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

ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation

Modern climate projections lack adequate spatial and temporal resolution due to computational constraints. A consequence is inaccurate and imprecise predictions of critical processes such as storms. Hybrid methods that combine physics with machine learning (ML) have introduced a new generation of higher fidelity climate simulators that can sidestep Moore's Law by outsourcing compute-hungry, short, high-resolution simulations to ML emulators. However, this hybrid ML-physics simulation approach requires domain-specific treatment and has been inaccessible to ML experts because of lack of training data and relevant, easy-to-use workflows. We present ClimSim, the largest-ever dataset designed for hybrid ML-physics research. It comprises multi-scale climate simulations, developed by a consortium of climate scientists and ML researchers. It consists of 5.7 billion pairs of multivariate input and output vectors that isolate the influence of locally-nested, high-resolution, high-fidelity physics on a host climate simulator's macro-scale physical state.The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators. We implement a range of deterministic and stochastic regression baselines to highlight the ML challenges and their scoring. The data (https://huggingface.co/datasets/LEAP/ClimSim_high-res) and code (https://leap-stc.github.io/ClimSim) are released openly to support the development of hybrid ML-physics and high-fidelity climate simulations for the benefit of science and society

On the nature of the atmospheric cloud radiative effect and its impact on tropical convection

Thesis (Ph.D.)--University of Washington, 2016-03Tropical high clouds have been shown to converge radiant energy within the atmosphere. We term this phenomenon the Atmospheric Cloud Radiative Effect (ACRE). The addition of energy unequally in space and time has profound effects on tropical convection. In this thesis, we show that the extra heating by clouds serves to enhance the divergence of energy transport out of the atmospheric column. Additionally, the water vapor lofted into the upper troposphere by convection behaves in a similar fashion to the clouds, heating the atmosphere and enhancing energy export. This atmospheric moisture radiative effect accounts for as much as a fifth of the total radiative heating owing to convection. Principal component analysis of satellite-retrieved cloud data reveal offsetting changes in cloud amount, cloud optical thickness, and cloud top height that give rise to an insensitivity in the top-of-atmosphere cloud radiative effect to changes in sea surface temperature. While increasing vertical motion makes the cloud radiative effect more negative, that decrease is offset by reductions in outgoing longwave radiation owing to increases in water vapor. The absorption of radiant energy by the clouds warms the upper tropical troposphere compared to simulations where ACRE is artificially removed. We show this increase in stability requires greater surface moist static energy to initiate convection, and hence, contracts the intertropical convergence zone (ITCZ) equatorward where sea surface temperatures are at a maximum. The meridional gradient in ACRE requires greater poleward energy transport. Thus, despite the increase in stability, the mean meridional circulation intensifies to export more energy out of the tropics. Finally, we show that the increase in stability owing to ACRE reduces cloud cover. ACRE influences the cloud cover in another way, however, and that is through destabilizing the cloud layer directly through absorption of longwave at cloud bottom and emission of longwave at cloud top. This cloud layer destabilization effect enhances the cloud areal coverage, offsetting some of the reduction from the tropospheric stability changes. The destabilization of the cloud layer also warms and thins the clouds, increasing the cloud radiative effect at the top of the atmosphere

Analysis scripts and dataset for Barthel et. al. (2023)

<p>This archive contains post-processed data and plotting scripts needed to generate the results described in Barthel et.al. (2023) <strong>Machine learning correction operators  for under-resolved climate models using nudged simulations. </strong>The archive includes processed data from simulations of a 2  layer Quasi-Geostrophic model generated using an in-house solver, simulations of DOE's E3SM Atmosphere Model Version 2 (EAMv2), and ERA5 reanalysis data.  A detailed description of the model and simulations can be found in Barthel et. al. (2023).</p&gt

Sensitivity of Surface Temperature to Oceanic Forcing via q-Flux Green’s Function Experiments. Part II: Feedback Decomposition and Polar Amplification

A large set of Green’s function-type experiments is performed with q-flux forcings mimicking the effects of the ocean heat uptake (OHU) to examine the global surface air temperature (SAT) sensitivities to the location of the forcing. The result of the experiments confirms the earlier notion derived from experiments with different model complexities that the global mean SAT is far more sensitive to the oceanic forcing from high latitudes than the tropics. Remarkably, no matter in which latitude the q-flux forcings are placed, the SAT response is always characterized by a feature of polar amplification, implicating that it is intrinsic to our climate system. Considerable zonal asymmetry is also present in the efficacy of the tropical OHU, with the tropical eastern Pacific being much more efficient than the Indian Ocean and tropical Atlantic in driving global SAT warming by exciting the leading neutral mode of the SAT that projects strongly onto global mean warming. Using a radiative kernel, feedback analysis is also conducted to unravel the underlying processes responsible for the spatial heterogeneity in the global OHU efficacy, the polar amplification structures, and the tropical altruism of sharing the warmth with remote latitudes. Warming “altruism” for a q flux at a given latitude is also investigated in terms of the ratio of the induced remote latitudes versus the directly forced local warming. It is found that the tropics are much more altruistic than higher latitudes because of the high-energy transport efficiency of the Hadley circulation

Diurnal Rainfall Response to the Physiological and Radiative Effects of CO2 in Tropical Forests in the Energy Exascale Earth System Model v1

Understanding how the connection between rainfall and tropical forests will respond to increasing CO2 concentrations is a key element in understanding how the tropical water cycle will respond to increasing CO2. The plant physiological and radiative impacts of CO2 on rainfall patterns over tropical forest regions are examined in the Energy Exascale Earth System Model version 1.1 (E3SMv1.1-BGC) biogeochemistry experiments. Composite analysis reveals a dampening of the diurnal cycle of rainfall over the Amazon, Congo, and Maritime Continent in response to rising CO2 levels, regardless of the sign of total rainfall change. A full factorial model experiment confirms that the CO2 radiative and CO2 plant physiological effects can individually or jointly reduce the magnitude of the rainfall diurnal cycle, though the physical pathway giving rise to the reduction differs between the two effects. For the physiological response, stomatal closure reduces evapotranspiration, which dries the boundary layer and raises the lifting condensation level. These effects combine to reduce deep convective rainfall during its peak occurrence in the late daytime to early nighttime period. For the radiative response, a relative reduction in daytime Convective Available Potential Energy (consistent with a reduction in the diurnal temperature range) leads to less frequent triggering of deep convection and a reduction of rainfall diurnal amplitude. These diurnal rainfall changes are structurally similar across seasons, and show little sensitivity to representation of nutrient coupling for the land biogeochemistry. In agreement with previous findings, the physiological response has only minor impact on extreme rainfall relative to the radiative response

An overview of cloud-radiation denial experiments for the Energy Exascale Earth System Model version 1

Academi

Characterizing Tropical Cyclones in the Energy Exascale Earth System Model Version 1

Abstract In this study, we analyze the realism with which tropical cyclones (TCs) are simulated in the fully coupled low‐ and high‐resolution Energy Exascale Earth System Model (E3SM) version 1, with a focus on the latter. Compared to the low‐resolution (grid spacing of ∼1°), the representation of TCs improves considerably in the high‐resolution configuration (grid spacing of ∼0.25°). Significant improvements are found in the global TC frequency, TC lifetime maximum intensities, and the relative distribution of TCs among the different basins. However, at both resolutions, spurious TC activity is found in some basins, notably in the subtropical regions. Contrasting the simulated large‐scale TC environment with observations reveals that the model environment is unrealistically conducive for TC development in those regions. Further analysis indicates that these biases are likely related to those in thermodynamic potential intensity, caused by systematic SST biases, and vertical wind shear in the coupled model. TC‐ocean interaction is also examined in the high‐resolution configuration of the model. The salient features of the ocean's response to TC‐induced mixing and the ocean's impact on TC intensification are well‐reproduced. Finally, an evaluation of the influence of El Niño Southern Oscillation (ENSO) on TCs in the high‐resolution configuration of the model reveals that the ENSO‐TC relationship in the model has the right sign and is significant for the North Atlantic and Northwest Pacific, albeit weaker than in observations. In summary, the high‐resolution configuration of the E3SM model simulates TC activity reasonably and hence could be a useful tool for TC‐related research

Technical descriptions of the experimental dynamical downscaling simulations over North America by the CAM5.4-MPAS4.0 variable-resolution model

Comprehensive assessment of climate datasets is important for communicating to stakeholders model projections and associated uncertainties. Uncertainties can arise not only from assumptions and biases within the model but also from external factors such as computational constraint and data processing. To understand sources of uncertainties in global variable-resolution (VR) dynamical downscaling, we produced a regional climate dataset using the Model for Prediction Across Scales dynamical core coupled to the Community Atmosphere Model version 5.4 (CAM-MPAS). This document provides technical details of the model configuration, simulations, computational requirements, post-processing, and data archive of the experimental CAM-MPAS downscaling data. The CAM-MPAS model is configured with VR meshes featuring higher resolutions over North America, as well as quasi-uniform resolution meshes across the globe. The dataset includes multiple uniform- (240 and 120 km) and variable-resolution (50–200, 25–100, and 12–46 km) simulations for both the present-day (1990–2010) and future (2080–2100) periods, closely following the protocol of the North American Coordinated Regional Climate Downscaling Experiment. A deviation from the protocol is the pseudo-warming experiment for the future period, using the ocean boundary conditions produced by adding the sea surface temperature and sea ice changes from the low resolution version of the Max Planck Institute Earth System Model in the Coupled Model Intercomparison Project phase five to the present-day ocean state from a reanalysis product. Some unique aspects of global VR models are evaluated to provide background knowledge to data users and to explore good practices for modelers who use VR models for regional downscaling. In the coarse-resolution domain, strong resolution-sensitivity of the hydrological cycles exists over the tropics but does not appear to affect the mid-latitude circulations in the Northern Hemisphere including the downscaling target of North America. The pseudo-warming experiment leads to similar responses of large-scale circulations to the imposed radiative and boundary forcings in the CAM-MPAS and MPI models, but their climatological states in the historical period differ over various regions including North America. Such differences are carried to the future period, suggesting the importance of the base state climatology. Within the refined domain, precipitation statistics improve with higher resolutions, and such statistical inference is verified to be negligibly influenced by horizontal remapping during post-processing. Limited (≈ 50 % slower) throughput of the current code is found on a recent many-core/wide-vector High Performance Computing system, which limits the lengths of the 12–46 km simulations and indirectly affects the uncertainty from sampling. Our experience shows that global and technical aspects of VR downscaling framework require further investigations to reduce uncertainties for regional refinement.This preprint is from Sakaguchi, K., Leung, L. R., Zarzycki, C. M., Jang, J., McGinnis, S., Harrop, B. E., Skamarock, W. C., Gettelman, A., Zhao, C., Gutowski, W. J., Leak, S., and Mearns, L.: Technical descriptions of the experimental dynamical downscaling simulations over North America by the CAM5.4-MPAS4.0 variable-resolution model, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1199, 2023. Posted with permission.This work is distributed under the Creative Commons Attribution 4.0 License