Abstract Agriculture and urbanization have profoundly altered the terrestrial nitrogen cycle, leading to widespread water quality degradation. Nitrogen leached from managed soils is transported and transformed through river networks, influencing downstream nutrient dynamics and ecosystem functioning. Limited knowledge of riverine nitrogen transport highlights the need for improved modeling of nitrogen transport and transformation in land models. This study presents first‐order estimates of large‐scale stream nitrate transport and reaction by using a newly implemented nitrogen routing scheme in the Model for Scale Adaptive River Transport (MOSART), the river routing scheme in the Community Land Model version 5 (CLM5). The new scheme explicitly simulates nitrate‐nitrogen concentrations in rivers by resolving the one‐dimensional advection‐dispersion‐reaction equation. The model is tested over the Great Lakes Basin (GLB) with fertilizer applications prescribed based on two different data sets. Results indicate that the model promisingly reproduces the observed magnitude and temporal variability of riverine nitrate concentrations. Further, nitrate delivery from lake‐specific contributing subbasins to the Great Lakes shorelines is quantified across fertilizer application scenarios. When normalized by lake volume, nitrate delivery was highest in Lakes Erie and Ontario, with concentrations of approximately 9.5 and 2.2 tons km−3, respectively. An analysis of simulation minus observation reveals a modest bias toward underestimating nitrate concentrations across observation sites, partly driven by low streamflow bias and elevated numerical dispersion inherent in the transport scheme. Overall, the implemented nitrate routing framework provides a physically‐based representation of large‐scale riverine nitrate transport and transformations and establishes a basis for future model development and nutrient assessments within CLM5‐MOSART.

The exchange of carbon, water, and energy fluxes between the land and the atmosphere plays a vital role in shaping global change and extreme events. Yet our understanding of the theory of this surface-atmosphere exchange, represented via land surface models (LSMs), continues to be limited, highlighted by marked biases in model-data benchmarking exercises. Here, we leveraged the PLUMBER2 dataset of observations and model simulations of terrestrial sensible heat, latent heat, and net ecosystem exchange fluxes from 153 international eddy-covariance sites to identify the meteorological conditions under which land surface models are performing worse than independent benchmark expectations. By defining performance relative to three sophisticated out-of-sample empirical models, we generated a lower bound of performance in turbulent flux prediction that can be achieved with the input information available to the land surface models during testing at flux tower sites. We found that land surface model performance relative to empirical models is worse at edge conditions - that is, LSMs underperform in timesteps where the meteorological conditions consist of coinciding relative extreme values. Conversely, LSMs perform much better under "typical" conditions within the centre of the meteorological variable distributions. Constraining analysis to exclude the edge conditions results in the LSMs outperforming strong empirical benchmarks. Encouragingly, we show that refinement of the performance of land surface models in these edge conditions, consisting of only 12 %-31 % of all site-timesteps, would see large improvements (22 %-114 %) in an aggregated performance metric. Better performance in the edge conditions could see mean relative improvements in the aggregated metric of 77 % for the latent heat flux, 48 % for the sensible heat flux, and 36 % for the net ecosystem exchange on average across all LSMs and sites. Precise targeting of model development towards these meteorological edge conditions offers a fruitful avenue to focus model development, ensuring future improvements have the greatest impact.

Abstract. Land carbon sinks are responsible for removing about a quarter of anthropogenic CO2 emissions, and make up approximately half of total global carbon sinks. Uncertainty in the response of land carbon sinks to climate and changing atmospheric CO2 are large, and dominate the uncertainty in total carbon sinks under future climate. Understanding the carbon cycle response to net-zero and net-negative emissions has important implications for projecting future climate. Experiments in the "flat10" model intercomparison were designed for directly estimating key climate metrics that underlie carbon budgeting frameworks. Here we characterize the response of land carbon pools and fluxes from ten emissions-driven Earth system models (ESMs) under positive, net-zero, and net-negative CO2 emissions. Although there are many differences in simulated land carbon pools and fluxes across models, we find some consistent behavior across ESMs. 1) During the positive emissions phase, carbon is gained on land primarily in vegetation pools. 2) Following net-negative emissions to the point of cumulative zero emissions, carbon is lost from land in tropical latitudes, primarily from vegetation pools, but in mid- and high-latitudes most models show net land carbon gain, primarily in soil pools. 3) Following an extended period of net-zero emissions, a majority of models again show carbon gain in mid- and high-latitudes and vegetation carbon loss in the tropics. Under net-negative emissions the timing of vegetation carbon response relative to peak emissions is relatively consistent across ESMs, but timing of soil carbon response varies widely, implying larger intermodel disagreement associated with responses of soil carbon which tends to have longer timescales relative to vegetation carbon. Our findings highlight that tropical carbon is most likely to be both gained and subsequently lost under positive, zero, declining, and negative emissions, with possible implications for carbon dioxide removal efforts.

Abstract. Land surface models (LSMs) are simulating land–atmosphere exchanges and are widely used in hydrology, operational weather prediction, research meteorology, and to assess land surface responses to future climate change. LSMs exhibit distinct differences in simulated water fluxes due to varying physical process representations and input land characteristics. We challenged seven state-of-the-art LSMs by altering soil hydraulic parameters from representing sand or silt to disentangle the responses of the water fluxes. The LSMs reacted differently due to complex, sometimes counter-intuitive interactions of infiltration, soil evaporation, and plant transpiration. We identified the representations of surface runoff and soil evaporation as the two main reasons behind model differences. We show how subgrid parameterization of a saturated fraction led to diverging sensitivities of runoff to soil parameters. Soil evaporation was the largest and most sensitive share of evapotranspiration in almost all models. Process parameterizations at the soil surface are identified as critical and should be improved to lead to more consistent flux partitioning. We demonstrate here that it is possible and worthwhile in model intercomparison studies to relate model results to specific process descriptions, helping users to understand model results of LSMs and helping modelling groups to identify weaknesses and move forward.

Agrivoltaics, combining agriculture with photovoltaic systems, offers a promising solution to address land-use conflict between food and energy production. However, the complexities of agrivoltaics and its effects on the water-energy-carbon interactions remain poorly understood. In this study, we developed a process-based agrivoltaic model within the Community Land model 5 to assess the impacts of agrivoltaics on water, energy, and carbon cycles. The model was validated using data from agrivoltaic sites in Illinois and Colorado, generally capturing spatiotemporal variations in light conditions, soil moisture, and biomass carbon. Simulation results suggest that agrivoltaics significantly impact water, energy, and carbon budgets at the patch and system levels for maize and soybean in Illinois and grass in Colorado (2000-2014). Our findings show that the impacts of agrivoltaics vary by climate conditions and plant types. In dry climates, rainfall redistribution and shading from agrivoltaics conserve soil moisture and enhance evapotranspiration, promoting greater carbon assimilation and soil carbon storage for C3 grass. Conversely, in wetter regions, reduced solar radiation from shading becomes the dominant factor, lowering carbon assimilation and sequestration for maize and soybean. These results suggest that agrivoltaics can help mitigate drought impacts in arid environments. Our analysis of land equivalent ratios across different photovoltaic ground coverage ratios (PV GCR) shows that a medium PV GCR (60%) under "AgPV" deployment, where PV and plants share the same land, maximizes land-use efficiency at the study sites. Our modeling study supports informed decision-making to promote sustainable management of water, energy, and food resources amid environmental change.

Climate models are increasingly used to inform water availability projections at regional scales. However, the models’ own runoff sensitivities—the change in runoff per unit change of precipitation or temperature—are often biased, which can degrade their projections of runoff change. Specifically, models tend to underestimate the runoff decline in response to a temperature increase. Here, we conduct a comprehensive analysis with multiple observational datasets, two climate model generations, and large ensemble sampling of internal climate variability to assess these biases and to constrain future runoff projections across major river basins globally. For basins that can be robustly constrained by available observations, the constraint indicates stronger runoff declines than raw model projections. The constrained projections thus indicate more severe impacts of climate change on water resources than indicated by direct climate model output. Global river runoff projections constrained by observations indicate stronger declines in runoff under climate change than raw model outputs, according to a study combining multiple observational datasets, two generations of climate models, and large ensemble sampling of internal variability.

The Land and Land Ice Theme in the Coupled Model Intercomparison Project Phase 7 (CMIP7) represents the current understanding of physical processes in land surface ecosystems, hydrology, cryosphere, and their physical interactions with other Earth system components. Simulations from Earth system models (ESMs) could provide crucial information for assessing planetary safety, such as critical tipping elements, and be used to inform climate risks for improving climate impact assessments and policy decisions. This paper presents a collaborative effort to identify scientific opportunities in the Land and Land Ice Theme of the CMIP7 Data Request. The proposed opportunities build upon advances in ESMs, including new freshwater system and land ice processes being included in CMIP7, as well as the scientific community's demand for high-frequency and sub-grid-scale land surface outputs. In total, 25 variable groups that contain 716 variables have been identified to be potentially available to the broad scientific audience for performing analysis in land-atmosphere coupling, hydrological processes and freshwater systems, glacier and ice sheet mass balance and their influence on the sea levels, land use, and plant phenology. Key reflections from this data request effort include advocacy for closer engagement between the user community and modeling groups, reduction in the technical barriers to tracking existing parameters and defining new variables, and more streamlined variable management. These will be essential to enhance the usability and reliability of CMIP7 outputs for climate and Earth system research and applications to a broad audience that relies on the CMIP7 endeavor.

During land model development, simulated carbon dynamics are often benchmarked against observational data sets to evaluate model performance. Functional relationship benchmarks are the relationship between a driving variable (e.g., temperature) and a response variable (e.g., ecosystem respiration) and are a promising tool for assessing model performance by evaluating modeled sensitivities to changing environmental conditions. However, observed functional relationships can be influenced by choices made during data collection and throughout the benchmarking process, impacting the inferred skill of land models. To avoid misrepresenting a model's true performance, it is necessary to systematically evaluate best practices when constructing functional relationship benchmarks. We developed a set of guidelines for constructing functional relationship benchmarks, considering the choice of data set, number of daily observations, temporal extent, and temporal resolution across Alaska and Canada over a 20-year period from 2001 to 2020. The temperature sensitivity of ecosystem respiration from observations, evaluated through an apparent Q 10, is highly variable both spatially and as a result of the data processing approach applied in the benchmark formation. When benchmarking 13 models from the Warming Permafrost Model Intercomparison Project (WrPMIP), the range in inferred model skill is substantially impacted by the choices applied in constructing functional relationship benchmarks. The inferred performance of a given model is most sensitive to the number of daily observations and temporal extent, followed by choice of benchmark data set and temporal averaging. Results from this analysis can guide the development of consistent and robust functional relationships for future model evaluation studies.

Increasing global demands for food and energy necessitate innovative land-use solutions. Agrivoltaics, colocating solar photovoltaics with agriculture, shows promise, but its widespread adoption faces complex biophysical and economic trade-offs in a changing climate. Here, we develop an integrated biophysical-economic modeling framework to quantify how agrivoltaics affect biophysical and economic impacts across the Midwestern United States under both current and project climate conditions. We find strong regional divergences driven by climate gradients. In the humid eastern Midwest, solar panel shading limits photosynthesis, leading to reduced yields (maize-24%; soybean-16%) and lower farmers' profitability (maize-16%; soybean-2%) compared to conventional agriculture. Conversely, in the semiarid western region, shading alleviates heat and water stress, moderating yield reductions for maize (-12%) and even boosting soybean yields (+6%), resulting in improved economic returns (-6% for maize; +9% for soybean), for a scenario with 33% photovoltaic ground coverage ratio. Although agrivoltaics generate substantial electrical energy across all regions, high upfront installation costs challenge solar developers compared to standalone solar photovoltaics. However, our analysis identifies "win-win" opportunities where soybean-based agrivoltaics in the semiarid region produce economic benefits for both farmers and solar developers, highlighting the necessity for region-specific designs tailored to local climate conditions. Critically, future climate projections indicate eastward expansion of semiarid conditions, broadening areas where agrivoltaics can mitigate crop yield penalties (even boosting yield) and improve overall profitability, especially under high-emission scenarios. The results provide a mechanistic and economically integrated understanding essential for developing evidence-based and region-specific strategies to scale agrivoltaics in a changing climate.

Drylands across the globe are experiencing intensifying water scarcity, land degradation, and hydroclimatic extremes. This review integrates evidence from multidecadal field studies, hydrologic monitoring, geomorphic and ecological assessments, remote sensing, and land–atmosphere science to evaluate how restoration influences key components of the terrestrial water cycle. Low-tech natural infrastructure in dryland streams (NIDS)—including check dams, leaky weirs, one-rock dams, and gabions—has emerged as a promising but under-synthesized nature-based solution for restoring hydrologic function in these environments. We describe the mechanisms through which these interventions modify runoff detention, infiltration, sediment and alluvial storage, shallow-groundwater recharge, vegetation recovery, and surface-energy partitioning, and we summarize outcomes across diverse dryland settings. Findings consistently show increased water residence time, enhanced soil-moisture storage, expanded riparian vegetation, extended flow duration, and shifts toward greater latent-heat flux—producing localized cooling and strengthened ecohydrological feedbacks. Building on these localized effects, we articulate a hypothesis that links the spatial extent of restoration, the density of NIDS per unit drainage area, and the magnitude of the latent-to-sensible-heat contrast generated by wetter post-rainfall conditions. Specifically, we hypothesize that when NIDS are implemented at densities permitted by topography and across areas large enough to maintain elevated soil moisture after storm events, the resulting increases in latent heat flux, surface cooling, and boundary-layer moistening may enhance moisture convergence and boundary-layer development, potentially increasing the likelihood or stability of convective precipitation, analogous to how reductions in these processes have contributed to regional drought intensification. These land–atmosphere feedbacks remain untested at scale but represent an important research Frontier. By integrating hydrologic, geomorphic, ecological, and atmospheric perspectives, this review provides a comprehensive framework for considering how low-tech, landscape-scale interventions can strengthen watershed resilience and contribute to climate-relevant nature-based solutions.

Historically, large areas have been deforested, croplands and rangelands have expanded, and the irrigation area has grown substantially. These land use and land cover changes have altered land surface properties, driving changes in near-surface air temperature. From limited observations and mostly idealized simulations, we know that altering sufficiently large land surface areas can lead to systematic changes in temperature and precipitation outside altered areas. The advection of temperature anomalies, atmosphere, land, and ocean feedbacks are known to be potential drivers of such non-local responses. We show that regionally, non-local temperature signals driven by land-use change can be robustly found in fully coupled Community Earth System Model 2 (CESM2) simulations of the historical period (1850-2014) with all forcings versus all-but-land-use-change forcings. With regional-scale warming of up to more than 1 K and cooling of up to more than 0.5 K, the modeled effects are commensurate to historical temperature effects of all forcings. Regional non-local warming and cooling balance out in the global mean to an effect that is small compared to internal variability (IntV). We analyze how the signal-to-noise ratio of spatially averaged signals depends on the number of ensemble members included from the CESM2 large ensemble. Furthermore, we discuss the ability of our and other signal separation techniques to distinguish different parts of the signal from each other and from IntV. Finally, we discuss future research needs for reliable conclusions on non-local biogeophysical effects of land use change to inform future land-based climate change mitigation strategies.

Fire is a global phenomenon and a key Earth system process. Extreme fire events have increased in recent years, and fire frequency and intensity are projected to rise across most regions and biomes, posing substantial challenges for ecosystems, the carbon cycle, and society. The Fire Model Intercomparison Project (FireMIP), launched in 2014, has advanced global fire modeling in Dynamic Global Vegetation Models (DGVMs) and improved understanding of fire's local and direct drivers and its local impacts on vegetation and land carbon budgets through land offline simulations (i.e., uncoupled from the atmosphere). We now bring FireMIP into Coupled Model Intercomparison Project Phase 7 (CMIP7) to: (1) evaluate fire simulations in state-of-the-art fully coupled Earth system models (ESMs); (2) assess fire regime changes in the past, present, and future, and identify their primary natural and anthropogenic forcings and causal pathways within the Earth system, including the associated uncertainties; and (3) quantify the impacts of fires and fire changes on climate, ecosystems, and society across Earth system components, regions, and timescales, and elucidate the underlying mechanisms. FireMIP in CMIP7 will advance the fire and fire-related modeling in fully coupled ESMs, and provide a quantitative, comprehensive, and process-based understanding of fire's role in the Earth system by using models that incorporate critical climate feedbacks and CMIP7 multi-model, multi-initial-condition, and multi-scenario ensemble. This protocol paper presents the motivation, scientific questions, experimental design and rationale, model inputs and outputs, and recommended analysis framework for FireMIP in CMIP7, providing guidance to Earth system modeling teams conducting simulations and informing communities studying fire, climate change, and climate solutions.

The development of a global data set consisting of a distributed set of geomorphic parameters suitable for use in a representative hillslope parameterization within an Earth System model (ESM) is described. An element of a representative hillslope is defined by six geomorphic properties: height above the stream channel, distance to the stream channel, width, slope, aspect, and area. The methodology employs spectral analysis to identify an appropriate spatial scale at which to resolve the stream network and catchments in an ESM gridcell. This objective method is applied to an ESM grid using a global high-resolution digital elevation model (DEM) as input. The resulting spatially varying length scales then determine the stream order used to delineate catchments from the DEM. The geomorphic parameters describing the representative hillslopes are obtained by discretizing the catchments into elements based on elevation and aspect, and then averaging the geomorphic properties within each element, resulting in a statistical representation of the hillslopes within the domain. The method is applied to a DEM having a spatial resolution of 3 arcseconds (about 90 m $\mathrm{m}$ at the equator) on a grid having approximately 1 degrees $1{}<^>{\circ}$ spatial resolution.

Irrigation plays an essential role in the Earth system by changing water, energy, and carbon fluxes, and then affecting the climate. Many previous studies have been conducted to explore its impacts on near-surface climate, highlighting its cooling effects on air temperature, especially during hot extremes. However, most studies do the exploration during the historical period and only focus on temperature. Projected greenhouse gas emissions and land use datasets have made it possible to extend the investigation under future scenarios, but there are no datasets about predicted irrigation techniques shares information. To address this issue, we create a dataset containing spatial distribution of drip, sprinkler, and flood irrigation techniques, based on a simple assumption that richer and drier countries will invest more in irrigation system upgrades. Then, the Community Earth System Model version 2 (CESM2) is developed to be able to represent different irrigation techniques for one crop type in one gridcell. Finally, with the newly created dataset and modified CESM2, we detect irrigation's impacts on heat and moist-heat stress under SSP1-2.6 and SSP3-7.0. Simulation outputs indicate that irrigation will experience various changes among regions and scenarios. In irrigation hot spots, irrigation will continue to reduce the probability of high-temperature extremes under both scenarios but cannot reverse the warming signal caused by other forcings. Moreover, irrigation's impacts on apparent temperature are very small, and even increase the hours exposed to wet bulb temperature extremes in some regions. This study reveals that irrigation's cooling impacts will persist in the future, but will not be an effective solution to the global warming issue. As for moist-heat stress, irrigation's effects are much more complicated due to its enhancing impacts on air humidity.

Climate change affects lightning frequency and wildfire intensity globally. To date, model limitations have prevented quantifying climate-lightning-wildfire interactions comprehensively. We exploit advances in Earth System modeling to examine these three-way interactions and their sensitivities to idealized CO2 forcing in 140-year simulations. Lightning sensitivity to global temperature change (+1.6 ± 0.1% per kelvin) is mitigated by compensating atmospheric effects. Global burned area sensitivity to temperature (+13.8 ± 0.3% per kelvin) is largely driven by intensified fire weather and increased biomass but marginally by lightning changes. We find a universal law characterizing regional-scale modeled fire activity and its CO2 sensitivity, consistent with basic principles of statistical mechanics. Last, a negative climate feedback through intensified aerosol direct effect from fire emissions reaches an equivalent decrease of 0.91 ± 0.01% in CO2 radiative forcing. However, this feedback contributes to polar amplification. Our analysis shows that climate-lightning-wildfire interactions involve multiple compensating and amplifying feedbacks, which are sensitive to anthropogenic CO2 forcing.

Idealized experiments with coupled climate-carbon Earth system models (ESMs) provide a basis for understanding the response of the carbon cycle to external forcing and for quantifying climate-carbon feedbacks. Here, we analyze globally-averaged results from idealized esm-flat10 experiments and show that most models exhibit a quasi-linear relationship between cumulative carbon uptake on land and in the ocean during a period of constant fossil fuel emissions of 10 Pg C yr−1. We hypothesize that this relationship does not depend on emission pathways. Further, as a simplification, we quantify the relationship between cumulative ocean carbon uptake and changes in ocean heat content using a linear approximation. In this way, changes in oceanic heat content and atmospheric CO2 concentration become interdependent variables, reducing the coupled temperature-CO2 system to just one differential equation. The equation can be solved analytically or numerically for the atmospheric CO2 concentration as a function of fossil fuel emissions. This approach leads to a simplified description of global carbon and climate dynamics, which could be used for applications beyond existing analytical frameworks.

Irrigation has been investigated as an important historical climate forcing, but there is no study exploring its future climatic impacts considering possible changes in both extent and efficiency. Here, we address these issues via developing irrigation efficiency scenarios in line with the Shared Socioeconomic Pathways (SSPs), implementing these in the Community Earth System Model, and applying them to generate projections over the period 2015-2074. We project that annual irrigation water withdrawal decreases under SSP1-2.6 (from  ~2100 to  ~1700 km3 yr-1) but increases under SSP3-7.0 (to  ~2400 km3 yr-1), with some new irrigation hot spots emerging, especially in Africa. Irrigation is projected to reduce the occurrence of dry-heat stress under both scenarios, but cannot reverse the warming trend due to greenhouse gas emission (e.g., increasing from  ~90 to around 600 and 1200 hours yr-1 in intensely irrigated areas, under two scenarios). Moreover, moist-heat extreme event frequency increases more substantially (by ≥1600 hours yr-1 under SSP3-7.0 in tropical regions), and irrigation further amplifies the hours of exposure (for example, by ≥100 hours yr-1 in South Asia), thereby raising the risk of moist-heat-related illnesses and mortality for exposed communities. Our results underscore the importance of reducing greenhouse gas emissions, limiting irrigation expansion and improving irrigation efficiency to preserve water resources and decelerate escalating exposure to dry- and moist-heat stress.

This study is the first to evaluate the state‐of‐the‐art coupled land‐atmosphere regional climate model WRF‐CTSM. It comprises the Weather Research and Forecasting model, WRF, and the Community Terrestrial Systems Model, CTSM (using a configuration that is the same as the Community Land Model Version 5, CLM5). The evaluation is conducted over Nordic Fennoscandia (Norway, Sweden, and Finland) since there is uncertainty in climate models' representation of key hydroclimatic variables in high‐latitude regions as they experience accelerated transformation in a changing climate. A 13‐year WRF‐CTSM simulation (2010–2022) is performed using a 10.5 km horizontal grid spacing to assess the model biases in simulating mean, minimum, and maximum 2 m temperature, precipitation, snow cover (snow depth, snow water equivalent, fractional snow‐covered duration), and surface energy balance components. The analysis is based on annual, seasonal, monthly, and daily mean comparisons against openly available observational data sets, comprising regional scale gridded station‐based and satellite‐based data sets, as well as point scale observations from ground stations. The model shows robust agreement with the evaluation data sets across all considered variables. Furthermore, in situ scale conditions in 2 m temperature, precipitation, snow cover variables, and latent heat are captured with considerable precision. WRF‐CTSM is thus considered a powerful research tool for the assessment of land‐atmosphere interactions over Nordic Fennoscandia.

Anthropogenic land-use change (LUC) substantially impacts climate dynamics, primarily through modifications in the surface biogeophysical (BGP) and biogeochemical (BGC) fluxes, which alter the exchange of energy, water, and carbon with the atmosphere. Despite the established significance of both the BGP and BGC effects, their relative contribution to climate change remains poorly quantified. In this study, we leveraged data from an unprecedented number of Earth system models (ESMs) of the latest generation that contributed to the Land Use Model Intercomparison Project (LUMIP), under the auspices of the Coupled Model Intercomparison Project Phase 6 (CMIP6). Our analysis of BGP effects indicates a range of global annual near-surface air temperature changes across ESMs due to historical LUC, from a cooling of −0.23 °C to a warming of 0.14 °C, with a multi-model mean and spread of -0.03±0.10 °C under present-day conditions relative to the pre-industrial era. Notably, the BGP effects indicate warming at high latitudes. Still, there is a discernible cooling pattern between 30° N and 60° N, extending across large landmasses from the Great Plains of North America to the Northeast Plain of Asia. The BGC effect shows substantial land carbon losses, amounting to -127±94 Gt C over the historical period, with decreased vegetation carbon pools driving the losses in nearly all analysed ESMs. Based on the transient climate response to cumulative emissions (TCRE), we estimate that LUC-induced carbon emissions result in a warming of approximately 0.21±0.14 °C, which is consistent with previous estimates. When the BGP and BGC effects are taken together, our results suggest that the net effect of LUC on historical climate change has been to warm the climate. To understand the regional drivers (and thus potential levers to alter the climate), we show the contribution of each grid cell to LUC-induced global temperature change, as a warming contribution over the tropics and subtropics with a nuanced cooling contribution over the mid-latitudes. Our findings indicate that, historically, the BGC temperature effects dominate the BGP temperature effects at the global scale. However, they also reveal substantial discrepancies across models in the magnitude, directional impact, and regional specificity of LUC impacts on global temperature and land carbon dynamics. This underscores the need for further improvement and refinement in model simulations, including the consideration and implementation of land-use data and model-specific parameterizations, to achieve more accurate and robust estimates of the climate effect of LUC.

The Community Earth System Model version 2 (CESM2) has a higher equilibrium climate sensitivity (ECS) than previous versions of CESM and many other Coupled Model Intercomparison Project models. Relatedly, CESM2 simulates too-cold ice-age and too-hot warm paleoclimates. An inappropriate ice number limiter in the CESM2 microphysics scheme was discovered, and some simulations indicate that the high ECS may be partially attributable to this inappropriate limiter. In light of those findings, we seek to provide users of CESM2 guidance on the fitness of CESM2 for a variety of applications. We find that despite concerns about its climate sensitivity and simulations of past climates, the transient climate response in CESM2 is moderate relative to the CMIP6 ensemble and robust across different versions of CESM. The changes made between CESM1 and CESM2 and the fixes to the microphysical issues of CESM2 have little impact on its simulated 20th and 21st century climates under SSP3-7.0. As a result, the simulated 20th and 21st century climates of CESM2 fall well within the range of the CMIP6 ensemble and agree well with observations over the historical record. However, hotter and colder paleoclimates simulated by CESM2 are inconsistent with paleoclimate evidence. A modified version of CESM2, PaleoCalibr CESM2, may be suitable for paleoclimate studies. Simulations past the end of the 21st century with default CESM2 and studies of microphysical processes in all GCMs should be analyzed with care.