It is still challenging to reproduce marine boundary layer (MBL) clouds well in large-scale models despite their importance to the Earth’s radiation budget and hydrological cycle. This study evaluates MBL and clouds in the Energy Exascale Earth System Model (E3SM) version 2. The E3SM simulation results are compared with remote sensing and reanalysis data during the Cloud System Evolution in the Trades (CSET) field campaign to better understand stratocumulus to cumulus cloud transition (SCT) over the northeast Pacific. E3SM results are extracted along the CSET Lagrangian trajectories. The comparison shows that the E3SM simulation applying horizontal wind nudging performs well in reproducing thermodynamic variables of the MBL and evolution trends of cloud variables along the trajectories. However, substantial overestimations of aerosol and cloud drop number ($N_d$) are observed, which is explained as an issue with version 2 of the model. Cloud fraction (CF) does decrease from the Californian coast to Hawaii in the E3SM simulation, but most CF values indicate an overcast or almost clear sky, which differ with satellite and reanalysis data. The effect of $N_d$ overestimation on CF evolution is assessed via prescribed $N_d$ simulations. Those simulations with $N_d$ modifications show negligible CF changes. A comparison of estimated inversion strength (EIS) also shows that the simulated EIS values are similar to those of reanalysis data. Our study suggests that cloud macrophysics and boundary layer processes are more important in improving the simulation rather than improving the model’s dynamics or cloud microphysics to capture SCT better in the model.

This paper provides an overview of the United States (U.S.) Department of Energy’s (DOE’s) Energy Exascale Earth System Model version 2.1 with an Arctic regionally refined mesh (RRM), hereafter referred to as E3SMv2.1-Arctic, for the atmosphere (25 km), land (25 km), and ocean/ice (10 km) components. We evaluate the atmospheric component and its interactions with land, ocean, and cryosphere by comparing the RRM (E3SM2.1-Arctic) historical simulations (1950-2014) with the uniform low-resolution (LR) counterpart, reanalysis products, and observational datasets. The RRM generally reduces biases in the LR model, improving simulations of Arctic large-scale mean fields, such as precipitation, atmospheric circulation, clouds, atmospheric river frequency, and sea ice dynamics. However, the RRM introduces a seasonally dependent surface air temperature bias, reducing the LR cold bias in summer but enhancing the LR warm bias in winter. The RRM also underestimates winter sea ice area and volume, consistent with its strong winter warm bias. Radiative feedback analysis shows similar climate feedback strengths in both RRM and LR, with the RRM exhibiting a more positive surface albedo feedback and contributing to a stronger surface warming than LR. These findings underscore the importance of high-resolution modeling for advancing our understanding of Arctic climate changes and their broader global impacts, although some persistent biases appear to be independent of model resolution at 10-100 km scales.

Discretized numerical models of the atmosphere are usually intended to faithfully represent an underlying set of continuous equations, but this necessary condition is violated sometimes by subtle pathologies that have crept into the discretized equations. Such pathologies can introduce undesirable artifacts, such as sawtooth noise, into the model solutions. The presence of these pathologies can be detected by numerical convergence testing. This study employs convergence testing to verify the discretization of the Cloud Layers Unified By Binormals (CLUBB) model of clouds and turbulence. That convergence testing identifies two aspects of CLUBB’s equation set that contribute to undesirable noise in the solutions. First, numerical limiters (i.e. clipping) used by CLUBB introduce discontinuities or slope discontinuities in model fields. Second, nonlinear numerical diffusion employed for improving numerical stability can introduce unintended small-scale features into the solution of the model equations. Smoothing the limiters and using linear diffusion (low-order hyperdiffusion) reduces the noise and restores the expected first-order convergence in CLUBB’s solutions. These model reformulations enhance our confidence in the trustworthiness of solutions from CLUBB by eliminating the unphysical oscillations in high-resolution simulations. The improvements in the results at coarser, near-operational grid spacing and timestep are also seen in cumulus cloud and dry turbulence tests. In addition, convergence testing is proven to be a valuable tool for detecting pathologies, including unintended discontinuities and grid dependence, in the model equation set.

Large-scale dynamical and thermodynamical processes are common environmental drivers of extreme weather events. However, such large-scale environmental conditions often display systematic biases in climate simulations, posing challenges to evaluating extreme weather events and associated risks in current and future climate. In this paper, a machine learning (ML) approach was employed to bias correct the large-scale wind, temperature, and humidity simulated by the E3SM atmosphere model at $\sim 1^\circ$ resolution. The usefulness of the proposed ML approach for extreme weather analysis was demonstrated with a focus on three extreme weather events, including tropical cyclones (TCs), extratropical cyclones (ETCs), and atmospheric rivers (ARs). We show that the ML model can effectively reduce climate bias in large-scale wind, temperature, and humidity while preserving their responses to imposed climate change perturbations. The bias correction is found to directly improve the water vapor transport associated with ARs, and the representations of thermodynamical flows associated with ETCs. When the bias-corrected large-scale winds are used to drive a synthetic TC track forecast model over the Atlantic basin, the resulting TC track density agrees better with that of the TC track model driven by observed winds. In addition, the ML model insignificantly interferes with the mean climate change signals of large-scale storm environments as well as the occurrence and intensity of three extreme events. This study suggests that the proposed ML approach can be used to improve the downscaling of extreme weather events by providing more realistic large-scale storm environments simulated by low-resolution climate models.

Society’s ability to make wise decisions depends onan accurate understanding of the current state of Earthand on an ability to predict future states.The Data Assimilation Research Testbed (DART) is an example of a suite of toolsdesigned to improve our understanding through the combination of observationswith our theoretical understanding embodied in forecast models.DART’s ensemble based data assimilation provides uncertainty quantification as a function of time, location, and variable.Current research using DART includes: Improving streamflow prediction during intense rainfall events, which lead to flooding, using DART and the Weather Research and Forecasting model and the Noah-MP land model (WRF-Hydro). Building an integrated atmosphere and ocean forecasting system using DART and WRF for the Red Sea Initiative. Understanding air pollution using a global meteorology-aerosol-chemistry prediction system that assimilates aerosol optical depth, carbon monoxide, and weather observations into the Community Atmosphere Model with Chemistry (CAM-Chem). Assimilating observations of the Earth system from satellites into the Model for Prediction Across Scales (MPAS; regional and global) using observation operators from the Joint Effort for Data assimilation Integration (JEDI), bias correction for satellite retrievals from the Gridpoint Statistical Interpolation (GSI), and the assimilation environment of DART. Deciphering the flow dependency of forecast errors in the tropics and the relative importance of wind and mass information for tropical analyses. Connecting the U.S. Department of Energy’s E3SM atmospheric model with a broad spectrum of observations to perform short ensemble hindcast simulations for model development and evaluation. Generating atmospheric reanalysis data sets from CAM, which enables efficient data assimilation in other components of the Earth system; ocean, land, cryosphere, … Improving DART by giving users more control over how observations are assimilated, and supporting the assimilation of additional observations, such as radiances through the use of the RTTOV software.

Discretized numerical models of the atmosphere are usually intended to faithfully represent an underlying set of continuous equations, but this necessary condition is violated sometimes by subtle pathologies that have crept into the discretized equations. Such pathologies can introduce undesirable artifacts, such as sawtooth noise, into the model solutions. The presence of these pathologies can be detected by numerical convergence testing. This study employs convergence testing to verify the discretization of the Cloud Layers Unified By Binormals (CLUBB) model of clouds and turbulence. That convergence testing identifies two aspects of CLUBB’s equation set that contribute to undesirable noise in the solutions. First, numerical limiters (i.e. clipping) used by CLUBB introduce discontinuities or slope discontinuities in model fields. Second, this noise can be amplified by an advective term in CLUBB’s background diffusion. Smoothing the limiters and removing the advective component of the background diffusion reduces the noise and restores the expected first-order convergence in CLUBB’s solutions. These model reformulations improve the results at coarser, near-operational grid spacing and time step in cumulus cloud and dry turbulence tests. In addition, convergence testing is proved to be a valuable tool for detecting pathologies, including unintended discontinuities and grid dependence, in the model equation set.