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Abstract. Sedimentary records indicate that atmospheric dust has increased substantially since preindustrial times. However, state-of-the-art global Earth system models (ESMs) are unable to capture this historical increase, posing challenges in assessing the impacts of desert dust on Earth's climate. To address this issue, we construct a globally gridded dust emission dataset (DustCOMMv1) spanning 1841–2000. We do so by combining 19 sedimentary records of dust deposition with observational and modeling constraints on the modern-day dust cycle. The derived emission dataset contains interdecadal variability of dust emissions as forced by the deposition flux records, which increased by approximately 50 % from 1851–1870 to 1981–2000. We further provide future dust emission datasets for 2000–2100 by assuming three possible scenarios for how future dust emissions will evolve. We evaluate the historical dust emission dataset and illustrate its effectiveness in enforcing a historical dust increase in ESMs by conducting a long-term (1851–2000) dust cycle simulation with the Community Earth System Model (CESM2). The simulated dust depositions are in reasonable agreement with the long-term increase in most sedimentary dust deposition records and with measured long-term trends in dust concentration at sites in Miami and Barbados. This contrasts with the CESM2 simulations using a process-based dust emission scheme and with simulations from the Coupled Model Intercomparison Project (CMIP6), which show little to no secular trends in dust deposition, concentration, and optical depth. The DustCOMM emissions thus enable ESMs to account for the historical radiative forcings (RFs), including due to dust direct interactions with radiation (direct RF). Our CESM2 simulations estimate a 1981–2000 minus 1851–1870 direct RF of −0.10 W m−2 by dust aerosols up to 10 µm in diameter (PM10) at the top of atmosphere (TOA). This global dust emission dataset thus enables models to more accurately account for historical aerosol forcings, thereby improving climate change projections such as those in the Intergovernmental Panel on Climate Change (IPCC) assessment reports. 

Abstract. The Arctic poses many challenges for Earth system and snow physics models, which are commonly unable to simulate crucial Arctic snowpack processes,such as vapour gradients and rain-on-snow-induced ice layers. These limitations raise concerns about the current understanding of Arctic warming and its impact on biodiversity, livelihoods, permafrost, and the global carbon budget. Recognizing that models are shaped by human choices, 18 Arctic researchers were interviewed to delve into the decision-making process behind model construction. Although data availability, issues of scale, internal model consistency, and historical and numerical model legacies were cited as obstacles to developing an Arctic snowpack model, no opinion was unanimous. Divergences were not merely scientific disagreements about the Arctic snowpack but reflected the broader research context. Inadequate and insufficient resources, partly driven by short-term priorities dominating research landscapes, impeded progress. Nevertheless, modellers were found to be both adaptable to shifting strategic research priorities – an adaptability demonstrated by the fact that interdisciplinary collaborations were the key motivation for model development – and anchored in the past. This anchoring and non-epistemic values led to diverging opinions about whether existing models were “good enough” and whether investing time and effort to build a new model was a useful strategy when addressing pressing research challenges. Moving forward, we recommend that both stakeholders and modellers be involved in future snow model intercomparison projects in order to drive developments that address snow model limitations currently impeding progress in various disciplines. We also argue for more transparency about the contextual factors that shape research decisions. Otherwise, the reality of our scientific process will remain hidden, limiting the changes necessary to our research practice. 

Arid and semi-arid regions of the world are particularly vulnerable to greenhouse gas–driven hydroclimate change. Climate models are our primary tool for projecting the future hydroclimate that society in these regions must adapt to, but here, we present a concerning discrepancy between observed and model-based historical hydroclimate trends. Over the arid/semi-arid regions of the world, the predominant signal in all model simulations is an increase in atmospheric water vapor, on average, over the last four decades, in association with the increased water vapor–holding capacity of a warmer atmosphere. In observations, this increase in atmospheric water vapor has not happened, suggesting that the availability of moisture to satisfy the increased atmospheric demand is lower in reality than in models in arid/semi-arid regions. This discrepancy is most clear in locations that are arid/semi-arid year round, but it is also apparent in more humid regions during the most arid months of the year. It indicates a major gap in our understanding and modeling capabilities which could have severe implications for hydroclimate projections, including fire hazard, moving forward. 

Abstract. Desert dust is an important atmospheric aerosol that affects the Earth's climate, biogeochemistry, and air quality. However, current Earth system models (ESMs) struggle to accurately capture the impact of dust on the Earth's climate and ecosystems, in part because these models lack several essential aeolian processes that couple dust with climate and land surface processes. In this study, we address this issue by implementing several new parameterizations of aeolian processes detailed in our companion paper in the Community Earth System Model version 2 (CESM2). These processes include (1) incorporating a simplified soil particle size representation to calculate the dust emission threshold friction velocity, (2) accounting for the drag partition effect of rocks and vegetation in reducing wind stress on erodible soils, (3) accounting for the intermittency of dust emissions due to unresolved turbulent wind fluctuations, and (4) correcting the spatial variability of simulated dust emissions from native to higher spatial resolutions on spatiotemporal dust variability. Our results show that the modified dust emission scheme significantly reduces the model bias against observations compared with the default scheme and improves the correlation against observations of multiple key dust variables such as dust aerosol optical depth (DAOD), surface particulate matter (PM) concentration, and deposition flux. Our scheme's dust also correlates strongly with various meteorological and land surface variables, implying higher sensitivity of dust to future climate change than other schemes' dust. These findings highlight the importance of including additional aeolian processes for improving the performance of ESM aerosol simulations and potentially enhancing model assessments of how dust impacts climate and ecosystem changes. 

Accurate representation of permafrost carbon emissions is crucial for climate projections, yet current Earth system models inadequately represent permafrost carbon. Sustained funding opportunities are needed from government and private sectors for prioritized model development. 

Abstract In this study, we investigate the air temperature response to land-use and land-cover change (LULCC; cropland expansion and deforestation) using subgrid land model output generated by a set of CMIP6 model simulations. Our study is motivated by the fact that ongoing land-use activities are occurring at local scales, typically significantly smaller than the resolvable scale of a grid cell in Earth system models. It aims to explore the potential for a multimodel approach to better characterize LULCC local climatic effects. On an annual scale, the CMIP6 models are in general agreement that croplands are warmer than primary and secondary land (psl; mainly forests, grasslands, and bare ground) in the tropics and cooler in the mid–high latitudes, except for one model. The transition from warming to cooling occurs at approximately 40°N. Although the surface heating potential, which combines albedo and latent heat flux effects, can explain reasonably well the zonal mean latitudinal subgrid temperature variations between crop and psl tiles in the historical simulations, it does not provide a good prediction on subgrid temperature for other land tile configurations (crop vs forest; grass vs forest) under Shared Socioeconomic Pathway 5–8.5 (SSP5–8.5) forcing scenarios. A subset of simulations with the CESM2 model reveals that latitudinal subgrid temperature variation is positively related to variation in net surface shortwave radiation and negatively related to variation in the surface energy redistribution factor, with a dominant role from the latter south of 30°N. We suggest that this emergent relationship can be used to benchmark the performance of land surface parameterizations and for prediction of local temperature response to LULCC. 

Key Points A new semi‐analytical spin‐up (SASU) framework combines the default accelerated spin‐up method and matrix analytical algorithm SASU accelerates CLIM5 spin‐up by tens of times, becoming the fastest method to our knowledge SASU is applicable to most biogeochemical models and enables computationally costly study, for example, sensitivity analysis