The DOE E3SM Coupled Model Version 1: Overview and Evaluation at Standard Resolution

This work documents the first version of the U.S. Department of Energy (DOE) new Energy Exascale Earth System Model (E3SMv1). We focus on the standard resolution of the fully coupled physical model designed to address DOE mission-relevant water cycle questions. Its components include atmosphere and land (110-km grid spacing), ocean and sea ice (60 km in the midlatitudes and 30 km at the equator and poles), and river transport (55 km) models. This base configuration will also serve as a foundation for additional configurations exploring higher horizontal resolution as well as augmented capabilities in the form of biogeochemistry and cryosphere configurations. The performance of E3SMv1 is evaluated by means of a standard set of Coupled Model Intercomparison Project Phase 6 (CMIP6) Diagnosis, Evaluation, and Characterization of Klima simulations consisting of a long preindustrial control, historical simulations (ensembles of fully coupled and prescribed SSTs) as well as idealized CO2 forcing simulations. The model performs well overall with biases typical of other CMIP-class models, although the simulated Atlantic Meridional Overturning Circulation is weaker than many CMIP-class models. While the E3SMv1 historical ensemble captures the bulk of the observed warming between preindustrial (1850) and present day, the trajectory of the warming diverges from observations in the second half of the twentieth century with a period of delayed warming followed by an excessive warming trend. Using a two-layer energy balance model, we attribute this divergence to the model’s strong aerosol-related effective radiative forcing (ERFari+aci = -1.65 W/m2) and high equilibrium climate sensitivity (ECS = 5.3 K).Plain Language SummaryThe U.S. Department of Energy funded the development of a new state-of-the-art Earth system model for research and applications relevant to its mission. The Energy Exascale Earth System Model version 1 (E3SMv1) consists of five interacting components for the global atmosphere, land surface, ocean, sea ice, and rivers. Three of these components (ocean, sea ice, and river) are new and have not been coupled into an Earth system model previously. The atmosphere and land surface components were created by extending existing components part of the Community Earth System Model, Version 1. E3SMv1’s capabilities are demonstrated by performing a set of standardized simulation experiments described by the Coupled Model Intercomparison Project Phase 6 (CMIP6) Diagnosis, Evaluation, and Characterization of Klima protocol at standard horizontal spatial resolution of approximately 1° latitude and longitude. The model reproduces global and regional climate features well compared to observations. Simulated warming between 1850 and 2015 matches observations, but the model is too cold by about 0.5 °C between 1960 and 1990 and later warms at a rate greater than observed. A thermodynamic analysis of the model’s response to greenhouse gas and aerosol radiative affects may explain the reasons for the discrepancy.Key PointsThis work documents E3SMv1, the first version of the U.S. DOE Energy Exascale Earth System ModelThe performance of E3SMv1 is documented with a set of standard CMIP6 DECK and historical simulations comprising nearly 3,000 yearsE3SMv1 has a high equilibrium climate sensitivity (5.3 K) and strong aerosol-related effective radiative forcing (-1.65 W/m2)Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151288/1/jame20860_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151288/2/jame20860.pd

Optimally merging precipitation to minimize land surface modeling errors

This paper introduces a new method to improve land surface model skill by merging different available precipitation datasets, given that an accurate land surface parameter ground truth is available. Precipitation datasets are merged with the objective of improving terrestrial water and energy cycle simulation skill, unlike most common methods in which the merging skills are evaluated by comparing the results with gauge data or selected reference data. The optimal merging method developed in this study minimizes the simulated land surface parameter (soil moisture, temperature, etc.) errors using the Noah land surface model with the Nelder–Mead (downhill simplex) method. In addition to improving the simulation skills, this method also impedes the adverse impacts of single-source precipitation data errors. Analysis has indicated that the results from the optimally merged precipitation product have fewer errors in other land surface states and fluxes such as evapotranspiration (ET), discharge R, and skin temperature T than do simulation results obtained by forcing the model using the precipitation products individually. It is also found that, using this method, the true knowledge of soil moisture information minimized land surface modeling errors better than the knowledge of other land surface parameters (ET, R, and T). Results have also shown that, although it does not have the true precipitation information, the method has associated heavier weights with the precipitation product that has intensity, amount, and frequency that are similar to those of the true precipitation

Comparison of Satellite-Derived TOA Shortwave Clear-Sky Fluxes to Estimates from GCM Simulations Constrained by Satellite Observations of Land Surface Characteristics

Clear-sky, upwelling shortwave flux at the top of the atmosphere (S(sub TOA raised arrow)), simulated using the atmospheric and land model components of the Community Climate System Model 3 (CCSM3), is compared to corresponding observational estimates from the Clouds and Earth's Radiant Energy System (CERES) sensor. Improvements resulting from the use of land surface albedo derived from Moderate Resolution Imaging Spectroradiometer (MODIS) to constrain the simulations are also examined. Compared to CERES observations, CCSM3 overestimates global, annual averaged S(sub TOA raised arrow) over both land and oceans. However, regionally, CCSM3 overestimates S(sub TOA raised arrow) over some land and ocean areas while underestimating it over other sites. CCSM3 underestimates S(sub TOA raised arrow) over the Saharan and Arabian Deserts and substantial differences exist between CERES observations and CCSM3 over agricultural areas. Over selected sites, after using groundbased observations to remove systematic biases that exist in CCSM computation of S(sub TOA raised arrow), it is found that use of MODIS albedo improves the simulation of S(sub TOA raised arrow). Inability of coarse resolution CCSM3 simulation to resolve spatial heterogeneity of snowfall over high altitude sites such as the Tibetan Plateau causes overestimation of S(sub TOA raised arrow) in these areas. Discrepancies also exist in the simulation of S(sub TOA raised arrow) over ocean areas as CCSM3 does not account for the effect of wind speed on ocean surface albedo. This study shows that the radiative energy budget at the TOA is improved through the use of MODIS albedo in Global Climate Models

Impact of Land Surface Heterogeneity on Mesoscale Atmospheric Dispersion

Prior numerical modelling studies show that atmospheric dispersion is sensitive to surface heterogeneities, but past studies do not consider the impact of a realistic distribution of surface heterogeneities on mesoscale atmospheric dispersion. While these focussed on dispersion in the convective boundary layer, the present work also considers dispersion in the nocturnal boundary layer and above. Using a Lagrangian particle dispersion model (LPDM) coupled to the Eulerian Regional Atmospheric Modeling System (RAMS), the impact of topographic, vegetation, and soil moisture heterogeneities on daytime and nighttime atmospheric dispersion is examined. In addition, the sensitivity to the use of Moderate Resolution Imaging Spectroradiometer (MODIS)-derived spatial distributions of vegetation characteristics on atmospheric dispersion is also studied. The impact of vegetation and terrain heterogeneities on atmospheric dispersion is strongly modulated by soil moisture, with the nature of dispersion switching from non-Gaussian to near- Gaussian behaviour for wetter soils (fraction of saturation soil moisture content exceeding 40%). For drier soil moisture conditions, vegetation heterogeneity produces differential heating and the formation of mesoscale circulation patterns that are primarily responsible for non-Gaussian dispersion patterns. Nighttime dispersion is very sensitive to topographic, vegetation, soil moisture, and soil type heterogeneity and is distinctly non-Gaussian for heterogeneous land-surface conditions. Sensitivity studies show that soil type and vegetation heterogeneities have the most dramatic impact on atmospheric dispersion. To provide more skillful dispersion calculations, we recommend the utilisation of satellite-derived vegetation characteristics coupled with data assimilation techniques that constrain soil-vegetation-atmosphere transfer (SVAT) models to generate realistic spatial distributions of surface energy fluxes

Effects of climate change on probable maximum precipitation: A sensitivity study over the Alabama-Coosa-Tallapoosa River Basin

Probable maximum precipitation (PMP), defined as the largest rainfall depth that could physically occur under a series of adverse atmospheric conditions, has been an important design criterion for critical infrastructures such as dams and nuclear power plants. To understand how PMP may respond to projected future climate forcings, we used a physics-based numerical weather simulation model to estimate PMP across various durations and areas over the Alabama-Coosa-Tallapoosa (ACT) River Basin in the southeastern United States. Six sets of Weather Research and Forecasting (WRF) model experiments driven by both reanalysis and global climate model projections, with a total of 120 storms, were conducted. The depth-area-duration relationship was derived for each set of WRF simulations and compared with the conventional PMP estimates. Our results showed that PMP driven by projected future climate forcings is higher than 1981–2010 baseline values by around 20% in the 2021–2050 near-future and 44% in the 2071–2100 far-future periods. The additional sensitivity simulations of background air temperature warming also showed an enhancement of PMP, suggesting that atmospheric warming could be one important factor controlling the increase in PMP. In light of the projected increase in precipitation extremes under a warming environment, the reasonableness and role of PMP deserve more in-depth examination

Impacts of land use/land cover change on climate and future research priorities

Several recommendations have been proposed for detecting land use and land cover change (LULCC) on the environment from, observed climatic records and to modeling to improve its understanding and its impacts on climate. Researchers need to detect LULCCs accurately at appropriate scales within a specified time period to better understand their impacts on climate and provide improved estimates of future climate. The US Climate Reference Network (USCRN) can be helpful in monitoring impacts of LULCC on near-surface atmospheric conditions, including temperature. The USCRN measures temperature, precipitation, solar radiation, and ground or skin temperature. It is recommended that the National Climatic Data Center (NCDC) and other climate monitoring agencies develop plans and seek funds to address any monitoring biases that are identified and for which detailed analyses have not been completed