We introduce the Clouds Above the United States and Errors at the Surface (CAUSES) project with its aim of better understanding the physical processes leading to warm screen temperature biases over the American Midwest in many numerical models. In this first of four companion papers, 11 different models, from nine institutes, perform a series of 5 day hindcasts, each initialized from reanalyses. After describing the common experimental protocol and detailing each model configuration, a gridded temperature data set is derived from observations and used to show that all the models have a warm bias over parts of the Midwest. Additionally, a strong diurnal cycle in the screen temperature bias is found in most models. In some models the bias is largest around midday, while in others it is largest during the night. At the Department of Energy Atmospheric Radiation Measurement Southern Great Plains (SGP) site, the model biases are shown to extend several kilometers into the atmosphere. Finally, to provide context for the companion papers, in which observations from the SGP site are used to evaluate the different processes contributing to errors there, it is shown that there are numerous locations across the Midwest where the diurnal cycle of the error is highly correlated with the diurnal cycle of the errorThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Abstract. Annual burned areas in the United States have increased 2-fold during the past decades. With more large fires resulting in more emissions of fine particulate matter, an accurate prediction of fire emissions is critical for quantifying the impacts of fires on air quality, human health, and climate. This study aims to construct a machine learning (ML) model with game-theory interpretation to predict monthly fire emissions over the contiguous US (CONUS) and to understand the controlling factors of fire emissions. The optimized ML model is used to diagnose the process-based models in the Fire Modeling Intercomparison Project (FireMIP) to inform future development. Results show promising performance for the ML model, Community Land Model (CLM), and Joint UK Land Environment Simulator-Interactive Fire And Emission Algorithm For Natural Environments (JULES-INFERNO) in reproducing the spatial distributions, seasonality, and interannual variability of fire emissions over the CONUS. Regional analysis shows that only the ML model and CLM simulate the realistic interannual variability of fire emissions for most of the subregions (r>0.95 for ML and r=0.14∼0.70 for CLM), except for Mediterranean California, where all the models perform poorly (r=0.74 for ML and r<0.30 for the FireMIP models). Regarding seasonality, most models capture the peak emission in July over the western US. However, all models except for the ML model fail to reproduce the bimodal peaks in July and October over Mediterranean California, which may be explained by the smaller wind speeds of the atmospheric forcing data during Santa Ana wind events and limitations in model parameterizations for capturing the effects of Santa Ana winds on fire activity. Furthermore, most models struggle to capture the spring peak in emissions in the southeastern US, probably due to underrepresentation of human effects and the influences of winter dryness on fires in the models. As for extreme events, both the ML model and CLM successfully reproduce the frequency map of extreme emission occurrence but overestimate the number of months with extremely large fire emissions. Comparing the fire PM2.5 emissions from the ML model with process-based fire models highlights their strengths and uncertainties for regional analysis and prediction and provides useful insights into future directions for model improvements.

Abstract. With the highest albedo of the land surface, snow plays a vital role in Earth’s surface energy and water cycles. Snow albedo is greatly affected by snow grain properties (e.g., size and shape) and light absorbing particles (LAPs) such as black carbon (BC) and dust. The mixing state of LAPs in snow also has large impacts on LAP-induced snow albedo reduction and surface radiative forcing (RF). However, most land surface models assume that snow grain shape is spherical and LAPs are externally mixed with the snow grains. This study improves the snow radiative transfer model in the land model (ELM v2.0) of the Energy Exascale Earth System Model version 2.0 (E3SM v2.0) by considering non-spherical snow grain shapes (i.e., spheroid, hexagonal plate and Koch snowflake) and internal mixing of dust-snow and systematically evaluates the impacts on surface energy and water balances over the Tibetan Plateau (TP). A series of ELM simulations with different treatments of snow grain shape, mixing state of BC-snow and dust-snow, and sub-grid topographic effects (TOP) on solar radiation are performed. Compared with two remote sensing snow products derived from the Moderate Resolution Imaging Spectroradiometer, the control ELM simulation with the default settings of spherical snow grain shape, internal mixing of BC-snow, external mixing of dust-snow and without TOP can capture the overall snow distribution reasonably. The estimated LAP-induced RF ranging from 0 to 21.9 W/m2 with the area-weighted average value of 1.3 W/m2 is comparable to reported values. Focusing on the snow-related processes and surface energy and water balances, Koch snowflake shape, among other non-spherical shapes, shows the largest difference from spherical shape in spring. The impacts of the mixing state of LAP-snow are smaller than the shape effects and depend on snow grain shape. Compared to external mixing, internal mixing of LAP-snow can lead to larger snow albedo reduction and snowmelt, which further affect surface energy and water cycles. Compared to the control simulation, the individual contributions of non-spherical snow shape, mixing state of LAP-snow, and local topography to the change of snow and surface fluxes have different signs and magnitudes, and their combined effects may be negative or positive due to complex and non-linear interactions among the factors. Overall, the changes of net solar radiation in spring due to individual and combined effects range from -28.6 to 16.9 W/m2 and -29.7 to 12.2 W/m2, respectively. This study advances understanding of the role of snow grain shape and mixing state of LAP-snow in land surface processes and offers guidance for improving snow simulations and RF estimates in Earth system models under climate change.

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Increasing temperature and water cycle changes due to warming climate may increase wildfire activities. Reliable projections of fire emissions are critical for informing fire management to address fire impacts on societies and ecosystems. Here, we construct a neural network (NN) model explained by the Shapley Additive explanation (SHAP) to predict fire PM emissions change and understand their drivers over the contiguous US (CONUS) in the mid-21st century under a high greenhouse gas emissions scenario (SSP5-8.5). Using future meteorology and leaf area index (LAI) simulated by eight global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), future population density, and present-day land use and land cover (LULC) as input to the NN model, the total fire PM emissions over CONUS are projected to increase by 4-75% (model spread). Among different regions, fire emissions in the western US are projected to increase more significantly in June-July-August (JJA) than in other seasons and regions, with the median ratios of future to present-day fire emissions ranging from 1.67 to 2.86. The increases in fire emissions are mainly driven by increasing normalized temperature (23-29%) and decreasing soil moisture (2-10%) in the future. When future LULC change is considered, the projected fire emissions further increase by 58%-83% over the western US compared to projections without LULC change because of future increases in vegetation fraction. The results highlight the important role of warmer temperature, decreasing soil moisture, and LULC change in increasing fire emissions in the future.