Bowen ratio reflects the partitioning between sensible and latent heat fluxes and plays a crucial role in land-atmosphere interaction. In this study, the spatiotemporal variations of Bowen ratio among 12 vegetation types were analyzed using observations from 203 FLUXNET sites worldwide and compared against Community Land Model. Results showed that the annual mean Bowen ratio across all sites was 1.48 ± 1.20 (mean ± SD). Sites with Bowen ratios less than 1 (39%, 80 sites) were found across all continents, and the ones with higher Bowen ratios (>3)(7%, 14 sites) appeared in dry and warm areas. Open shrublands showed the highest Bowen ratio (3.04 ± 0.58), whereas wetlands showed the lowest (0.74 ± 0.09). In terms of seasonality, Bowen ratio showed a U-curve with lower values in local summer and higher in spring and autumn in the northern hemisphere; the opposite occurred in the southern hemisphere. The spatiotemporal variations in Bowen ratio can be explained by climatic, geographical, and biological factors, with climate factors having the greatest impact. Bowen ratio increased under higher VPD (R = 0.45) and hotter (R=0.14) conditions with more shortwave radiation (R=0.39), and decreased with higher precipitation (R=-0.34), albedo (R=-0.16), and leaf area index (R=-0.25). CLM well reproduced the global annual mean Bowen ratio, but showed larger differences for certain vegetations types such as open shrublands (-1.51), woody savannas (+0.98). Our results could enhance our understanding of biotic and environmental controls on land surface energy fluxes and help improve land surface and climate models.

The Global Burden of Disease attributes millions of premature deaths to ambient air pollution each year, making it one of the largest environmental health risks faced by society. This mortality is largely due to exposure to fine particulate matter (PM2.5). In the United States, the Environmental Protection Agency estimated that 50.5 million people lived in counties with PM2.5 concentrations above the level of the National Ambient Air Quality Standards in 2020. PM2.5 levels can be derived from satellite aerosol optical depth (AOD) measurements providing comprehensive spatial and temporal coverage. However, the chemical composition of PM2.5 affects the mechanisms by which adverse health effects occur, and thus there is a pressing need for linking satellite data with high-resolution atmospheric modeling of PM2.5 composition. In order to better inform public health policy and decision-making, we aim to estimate near-real-time (NRT) surface PM2.5 composition informed by satellite AOD measurements and chemical transport modeling for the first time. Here, we demonstrate this framework for hindcast estimates in year 2021. NRT AOD is collected from multi-source remote sensing data including Moderate Resolution Imaging Spectroradiometer (MODIS; Aqua and Terra), the Visible Infrared Imaging Radiometer Suite (VIIRS; Dark Target and Deep Blue), and Multi-Angle Imaging SpectroRadiometer (MISR). The data obtained from these products are combined into daily, 10-km AOD estimates and used to scale simulated total PM2.5. GEOS-Chem (v13.1.2) nested regional simulations are run over North America with GEOS-Forward Processing (FP) assimilated meteorology at resolution 0.25° lat. x0.3125° lon. (approximately 20-30km) to simulate daily AOD and get an initial estimate of PM2.5 composition. This estimate is interpolated into the 10-km grid and multiplied with the satellite-adjusted total PM2.5 composition to produce concentrations of each PM2.5 species. This satellite-constrained chemical transport model framework estimates of PM2.5 will ultimately be evaluated against observations and compared to estimates using standard satellite products to inform future use of this framework to predict ambient air pollution health risks in true near-real-time.

Forests play a pivotal role in regulating climate and sustaining the hydrological cycle. The biophysical impacts of forest on clouds, however, remain unclear due to the lack of direct observations. In this first global-scale observational study, we use long-term satellite-derived cloud cover data to show that forests can have opposite effects on summer cloud cover. We find enhanced cloud cover over most temperate and boreal forests, but inhibited cloud cover over Amazon, central Africa, and Southeast US. These cloud effects mainly arise from convection processes associated with forests. The spatial variation in the sign of cloud effects is driven by sensible heating where cloud enhancement (inhibition) is more likely to occur when sensible heat in forest is larger (smaller) than nearby nonforest. Ongoing forest cover loss has led to opposite cloud cover changes, with local cloud increase over forest loss hotspots in the Amazon (+0.78%), Indonesia (+1.19%), and Southeast US (+0.09%), but cloud reduction in East Siberia (-0.20%) from 2002-2018. Our data-driven assessment informs the climate effects of local-scale forest cover change and improves mechanistic understanding of forest-cloud interactions, the latter of which remains uncertain in Earth system models.