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Multiple abrupt warming events (hyperthermals) punctuated the Early Eocene and were associated with deep-sea temperature increases of 2 to 4 °C, seafloor carbonate dissolution, and negative carbon isotope (δ13C) excursions. Whether hyperthermals were associated with changes in the global ocean overturning circulation is important for understanding their driving mechanisms and feedbacks and for gaining insight into the circulations sensitivity to climatic warming. Here, we present high-resolution benthic foraminiferal stable isotope records (δ13C and δ18O) throughout the Early Eocene Climate Optimum (~53.26 to 49.14 Ma) from the deep equatorial and North Atlantic. Combined with existing records from the South Atlantic and Pacific, these indicate consistently amplified δ13C excursion sizes during hyperthermals in the deep equatorial Atlantic. We compare these observations with results from an intermediate complexity Earth system model to demonstrate that this spatial pattern of δ13C excursion size is a predictable consequence of global warming-induced changes in ocean overturning circulation. In our model, transient warming drives the weakening of Southern Ocean-sourced overturning circulation, strengthens Atlantic meridional water mass aging gradients, and amplifies the magnitude of negative δ13C excursions in the equatorial to North Atlantic. Based on model-data consistency, we conclude that Eocene hyperthermals coincided with repeated weakening of the global overturning circulation. Not accounting for ocean circulation impacts on δ13C excursions will lead to incorrect estimates of the magnitude of carbon release driving hyperthermals. Our finding of weakening overturning in response to past transient climatic warming is consistent with predictions of declining Atlantic Ocean overturning strength in our warm future.

The parameterization of subgrid‐scale processes such as boundary layer (PBL) turbulence introduces uncertainty in Earth System Model (ESM) results. This uncertainty can contribute to or exacerbate existing biases in representing key physical processes. This study analyzes the influence of tunable parameters in an experimental version of the Cloud Layers Unified by Binormals (CLUBBX) scheme. CLUBB is the operational PBL parameterization in the Community Atmosphere Model version 6 (CAM6), the atmospheric component of the Community ESM version 2 (CESM2). We perform the Morris one‐at‐a‐time (MOAT) parameter sensitivity analysis using short‐term (3‐day), initialized hindcasts of CAM6‐CLUBBX with 24 unique initial conditions. Several input parameters modulating vertical momentum flux appear most influential for various regionally‐averaged quantities, namely surface stress and shortwave cloud forcing (SWCF). These parameter sensitivities have a spatial dependence, with parameters governing momentum flux most influential in regions of high vertical wind shear (e.g., the mid‐latitude storm tracks). We next evaluate several experimental 20‐year simulations of CAM6‐CLUBBX with targeted parameter perturbations. We find that parameter perturbations produce similar physical mechanisms in both short‐term and long‐term simulations, but these physical responses can be muted due to nonlinear feedbacks manifesting over time scales longer than 3 days, thus causing differences in how output metrics respond in the long‐term simulations. Analysis of turbulent fluxes in CLUBBX indicates that the influential parameters affect vertical fluxes of heat, moisture, and momentum, providing physical pathways for the sensitivities identified in this study. Plain Language Summary: Models struggle with certain aspects of predicting the Earth's current and future climate. To achieve better predictions in the future, it is important to understand which parts of the model need to be improved. This study explores how changing certain model characteristics influences what the model outputs. We find that changing how the model estimates small‐scale motions in the atmosphere improves the model's accuracy. Furthermore, these changes affect both short‐term (several days) and long‐term (several decades) model simulations. The results of this study can help scientists understand the physical behavior of climate models and help inform future improvements to enhance model accuracy. Key Points: A computationally‐efficient sensitivity analysis identifies key parameters and physical mechanisms for global climate propertiesCertain parameter sensitivities in short‐term, initialized hindcasts are consistent with those seen in multidecadal climate simulationsParameters governing the degree of turbulent mixing in the presence of vertical wind shear are influential for surface stress representation [ABSTRACT FROM AUTHOR]

The last deglaciation experienced the retreat of massive ice sheets and a transition from the cold Last Glacial Maximum to the warmer Holocene. Key simulation challenges for this period include the timing and extent of ice sheet decay and meltwater input into the oceans. Here, major uncertainties and forcing factors for the last deglaciation are evaluated. Two sets of transient simulations are performed based on the novel ice‐sheet reconstruction PaleoMist and the more established GLAC1D. The simulations reveal that the proximity of the Atlantic meridional overturning circulation (AMOC) to a bifurcation point, where it can switch between on‐ and off‐modes, is primarily determined by the interplay of greenhouse gas concentrations, orbital forcing and freshwater forcing. The PaleoMist simulation qualitatively replicates the Bølling‐Allerød (BA)/Younger Dryas (YD) sequence: a warming in Greenland and Antarctica during the BA, followed by a cooling northern North Atlantic and an Antarctic warming during the YD. Plain Language Summary: The last deglaciation, spanning roughly 20,000 to 10,000 years ago, marked a period of Earth's history characterized by the retreat of massive ice sheets that had covered large parts of the planet. During this phase, a drastic transition occurred from the cold Last Glacial Maximum to the warmer and more stable climate of the Holocene. A main challenge for simulating the last deglaciation is the timing and amplitude of the ice sheet decay and the amount of meltwater that enters into the oceans. Using two different reconstructions of ice sheets, we employ an efficient climate model to explore changes at the end of the last ice age. Our comparison shows notable differences in the timing and amplitude of abrupt climate events in the simulations using two different ice‐sheet reconstructions. Furthermore, we investigate the effects of factors such as greenhouse gases and Earth's orbital changes on the large‐scale ocean currents with respect to underlying ice sheets. Ultimately, our study sheds light on how different elements of the Earth's system shape the termination of the last ice age, enriching our understanding of Earth's climate history and guiding further deglaciation scenarios. Key Points: Transient simulations of the last deglaciation using GLAC1D and the new PaleoMist ice sheet reconstruction are compared for the first timeAMOC stability is impacted by ice sheet reconstruction used and the combination of forcings, indicating its proximity to a bifurcation pointPaleoMist simulation captures the BA/YD sequence signature in the northern North Atlantic [ABSTRACT FROM AUTHOR]

The first operational version of the coupled Hurricane Analysis and Forecast System (HAFSv1) launched in 2023 consists of the HYbrid Coordinate Ocean Model (HYCOM) and finite-volume cubed-sphere (FV3) dynamic atmosphere model. This system is a product of efforts involving improvements and updates over a 4-year period (2019-2022) through extensive collaborations between the Environmental Modeling Center at the US National Centers for Environmental Prediction (NCEP) and NOAA Atlantic Oceanography and Meteorology Laboratory. To provide two sets of numerical guidance, the initial operational capability of HAFSv1 was configured to two systems -HFSA and HFSB. In this study, we present in-depth analysis of the forecast skills of the upper ocean that was co-evolved by the HFSA and HFSB. We chose hurricane Laura (2020) as an example to demonstrate the interactions between the storm and oceanic mesoscale features. Comparisons performed with the available in situ observations from gliders as well as Argos and National Data Buoy Center moorings show that the HYCOM simulations have better agreement for weak winds than high winds (greater than Category 2). The skill metrics indicate that the model sea-surface temperature (SST) and mixed layer depth (MLD) have a relatively low correlation. The SST, MLD, mixed layer temperature (MLT), and ocean heat content (OHC) are negatively biased. For high winds, SST and MLT are more negative, while MLD is closer to the observations with improvements of about 8%-19%. The OHC discrepancy is proportional to predicted wind intensity. Contrarily, the mixed layer salinity (MLS) uncertainties are smaller and positive for higher winds, probably owing to the higher MLD. The less-negative bias of MLD for high winds implies that the wind-force mixing is less effective owing to the higher MLD and high buoyancy stability (approx. 1.5-1.7 times) than the observations. The heat budget analysis suggests that the maximum heat loss by hurricane Laura was 0(< 3°C per day). The main contributor here is advection, followed by entrainment, which act against or with each other depending on the storm quadrant. We also found relatively large unaccountable heat residuals for the in-storm period, and the residuals notably led the heat tendency, meaning that further improvements of the subscale simulations are warranted. In summary, HYCOM simulations showed no systematic differences forced by either HFSA or HFSB. [ABSTRACT FROM AUTHOR]

Sea‐level rise (SLR) poses a severe threat to the coastal environment through seawater intrusion into freshwater aquifers. The rising groundwater table also exacerbates the risk of pluvial, fluvial, and groundwater flooding in coastal regions. However, current Earth system models (ESMs) commonly ignore the exchanges of water at the land‐ocean interface. To address this gap, we developed a novel land‐ocean hydrologic coupling scheme in a state‐of‐the‐science ESM, the Energy Exascale Earth System Model version 2 (E3SMv2). The new scheme includes the lateral exchange between seawater and groundwater and the vertical infiltration of seawater driven by the SLR‐induced inundation. Simulations were performed with the updated E3SMv2 for the global land‐ocean interface to assess the impacts of SLR on coastal groundwater under a high CO2 emission scenario. By the middle of this century, seawater infiltration on the inundated areas will be the dominant component in the land‐ocean coupling process, while the lateral subsurface flow exchange will be much smaller. The SLR‐induced seawater infiltration will raise the groundwater levels, enhance evapotranspiration, and increase runoff with distinct spatial patterns globally in the future. Although the coupling process is induced by SLR, we found topography and warming temperature have more control on the coupling impacts, probably due to the relatively modest magnitude of SLR during the selected future period. Overall, our study suggests significant groundwater and seawater exchange at the land‐ocean interface, which needs to be considered in ESMs. Plain Language Summary: As ocean levels rise, seawater threatens to intrude into coastal freshwater aquifers that millions of people depend on for drinking water and irrigation. While regional studies have examined the impacts of sea‐level rise (SLR) on coastal groundwater systems, current Earth system models (ESMs) overlook the exchange of water between ocean and groundwater. Our work addresses this gap by developing a water exchange process between ocean and land components in a state‐of‐the‐science ESM. This coupling scheme includes lateral exchanges between seawater and groundwater, as well as vertical seawater infiltration resulting from oceanic inundation. We used this new coupled model to assess SLR impacts on the global coastal groundwater table under a higher CO2 emission scenario. We found that SLR will raise groundwater levels and intensify the hydrological cycle by midcentury mainly due to increased seawater infiltration. Further, while SLR triggers this increased seawater infiltration, topography and warming temperature play more significant roles in determining its magnitude. Key Points: A novel land‐ocean coupling scheme is developed and implemented in Energy Exascale Earth System Model version 2 to evaluate the hydrologic exchange at the land‐ocean interfaceThe impact of land‐ocean coupling on the groundwater table is dominated by seawater infiltration, while lateral exchange is negligibleFuture sea‐level rise will induce significant seawater intrusion into the coastal groundwater system [ABSTRACT FROM AUTHOR]

Coastal zone compound flooding (CF) can be caused by the interactive fluvial and oceanic processes, particularly when coastal backwater propagates upstream and interacts with high river discharge. The modeling of CF is limited in existing Earth System Models (ESMs) due to coarse mesh resolutions and one‐way coupled river‐ocean components. In this study, we present a novel multi‐scale coupling framework within the Energy Exascale Earth System Model (E3SM), integrating global atmosphere and land with interactively coupled river and ocean models using different meshes with refined resolutions near the coastline. To evaluate this framework, we conducted ensemble simulations of a CF event (Hurricane Irene in 2011) in a Mid‐Atlantic estuary. The results demonstrate that the novel E3SM configuration can reasonably reproduce river discharge and sea surface height variations. The two‐way river‐ocean coupling improves the representation of coastal backwater effects at the terrestrial‐aquatic interface that are caused by the combined actions of tide and storm surge during the CF event, thus providing a valuable modeling tool for better understanding the river‐estuary‐ocean dynamics in extreme events under climate change. Notably, our results show that the most significant CF impacts occur when the highest storm surge generated by a tropical cyclone meets with a moderate river discharge. This study highlights the state‐of‐the‐art advancements developed within E3SM for simulating multi‐scale coastal processes. Plain Language Summary: Compound flooding (CF) happens when rivers and oceans interact in the coastal zone. There are limitations in current models to accurately simulate these processes because of the insufficient resolutions in the computational meshes and lack of details on how rivers and oceans are connected. This study creates a comprehensive framework for the Energy Exascale Earth System Model (E3SM) and demonstrates its ability to simulate a specific compound flooding event in a Mid‐Atlantic estuary. Our framework combines models of the atmosphere, land, river, and ocean, each with its own level of detail near the coastline to account for their different physical processes. Our results show that the E3SM framework can reproduce the river discharge and sea level variations reasonably well. By simulating the interaction between river and ocean, we can better understand the effects of coastal water on river discharge forced by tides and storm surges during the CF event. Our simulation reveals that CF is most significant when a tropical cyclone produces the highest storm surge but moderate river discharge. This study demonstrates the capability of the E3SM model to accurately simulate the detailed coastal processes. Key Points: An Energy Exascale Earth System Model (E3SM) configuration is developed to integrate variable‐resolution meshes with component advancementsThe two‐way river‐ocean coupling scheme developed in E3SM significantly improves the representation of river‐ocean interactionsThe new coupled E3SM configuration provides insights into the nonlinear interactions between storm surge and river discharge during compound flooding [ABSTRACT FROM AUTHOR]

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, 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 next generation climate services. Plain Language Summary: 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 currently at hand, and working with stakeholders to co‐produce the information that they require. 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. Key Points: Facing the uncharted territory of a changed climate and increasingly complex socio‐economic systems requires the best possible predictionsScientific and technological opportunities are at hand to accelerate progress on U.S. climate predictions and projectionsCapitalizing on the most transformative opportunities calls for an unprecedented level of coordination and resources [ABSTRACT FROM AUTHOR]

Abstract The parameterization of subgrid‐scale processes such as boundary layer (PBL) turbulence introduces uncertainty in Earth System Model (ESM) results. This uncertainty can contribute to or exacerbate existing biases in representing key physical processes. This study analyzes the influence of tunable parameters in an experimental version of the Cloud Layers Unified by Binormals (CLUBBX) scheme. CLUBB is the operational PBL parameterization in the Community Atmosphere Model version 6 (CAM6), the atmospheric component of the Community ESM version 2 (CESM2). We perform the Morris one‐at‐a‐time (MOAT) parameter sensitivity analysis using short‐term (3‐day), initialized hindcasts of CAM6‐CLUBBX with 24 unique initial conditions. Several input parameters modulating vertical momentum flux appear most influential for various regionally‐averaged quantities, namely surface stress and shortwave cloud forcing (SWCF). These parameter sensitivities have a spatial dependence, with parameters governing momentum flux most influential in regions of high vertical wind shear (e.g., the mid‐latitude storm tracks). We next evaluate several experimental 20‐year simulations of CAM6‐CLUBBX with targeted parameter perturbations. We find that parameter perturbations produce similar physical mechanisms in both short‐term and long‐term simulations, but these physical responses can be muted due to nonlinear feedbacks manifesting over time scales longer than 3 days, thus causing differences in how output metrics respond in the long‐term simulations. Analysis of turbulent fluxes in CLUBBX indicates that the influential parameters affect vertical fluxes of heat, moisture, and momentum, providing physical pathways for the sensitivities identified in this study.

Abstract Sea‐level rise (SLR) poses a severe threat to the coastal environment through seawater intrusion into freshwater aquifers. The rising groundwater table also exacerbates the risk of pluvial, fluvial, and groundwater flooding in coastal regions. However, current Earth system models (ESMs) commonly ignore the exchanges of water at the land‐ocean interface. To address this gap, we developed a novel land‐ocean hydrologic coupling scheme in a state‐of‐the‐science ESM, the Energy Exascale Earth System Model version 2 (E3SMv2). The new scheme includes the lateral exchange between seawater and groundwater and the vertical infiltration of seawater driven by the SLR‐induced inundation. Simulations were performed with the updated E3SMv2 for the global land‐ocean interface to assess the impacts of SLR on coastal groundwater under a high CO2 emission scenario. By the middle of this century, seawater infiltration on the inundated areas will be the dominant component in the land‐ocean coupling process, while the lateral subsurface flow exchange will be much smaller. The SLR‐induced seawater infiltration will raise the groundwater levels, enhance evapotranspiration, and increase runoff with distinct spatial patterns globally in the future. Although the coupling process is induced by SLR, we found topography and warming temperature have more control on the coupling impacts, probably due to the relatively modest magnitude of SLR during the selected future period. Overall, our study suggests significant groundwater and seawater exchange at the land‐ocean interface, which needs to be considered in ESMs.

Abstract The last deglaciation experienced the retreat of massive ice sheets and a transition from the cold Last Glacial Maximum to the warmer Holocene. Key simulation challenges for this period include the timing and extent of ice sheet decay and meltwater input into the oceans. Here, major uncertainties and forcing factors for the last deglaciation are evaluated. Two sets of transient simulations are performed based on the novel ice‐sheet reconstruction PaleoMist and the more established GLAC1D. The simulations reveal that the proximity of the Atlantic meridional overturning circulation (AMOC) to a bifurcation point, where it can switch between on‐ and off‐modes, is primarily determined by the interplay of greenhouse gas concentrations, orbital forcing and freshwater forcing. The PaleoMist simulation qualitatively replicates the Bølling‐Allerød (BA)/Younger Dryas (YD) sequence: a warming in Greenland and Antarctica during the BA, followed by a cooling northern North Atlantic and an Antarctic warming during the YD.

The first operational version of the coupled Hurricane Analysis and Forecast System (HAFSv1) launched in 2023 consists of the HYbrid Coordinate Ocean Model (HYCOM) and finite-volume cubed-sphere (FV3) dynamic atmosphere model. This system is a product of efforts involving improvements and updates over a 4-year period (2019–2022) through extensive collaborations between the Environmental Modeling Center at the US National Centers for Environmental Prediction (NCEP) and NOAA Atlantic Oceanography and Meteorology Laboratory. To provide two sets of numerical guidance, the initial operational capability of HAFSv1 was configured to two systems—HFSA and HFSB. In this study, we present in-depth analysis of the forecast skills of the upper ocean that was co-evolved by the HFSA and HFSB. We chose hurricane Laura (2020) as an example to demonstrate the interactions between the storm and oceanic mesoscale features. Comparisons performed with the available in situ observations from gliders as well as Argos and National Data Buoy Center moorings show that the HYCOM simulations have better agreement for weak winds than high winds (greater than Category 2). The skill metrics indicate that the model sea-surface temperature (SST) and mixed layer depth (MLD) have a relatively low correlation. The SST, MLD, mixed layer temperature (MLT), and ocean heat content (OHC) are negatively biased. For high winds, SST and MLT are more negative, while MLD is closer to the observations with improvements of about 8%–19%. The OHC discrepancy is proportional to predicted wind intensity. Contrarily, the mixed layer salinity (MLS) uncertainties are smaller and positive for higher winds, probably owing to the higher MLD. The less-negative bias of MLD for high winds implies that the wind-force mixing is less effective owing to the higher MLD and high buoyancy stability (approx. 1.5–1.7 times) than the observations. The heat budget analysis suggests that the maximum heat loss by hurricane Laura was O(< 3°C per day). The main contributor here is advection, followed by entrainment, which act against or with each other depending on the storm quadrant. We also found relatively large unaccountable heat residuals for the in-storm period, and the residuals notably led the heat tendency, meaning that further improvements of the subscale simulations are warranted. In summary, HYCOM simulations showed no systematic differences forced by either HFSA or HFSB.

Abstract 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, 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 next generation climate services.

Abstract Coastal zone compound flooding (CF) can be caused by the interactive fluvial and oceanic processes, particularly when coastal backwater propagates upstream and interacts with high river discharge. The modeling of CF is limited in existing Earth System Models (ESMs) due to coarse mesh resolutions and one‐way coupled river‐ocean components. In this study, we present a novel multi‐scale coupling framework within the Energy Exascale Earth System Model (E3SM), integrating global atmosphere and land with interactively coupled river and ocean models using different meshes with refined resolutions near the coastline. To evaluate this framework, we conducted ensemble simulations of a CF event (Hurricane Irene in 2011) in a Mid‐Atlantic estuary. The results demonstrate that the novel E3SM configuration can reasonably reproduce river discharge and sea surface height variations. The two‐way river‐ocean coupling improves the representation of coastal backwater effects at the terrestrial‐aquatic interface that are caused by the combined actions of tide and storm surge during the CF event, thus providing a valuable modeling tool for better understanding the river‐estuary‐ocean dynamics in extreme events under climate change. Notably, our results show that the most significant CF impacts occur when the highest storm surge generated by a tropical cyclone meets with a moderate river discharge. This study highlights the state‐of‐the‐art advancements developed within E3SM for simulating multi‐scale coastal processes.

Astronomical cycles are strongly expressed in marine geological records, providing important insights into Earth system dynamics and an invaluable means of constructing age models. However, how various astronomical periods are filtered by the Earth system and the mechanisms by which carbon reservoirs and climate components respond, particularly in absence of dynamic ice sheets, is unclear. Using an Earth system model that includes feedbacks between climate, ocean circulation, and inorganic (carbonate) carbon cycling relevant to geological timescales, we systematically explore the impact of astronomically modulated insolation forcing and its expression in model variables most comparable to key paleoceanographic proxies (temperature, the δ13C of inorganic carbon, and sedimentary carbonate content). Temperature predominately responds to short and long eccentricity and is little influenced by the modeled carbon cycle feedbacks. In contrast, the cycling of nutrients and carbon in the ocean generates significant precession power in atmospheric CO2, benthic ocean δ13C, and sedimentary wt% CaCO3, while inclusion of marine sedimentary and weathering processes shifts power to the long eccentricity period. Our simulations produce reduced pCO2 and dissolved inorganic carbon (DIC) δ13C at long eccentricity maxima and, contrary to early Cenozoic marine records, CaCO3 preservation in the model is enhanced during eccentricity-modulated warmth. Additionally, the magnitude of δ13C variability simulated in our model underestimates marine proxy records. These model-data discrepancies hint at the possibility that the Paleogene silicate weathering feedback was weaker than modeled here and that additional organic carbon cycle feedbacks are necessary to explain the full response of the Earth system to astronomical forcing.

The Arctic is home to several groups of Indigenous Peoples, each with distinct ways of interacting with their environment and ways of life. Arctic, Indigenous Peoples’ food sovereignty is tightly linked with food security. Subsistence harvesting activities provide nutritious and culturally vital foods for Alaska Native households and communities. Climate change is causing rapid and more unpredictable shifts in environmental conditions that impact three of the key aspects of food security, availability, stability, and accessibility. While communities monitor the abundance and health of food webs and environments as part of subsistence harvest practices, anticipating major transformations and changes in these systems is challenging. We explored the potential of Earth System Model output in helping anticipate or project physical or ecosystem changes relevant to Alaska Indigenous peoples’ food security needs. Through examples of model products, that provide measures of accessibility and availability of marine resources, we show that modern models, such as the Energy Exascale Earth System Model presented here, can provide estimates of a broad suite of variables relevant to food security. We investigate how Earth System Model output could contribute to exploring questions related to aspects of Arctic food security such as accessibility and availability and highlight present model shortcomings that, if addressed, would move Earth System Models closer to being a useful tool for understanding environmentally driven changes to the availability and accessibility of harvestable food resources. Our example model-derived food security indicators illustrate how Earth System Model output could be combined with relevant, non-model, information sources; These model products are meant only as a starting point and a tool for engaging community members and to present, in an accessible way, the model’s potential utility, or current lack thereof, to rights holders and stakeholders concerned about food security. We are hopeful that with example products in hand, additional model development efforts will have a higher likelihood of success in achieving an iterative discussion with stakeholders regarding feasible and desired products.

The Earth System modeling community uses different methods to discretize a landscape in model elements, such as square grids, triangles, or irregular shapes. Mapping data from one spatial configuration to another is an essential part of environmental modeling, and can be time-consuming and cumbersome. In this work, we present a Python package called EASYMORE. EASYMORE stands for EArth SYstem MOdeling REmapper and enables users to quickly and efficiently remap variables, such as precipitation or temperature, from one spatial representation (e.g., unstructured grids) to another (e.g., sub-basins). The package is aimed to increase the efficiency of data preparation for Earth System modeling in a reproducible and transparent manner. The remapped variables, provided in netCDF or CSV formats, can then be used directly or changed to the format needed for intended uses. This manuscript presents examples that show various applications of EASYMORE.

The climate model hierarchy encompasses models of varying complexity along different axes, ranging from idealized models that elegantly describe isolated mechanisms to fully coupled Earth system models that aspire to provide useable climate projections. Based on the second Model Hierarchies Workshop, which took place in 2022, we present perspectives on how this field has evolved since the first Model Hierarchies Workshop in 2016. In this period, we have witnessed a dramatic increase in the use of (a) machine learning in climate modeling and (b) climate models to estimate risks and influence decision making under climate change. Here, we discuss the implications of these growing areas of research and how we expect them to become integrated into the model hierarchies framework. Plain Language Summary: The climate modeling community often describes the variety of models used to understand and simulate climate processes as a hierarchy in complexity. Simple idealized models exist at the bottom of the hierarchy and are useful for explaining underlying physics, while fully coupled Earth system models exist at the top of the hierarchy and aim to provide useable climate projections. We present perspectives on how the model hierarchy field is evolving, focusing on two noticeable changes in recent years. Firstly, models are increasingly using machine learning, and secondly, there has been a growing interest in the usability of climate models, for instance, for estimating risks associated with climate change. Here, we discuss the implications of these growing areas of interest and how we expect them to become integrated into the model hierarchies framework. Key Points: Inspired by the 2022 Model Hierarchies workshop, we present perspectives on how the field is evolvingFirstly, we note the growing interest in the use of machine learning which presents another way to ascend or descend the model hierarchySecondly, we discuss the role of the model hierarchy for actionable climate science and decision‐making [ABSTRACT FROM AUTHOR]

With the recent advances in data science, machine learning has been increasingly applied to convection and cloud parameterizations in global climate models (GCMs). This study extends the work of Han et al. (2020, https://doi.org/10.1029/2020MS002076) and uses an ensemble of 32‐layer deep convolutional residual neural networks, referred to as ResCu‐en, to emulate convection and cloud processes simulated by a superparameterized GCM, SPCAM. ResCu‐en predicts GCM grid‐scale temperature and moisture tendencies, and cloud liquid and ice water contents from moist physics processes. The surface rainfall is derived from the column‐integrated moisture tendency. The prediction uncertainty inherent in deep learning algorithms in emulating the moist physics is reduced by ensemble averaging. Results in 1‐year independent offline validation show that ResCu‐en has high prediction accuracy for all output variables, both in the current climate and in a warmer climate with +4K sea surface temperature. The analysis of different neural net configurations shows that the success to generalize in a warmer climate is attributed to convective memory and the 1‐dimensional convolution layers incorporated into ResCu‐en. We further implement a member of ResCu‐en into CAM5 with real world geography and run the neural‐network‐enabled CAM5 (NCAM) for 5 years without encountering any numerical integration instability. The simulation generally captures the global distribution of the mean precipitation, with a better simulation of precipitation intensity and diurnal cycle. However, there are large biases in temperature and moisture in high latitudes. These results highlight the importance of convective memory and demonstrate the potential for machine learning to enhance climate modeling. Plain Language Summary: The representation of storms and clouds through empirical algorithms known as parameterizations in global climate models (GCMs) is one of the main sources of biases in the simulation of rainfall and atmospheric circulation. Here an ensemble of 8 deep neural networks are used to replace the conventional parameterization of atmospheric moist physics processes. They are trained on data sampled from 1‐year present‐day climate simulation by a "superparameterized" climate model, which uses a two‐dimensional cloud‐scale model to explicitly simulate convection and clouds inside each GCM grid box. On ensemble averaging, the neural nets produce highly accurate predictions of precipitation characteristics including global distribution and intensity. Furthermore, the machine‐learned emulator trained on data in the current climate also represents convection and precipitation extremely well in a warmer climate. A member of the ensemble of the neural nets is implemented into a GCM. The model is then integrated for 5 years, producing reasonable results. Key Points: An ensemble of deep convolutional residual neural networks is used to reduce the uncertainty in moist physics emulationsThe ensemble of the neural networks trained on data from a present‐day climate simulation generalizes well to a +4K warm climate offlineA multi‐year stable online integration is achieved in a real‐geography GCM with reasonable results [ABSTRACT FROM AUTHOR]

Abstract With the recent advances in data science, machine learning has been increasingly applied to convection and cloud parameterizations in global climate models (GCMs). This study extends the work of Han et al. (2020, https://doi.org/10.1029/2020MS002076) and uses an ensemble of 32‐layer deep convolutional residual neural networks, referred to as ResCu‐en, to emulate convection and cloud processes simulated by a superparameterized GCM, SPCAM. ResCu‐en predicts GCM grid‐scale temperature and moisture tendencies, and cloud liquid and ice water contents from moist physics processes. The surface rainfall is derived from the column‐integrated moisture tendency. The prediction uncertainty inherent in deep learning algorithms in emulating the moist physics is reduced by ensemble averaging. Results in 1‐year independent offline validation show that ResCu‐en has high prediction accuracy for all output variables, both in the current climate and in a warmer climate with +4K sea surface temperature. The analysis of different neural net configurations shows that the success to generalize in a warmer climate is attributed to convective memory and the 1‐dimensional convolution layers incorporated into ResCu‐en. We further implement a member of ResCu‐en into CAM5 with real world geography and run the neural‐network‐enabled CAM5 (NCAM) for 5 years without encountering any numerical integration instability. The simulation generally captures the global distribution of the mean precipitation, with a better simulation of precipitation intensity and diurnal cycle. However, there are large biases in temperature and moisture in high latitudes. These results highlight the importance of convective memory and demonstrate the potential for machine learning to enhance climate modeling.

Abstract The climate model hierarchy encompasses models of varying complexity along different axes, ranging from idealized models that elegantly describe isolated mechanisms to fully coupled Earth system models that aspire to provide useable climate projections. Based on the second Model Hierarchies Workshop, which took place in 2022, we present perspectives on how this field has evolved since the first Model Hierarchies Workshop in 2016. In this period, we have witnessed a dramatic increase in the use of (a) machine learning in climate modeling and (b) climate models to estimate risks and influence decision making under climate change. Here, we discuss the implications of these growing areas of research and how we expect them to become integrated into the model hierarchies framework.

The Community Land Model (CLM) is the land component of the Community Earth System Model (CESM) and is used in several global and regional modeling systems. In this paper, we introduce model developments included in CLM version 5 (CLM5), which is the default land component for CESM2. We assess an ensemble of simulations, including prescribed and prognostic vegetation state, multiple forcing data sets, and CLM4, CLM4.5, and CLM5, against a range of metrics including from the International Land Model Benchmarking (ILAMBv2) package. CLM5 includes new and updated processes and parameterizations: (1) dynamic land units, (2) updated parameterizations and structure for hydrology and snow (spatially explicit soil depth, dry surface layer, revised groundwater scheme, revised canopy interception and canopy snow processes, updated fresh snow density, simple firn model, and Model for Scale Adaptive River Transport), (3) plant hydraulics and hydraulic redistribution, (4) revised nitrogen cycling (flexible leaf stoichiometry, leaf N optimization for photosynthesis, and carbon costs for plant nitrogen uptake), (5) global crop model with six crop types and time-evolving irrigated areas and fertilization rates, (6) updated urban building energy, (7) carbon isotopes, and (8) updated stomatal physiology. New optional features include demographically structured dynamic vegetation model (Functionally Assembled Terrestrial Ecosystem Simulator), ozone damage to plants, and fire trace gas emissions coupling to the atmosphere. Conclusive establishment of improvement or degradation of individual variables or metrics is challenged by forcing uncertainty, parametric uncertainty, and model structural complexity, but the multivariate metrics presented here suggest a general broad improvement from CLM4 to CLM5.

Stratospheric injections of sulfur dioxide from major volcanic eruptions perturb the Earth's global radiative balance and dominate variability in stratospheric sulfur loading. The atmospheric component of the GFDL Earth System Model (ESM4.1) uses a bulk aerosol scheme and previously prescribed the distribution of aerosol optical properties in the stratosphere. To quantify volcanic contributions to the stratospheric sulfur cycle and the resulting climate impact, we modified ESM4.1 to simulate stratospheric sulfate aerosols prognostically. Driven by explicit volcanic emissions of aerosol precursors and non‐volcanic sources, we conduct ESM4.1 simulations from 1989 to 2014, with a focus on the Mt. Pinatubo eruption. We evaluate our interactive representation of the stratospheric sulfur cycle against data from Moderate Resolution Imaging Spectroradiometer, Multi‐angle Imaging SpectroRadiometer, Advanced Very High Resolution Radiometer, High Resolution Infrared Radiation Sounder, and Stratospheric Aerosol and Gas Experiment II. To assess the key processes associated with volcanic aerosols, we performed a sensitivity analysis of sulfate burden from the Mt. Pinatubo eruption by varying injection heights, emission amount, and stratospheric sulfate's dry effective radius. We find that the simulated stratospheric sulfate mass burden and aerosol optical depth in the model are sensitive to these parameters, especially volcanic SO2 injection height, and the optimal combination of parameters depends on the metric we evaluate. Plain Language Summary: Major volcanic eruptions emit sulfur dioxide into the stratosphere and affect the Earth's global radiative balance as well as the stratospheric sulfur abundance. The GFDL Earth System Model (ESM4.1) previously uses prescribed aerosol optical properties, and in this paper, we replace it with explicit volcanic emissions to study the volcanic contribution to the stratospheric sulfur cycle and its impact. We simulate years from 1989 to 2014, with a focus on the Mt. Pinatubo eruption as a benchmark. We also evaluate the new improvements against observations and performed sensitivity analyses of the sulfate burden from the Mt. Pinatubo eruptions. We find that the simulated stratospheric sulfate amount and aerosol optical depth are sensitive to injection height, emission amount, and aerosol size, and while the injection height is the most sensitive, the best combination of parameters depends on the chosen observational metric. Key Points: We present an interactive representation of the stratospheric sulfur cycle in GFDL ESM4 that replaced prescribed aerosol optical propertiesWe simulated years 1989–2014 with a focus on the Mt. Pinatubo eruption and evaluated against observationsSimulated stratospheric sulfate burden and optical depth are sensitive to injection height, emission amount, and aerosol size [ABSTRACT FROM AUTHOR]

Discretized numerical models of the atmosphere are usually intended to faithfully represent an underlying set of continuous equations, but this necessary condition is violated sometimes by subtle pathologies that have crept into the discretized equations. Such pathologies can introduce undesirable artifacts, such as sawtooth noise, into the model solutions. The presence of these pathologies can be detected by numerical convergence testing. This study employs convergence testing to verify the discretization of the Cloud Layers Unified By Binormals (CLUBB) model of clouds and turbulence. That convergence testing identifies two aspects of CLUBB's equation set that contribute to undesirable noise in the solutions. First, numerical limiters (i.e., clipping) used by CLUBB introduce discontinuities or slope discontinuities in model fields. Second, nonlinear artificial diffusion employed for improving numerical stability can introduce unintended small‐scale features into the solution of the model equations. Smoothing the limiters and using linear artificial diffusion reduces the noise and restores the expected first‐order convergence in CLUBB's solutions. These model reformulations enhance our confidence in the trustworthiness of solutions from CLUBB by eliminating the unphysical oscillations in high‐resolution simulations. The improvements in the results at coarser, near‐operational grid spacing and timestep are also seen in cumulus cloud and dry turbulence tests. In addition, convergence testing is proven to be a valuable tool for detecting pathologies, including unintended discontinuities and grid dependence, in the model equation set. Plain Language Summary: In order to detect pathologies in a numerical model of the atmosphere, a diagnostic test is used here that has been widely adopted in the applied mathematics community. The diagnostic test, if set up properly, proves to be effective at detecting the pathologies caused by issues associated with numerical discretization. Both the method of testing and the particular pathologies detected are expected to be relevant to other atmospheric models. Key Points: Numerical limiters and nonlinear artificial diffusion can inject unintended small‐scale features into the solution of the model equationsImproved treatment of limiters and artificial diffusion can reduce noise and improve the quality of solutions and numerical convergenceConvergence testing is a valuable tool for detecting noise in the model solution, as well as unintended discontinuities and grid dependence [ABSTRACT FROM AUTHOR]

El Niño‐Southern Oscillation (ENSO) can effectively modulate global tropical cyclone (TC) activity, but the role TCs may play in determining ENSO characteristics remains unclear. Here we investigate the impact of TC winds on ENSO using a suite of Earth system model experiments where we insert TC winds, extracted from a TC‐permitting high‐resolution simulation, into a low‐resolution model configuration with nearly no intrinsic TCs. The presence of TC winds in the model increases ENSO power and shifts ENSO frequency closer to what we observe. TCs lead to an increase of strong to extreme El Niño events seen in observations and not simulated in the low‐resolution model without intrinsic TCs, mainly through enhanced zonal advection feedback and thermocline feedback. Our results indicate that TCs play a fundamental role in producing the ENSO characteristics we experience today in the climate system and point to a two‐way climatological interaction between TCs and ENSO. Plain Language Summary: El Niño‐Southern Oscillation (ENSO) can influence global tropical cyclone (TC) activity by altering the large‐scale conditions. TCs, as transient yet powerful weather events, can cause strong air‐sea interactions over the tropical ocean that may consequently influence the climate mean state. Here we show how TCs could be essential to the characteristics of ENSO using a suite of Earth system model experiments where we insert TC winds, extracted from a high‐resolution model simulation, into a low‐resolution model simulation with nearly no intrinsic TCs. The added TC winds in the model increases ENSO power and shifts ENSO frequency closer to the observations. TCs lead to an increase of strong to extreme El Niño events seen in observations and not simulated in the low‐resolution model without intrinsic TCs. Our results indicate that TCs play a fundamental role in producing the ENSO characteristics we experience today in the climate system and point to a previously unidentified two‐way climatological interaction between TCs and ENSO. Key Points: The climatological impacts of tropical cyclone (TC) winds on El Niño‐Southern Oscillation (ENSO) are investigated for the first time in a fully coupled Earth system modelTC winds impact ENSO magnitude, frequency, and the occurrence of strong to extreme El Niño eventsModel proved that TC winds affect El Niño by enhancing the thermocline feedback and the zonal advection feedback [ABSTRACT FROM AUTHOR]

Our understanding of the long-term evolution of the Earth system is based on the assumption that terrestrial weathering rates should respond to, and hence help regulate, atmospheric CO2 and climate. Increased terrestrial weathering requires increased carbonate accumulation in marine sediments, which in turn is expected to result in a long-term deepening of the carbonate compensation depth (CCD). Here, we critically assess this long-term relationship between climate and carbon cycling. We generate a record of marine deep-sea carbonate abundance from selected late Paleocene through early Eocene time slices to reconstruct the position of the CCD. Although our data set allows for a modest CCD deepening, we find no statistically significant change in the CCD despite >3 °C global warming, highlighting the need for additional deep-sea constraints on carbonate accumulation. Using an Earth system model, we show that the impact of warming and increased weathering on the CCD can be obscured by the opposing influences of ocean circulation patterns and sedimentary respiration of organic matter. From our data synthesis and modeling, we suggest that observations of warming, declining δ13C and a relatively stable CCD can be broadly reproduced by mid-Paleogene increases in volcanic CO2 outgassing and weathering. However, remaining data-model discrepancies hint at missing processes in our model, most likely involving the preservation and burial of organic carbon. Our finding of a decoupling between the CCD and global marine carbonate burial rates means that considerable care is needed in attempting to use the CCD to directly gauge global carbonate burial rates and hence weathering rates.

Water availability has historically been one of the most significant threats to African regional social and economic well-being. Over the Sahel region, a megadrought during the 1960s and 1970s induced by an abrupt and substantial rainfall reduction caused widespread famine and death. The postdrought recovery, which is still ongoing, has been characterized by gradual increases in rainfall, but with dramatic fluctuations. The large negative human impacts, slow recovery, and variability raise important questions of why and how rainfall dynamics evolve and interact with other components of the regional climate system. Here we provide an observational assessment of rainfall interactions mechanisms (informed by directional transfer of information entropy) that regulate Sahel rainfall. We quantitatively demonstrate that (1) sea surface temperature over the Gulf of Guinea controls moisture advection and transport to the West Sahel region and (2) strong bidirectional interactions exist between local vegetation dynamics and rainfall patterns. Then we assess the directional interaction patterns from nine state-of-the-art Earth System Models (ESMs). We find that most ESMs are able to represent either the unidirectional control of sea surface temperature on precipitation or the bidirectional interaction between vegetation and precipitation. However, none of the ESMs represents both interactive patterns. The GFDL and IPSL-CM5A-LR models successfully reproduced observed patterns over ~50% of the West Sahel region but were not accurate in reproducing observed precipitation regional trends or interannual variation. We propose that the directional information transfer is a powerful mechanistic benchmark to assess model fidelity at the process level.

Abstract Stratospheric injections of sulfur dioxide from major volcanic eruptions perturb the Earth's global radiative balance and dominate variability in stratospheric sulfur loading. The atmospheric component of the GFDL Earth System Model (ESM4.1) uses a bulk aerosol scheme and previously prescribed the distribution of aerosol optical properties in the stratosphere. To quantify volcanic contributions to the stratospheric sulfur cycle and the resulting climate impact, we modified ESM4.1 to simulate stratospheric sulfate aerosols prognostically. Driven by explicit volcanic emissions of aerosol precursors and non‐volcanic sources, we conduct ESM4.1 simulations from 1989 to 2014, with a focus on the Mt. Pinatubo eruption. We evaluate our interactive representation of the stratospheric sulfur cycle against data from Moderate Resolution Imaging Spectroradiometer, Multi‐angle Imaging SpectroRadiometer, Advanced Very High Resolution Radiometer, High Resolution Infrared Radiation Sounder, and Stratospheric Aerosol and Gas Experiment II. To assess the key processes associated with volcanic aerosols, we performed a sensitivity analysis of sulfate burden from the Mt. Pinatubo eruption by varying injection heights, emission amount, and stratospheric sulfate's dry effective radius. We find that the simulated stratospheric sulfate mass burden and aerosol optical depth in the model are sensitive to these parameters, especially volcanic SO2 injection height, and the optimal combination of parameters depends on the metric we evaluate.

Abstract Discretized numerical models of the atmosphere are usually intended to faithfully represent an underlying set of continuous equations, but this necessary condition is violated sometimes by subtle pathologies that have crept into the discretized equations. Such pathologies can introduce undesirable artifacts, such as sawtooth noise, into the model solutions. The presence of these pathologies can be detected by numerical convergence testing. This study employs convergence testing to verify the discretization of the Cloud Layers Unified By Binormals (CLUBB) model of clouds and turbulence. That convergence testing identifies two aspects of CLUBB's equation set that contribute to undesirable noise in the solutions. First, numerical limiters (i.e., clipping) used by CLUBB introduce discontinuities or slope discontinuities in model fields. Second, nonlinear artificial diffusion employed for improving numerical stability can introduce unintended small‐scale features into the solution of the model equations. Smoothing the limiters and using linear artificial diffusion reduces the noise and restores the expected first‐order convergence in CLUBB's solutions. These model reformulations enhance our confidence in the trustworthiness of solutions from CLUBB by eliminating the unphysical oscillations in high‐resolution simulations. The improvements in the results at coarser, near‐operational grid spacing and timestep are also seen in cumulus cloud and dry turbulence tests. In addition, convergence testing is proven to be a valuable tool for detecting pathologies, including unintended discontinuities and grid dependence, in the model equation set.

Abstract El Niño‐Southern Oscillation (ENSO) can effectively modulate global tropical cyclone (TC) activity, but the role TCs may play in determining ENSO characteristics remains unclear. Here we investigate the impact of TC winds on ENSO using a suite of Earth system model experiments where we insert TC winds, extracted from a TC‐permitting high‐resolution simulation, into a low‐resolution model configuration with nearly no intrinsic TCs. The presence of TC winds in the model increases ENSO power and shifts ENSO frequency closer to what we observe. TCs lead to an increase of strong to extreme El Niño events seen in observations and not simulated in the low‐resolution model without intrinsic TCs, mainly through enhanced zonal advection feedback and thermocline feedback. Our results indicate that TCs play a fundamental role in producing the ENSO characteristics we experience today in the climate system and point to a two‐way climatological interaction between TCs and ENSO.

Representing subgrid variabilities of land surface processes and their upscaled effects is crucial for global climate modeling. Here, we implement a multiple atmosphere multiple land (MAML) framework in the superparamaterized version of E3SM (SP‐E3SM) to explicitly simulate the subgrid variabilities of land states and fluxes at cloud‐resolving scale and their interactions with atmosphere. Comparing to the standard SP‐E3SM in which all the atmospheric columns of the cloud resolving model embedded within the global atmospheric model grid interact with the same land surface (i.e., multiple atmosphere single land (MASL)), the impact of MAML on the strength of land‐atmosphere coupling is limited, partly because the current implementation mainly facilitates one‐way coupling between the cloud‐resolving model and the land surface model. Despite such limitation, MAML increases the surface latent heat flux at the expense of sensible heat flux, and increases precipitation in India, Amazon, and Central Africa, reducing the model dry bias compared to the standard SP‐E3SM. By employing a normalized gross moist stability (NGMS) diagnostic framework, we find that the increase in precipitation minus evaporation (P‐E) is primarily driven by the change in large‐scale moisture convergence, particularly by the increase of water vapor in the lower atmosphere, while the local effect of total surface energy flux plays a minor role in the P‐E change. More specifically, MAML changes the surface energy partitioning (evaporative fraction), increases the atmosphere water vapor, and further increases P‐E by decreasing the NGMS. Finally, future development in the MAML framework is discussed. Plain Language Summary: Land‐atmosphere interactions such as soil moisture‐precipitation feedback occur at a wide range of spatial scales. For example, spatial variability of surface fluxes such as radiation and precipitation can influence surface latent and sensible heat fluxes, which further influence turbulent mixing processes in the boundary layer and cloud and precipitation. Current global climate models (GCMs) have difficulties in representing land‐atmosphere interactions at scales smaller than the GCM grid (subgrid scales). To meet this challenge, we employ a novel framework, called multiple atmosphere multiple land (MAML), in a superparamaterized version of E3SM (SP‐E3SM) in which a cloud resolving model is embedded within each GCM grid to better resolve clouds and convection. MAML explicitly simulates the subgrid variabilities of land states and fluxes at scale of ∼1 km and provides feedback to the atmosphere. The use of MAML is shown to have a limited effect on the strength of land‐atmosphere coupling due to the current one‐way subgrid land‐atmosphere interaction setup. Despite the limited effect, MAML increases the rainfall in India, Amazon, and Central Africa, which reduces the dry bias in the model. Key Points: A multiple atmosphere multiple land (MAML) framework of land‐atmosphere coupling is implemented in the super‐parameterized E3SM (SP‐E3SM)MAML increases precipitation in India, Central Africa, and Amazon by increasing water vapor and hence large‐scale moisture convergenceMAML's impact on land‐atmosphere interactions is limited by the current setup of one‐way coupling on cloud‐resolving scales [ABSTRACT FROM AUTHOR]

How confident are we that CO2 emissions must reach net zero or below to halt CO2‐induced warming? The IPCC's sixth assessment report concluded that "limiting human‐induced global warming to a specific level requires ... reaching at least net zero CO2 emissions." This is much stronger language than the special report on the global warming of 1.5°C, which concluded that reaching net zero CO2 emissions would be sufficient. Here we show that "approximately net zero" is better supported than "at least net zero." We estimate the rate of adjustment to zero emissions (RAZE) parameter (−0.24 to +0.17%/yr), defined as the fractional change in CO2‐induced warming after CO2 emissions cease. The RAZE determines the CO2 emissions compatible with halting warming over multiple decades: in 1.5°C‐consistent scenarios, CO2 emissions consistent with halting anthropogenic warming are +2.2 GtCO2/yr (5–95th percentile range spans −7.3 to +6.2 GtCO2/yr), similar to the expected emissions from unmodelled Earth system feedbacks. Plain Language Summary: How confident are we that CO2 emissions will need to reach net zero (where human‐made CO2 emissions into the atmosphere are approximately balanced with CO2 removals through carbon capture and storage, nature‐based solutions, etc.) or below to halt human‐induced warming? Here we show that "approximately net zero" is the best‐estimate requirement to stabilize warming. To do this we show that the behavior of the climate system's temperature response following net zero can be defined using a new parameter (the rate of adjustment to zero emissions, RAZE), which spans −0.24 to +0.17%/yr. The RAZE determines the ongoing rate of CO2 emissions or removals compatible with halting warming. In scenarios which reach approximately 1.5°C warming, CO2 emissions consistent with halting human‐made warming over multi‐decadal timescales span −7.3 to +6.2 GtCO2/yr with a best‐estimate of +2.2 GtCO2/yr. Planning for net negative global CO2 emissions remains important, given the chance of global temperatures overshooting 1.5°C, along with research to better understand the emissions consistent with warming stabilization. Key Points: The conditions for stabilization of global temperature at any level depend on the multi‐century carbon and thermal cycle responseThis is described by the rate of adjustment to zero emissions parameter, spanning −0.24 to +0.17%/yr (−0.036 to 0.025°C/decade at 1.5°C)In 1.5°C scenarios, CO2 emissions consistent with halting anthropogenic warming over multi‐decadal timescales span −7.3 to +6.2 GtCO2/yr [ABSTRACT FROM AUTHOR]

Abstract Representing subgrid variabilities of land surface processes and their upscaled effects is crucial for global climate modeling. Here, we implement a multiple atmosphere multiple land (MAML) framework in the superparamaterized version of E3SM (SP‐E3SM) to explicitly simulate the subgrid variabilities of land states and fluxes at cloud‐resolving scale and their interactions with atmosphere. Comparing to the standard SP‐E3SM in which all the atmospheric columns of the cloud resolving model embedded within the global atmospheric model grid interact with the same land surface (i.e., multiple atmosphere single land (MASL)), the impact of MAML on the strength of land‐atmosphere coupling is limited, partly because the current implementation mainly facilitates one‐way coupling between the cloud‐resolving model and the land surface model. Despite such limitation, MAML increases the surface latent heat flux at the expense of sensible heat flux, and increases precipitation in India, Amazon, and Central Africa, reducing the model dry bias compared to the standard SP‐E3SM. By employing a normalized gross moist stability (NGMS) diagnostic framework, we find that the increase in precipitation minus evaporation (P‐E) is primarily driven by the change in large‐scale moisture convergence, particularly by the increase of water vapor in the lower atmosphere, while the local effect of total surface energy flux plays a minor role in the P‐E change. More specifically, MAML changes the surface energy partitioning (evaporative fraction), increases the atmosphere water vapor, and further increases P‐E by decreasing the NGMS. Finally, future development in the MAML framework is discussed.

As global temperatures increase, sea ice loss will increasingly enable commercial shipping traffic to cross the Arctic Ocean, where the ships' gas and particulate emissions may have strong regional effects. Here we investigate impacts of shipping emissions on Arctic climate using a fully coupled Earth system model (CESM 1.2.2) and a suite of newly developed projections of 21st-century trans-Arctic shipping emissions. We find that trans-Arctic shipping will reduce Arctic warming by nearly 1 °C by 2099, due to sulfate-driven liquid water cloud formation. Cloud fraction and liquid water path exhibit significant positive trends, cooling the lower atmosphere and surface. Positive feedbacks from sea ice growth-induced albedo increases and decreased downwelling longwave radiation due to reduced water vapor content amplify the cooling relative to the shipping-free Arctic. Our findings thus point to the complexity in Arctic climate responses to increased shipping traffic, justifying further study and policy considerations as trade routes open.

The leaf photosynthesis models developed by Farquhar et al. (1980, https://doi.org/10.1007/BF00386231) (FvCB) and Goudriaan et al. (1985, https://doi.org/10.1007/978‐1‐4899‐3665‐3%5f10) (G85) are both used in Earth system and weather models to quantify ecosystem carbon assimilation. Despite their common role, a systematic comparison between these two photosynthesis models is currently lacking in scientific literature. In this technical report we compared the two models in a systematic way. Hereto we performed a comparative analysis of the model structures as well as the modeled responses of photosynthesis to light and CO2 at leaf level. To facilitate future model comparison, we also constructed a lookup table that presents FvCB model parameters fitted to CO2‐response curves from G85 in a wide natural range in photosynthetic capacity at standardized temperature. The structure of the FvCB model differs fundamentally from the G85 model as the FvCB model considers rate‐limiting processes due to Rubisco capacity, electron transport and triose phosphate utilization in parallel, whereas the G85 model considers Rubisco activity and triose phosphate utilization limitation to act in series scaled with quantum use efficiency. The models also differ fundamentally in terms of the parametrization of dark respiration. Still, both models calculate near‐similar responses of photosynthesis to changes in light and CO2 across a wide range in photosynthetic capacity with only two free parameters each. Our work thereby highlights functional similarities between these model approaches despite fundamental differences in model structure. Hence, standardized parameter sets that yield similar photosynthesis responses to light and CO2 may facilitate intercomparison of Earth system and weather models. Plain Language Summary: In Earth system and weather models the exchange of CO2 between the vegetation and the atmosphere is primarily determined by photosynthesis at leaf level. Two commonly applied representations of leaf photosynthesis are based on the models of Farquhar et al. (1980, https://doi.org/10.1007/BF00386231) (FvCB) and Goudriaan et al. (1985, https://doi.org/10.1007/978‐1‐4899‐3665‐3%5f10) (G85). A systematic comparison between the two model approaches is currently lacking in scientific literature. The objective of our technical report is to provide such a comparison and thereby to contribute to an exchange of ideas, data and applications between users of the respective models. We found that the two models calculate a similar response of net photosynthesis to light and CO2, despite having considerable differences in their model structure and representation of physiological processes. The main difference in model structure is that the limitations to photosynthesis in the FvCB model are applied in parallel, whereas in the G85 model they are in series. We also present a lookup table to express parameters from the G85 model in terms of their equivalent FvCB parameters. This technical report thereby contributes to the comparison of models used across the Earth system and weather modeling communities. Key Points: We compare two leaf photosynthesis models, based on different biophysical assumptions, that are both used in Earth system and weather modelingThe two models calculate a near‐similar response of net photosynthesis to light and CO2 despite having considerable differences in their structure and process representation. This finding is significant across a wide range of naturally occurring photosynthetic capacities in vegetationThrough a flowchart, a comparison of photosynthetic response to light and CO2 and a parameter conversion table, we aim to enable more communication and exchange of data between users of the two models within the Earth system and weather modeling communities [ABSTRACT FROM AUTHOR]

Irrigation is the process of artificially providing water to agricultural lands in order to provide crops with the necessary water supply to ensure or foster the growth of the plants. However, its implications reach beyond the agro-economic aspect as irrigation affects the soil-land-atmosphere interactions and thus influences the water and energy cycles in the Earth system. Past studies have shown how through these interactions, an increase in soil moisture due to irrigation also affects the atmospheric state and its dynamics. Thus, the lack of representation of irrigation in numerical Earth system models—be it for reanalysis, weather forecasting or climate prediction—can lead to significant errors and biases in various parameters of the system including but not limited to surface temperature and precipitation. In this study, we aim to summarize and discuss currently available irrigation parameterizations across different numerical models. This provides a reference framework to understand the impact of irrigation on the various components of Earth system models. Specifically, we discuss the impact of these parameterizations in the context of their spatio-temporal scale representation and point out the benefits and limitations of the various approaches. In fact, most of the parameterizations use irrigation as a direct modification of soil moisture with just a few implementations add irrigation as a form of surface water. While the former method might be suitable for coarse spatio-temporal scales, the latter better resembles the range of employed irrigation techniques. From the analysis, we find that not only the method or the spatio-temporal scales but the actual amount of water used is of great importance to the response of the Earth system model.

Computational models of the Earth System are critical tools for modern scientific inquiry. Efforts toward evaluating and improving errors in representations of physical and chemical processes in these large computational systems are commonly stymied by highly nonlinear and complex error behavior. Recent work has shown that these errors can be effectively predicted using modern Artificial Intelligence (A.I.) techniques. In this work, we go beyond these previous studies to apply an explainable A.I. technique to not only predict model errors but also move toward understanding the underlying reasons for successful error prediction. We use XGBoost classification trees and SHapley Additive exPlanations analysis to explore the errors in the prediction of lightning occurrence in the NASA Goddard Earth Observing System model, a widely used Earth System Model. This explainable error prediction system can effectively predict the model error and indicates that the errors are strongly related to convective processes and the characteristics of the land surface. Plain Language Summary: Computer models of the Earth are very important tools in the modern Earth scientist's toolkit. Understanding when and why these models are wrong is a major challenge facing the scientific community. Work published in the last few years has shown that you can actually predict when these models are wrong using artificial intelligence (A.I.). We build on that work by applying existing fancy mathematical tools to these A.I. methods to understand why these computer models are wrong. We demonstrate this approach to predictions of lightning in a model created by NASA, and find that the lightning in the model is wrong in ways that are strongly related to convection in the atmosphere and some aspects of the land surface. Key Points: Errors in simulated lightning flash occurrence are learned through a machine learning classification approachAn explainable machine learning technique is used to explore the drivers of the simulation errorsThis error prediction system indicates that errors are strongly related to convective processes and the characteristics of the land surface [ABSTRACT FROM AUTHOR]

The treatment of cloud structure in numerical weather and climate models is often greatly simplified to make them computationally affordable. Here we propose to correct the European Centre for Medium‐Range Weather Forecasts 1D radiation scheme ecRad for 3D cloud effects using computationally cheap neural networks. 3D cloud effects are learned as the difference between ecRad's fast 1D Tripleclouds solver that neglects them and its 3D SPARTACUS (SPeedy Algorithm for Radiative TrAnsfer through CloUd Sides) solver that includes them but is about five times more computationally expensive. With typical errors between 20% and 30% of the 3D signal, neural networks improve Tripleclouds' accuracy for about 1% increase in runtime. Thus, rather than emulating the whole of SPARTACUS, we keep Tripleclouds unchanged for cloud‐free parts of the atmosphere and 3D‐correct it elsewhere. The focus on the comparably small 3D correction instead of the entire signal allows us to improve predictions significantly if we assume a similar signal‐to‐noise ratio for both. Plain Language Summary: Solar and terrestrial radiation is the primary driver of the Earth's weather and their detailed representation is essential for improving weather predictions and climate projections. Several aspects, however, such as the flow of radiation through the side of clouds and other three‐dimensional effects are often too costly to compute routinely. In this paper we describe how machine learning can help account for these effects cheaply. Key Points: Emulators are used to add 3D cloud effects to the fast 1D radiation solver Tripleclouds cheaplyEmulators are trained on 3D cloud effects from SPARTACUS, which is five times slower than TriplecloudsFor a 1% slowdown in Tripleclouds' runtime, 3D fluxes are emulated with an average error of about 20%–30% [ABSTRACT FROM AUTHOR]

Abstract Computational models of the Earth System are critical tools for modern scientific inquiry. Efforts toward evaluating and improving errors in representations of physical and chemical processes in these large computational systems are commonly stymied by highly nonlinear and complex error behavior. Recent work has shown that these errors can be effectively predicted using modern Artificial Intelligence (A.I.) techniques. In this work, we go beyond these previous studies to apply an explainable A.I. technique to not only predict model errors but also move toward understanding the underlying reasons for successful error prediction. We use XGBoost classification trees and SHapley Additive exPlanations analysis to explore the errors in the prediction of lightning occurrence in the NASA Goddard Earth Observing System model, a widely used Earth System Model. This explainable error prediction system can effectively predict the model error and indicates that the errors are strongly related to convective processes and the characteristics of the land surface.

Abstract The treatment of cloud structure in numerical weather and climate models is often greatly simplified to make them computationally affordable. Here we propose to correct the European Centre for Medium‐Range Weather Forecasts 1D radiation scheme ecRad for 3D cloud effects using computationally cheap neural networks. 3D cloud effects are learned as the difference between ecRad's fast 1D Tripleclouds solver that neglects them and its 3D SPARTACUS (SPeedy Algorithm for Radiative TrAnsfer through CloUd Sides) solver that includes them but is about five times more computationally expensive. With typical errors between 20% and 30% of the 3D signal, neural networks improve Tripleclouds' accuracy for about 1% increase in runtime. Thus, rather than emulating the whole of SPARTACUS, we keep Tripleclouds unchanged for cloud‐free parts of the atmosphere and 3D‐correct it elsewhere. The focus on the comparably small 3D correction instead of the entire signal allows us to improve predictions significantly if we assume a similar signal‐to‐noise ratio for both.

As an episode of rapid global warming associated with the release of massive quantities of carbon to the atmosphere and oceans, the Paleocene-Eocene Thermal Maximum (PETM, ~56 Ma) is considered a potential analog for modern anthropogenic carbon emissions. However, the prevailing order of magnitude uncertainty in the rate of carbon release during the PETM precludes any straightforward comparison between the paleo-record and the modern. Similar barriers exist to the interpretation of many other carbon isotope excursions in the geological record. Here we use the Earth system model cGENIE to quantify the consequences of differing carbon emissions rates on the isotopic record of different carbon reservoirs. We explore the consequences of a range of emissions scenarios - from durations of carbon input of years to millennia and constant versus pulsed emissions rates, and trace how the isotopic signal is imprinted on the different carbon reservoirs. From this, we identify a characteristic relationship between the difference in carbon isotope excursion sizes between atmospheric CO2 and dissolved inorganic carbon (DIC) and the duration of carbon emissions. To the extent that available isotopic data spanning the PETM constrain the size of the marine and atmospheric carbon isotopic excursions, applying this empirical relationship suggests the duration of the component of carbon emissions that dominates the isotopic signal could be less than 3000 yr. However, utilizing the ratio of excursion size in the atmosphere to ocean as a metric to constrain duration of carbon emissions highlights the necessity to strengthen estimates for these two measurements across the PETM. Our general interpretive framework could be equally applied in assessing rates of carbon emissions for other geological events.

As an episode of rapid global warming associated with the release of massive quantities of carbon to the atmosphere and oceans, the Paleocene-Eocene Thermal Maximum (PETM, ~56 Ma) is considered a potential analog for modern anthropogenic carbon emissions. However, the prevailing order of magnitude uncertainty in the rate of carbon release during the PETM precludes any straightforward comparison between the paleo-record and the modern. Similar barriers exist to the interpretation of many other carbon isotope excursions in the geological record. Here we use the Earth system model cGENIE to quantify the consequences of differing carbon emissions rates on the isotopic record of different carbon reservoirs. We explore the consequences of a range of emissions scenarios - from durations of carbon input of years to millennia and constant versus pulsed emissions rates, and trace how the isotopic signal is imprinted on the different carbon reservoirs. From this, we identify a characteristic relationship between the difference in carbon isotope excursion sizes between atmospheric CO2 and dissolved inorganic carbon (DIC) and the duration of carbon emissions. To the extent that available isotopic data spanning the PETM constrain the size of the marine and atmospheric carbon isotopic excursions, applying this empirical relationship suggests the duration of the component of carbon emissions that dominates the isotopic signal could be less than 3000 yr. However, utilizing the ratio of excursion size in the atmosphere to ocean as a metric to constrain duration of carbon emissions highlights the necessity to strengthen estimates for these two measurements across the PETM. Our general interpretive framework could be equally applied in assessing rates of carbon emissions for other geological events.

Precipitation‐derived soil moisture in the active aeolian layer (the top ∼2 mm of the soil) impacts dust emission by increasing the threshold for emission. The purpose of this study was to use data similar to those used in global and regional dust emission models to calculate the probability that a high wind event happens during the period that antecedent precipitation would have left the active layer wet. The results indicate that the answer to this question is more strongly related to regional climate than soil texture. For more than half of the global drylands, the probability of precipitation influencing dust emission is greater than 30%–40%. Thus, rain‐derived soil moisture in the active layer should not be ignored in models throughout much of the world's dust producing regions. However, because the thin active layer dries faster than the underlying soil, models that use thick surface soil layer moisture for this may underestimate dust emission. Plain Language Summary: Mineral dust, emitted during strong wind events in deserts, is an important component of the Earth System. Rain can reduce dust emission, but this can only happen if the topmost soil layer is wet during high wind events. Whether rain can suppress dust emission depends on how frequently strong winds occur while the "active layer" of the soil (i.e., the top ∼2 mm) is wet. Using global meteorological products like those used in dust emission models, we investigated the potential of rain to influence dust emission by calculating the fraction of wind events that occur while the soil is wet. The impact of rain on dust emission is highly dependent upon regional climate. In certain regions, the dusty season and the rainy season occur during different parts of the year and rain will have little effect on dust emission. In other regions, storms that bring rain also bring winds, and rain has the potential to strongly effect dust emission. These results should inform models of dust emission by providing insight into where dust emission is most sensitive to precipitation‐derived moisture in the topmost layers of the soil. Special care in these areas must be used when interpreting modeled dust emission and significant improvements in dust emission estimation might be obtained by employing a scheme for accounting for drying of the active layer. Key Points: Rain can suppress dust emission from drylands by wetting the soil surface, but only if the emissive winds occur while the soil is wetThe frequency of dust emission suppression by rain varies geographicallyRain suppresses dust emission from more than half of the world's drylands at least 30%–40% of the time [ABSTRACT FROM AUTHOR]

The present study examines the characteristics of the Madden–Julian Oscillation (MJO) events represented in the Energy Exascale Earth System Model version 1 (E3SMv1), DOE's new Earth system model. The coupled E3SMv1 realistically simulates the eastward propagation of precipitation and moist static energy (MSE) anomalies associated with the MJO. As in observations, horizontal moisture advection and longwave radiative feedback are found to be the dominant processes in E3SMv1 that lead to the eastward movement and maintenance of the MJO MSE anomalies, respectively. Modulation of the diurnal cycle of precipitation in the Maritime Continent region by the MJO is also well represented in the model despite systematic biases in the magnitude and phase of the precipitation diurnal cycle. On the MJO impact over the midlatitude, E3SMv1 reasonably captures the pattern of the MJO teleconnections across the North Pacific and North America, with improvement in the performance in a high‐resolution version, despite the magnitude being a bit weaker than the observed. Regarding the interannual variability of the MJO, the El Niño‐Southern Oscillation (ENSO) modulation of the zonal extent of MJO's eastward propagation, as well as associated changes in the mean state moisture gradient in the tropical west Pacific, are well reproduced in the model. However, MJO in E3SMv1 exhibits no sensitivity to the Quasi‐Biennial Oscillation (QBO), with the MJO propagation characteristics being almost identical between easterly QBO and westerly QBO years. Processes that have been suggested as critical to MJO simulation are also examined by utilizing recently developed process‐oriented diagnostics. Plain Language Summary: The United States Department of Energy developed a new computer model that simulates Earth's climate systems, called Energy Exascale Earth System Model version 1 (E3SMv1). This study examines how well the model reproduces the characteristics of the Madden–Julian Oscillation (MJO), a tropical climate phenomenon that impacts weather and climate around the globe. We find that the strength and eastward movement of the MJO is realistically represented in the model. Variability of water vapor and radiation are the dominant processes for the MJO simulation, which agree well with the real‐world observations. Despite some unrealistic features, E3SMv1 successfully simulates the impact of the MJO on tropical precipitation at shorter than daily time scale and on large‐scale atmospheric circulation in the midlatitude. The model also exhibits realistic year‐to‐year changes in east‐west expansion of the MJO by the El Niño‐Southern Oscillation, while no noticeable changes can be detected when stratospheric wind reverses its direction over the equator in every 1 or 2 years. Key Points: Energy Exascale Earth System Model version 1 (E3SMv1) simulates Madden‐Julian Oscillation (MJOs) that exhibit realistic eastward propagation over the Indo‐Pacific warm poolModeled processes of the MJO, revealed through column‐integrated moist static energy anomalies, match well with those in observationsDespite the decent MJO simulation fidelity, the observed MJO–Quasi‐Biennial Oscillation coupling is not simulated in E3SMv1 [ABSTRACT FROM AUTHOR]

Abstract Accurate simulations of land processes are crucial for many purposes, such as climate simulation, weather, flood, and drought prediction, and climate change impact assessment studies. In this paper, we present a new land simulator called the Integrated Land Simulator (ILS). The ILS consists of multiple models that represent processes related to land (hereafter, referred to as “land models”). They are coupled by a general-purpose coupler, Jcup, and executed using the Multiple Program Multiple Data approach. Currently, ILS includes a physical land surface model, the Minimal Advanced Treatments of Surface Interaction and Runoff model, and a hydrodynamic model, the Catchment-based Macro-scale Floodplain model, and the inclusion of additional land models is planned. We conducted several test simulations to evaluate the computational speed and scalability and the basic physical performance of the ILS. The results will become a benchmark for further development.