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.

This work describes the implementation and evaluation of the Slab Ocean Model component of the Energy Exascale Earth System Model version 2 (E3SMv2-SOM), and its application to understanding the climate sensitivity to ocean heat transport (OHT) strength and CO$_{2}$ forcing. E3SMv2-SOM reproduces the baseline climate and Equilibrium Climate Sensitivity (ECS) of the E3SMv2 fully coupled experiments, reasonably well, with a pattern correlation close to 1 and global mean bias that is less than 1$\%$ of the fully coupled surface temperature, precipitation and sea ice extent and volume. Similar to other model behaviour, the ECS estimated from the SOM (4.5$^\circ$C) is greater than the estimate from fully coupled model (4.0$^\circ$C; from 150 years regression). The E3SMv2 baseline climate is also very sensitive to the strength of the OHT from which the prescribed ocean heat convergence (OHC) for the SOM is derived, with a surface temperature difference of about 4.0$^\circ$C between high- and low-OHT SOM experiments. The surface temperature response in the high/low-OHT experiments occur through a positive/negative Shortwave cloud radiative effect, caused by a decrease/increase in marine low-level clouds over subpolar regions. This surface temperature sensitivity to prescribed OHCs is particularly large in the Southern hemisphere and is associated with an overcompensation of between prescribed OHC/OHT by atmosphere heat transports. This large sensitivity indicates stronger low-level cloud feedbacks in E3SM. The SOMâ\euro™s ECS estimate is also sensitive to the baseline climate it is initialized from, with an ECS difference of 0.5$^\circ$C between the high- and low- OHT CO$_2$ increase experiments.

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 Community Earth System Model 2 (CESM2) experiments. We assess the MCB impact on tipping element-related metrics to estimate the extent to which such interventions might reduce tipping element risks. Both the pattern and magnitude of the MCB cooling depend strongly on location of the MCB intervention. We find the MCB cooling effect reduces most tipping element impacts; though differences in MCB versus GHG climate response patterns mean MCB is an imperfect remedy. However, MCB applied in certain regions may exacerbate certain GHG tipping element impacts. Thus, it is crucial to carefully consider the pattern of MCB interventions and their teleconnected responses to avoid unintended climate effects.

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.

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.

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.