measurable salinization by the year 2100. Changes in recharge have a greater effect on the magnitude of salinization, whereas sea level rise drives the widespread extensiveness of salinization around the global coast. Our results highlight the variable pressures of climate change on coastal regions and have implications for prioritizing management solutions. Key Points First global analysis of future saltwater intrusion vulnerability responding to spatially variable recharge and sea level rise is provided Recharge drives the extreme cases of saltwater intrusion, while sea level rise is responsible for its global pervasiveness Nearly 77% of global coastal areas below 60° north will undergo saltwater intrusion by 2100

Anthropogenic aerosols (AER) and greenhouse gases (GHG)—the leading drivers of the forced historical change—produce different large‐scale climate response patterns, with correlations trending from negative to positive over the past century. To understand what caused the time‐evolving comparison between GHG and AER response patterns, we apply a low‐frequency component analysis to historical surface ocean changes from CESM1 single‐forcing large‐ensemble simulations. While GHG response is characterized by its first leading mode, AER response consists of two distinct modes. The first one, featuring long‐term global AER increase and global cooling, opposes GHG response patterns up to the mid‐twentieth century. The second one, featuring multidecadal variations in AER distributions and interhemispheric asymmetric surface ocean changes, appears to reinforce the GHG warming effect over recent decades. AER thus can have both competing and synergistic effects with GHG as their emissions change temporally and spatially. Plain Language Summary Anthropogenically forced climate change over the past century has been mainly caused by two types of emissions: greenhouse gases (GHG) and aerosols (AER). In general, sulfate aerosols from industrial sources can reflect shortwave radiation to yield a cooling effect opposite to the GHG warming effect. However, model simulations isolating GHG and AER forcings show that the large‐scale climate effect of AER does not always dampen the GHG effect. Instead, over recent decades, AER have produced surface ocean response patterns more like the GHG response. Using a novel low‐frequency statistical decomposion, we find that aerosols have driven two distinct modes of climate change patterns over the historical period. The first mode is associated with global aerosol increase, resulting in global‐wide cooling damping the GHG‐induced warming. The second mode is associated with the shift in aerosol emissions from north America/western Europe to southeast Asia, which drives regional changes enhancing the GHG effect. Our results highlight the importance of considering the temporal and spatial evolutions of AER emissions in assessing GHG and AER climate effects and attributing historical anthropogenic climate changes to GHG and AER forcings. Key Points Over the past century, GHG forced response is characterized by a single dominant mode while AER response consists of two distinct modes Monotonic global aerosol increases, mainly from Southeast Asia emissions, produce a global aerosol cooling mode opposing greenhouse warming Important in recent decades, geographic redistribution of AER emissions produces a second aerosol mode that reinforces greenhouse warming

Fresh submarine groundwater discharge (FSGD) can deliver significant fluxes of water and solutes from land to sea. In the Arctic, which accounts for ∼34% of coastlines globally, direct observations and knowledge of FSGD are scarce. Through integration of observations and process‐based models, we found that regardless of ice‐bonded permafrost depth at the shore, summer SGD flow dynamics along portions of the Beaufort Sea coast of Alaska are similar to those in lower latitudes. Calculated summer FSGD fluxes in the Arctic are generally higher relative to low latitudes. The FSGD organic carbon and nitrogen fluxes are likely larger than summer riverine input. The FSGD also has very high CO2 making it a potentially significant source of inorganic carbon. Thus, the biogeochemistry of Arctic coastal waters is potentially influenced by groundwater inputs during summer. These water and solute fluxes will likely increase as coastal permafrost across the Arctic thaws. Plain Language Summary Groundwater flows from land to sea, transporting freshwater, organic matter, nutrients, and other solutes that impact coastal ecosystems. However, along coasts of the rapidly‐warming Arctic, there is limited knowledge regarding how much fresh groundwater enters the ocean. Using field observations and numerical models, we show that groundwater flowing from tundra in northern coastal Alaska carries large amounts of freshwater, organic matter, and carbon dioxide to the Arctic lagoons during summer. These inputs are likely significant to coastal biogeochemical cycling and marine food webs. Groundwater discharge and the associated transport of dissolved materials are expected to increase due to longer periods of above‐zero temperatures that thaw frozen soils below the tundra. Key Points Summer fresh submarine groundwater discharge (FSGD) to the Alaskan Beaufort Sea is only 3%–7% of rivers but carries as much organic matter Summer FSGD delivers a median of 116 (interquartile range: 35–405) and 6 (2–21) kg/d per km dissolved organic carbon and nitrogen Fresh groundwater at the beach of Simpson Lagoon (SL) has a median PCO2 of ∼33,000 μatm implying substantial CO2 flux

Severe convective storms are among the most dangerous weather phenomena and accurate forecasts mitigate their impacts. The recently released suite of AI‐based weather models produces medium‐range forecasts within seconds, with a skill similar to state‐of‐the‐art operational forecasts for variables on single levels. However, predicting severe thunderstorm environments requires accurate combinations of dynamic and thermodynamic variables and the vertical structure of the atmosphere. Advancing the assessment of AI‐models toward process‐based evaluations lays the foundation for hazard‐driven applications. We assess the forecast skill of the top‐performing AI‐models GraphCast, Pangu‐Weather and FourCastNet for convective parameters at lead‐times up to 10 days against reanalysis and ECMWF's operational numerical weather prediction model IFS. In a case study and seasonal analyses, we see the best performance by GraphCast and Pangu‐Weather: these models match or even exceed the performance of IFS for instability and shear. This opens opportunities for fast and inexpensive predictions of severe weather environments. Plain Language Summary Over the past year, several global AI‐based weather models were released and produce a similar quality of forecasts as traditional weather models. AI‐models are very fast and computationally cheap to produce forecasts. The evaluation of AI‐models has largely focused on single atmospheric variables at certain heights. To forecast specific phenomena, such as thunderstorms, a combination of variables must be accurate at multiple heights. Here we use the output of AI‐models to derive thunderstorm‐related ingredients. We compare 10‐day‐forecasts between the AI‐models GraphCast, Pangu‐Weather and FourCastNet and a state‐of‐the‐art traditional weather model while using a reanalysis data set as the reference. The example of a tornado outbreak in the southern United States shows that all models are capable of forecasting thunderstorm ingredients multiple days in advance. To obtain a robust assessment, we evaluate the entire thunderstorm season in 2020 in North America, Europe, Argentina, and Australia, where severe thunderstorms occur frequently. Two of the three AI‐models achieve similar or even better results than the traditional weather model while being much cheaper to operate computationally. Forecasting thunderstorm parameters directly, instead of calculating them afterward, is likely to produce even better results. This opens opportunities for rapid and accessible forecasts for severe thunderstorm phenomena. Key Points AI‐based global weather models produce forecasts with sufficient accuracy to derive instability and shear metrics skillfully The best AI‐based weather models are capable of competing with state‐of‐the‐art numerical weather predictions of instability and shear This is a major step toward computationally inexpensive and fast convective outlooks

Air‐sea heat and moisture fluxes modulate the surface energy balance and oceanic and atmospheric heat transport across all timescales. Spatial gradients of these fluxes, on a multitude of spatial scales, also have significant impacts on the ocean and atmosphere. Nevertheless, analysis of these gradients, and discussion regarding our ability to represent them, is relatively absent within the community. This letter discusses their importance and presents a wintertime climatology. Their sensitivity to spatiotemporal scale and choice of data set is also examined in the mid‐latitudes. A lead‐lag analysis illustrates that wintertime air‐sea heat flux gradients in the Gulf Stream can precede the North Atlantic Oscillation by ∼1 month. A lack of observations and thus validation of air‐sea heat flux gradients represents a significant gap in our understanding of how air‐sea processes affect weather and climate, and warrants increased attention from the observational and modeling communities. Plain Language Summary The oceans impact both weather and climate by heating and cooling the lower atmosphere. Surface latent (sensible) heat flux is a quantity that measures the exchange of heat associated with evaporation of seawater (an air‐sea temperature difference). In addition to the absolute exchange, the manner in which the exchange varies spatially (the heat flux gradients) is also known to be important for the development of weather systems and longer‐term climate. Despite this, relatively little attention is paid in the literature to variability in these gradients. This study provides a brief overview of their importance and provides a wintertime climatology in these gradients. It is also illustrated that when considering gradients, the importance of specifying the spatial scale over which the gradient is calculated is critical. Although many differences exist between air‐sea heat flux data products in these gradients, there are currently almost no observations to validate them in key areas of interest, which represents a significant deficiency in our understanding of ocean‐atmosphere interactions. This is emphasized by demonstrating that these gradients in the mid‐latitudes can statistically precede variability in the North Atlantic Oscillation, the most important mode of monthly atmospheric variability in the North Atlantic. Key Points Air‐sea heat and moisture flux gradients modulate important oceanic and atmospheric processes across a multitude of spatiotemporal scales Air‐sea heat flux gradient variability can statistically precede mid‐latitude atmospheric variability Notable air‐sea heat and moisture flux gradient inconsistencies exist in data products, yet the ability to validate them remains elusive

Compound climate extremes, such as events with concurrent temperature and precipitation extremes, have significant impacts on the health of humans and ecosystems. This paper aims to analyze temporal and spatial characteristics of compound extremes of monthly temperature and precipitation, evaluate the performance of the sixth phase of the Coupled Model Intercomparison Project (CMIP6) models in simulating compound extremes, and investigate their future changes under Shared Socioeconomic Pathways (SSPs). The results show a significant increase in the frequency of compound warm extremes (warm/dry and warm/wet) but a decrease in compound cold extremes (cold/dry and cold/wet) during 1985–2014 relative to 1955–1984. The observed upward trends of compound warm extremes over China are much higher than those worldwide during the period of interest. A multi‐model ensemble (MME) of CMIP6 models performs well in simulating temporal changes of warm/wet extremes, and temporal correlation coefficients between MME and observations are above 0.86. Under future scenarios, CMIP6 simulations show substantial rises in compound warm extremes and declines in compound cold extremes. Globally, the average frequency of warm/wet extremes over a 30‐yr period is projected to increase for 2070–2099 relative to 1985–2014 by 18.53, 34.15, 48.79, and 59.60 under SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5, respectively. Inter‐model uncertainties for the frequencies of compound warm extremes are considerably higher than those of compound cold extremes. The projected uncertainties in the global occurrences of warm/wet extremes are 3.82 times those of warm/dry extremes during 2070–2099 and especially high for the Amazon and the Tibetan Plateau. Plain Language Summary Compound climate extremes, such as the events with concurrent temperature and precipitation extremes, have significant impacts on the health of humans and ecosystems. Can climate model simulate the historical compound extremes? If yes, how the global compound extremes will change in the future? In this study, we found that the global climate model performs well in simulating temporal changes of warm/wet and warm/dry extremes during the period 1955–2014. With greenhouse gas emissions continuing to increase in the future, compound warm/dry and warm/wet extremes show a continuous increase in frequency in the next few decades, while compound cold/dry and cold/wet extremes are projected to occur less frequently. Key Points A multi‐model ensemble of CMIP6 models performs well in simulating temporal changes of warm/wet extremes The inter‐model uncertainties for the frequencies of compound warm extremes are considerably higher than those of compound cold extremes The projected uncertainties in the global occurrences of warm/wet extremes are 3.82 times those of warm/dry extremes during 2070–2099

Sea level rise (SLR) is a long-lasting consequence of climate change because global anthropogenic warming takes centuries to millennia to equilibrate for the deep ocean and ice sheets. SLR projections based on climate models support policy analysis, risk assessment and adaptation planning today, despite their large uncertainties. The central range of the SLR distribution is estimated by process-based models. However, risk-averse practitioners often require information about plausible future conditions that lie in the tails of the SLR distribution, which are poorly defined by existing models. Here, a community effort combining scientists and practitioners builds on a framework of discussing physical evidence to quantify high-end global SLR for practitioners. The approach is complementary to the IPCC AR6 report and provides further physically plausible high-end scenarios. High-end estimates for the different SLR components are developed for two climate scenarios at two timescales. For global warming of +2°C in 2100 (RCP2.6/SSP1-2.6) relative to pre-industrial values our high-end global SLR estimates are up to 0.9 m in 2100 and 2.5 m in 2300. Similarly, for a (RCP8.5/SSP5-8.5), we estimate up to 1.6 m in 2100 and up to 10.4 m in 2300. The large and growing differences between the scenarios beyond 2100 emphasize the long-term benefits of mitigation. However, even a modest 2°C warming may cause multi-meter SLR on centennial time scales with profound consequences for coastal areas. Earlier high-end assessments focused on instability mechanisms in Antarctica, while here we emphasize the importance of the timing of ice shelf collapse around Antarctica. This is highly uncertain due to low understanding of the driving processes. Hence both process understanding and emission scenario control high-end SLR.

Global warming is shifting the thermal dynamics of lakes, with resulting climatic variability heavily affecting their mixing dynamics. We present a dual ensemble workflow coupling climate models with lake models. We used a large set of simulations across multiple domains, multi‐scenario, and multi GCM‐ RCM combinations from CORDEX data. We forced a set of multiple hydrodynamic lake models by these multiple climate simulations to explore climate change impacts on lakes. We also quantified the contributions from the different models to the overall uncertainty. We employed this workflow to investigate the effects of climate change on Lake Sevan (Armenia). We predicted for the end of the 21st century, under RCP 8.5, a sharp increase in surface temperature (4.3±0.7K)$(4.3\\pm 0.7\\,\\mathrm{K})$and substantial bottom warming (1.7±0.7K)$(1.7\\pm 0.7\\,\\mathrm{K})$ , longer stratification periods (+55 days) and disappearance of ice cover leading to a shift in mixing regime. Increased insufficient cooling during warmer winters points to the vulnerability of Lake Sevan to climate change. Our workflow leverages the strengths of multiple models at several levels of the model chain to provide a more robust projection and at the same time a better uncertainty estimate that accounts for the contributions of the different model levels to overall uncertainty. Although for specific variables, for example, summer bottom temperature, single lake models may perform better, the full ensemble provides a robust estimate of thermal dynamics that has a high transferability so that our workflow can be a blueprint for climate impact studies in other systems. Plain Language Summary Lakes are threatened by climate change because of effects related to the increasing temperature, long stratification, and ice disappearance. One of the best tools to predict these effects on lakes is numerical modeling of lakes that benefit from climate modeling. Climate modeling is normally done globally or in the so‐called general circulation model (GCM) or more detailed simulations on regional levels (RCM) like the CORDEX data set. In this study, we used the CORDEX data, which employed several climate models from several regions (domains) for several climatic scenarios (emissions scenarios) to force multiple lake models. This approach gave us an extensive prediction about various possible outputs. We applied this approach to Lake Sevan (Armenia), a large mountain lake. Our study predicted for the worst‐case scenario, an increase of the surface temperature by almost 4.3 K by the end of the 21st century, 1.75 K for bottom temperature, a total disappearance of ice cover, and about 55 extra days of stratification, showing its vulnerability for climate change. This optimized workflow uses the strength of a wide variety of models on the climate and lake levels to better understand the impact of climate change and quantify the sources of uncertainty in the workflow. Key Points Dual multi‐model ensemble of climate data and lake models is used for robust projections of climate change impacts Variance decomposition effectively identified the sources of uncertainty and contributions of different models to the overall uncertainty Significant warming, longer stratification periods, and loss of ice cover are predicted for Lake Sevan by the end of the 21st century

The feedback of topsoil moisture (SM) content on convective clouds and precipitation is not well understood and represented in the current generation of weather and climate models. Here, we use functional decomposition of satellite‐derived SM and cloud vertical profiles (CVP) to quantify the relationship between SM and the vertical distribution of cloud water in the central US. High‐dimensional model representation is used to disentangle the contributions of SM and other land‐surface and atmospheric variables to the CVP. Results show that the sign and strength of the SM‐cloud‐precipitation feedback varies with cloud height and time lag and displays a large spatial variability. Positive anomalies in antecedent 7‐hr SM and land‐surface temperature enhance cloud reflectivity up to 4 dBZ in the lower atmosphere about 1–3 km above the surface. Our approach presents new insights into the SM‐cloud‐precipitation feedback and aids in the diagnosis of land‐atmosphere interactions simulated by weather and climate models. Plain Language Summary This paper focuses on the observational analysis of how soil moisture (SM) influences the vertical cloud‐water distribution throughout the day. By analyzing data from Soil Moisture Active Passive (SMAP) and Dual‐frequency Precipitation Radar (DPR), we gain insights into how antecedent SM affects cloud‐water reflectivity at different heights in the lower atmosphere. Our data‐driven approach produces spatial maps of SM's contribution to cloud reflectivity and rainfall in the central US as a function of cloud height and SM time lag. Our method will help diagnose weather and climate model biases. Key Points Functional decomposition of satellite‐measured soil moisture (SM) and cloud vertical profiles (CVP) provides insights into SM‐CVP feedbacks The sign and strength of this feedback varies with height, time lag, and geographic location, in agreement with qualitative studies Our approach can be used to diagnose weather and climate model biases as they relate to land‐atmosphere coupling

Elevated concentrations of atmospheric bromine are known to cause ozone depletion in the Arctic, which is most frequently observed during springtime. We implement a detailed description of bromine and chlorine chemistry within the WRF‐Chem 4.1.1 model, and two different descriptions of Arctic bromine activation: (1) heterogeneous chemistry on surface snow on sea ice, triggered by ozone deposition to snow (Toyota et al., 2011 https://doi.org/10.5194/acp-11-3949-2011), and (2) heterogeneous reactions on sea salt aerosols emitted through the sublimation of lofted blowing snow (Yang et al., 2008, https://doi.org/10.1029/2008gl034536). In both mechanisms, bromine activation is sustained by heterogeneous reactions on aerosols and surface snow. Simulations for spring 2012 covering the entire Arctic reproduce frequent and widespread ozone depletion events, and comparisons with observations of ozone show that these developments significantly improve model predictions during the Arctic spring. Simulations show that ozone depletion events can be initiated by both surface snow on sea ice, or by aerosols that originate from blowing snow. On a regional scale, in spring 2012, snow on sea ice dominates halogen activation and ozone depletion at the surface. During this period, blowing snow is a major source of Arctic sea salt aerosols but only triggers a few depletion events. Plain Language Summary During Arctic spring, ground level ozone is often depleted to very low concentrations compared to background levels. This surface ozone depletion is caused by reactive halogen species in the atmosphere, especially bromine. In this study, we implement a detailed description of chlorine and bromine chemistry in the regional atmospheric model WRF‐Chem 4.1.1. We also compare two different bromine sources capable of triggering these events: first, chemical reactions on surface snow over sea ice, and second, sea salt particles emitted by the sublimation of salty “blowing snow” lofted by strong winds. These developments are used to investigate the origins of Arctic bromine and of ozone depletion events, and to improve the representation of Arctic ozone in the model. We find that, in spring 2012, both bromine sources can cause ozone depletion events, but that over the entire Arctic, snow on sea ice dominates halogen activation and causes ground level ozone depletion. Key Points Halogen activation and its role in Arctic surface ozone depletion events (ODEs) is modeled using WRF‐Chem Two halogen activation mechanisms are implemented (1) surface snow and (2) blowing snow A spring 2012 case study indicates that both mechanisms can trigger near‐surface ODEs, but that surface snow dominates