It has been proposed that increasing greenhouse gas (GHG)-driven climate tipping point risks may prompt consideration of Solar Radiation Modification (SRM) climate intervention to reduce those risks. Here, we study marine cloud brightening (MCB) SRM interventions in three subtropical oceanic regions using the Community Earth System Model 2 (CESM2) experiments. We assess the response of tipping point-related metrics to estimate the extent to which such interventions could reduce tipping point risk. Both the pattern and magnitude of the MCB cooling depend strongly on location of the MCB intervention. We find the MCB cooling effect reduces tipping point risk overall; however, the distinct pattern effects of MCB versus GHG means it is an imperfect remedy. Indeed, if MCB is applied in certain oceanic regions, it may exacerbate some tipping point risks. It is therefore crucial to carefully assess the potential remote teleconnected response to MCB interventions to reduce unintended climate impacts.

The magnitude of the aerosol forcing remains among the largest unknowns when assessing climate sensitivity over the historical period. Here, we describe a previously unconsidered source of uncertainty in aerosol forcing: the temporal variability of aerosol emissions. We show that time-variability in biomass burning (BB) emissions weakens the time-averaged total aerosol forcing, particularly in the Northern Hemisphere mid- to high-latitudes. BB emissions variability produces weaker (less negative) mean effective radiative forcing (ERF) compared to scenarios with no interannual variability in emissions. Satellite-estimated BB emissions (and associated variability) results in a June-September absolute ERF (relative to zero BB emissions) of -7.7 W⋅m-2 from 50-70ºN, compared to -10.4 W⋅m-2 when no emissions variability is used in the Community Earth System Model version 2 (CESM2). This difference in forcing is attributable to nonlinear aerosol-cloud interactions. Aerosol forcing will be overestimated (i.e. more negative) if emissions are temporally-smoothed.

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.

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 work describes the implementation and evaluation of the Slab Ocean Model com16 ponent of the Energy Exascale Earth System Model version 2 (E3SMv2-SOM) and its application to understanding the climate sensitivity to ocean heat transports (OHTs) and CO2 forcing. E3SMv2-SOM reproduces the baseline climate and Equilibrium Climate Sensitivity (ECS) of the fully coupled E3SMv2 experiments reasonably well, with a pattern correlation close to 1 and a global mean bias of less than 1% of the fully coupled surface temperature and precipitation. Sea ice extent and volume are also well reproduced in the SOM. Consistent with general model behavior, the ECS estimated from the SOM (4.5K) exceeds the effective climate sensitivity obtained from extrapolation to equilibrium in the fully coupled model (4.0K). The E3SMv2 baseline climate also shows a large sensitivity to OHT strengths, with a global surface temperature difference of about 4.0◦ C between high-/low-OHT experiments with prescribed forcings derived from fully coupled experiments with realistic/weak ocean circulation strengths. Similar to their forc ng pattern, the surface temperature response occurs mainly over the subpolar regions in both hemispheres. However, the Southern Ocean shows more surface temperature sensitivity to high/low-OHT forcing due to a positive/negative shortwave cloud radiative effect caused by decreases/increases in mid-latitude marine low-level clouds. This large temperature sensitivity also causes an overcompensation between the prescribed OHTs and atmosphere heat transports. The SOM’s ECS estimate is also sensitive to the prescribed OHT and the associated baseline climate it is initialized from; the high-OHT ECS is 0.5K lower than the low-OHT ECS.

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.

We study the sensitivity of South Asian Summer Monsoon (SASM) precipitation to Southern Hemisphere (SH) subtropical Absorbed Solar Radiation (ASR) changes using Community Earth System Model 2 simulations. Reducing positive ASR biases over the SH subtropics impacts SASM, and is sensitive to the ocean basin where changes are imposed. Radiation changes over the SH subtropical Indian Ocean (IO) shifts rainfall over the equatorial IO northward causing 1-2 mm/day drying south of equator, changes over the SH subtropical Pacific increases precipitation over northern continental regions by 1-2 mm/day, and changes over the SH subtropical Atlantic have little effect on SASM precipitation. Radiation changes over the subtropical Pacific impacts the SASM through zonal circulation changes, while changes over the IO modify meridional circulation to bring about changes in precipitation over northern IO. Our findings suggest that reducing SH subtropical radiation biases in climate models may also reduce SASM precipitation biases.

As global temperatures increase, Antarctica is likely to experience increased frequency, duration, and intensity of extreme temperature events. Here we investigate how the characteristics of summer extreme temperature events - heatwaves and incidence of melt days - may change over Antarctica using daily historical and SSP5-8.5 Coupled Model Intercomparison Project phase 6 (CMIP6) output from 1950-2099. CMIP6 models robustly project that Antarctica’s lowest elevation regions and the West Antarctic ice sheet will reach 0C for an average of 6-12 days during summer by 2099. Modelled summer heatwaves become more intense across the entire continent, but less frequent and shorter everywhere except the East Antarctic Plateau due to declining temperature variability as surface temperatures approach the melting point of ice. Our results imply that the increasing frequency of 0C days and greater heatwave intensity will contribute to increasing ice sheet surface melt and accelerating global sea level rise over the coming century.

Polycyclic aromatic hydrocarbons (PAHs), emitted from combustion of biofuels and fossil fuels, are toxic compounds and known to cause lung-cancer. Integrating a global atmospheric chemistry model and plausible future emissions trajectories, we assess how global PAHs and their associated lung cancer risk will likely change in the future. Benzo(a)pyrene (BaP) is used as an indicator of cancer risk from PAH mixtures. From 2008 to 2050, the population-weighted global average BaP concentrations under all RCPs consistently exceeded the WHO-recommended limits, primarily attributed to residential biofuel use. In developing regions of Africa and South Asia, PAH-associated lung-cancer risk increased by 30-64% from 2008 to 2050, due to increasing use of traditional biofuels with population growth. With the stringent air quality policy, PAH lung-cancer risk substantially decreases by ~80% in developed countries. Climate change is likely to have minor effects on PAH lung-cancer risk compared with the impact of emissions.

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.