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  1. 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 
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    Free, publicly-accessible full text available August 1, 2024
  2. Abstract

    Vegetation and precipitation are known to fundamentally influence each other. However, this interdependence is not fully represented in climate models because the characteristics of land surface (canopy) conductance to water vapor and CO2are determined independently of precipitation. Working within a coupled atmosphere and land modelling framework (CAM6/CLM5; coupled Community Atmosphere Model v6/Community Land Model v5), we have developed a new theoretical approach to characterizing land surface conductance by explicitly linking its dynamic properties to local precipitation, a robust proxy for moisture available to vegetation. This will enable regional surface conductance characteristics to shift fluidly with climate change in simulations, consistent with general principles of co‐evolution of vegetation and climate. Testing within the CAM6/CLM5 framework shows that climate simulations incorporating the new theory outperform current default configurations across several error metrics for core output variables when measured against observational data. In climate simulations for the end of this century the new, adaptive stomatal conductance scheme provides a revised prognosis for average and extreme temperatures over several large regions, with increased primary productivity through central and east Asia, and higher rainfall through North Africa and the Middle East. The new projections also reveal more frequent heatwaves than originally estimated for the south‐eastern US and sub‐Saharan Africa but less frequent heatwaves across east Europe and northeast Asia. These developments have implications for evaluating food security and risks from extreme temperatures in areas that are vulnerable to climate change.

     
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  3. Abstract

    Urban heat waves (UHWs) are strongly associated with socioeconomic impacts. Here, we use an urban climate emulator combined with large ensemble global climate simulations to show that, at the urban scale a large proportion of the variability results from the model structural uncertainty in projecting UHWs in the coming decades under climate change. Omission of this uncertainty would considerably underestimate the risk of UHW. Results show that, for cities in four high-stake regions – the Great Lakes of North America, Southern Europe, Central India, and North China – a virtually unlikely (0.01% probability) UHW projected by single-model ensembles is estimated by our model with probabilities of 23.73%, 4.24%, 1.56%, and 14.76% respectively in 2061–2070 under a high-emission scenario. Our findings suggest that for urban-scale extremes, policymakers and stakeholders will have to plan for larger uncertainties than what a single model predicts if decisions are informed based on urban climate simulations.

     
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  4. Abstract

    Prediction systems to enable Earth system predictability research on the subseasonal time scale have been developed with the Community Earth System Model, version 2 (CESM2) using two configurations that differ in their atmospheric components. One system uses the Community Atmosphere Model, version 6 (CAM6) with its top near 40 km, referred to as CESM2(CAM6). The other employs the Whole Atmosphere Community Climate Model, version 6 (WACCM6) whose top extends to ∼140 km, and it includes fully interactive tropospheric and stratospheric chemistry [CESM2(WACCM6)]. Both systems are utilized to carry out subseasonal reforecasts for the 1999–2020 period following the Subseasonal Experiment’s (SubX) protocol. Subseasonal prediction skill from both systems is compared to those of the National Oceanic and Atmospheric Administration CFSv2 and European Centre for Medium-Range Weather Forecasts (ECMWF) operational models. CESM2(CAM6) and CESM2(WACCM6) show very similar subseasonal prediction skill of 2-m temperature, precipitation, the Madden–Julian oscillation, and North Atlantic Oscillation to its previous version and to the NOAA CFSv2 model. Overall, skill of CESM2(CAM6) and CESM2(WACCM6) is a little lower than that of the ECMWF system. In addition to typical output provided by subseasonal prediction systems, CESM2 reforecasts provide comprehensive datasets for predictability research of multiple Earth system components, including three-dimensional output for many variables, and output specific to the mesosphere and lower-thermosphere (MLT) region from CESM2(WACCM6). It is shown that sudden stratosphere warming events, and the associated variability in the MLT, can be predicted ∼10 days in advance. Weekly real-time forecasts and reforecasts with CESM2(CAM6) and CESM2(WACCM6) are freely available.

    Significance Statement

    We describe here the design and prediction skill of two subseasonal prediction systems based on two configurations of the Community Earth System Model, version 2 (CESM2): CESM2 with the Community Atmosphere Model, version 6 [CESM2(CAM6)] and CESM 2 with Whole Atmosphere Community Climate Model, version 6 [CESM2(WACCM6)] as its atmospheric component. These two systems provide a foundation for community-model based subseasonal prediction research. The CESM2(WACCM6) system provides a novel capability to explore the predictability of the stratosphere, mesosphere, and lower thermosphere. Both CESM2(CAM6) and CESM2(WACCM6) demonstrate subseasonal surface prediction skill comparable to that of the NOAA CFSv2 model, and a little lower than that of the ECMWF forecasting system. CESM2 reforecasts provide a comprehensive dataset for predictability research of multiple aspects of the Earth system, including the whole atmosphere up to 140 km, land, and sea ice. Weekly real-time forecasts, reforecasts, and models are publicly available.

     
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  5. Abstract

    Agricultural expansion and management have greatly increased global food production and altered Earth's climate by changing physical and biogeochemical properties of terrestrial ecosystems. Few Earth system models represent agricultural management practices due to the complexity of the interactions between human decisions and biological processes on global scales. We describe the new capabilities of representing crop distributions and management in the Community Land Model (CLM) Version 5, which includes time‐varying spatial distributions of major crop types and their management through fertilization and irrigation, and temperature‐based phenological triggers. Including active crop management increases peak growing season gross primary productivity (GPP), increases the amplitude of Northern Hemisphere net ecosystem exchange, and changes seasonal and annual patterns of latent and sensible heat fluxes. The CLM5 crop model simulates the global observed historical trend of crop yields with relative fidelity from 1850 to 1990. Cropland expansion was important for increasing crop production, especially during the first century of the simulations, while fertilization and irrigation were important for increasing yields from 1950 onward. From 1990 to present day, observed crop production continued to increase while CLM5 production levels off, likely because intensification practices are not represented in the model. Specifically, CLM does not currently include increasing planting density, crop breeding and genetic modification, representations of tillage, or other management practices that may also affect crop‐climate and crop‐carbon cycle interactions and alter trends in yields. These results highlight the importance of including crop management in Earth system models, particularly as global data sets for parameterization and evaluation become more readily available.

     
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  6. Abstract

    Earth system models (ESMs) rely on the calculation of canopy conductance in land surface models (LSMs) to quantify the partitioning of land surface energy, water, andCO2fluxes. This is achieved by scaling stomatal conductance,gw, determined from physiological models developed for leaves. Traditionally, models forgwhave been semi‐empirical, combining physiological functions with empirically determined calibration constants. More recently, optimization theory has been applied to modelgwinLSMs under the premise that it has a stronger grounding in physiological theory and might ultimately lead to improved predictive accuracy. However, this premise has not been thoroughly tested. Using original field data from contrasting forest systems, we compare a widely used empirical type and a more recently developed optimization‐typegwmodel, termedBBandMED, respectively. Overall, we find no difference between the two models when used to simulategwfrom photosynthesis data, or leaf gas exchange from a coupled photosynthesis‐conductance model, or gross primary productivity and evapotranspiration for aFLUXNETtower site with theCLM5 communityLSM. Field measurements reveal that the key fitted parameters forBBandMED,g1Bandg1M,exhibit strong species specificity in magnitude and sensitivity toCO2, andCLM5 simulations reveal that failure to include this sensitivity can result in significant overestimates of evapotranspiration for high‐CO2scenarios. Further, we show thatg1Bandg1Mcan be determined from meanci/ca(ratio of leaf intercellular to ambientCO2concentration). Applying this relationship withci/cavalues derived from a leaf δ13C database, we obtain a global distribution ofg1Bandg1M, and these values correlate significantly with mean annual precipitation. This provides a new methodology for global parameterization of theBBandMEDmodels inLSMs, tied directly to leaf physiology but unconstrained by spatial boundaries separating designated biomes or plant functional types.

     
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