Wildfires inject aerosols into the atmosphere at varying altitudes, modifying long-range transport, which impacts Earth’s climate system and air quality. Most global climate models use prescribed fixed-height injections, not accounting for the dynamic variability of wildfires. In this study, we enhance the injection method of biomass burning aerosols implemented in the Geophysical Fluid Dynamic Laboratory’s Atmospheric Model version 4.0, shifting to a more mechanistic approach. We test several injection height schemes to assess their impact on the Earth’s radiation budget by performing 18-year global simulations. Comparison of modeled injection height from the mechanistic scheme with observations indicates error within instrumental uncertainty (less than 500 meters). Aerosol Optical Depth (AOD) is systematically underestimated due to biases in the emission dataset, but the mechanistic scheme significantly reduces this bias by up to 0.5 optical depth units during extreme wildfire seasons over boreal forests. In term of the vertical profile of the aerosol extinction coefficient, a comparison with satellite observations indicates significant improvement below 4 km altitude. Dynamic injection of biomass burning emissions changed the net radiative flux at top of the atmosphere regionally (±1.5 Wm-2) and reduced it by -0.38 Wm-2 at the surface globally, relative to a baseline with no fire emissions. The temperature gradient anomaly associated with the dynamic injection of absorbing aerosols affects the atmospheric stability and circulation patterns. This study highlights the need to implement dynamic injection of fire emissions to simulate more accurately the atmospheric distribution of aerosols and their interactions with Earth’s climate system.

Biases in aerosol optical depths (AOD) and land surface albedos in the AeroCom models are manifested in the top-of-atmosphere (TOA) clear-sky reflected shortwave (SW) fluxes. Biases in the SW fluxes from AeroCom models are quantitatively related to biases in AOD and land surface albedo by using their radiative kernels. Over ocean, AOD contributes about 25% to the 60°S-60°N mean SW flux bias for the multi-model mean (MMM) result. Over land, AOD and land surface albedo contribute about 40% and 30%, respectively, to the 60°S-60°N mean SW flux bias for the MMM result. Furthermore, the spatial patterns of the SW flux biases derived from the radiative kernels are very similar to those between models and CERES observation, with the correlation coefficient of 0.6 over ocean and 0.76 over land for MMM using data of 2010. Satellite data used in this evaluation are derived independently from each other, consistencies in their bias patterns when compared with model simulations suggest that these patterns are robust. This highlights the importance of evaluating related variables in a synergistic manner to provide an unambiguous assessment of the models, as results from single parameter assessments are often confounded by measurement uncertainty. We also compare the AOD trend from three models with the observation-based counterpart. These models reproduce all notable trends in AOD (i.e. decreasing trend over eastern United States and increasing trend over India) except the decreasing trend over eastern China and the adjacent oceanic regions due to limitations in the emission dataset.

The COVID-19 pandemic led to a widespread reduction in aerosol emissions. Anecdotal effects on air quality and visibility were widely reported. Less known are the impacts on the planetary energy balance. Using satellite observations and climate model simulations, we 15 study the underlying mechanisms of the large, precipitous decreases in solar clear-sky reflection (3.8 W m-2 or 7%) and aerosol optical depth (0.16 or 32%) over the East Asian Marginal Seas in March 2020. By separating the impacts due to meteorology and emissions in the model simulations, we find that about one-third of the anomalies can be attributed to pandemic-related emission reductions, and the rest to weather variability and long-term emission trends. The 20 current observational and modeling capabilities will be critical for monitoring, understanding, and predicting the radiative forcing and climate impacts of the ongoing crisis.

We describe the baseline model configuration and simulation characteristics of GFDL’s Atmosphere Model version 4.1 (AM4.1), which builds on developments at GFDL over 2013–2018 for coupled carbon-chemistry-climate simulation as part of the sixth phase of the Coupled Model Intercomparison Project. In contrast with GFDL’s AM4.0 development effort, which focused on physical and aerosol interactions and which is used as the atmospheric component of CM4.0, AM4.1 focuses on comprehensiveness of Earth system interactions. Key features of this model include doubled horizontal resolution of the atmosphere (~200 km to ~100 km) with revised dynamics and physics from GFDL’s previous-generation AM3 atmospheric chemistry-climate model. AM4.1 features improved representation of atmospheric chemical composition, including aerosol and aerosol precursor emissions, key land-atmosphere interactions, comprehensive land-atmosphere-ocean cycling of dust and iron, and interactive ocean-atmosphere cycling of reactive nitrogen. AM4.1 provides vast improvements in fidelity over AM3, captures most of AM4.0’s baseline simulations characteristics and notably improves on AM4.0 in the representation of aerosols over the Southern Ocean, India, and China—even with its interactive chemistry representation—and in its manifestation of sudden stratospheric warmings in the coldest months. Distributions of reactive nitrogen and sulfur species, carbon monoxide, and ozone are all substantially improved over AM3. Fidelity concerns include degradation of upper atmosphere equatorial winds and of aerosols in some regions.