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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]

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 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 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 To monitor the success of carbon dioxide removal (CDR) or solar radiation management (SRM) that offset anthropogenic climate change, the forced response to any external forcing is required to be detectable against internal variability. Thus far, only the detectability of SRM has been examined using both a stationary and nonstationary detection and attribution method. Here, the spatiotemporal detectability of the forced response to artificial ocean alkalinization (AOA) and stratospheric aerosol injection (SAI) as exemplary methods for CDR and SRM, respectively, is compared in Max Planck Institute Earth System Model (MPI‐ESM) experiments using regularized optimal fingerprinting and single‐model estimates of internal variability, while working under a stationary or nonstationary null hypothesis. Although both experiments are forced by emissions according to the Representative Concentration Pathway 8.5 (RCP8.5) and target the climate of the RCP4.5 scenario using AOA or SAI, detection timescales reflect the fundamentally different forcing agents. Moreover, detectability timescales are sensitive to the choice of null hypothesis. Globally, changes in the CO2 system in seawater are detected earlier than the response in temperature to AOA but later in the case of SAI. Locally, the detection time scales depend on the physical, chemical, and radiative impacts of CDR and SRM forcing on the climate system, as well as patterns of internal variability, which is highlighted for oceanic heat and carbon storage.

To monitor the success of carbon dioxide removal (CDR) or solar radiation management (SRM) that offset anthropogenic climate change, the forced response to any external forcing is required to be detectable against internal variability. Thus far, only the detectability of SRM has been examined using both a stationary and nonstationary detection and attribution method. Here, the spatiotemporal detectability of the forced response to artificial ocean alkalinization (AOA) and stratospheric aerosol injection (SAI) as exemplary methods for CDR and SRM, respectively, is compared in Max Planck Institute Earth System Model (MPI‐ESM) experiments using regularized optimal fingerprinting and single‐model estimates of internal variability, while working under a stationary or nonstationary null hypothesis. Although both experiments are forced by emissions according to the Representative Concentration Pathway 8.5 (RCP8.5) and target the climate of the RCP4.5 scenario using AOA or SAI, detection timescales reflect the fundamentally different forcing agents. Moreover, detectability timescales are sensitive to the choice of null hypothesis. Globally, changes in the CO2 system in seawater are detected earlier than the response in temperature to AOA but later in the case of SAI. Locally, the detection time scales depend on the physical, chemical, and radiative impacts of CDR and SRM forcing on the climate system, as well as patterns of internal variability, which is highlighted for oceanic heat and carbon storage. Key Points: Detectability is robust using regularized optimal fingerprinting and single‐model estimates of internal variabilityChanges in the CO2 system in seawater are detected earlier than the thermodynamic response to AOA but later in the case of SAILocal detectability depends on patterns of internal variability and physical, chemical, and radiative impacts of external forcing [ABSTRACT FROM AUTHOR]

Abstract: The impacts of land use have been shown to have considerable influence on regional climate. With the recent international commitment to limit global warming to well below 2°C, emission reductions need to be ambitious and could involve major land‐use change (LUC). Land‐based mitigation efforts to curb emissions growth include increasing terrestrial carbon sequestration through reforestation, or the adoption of bioenergy crops. These activities influence local climate through biogeophysical feedbacks, however, it is uncertain how important they are for a 1.5° climate target. This was the motivation for HAPPI‐Land: the half a degree additional warming, prognosis, and projected impacts—land‐use scenario experiment. Using four Earth system models, we present the first multimodel results from HAPPI‐Land and demonstrate the critical role of land use for understanding the characteristics of regional climate extremes in low‐emission scenarios. In particular, our results show that changes in temperature extremes due to LUC are comparable in magnitude to changes arising from half a degree of global warming. We also demonstrate that LUC contributes to more than 20% of the change in temperature extremes for large land areas concentrated over the Northern Hemisphere. However, we also identify sources of uncertainty that influence the multimodel consensus of our results including how LUC is implemented and the corresponding biogeophysical feedbacks that perturb climate. Therefore, our results highlight the urgent need to resolve the challenges in implementing LUC across models to quantify the impacts and consider how LUC contributes to regional changes in extremes associated with sustainable development pathways. [ABSTRACT FROM AUTHOR]