Abstract. Constraining the Southern Ocean carbon sink is vital for understanding global carbon cycling and climate. Monthly 1°x1° gridded products of surface seawater partial pressure of carbon dioxide (pCO2) are routinely used to detect change in Southern Ocean carbon cycling. Despite Southern Ocean sampling being sparse and skewed to summer months, the uncertainty arising from incomplete sampling at the monthly 1°x1° scale is unquantified to date, and it remains unclear how this pCO2 sampling error affects estimates of monthly gridded pCO2 products and derived air-sea CO2 fluxes. Here, we quantify the Southern Ocean pCO2 sampling error using synthetic observations from an eddy-permitting ocean-sea ice-biogeochemistry configuration of the Energy Exascale Earth System Model. Using instantaneous snapshots of the model state at the times and locations of real-world ship and float observations, we reconstruct gridded monthly 1°x1° pCO2 datasets over 2014–2023 and compare them with the simulated model pCO2 output, isolating the error due to incomplete observational coverage at the monthly 1°x1° scale. We find that, on average, pCO2 derived from synthetic observations is 13–14 μatm lower than the modeled monthly mean, with individual grid cells exhibiting uncertainties up to an order of magnitude larger, primarily caused by undersampling during high-wind events in the Southern Ocean. Our results imply that the rate of oceanic CO2 uptake derived from available pCO2 observations is biased high. Since the pCO2 uncertainty due to incomplete observational coverage exceeds other sources of uncertainty in gridded pCO2 data products, e.g., measurement error, it represents a substantial, previously overlooked contribution that should be propagated through gap-filled pCO2 data products and estimates of the Southern Ocean and global ocean carbon sink.

Meltwater from the Greenland Ice Sheet can alter the continental shelf/slope circulation and cross -shelf freshwater fluxes and limit deep convection in adjacent basins through surface freshening. We explore the impacts on the West Greenland Current and eastern Labrador Sea with different vertical distributions of the meltwater forcing. In this study, we present the results from global coupled ocean/sea ice simulations, forced with atmospheric reanalysis, that are mesoscale eddy -active ( similar to 2 - 3-km horizontal spacing) and eddy -permitting ( similar to 6 - 7-km horizontal spacing) in the study region. We compare the West Greenland Current in mesoscale eddy -active and eddy -permitting without meltwater to highlight the role of small-scale features. The mesoscale eddy -active con fi guration is then used to assess the change in the eastern Labrador Sea when meltwater is added to the surface or vertically distributed to account for mixing within fjords. In both simulations with meltwater, the West Greenland and West Greenland Coastal Currents are faster than in the simulation with no meltwater; their mean surface speeds are the highest in the vertical distribution case. In the latter case, there is enhanced baroclinic conversion at the shelf break compared to the simulation with no meltwater. When meltwater is vertically distributed, there is an increase in baroclinic conversion at the shelf break associated with increased eddy kinetic energy. In addition, in the eastern Labrador Sea, the salinity is lower and the meltwater volume is greater when meltwater is vertically distributed. Therefore, the West Greenland Current is sensitive to how meltwater is added to the ocean with implications for the freshening of the Labrador Sea. SIGNIFICANCE STATEMENT: Our goal is to understand how the flux of freshwater across the West Greenland continental slope into the Labrador Sea is modified by meltwater from the Greenland Ice Sheet. We compare the simulations of the ocean that capture key dynamics along the West Greenland continental slope that have no meltwater, meltwater added to the ocean surface, and meltwater distributed vertically to represent the mixing within fjords. When meltwater is added, the currents along the continental slope are faster, with the greatest increase when meltwater is vertically distributed. In that case, there is enhanced freshening of the Labrador Sea because modi fi ed density gradients generate more eddies. Proper representation of the vertical structure of meltwater is important for projecting the impact of freshwater on the subpolar North Atlantic.

Abstract. Since their advent over two decades ago, autonomous Argo floats have revolutionized the field of oceanography, and more recently, the addition of biogeochemical and biological sensors to these floats has greatly improved our understanding of carbon, nutrient, and oxygen cycling in the ocean. While Argo floats offer unprecedented horizontal, vertical, and temporal coverage of the global ocean, uncertainties remain about whether Argo sampling frequency and density capture the true spatio-temporal variability of physical, biogeochemical, and biological properties. As the true distributions of, e.g., temperature or oxygen are unknown, these uncertainties remain difficult to address with Argo floats alone. Numerical models with synthetic observing systems offer one potential avenue to address these uncertainties. Here, we implement synthetic biogeochemical Argo floats into the Energy Exascale Earth System Model version 2 (E3SMv2). Since the synthetic floats sample the model fields at model run time, the end-user defines the sampling protocol ahead of any model simulation, including the number and distribution of synthetic floats to be deployed, their sampling frequency, and the prognostic or diagnostic model fields to be sampled. Using a six-year proof-of-concept simulation, we illustrate the utility of the synthetic floats in different case studies. In particular, we quantify the impact of i) sampling density on the float-derived detection of deep-ocean change in temperature or oxygen and on float-derived estimates of phytoplankton phenology, ii) sampling frequency and sea-ice cover on float trajectory lengths and hence float-derived estimates of current velocities, and iii) short-term variability in ecosystem stressors on estimates of seasonal variability.

Since their advent over 2 decades ago, autonomous Argo floats have revolutionized the field of oceanography, and, more recently, the addition of biogeochemical and biological sensors to these floats has greatly improved our understanding of carbon, nutrient, and oxygen cycling in the ocean. While Argo floats offer unprecedented horizontal, vertical, and temporal coverage of the global ocean, uncertainties remain about whether Argo sampling frequency and density capture the true spatiotemporal variability in physical, biogeochemical, and biological properties. As the true distributions of, e.g., temperature or oxygen are unknown, these uncertainties remain difficult to address with Argo floats alone. Numerical models with synthetic observing systems offer one potential avenue to address these uncertainties. Here, we implement synthetic biogeochemical Argo floats into the Energy Exascale Earth System Model version 2 (E3SMv2), which build on the Lagrangian In Situ Global High-Performance Particle Tracking (LIGHT) module in E3SMv2 (E3SMv2-LIGHT-bgcArgo-1.0). Since the synthetic floats sample the model fields at model run time, the end user defines the sampling protocol ahead of any model simulation, including the number and distribution of synthetic floats to be deployed, their sampling frequency, and the prognostic or diagnostic model fields to be sampled. Using a 6-year proof-of-concept simulation, we illustrate the utility of the synthetic floats in different case studies. In particular, we quantify the impact of (i) sampling density on the float-derived detection of deep-ocean change in temperature or oxygen and on float-derived estimates of phytoplankton phenology, (ii) sampling frequency and sea-ice cover on float trajectory lengths and hence float-derived estimates of current velocities, and (iii) short-term variability in ecosystem stressors on estimates of their seasonal variability.

Couplings between land and the near surface atmosphere are modulated by interactions between soil conditions, vegetation dynamics, turbulent fluxes, and atmospheric properties. How the land‐atmosphere coupling responds to warming and elevated CO2 are important for understanding the land surface carbon, energy, and water cycles. In this work, we documented this coupled land‐atmosphere network based on observations and the Energy Exascale Earth System Model (E3SM) simulations over extratropical forest ecosystems. We employed a transfer entropy approach and novel network metrics to reveal patterns and strength of the land‐atmosphere coupling under historical conditions and a future high emission scenario (SSP585). We found that, in observations, the present‐day extratropical forest coupling network has high network connectivity (72%–88% of the targeted processes are coupled). E3SM reasonably captured the extratropical forest coupling network (modeled network connectivity was 81%–96%) and predicted that the coupling strength would significantly increase by 28% (±3%) under warming and elevated CO2 conditions. Furthermore, E3SM factorial coupled experiments suggested that warming enhanced soil nitrogen mineralization favoring plant nitrogen uptake and vegetation growth were responsible for the strengthening future land‐atmosphere coupling. This work provides new metrics to analyze and document complex couplings for coupled earth system processes and highlights the important roles soil nutrient availability and biogeochemistry have on land‐atmosphere coupling.

This paper provides an overview of the United States (US) Department of Energy's (DOE's) Energy Exascale Earth System Model version 2 (E3SMv2) fully coupled regionally refined model (RRM) and documents the overall atmosphere, land, and river results from the Coupled Model Intercomparison Project 6 (CMIP6) DECK (Diagnosis, Evaluation, and Characterization of Klima) and historical simulations - a first-of-its-kind set of climate production simulations using RRM. The North American (NA) RRM (NARRM) is developed as the high-resolution configuration of E3SMv2 with the primary goal of more explicitly addressing DOE's mission needs regarding impacts to the US energy sector facing Earth system changes. The NARRM features finer horizontal resolution grids centered over NA, consisting of 25 -> 100 km atmosphere and land, a 0.125 degrees river-routing model, and 14 -> 60 km ocean and sea ice. By design, the computational cost of NARRM is similar to 3 x of the uniform low-resolution (LR) model at 100 km but only similar to 10 %-20% of a globally uniform high-resolution model at 25 km. A novel hybrid time step strategy for the atmosphere is key for NARRM to achieve improved climate simulation fidelity within the high-resolution patch without sacrificing the overall global performance. The global climate, including climatology, time series, sensitivity, and feedback, is confirmed to be largely identical between NARRM and LR as quantified with typical climate metrics. Over the refined NA area, NARRM is generally superior to LR, including for precipitation and clouds over the contiguous US (CONUS), summertime marine stratocumulus clouds off the coast of California, liquid and ice phase clouds near the North Pole region, extratropical cyclones, and spatial variability in land hydrological processes. The improvements over land are related to the better-resolved topography in NARRM, whereas those over ocean are attributable to the improved air-sea interactions with finer grids for both atmosphere and ocean and sea ice. Some features appear insensitive to the resolution change analyzed here, for instance the diurnal propagation of organized mesoscale convective systems over CONUS and the warm-season land-atmosphere coupling at the southern Great Plains. In summary, our study presents a realistically efficient approach to leverage the fully coupled RRM framework for a standard Earth system model release and high-resolution climate production simulations.

Mechanical coupling of the atmosphere to the ocean surface in general circulation models is represented using bulk wind stress formulations. The stress is often based on either absolute wind velocity, τa, or the more correct wind velocity relative to the ocean surface currents, τr. Here, we use coarse-graining to disentangle wind work by these formulations at different length-scales. We show that both can be reasonably accurate in forcing the ocean at length-scales larger than the mesoscales, with τa overestimating wind work by 10%. However, τa and τr show stark and opposing systematic biases in how they drive the mesoscales; τa does negligible (albeit positive) work on the mesoscales, while τr yields eddy-killing (negative work) that is artificially exaggerated by a factor of ≈4. We derive an analytical criterion for eddy-killing to occur, which shows that exaggerated eddy killing is due to resolution mismatch between the atmosphere and ocean. Our criterion highlights the disproportionate effect small-scale winds Ο(100)km can have on the dynamics of mesoscale ocean eddies, despite the dominant atmospheric motions being at length-scales larger than Ο(103) km. The eddy-killing criterion shows that large-scale winds do not necessarily cause eddy-killing but are merely an amplification factor for wind work on the mesoscales, which can be either positive or negative depending on the local alignment of small-scale winds with the ocean eddies. We propose a simple reformulation of τr, without introducing tuning parameters, to remove spurious eddy-killing from air-sea resolution mismatch that is often present in climate models.

facilitate the future routine inclusion of polar tides and eddies in Earth System Models when computing power allows. As such, the proposed research addresses the science in support of the BER’s Climate and Environmental Sciences Division Long Term Measure as it will improve the ocean and sea ice model components as well as the fully coupled RASM and Community Earth System Model (CESM) and it will make them more accurate and computationally efficient.

Understanding how the connection between rainfall and tropical forests will respond to increasing CO 2 concentrations is a key element in understanding how the tropical water cycle will respond to increasing CO 2 . The plant physiological and radiative impacts of CO 2 on rainfall patterns over tropical forest regions are examined in the Energy Exascale Earth System Model version 1.1 (E3SMv1.1‐BGC) biogeochemistry experiments. Composite analysis reveals a dampening of the diurnal cycle of rainfall over the Amazon, Congo, and Maritime Continent in response to rising CO 2 levels, regardless of the sign of total rainfall change. A full factorial model experiment confirms that the CO 2 radiative and CO 2 plant physiological effects can individually or jointly reduce the magnitude of the rainfall diurnal cycle, though the physical pathway giving rise to the reduction differs between the two effects. For the physiological response, stomatal closure reduces evapotranspiration, which dries the boundary layer and raises the lifting condensation level. These effects combine to reduce deep convective rainfall during its peak occurrence in the late daytime to early nighttime period. For the radiative response, a relative reduction in daytime Convective Available Potential Energy (consistent with a reduction in the diurnal temperature range) leads to less frequent triggering of deep convection and a reduction of rainfall diurnal amplitude. These diurnal rainfall changes are structurally similar across seasons, and show little sensitivity to representation of nutrient coupling for the land biogeochemistry. In agreement with previous findings, the physiological response has only minor impact on extreme rainfall relative to the radiative response.

2 (E3SMv2) is a significant evolution from its predecessor E3SMv1, resulting in a model that is nearly twice as fast and with a simulated climate that is improved in many metrics.We describe the physical climate model in its lower horizontal resolution configuration consisting of 110 km atmosphere, 165 km land, 0.5°river routing model, and an ocean and sea ice with mesh spacing varying between 60 km in the mid-latitudes and 30 km at the equator and poles.The model performance is evaluated by means of a standard set of Coupled Model Intercomparison Project Phase 6 (CMIP6) Diagnosis, Evaluation, and Characterization of Klima (DECK) simulations augmented with historical simulations as well as simulations to evaluate impact of different forcing agents.The simulated climate is generally realistic, with notable improvements in clouds and precipitation compared to E3SMv1.E3SMv1 suffered from an excessively high equilibrium climate sensitivity (ECS) of 5.3 K.In E3SMv2, ECS is reduced to 4.0 K which is now within the plausible range based on a recent World Climate Research Programme (WCRP) assessment.However, E3SMv2 significantly underestimates the global mean temperature in the second half of the historical record.An analysis of single-forcing simulations indicates that correcting the historical temperature bias would require a substantial reduction in the magnitude of the aerosol-related forcing.

Global climate models (GCMs) and Earth system models (ESMs) provide many climate services with environmental relevance. The High Resolution Model Inter-comparison Project (HighResMIP) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) provides model runs of GCMs and ESMs to address regional phenomena. Developing a parsimonious ensemble of CMIP6 requires multiple ensemble methods such as independent-model subset selection, prescreening-based subset selection, and model weighting. The work presented here focuses on application-specific optimal model weighting, with prescreening-based subset selection. As such, independent ensemble members are categorized, selected, and weighted based on their ability to reproduce physically-interpretable features of interest that are problem-specific. We discuss the strengths and caveats of optimal model weighting using a case study of red tide prediction in the Gulf of Mexico along the West Florida Shelf. Red tide is a common name of specific harmful algal blooms that occur worldwide, causing adverse socioeconomic and environmental impacts. Our results indicate the importance of prescreening-based subset selection as optimal model weighting can underplay robust ensemble members by optimizing error cancellation. Prescreening-based subset selection also provides insights about the validity of the model weights. By illustrating the caveats of using non-representative models when optimal model weighting is used, the findings and discussion of this study are pertinent to many other climate services.

This work documents version two of the Department of Energy's Energy Exascale Earth System Model (E3SM). E3SMv2 is a significant evolution from its predecessor E3SMv1, resulting in a model that is nearly twice as fast and with a simulated climate that is improved in many metrics. We describe the physical climate model in its lower horizontal resolution configuration consisting of 110 km atmosphere, 165 km land, 0.5° river routing model, and an ocean and sea ice with mesh spacing varying between 60 km in the mid‐latitudes and 30 km at the equator and poles. The model performance is evaluated with Coupled Model Intercomparison Project Phase 6 Diagnosis, Evaluation, and Characterization of Klima simulations augmented with historical simulations as well as simulations to evaluate impacts of different forcing agents. The simulated climate has many realistic features of the climate system, with notable improvements in clouds and precipitation compared to E3SMv1. E3SMv1 suffered from an excessively high equilibrium climate sensitivity (ECS) of 5.3 K. In E3SMv2, ECS is reduced to 4.0 K which is now within the plausible range based on a recent World Climate Research Program assessment. However, a number of important biases remain including a weak Atlantic Meridional Overturning Circulation, deficiencies in the characteristics and spectral distribution of tropical atmospheric variability, and a significant underestimation of the observed warming in the second half of the historical period. An analysis of single‐forcing simulations indicates that correcting the historical temperature bias would require a substantial reduction in the magnitude of the aerosol‐related forcing.

We present the ensemble method of prescreening-based subset selection to improve ensemble predictions of Earth system models (ESMs). In the prescreening step, the independent ensemble members are categorized based on their ability to reproduce physically-interpretable features of interest that are regional and problem-specific. The ensemble size is then updated by selecting the subsets that improve the performance of the ensemble prediction using decision relevant metrics. We apply the method to improve the prediction of red tide along the West Florida Shelf in the Gulf of Mexico, which affects coastal water quality and has substantial environmental and socioeconomic impacts on the State of Florida. Red tide is a common name for harmful algal blooms that occur worldwide, which result from large concentrations of aquatic microorganisms, such as dinoflagellate Karenia brevis, a toxic single celled protist. We present ensemble method for improving red tide prediction using the high resolution ESMs of the Coupled Model Intercomparison Project Phase 6 (CMIP6) and reanalysis data. The study results highlight the importance of prescreening-based subset selection with decision relevant metrics in identifying non-representative models, understanding their impact on ensemble prediction, and improving the ensemble prediction. These findings are pertinent to other regional environmental management applications and climate services. Additionally, our analysis follows the FAIR Guiding Principles for scientific data management and stewardship such that data and analysis tools are findable, accessible, interoperable, and reusable. As such, the interactive Colab notebooks developed for data analysis are annotated in the paper. This allows for efficient and transparent testing of the results' sensitivity to different modeling assumptions. Moreover, this research serves as a starting point to build upon for red tide management, using the publicly available CMIP, Coordinated Regional Downscaling Experiment (CORDEX), and reanalysis data.

Earth system models (ESMs) serve as a unique research infrastructure for quality climate services, yet their application for environmental management at regional scale has not yet been fully explored. The unprecedented resolution and model fidelity of the Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations, especially of the High-Resolution Model Intercomparison Project (HighResMIP) focusing on regional phenomena, offer opportunities for such applications. This article presents the first venture into using the HighResMIP simulations to tackle a regional environmental issue, the Florida Red Tide. This is a harmful algae bloom caused by the dinoflagellate Karenia brevis, a toxic single-celled microscopic protist. We use CMIP6 historical simulations to establish a causal agreement between the position of Loop Current, a warm ocean current that moves into the Gulf of Mexico, and the occurrence of K. brevis blooms on the Western Florida shelf. Results show that the high-resolution ESMs are capable of simulating the phenomena of interest (i.e., Loop Current) at the regional spatial scale with generally adequate data-model agreement in the context of the relation between Loop Current and red tide. We use this case study to elaborate on the prospects and limitations of using publicly available CMIP data for regional environmental management. We highlight the current gaps and the developmental needs for the next generation ESMs, and discuss the role of stakeholder participation in future ESMs development to facilitate the translation of scientific understanding to better inform decision-making of regional environmental management.

We present a framework for estimating concentrations of episodically elevated high-temperature marine ice nucleating particles (INPs) in the sea surface microlayer and their subsequent emission into the atmospheric boundary layer. These episodic INPs have been observed in multiple ship-based and coastal field campaigns, but the processes controlling their ocean concentrations and transfer to the atmosphere are not yet fully understood. We use a combination of empirical constraints and simulation outputs from an Earth system model to explore different hypotheses for explaining the variability of INP concentrations, and the occurrence of episodic INPs, in the marine atmosphere. In our calculations, we examine the following two proposed oceanic sources of high-temperature INPs: heterotrophic bacteria and marine biopolymer aggregates (MBPAs). Furthermore, we assume that the emission of these INPs is determined by the production of supermicron sea spray aerosol formed from jet drops, with an entrainment probability that is described by Poisson statistics. The concentration of jet drops is derived from the number concentration of supermicron sea spray aerosol calculated from model runs. We then derive the resulting number concentrations of marine high-temperature INPs (at 253 K) in the atmospheric boundary layer and compare their variability to atmospheric observations of INP variability. Specifically, we compare against concentrations of episodically occurring high-temperature INPs observed during field campaigns in the Southern Ocean, the Equatorial Pacific, and the North Atlantic. In this case study, we evaluate our framework at 253 K because reliable observational data at this temperature are available across three different ocean regions, but suitable data are sparse at higher temperatures. We find that heterotrophic bacteria and MBPAs acting as INPs provide only a partial explanation for the observed high INP concentrations. We note, however, that there are still substantial knowledge gaps, particularly concerning the identity of the oceanic INPs contributing most frequently to episodic high-temperature INPs, their specific ice nucleation activity, and the enrichment of their concentrations during the sea–air transfer process. Therefore, targeted measurements investigating the composition of these marine INPs and drivers for their emissions are needed, ideally in combination with modeling studies focused on the potential cloud impacts of these high-temperature INPs.

Larger Weddell Sea polynyas (WSPs), differentiated in this study from the smaller Maud Rise Polynyas (MRPs) that form to the east of the prime meridian in the proximity of the Maud Rise seamount, have last been observed in the 1970s. We investigate WSPs that grow realistically out of MRPs in a high-resolution preindustrial simulation with the Energy Exascale Earth System Model, version 0.1. The formation of MRPs requires high resolution to simulate the detailed flow around Maud Rise, whereas the realistic formation of WSPs requires a model to produce MRPs. Furthermore, WSPs tend to follow periods of a prolonged buildup of a heat reservoir at depth and weakly negative wind stress curl in association with the core of the Southern Hemisphere westerlies at an anomalously northern position. While this scenario also leads to drier conditions over the central Weddell Sea, which some literature claims to be a necessary condition for the formation of WSPs, our model results indicate that open-ocean polynyas do not occur during periods of weakly negative wind stress curl despite drier atmospheric conditions. Our study supports the hypothesis noted in earlier studies that a shift from a weakly negative to a strongly negative wind stress curl over the Weddell Sea is a prerequisite for WSPs to form, together with a large heat reservoir at depth. However, the ultimate trigger is a pronounced MRP, whose associated convection creates high surface salinity anomalies that propagate westward with the flow of the Weddell Gyre. If large enough, these anomalies trigger the formation of a WSP and a pulse of newly formed Antarctic Bottom Water.

Several problems in earth system modeling are dependent on highly multiscale phenomena, such as turbulence, where computational modeling is challenging and expensive. This issue is exacerbated in atmospheric and oceanic domains, due to inherent high-dimensionality of the problem. One approach to this problem has been reduced order modeling (ROM); which aims to represent the key physics of the phenomena as a low-dimensional system. AI methods have huge potential in building accurate, stable ROMs and parameter estimation for these ROMs, as it requires extracting nonlinearities and patterns from simulation and/or observational data. Developing physics-based AI approaches specialized for the complexities of multiscale data, along with strategies to account for uncertainties, will revolutionize rapid modeling, analysis and decision making for earth system problems of practical interest.