Basudev Swain

Abstract:

This study focuses on the role of human activities in shaping climate forcings and their impact on surface air temperature (SAT) and drought intensification over Africa, emphasizing the human contributions to these phenomena. Through the analysis of observations, various model experiments, and Regularized Optimal Fingerprinting detection technique, our findings indicate that human-induced factors have contributed to an increase in surface air temperatures ranging from 0.8 to C above pre-industrial benchmarks. Greenhouse gases (GHGs) emerge as the primary driver of this rise (0.47 to C), followed by land use (LU) changes (0.47 to C). In contrast, anthropogenic aerosols (Aaer) exert a cooling effect (-1.82 to C) on SAT. The analysis reveals that SAT anomalies, particularly during the industrial period, have significantly contributed to the intensification of drought-prone climatic conditions. During the pre-industrial period, the absence of anthropogenic warming kept SAT stable, resulting in mildly wet conditions (Standardized Precipitation Evapotranspiration Index (SPEI)=0.54). However, in the industrial period, the sharp rise in SAT due to GHG and LU forcings led towards significantly drought-prone climatic conditions (SPEI=-0.73), while the cooling effect of Aaer was insufficient to offset the warming trend. Estimates based on Representative Concentration Pathways (RCP) 4.5 and 8.5 suggest that the SAT over Africa could rise by around C and C, respectively, by the end of the century, highlighting the significant influence of human-driven factors in driving temperature rise. Strategic oversight of GHG emissions, LU changes, and aerosol concentrations in Africa offers the possibility potential to mitigate further warming and consequent drought intensification in this region.

Abstract:

The Arctic is heating far more rapidly than the global mean, and clarifying the influence of aerosols in this intensification demands accurate and reliable observational records. The Arctic exhibits a distinct seasonal aerosol cycle, springtime ”Arctic Haze” with elevated AOD and summertime “Clean Air” with low AOD. Thus, it is critical to evaluate how well various datasets capture this seasonality relative to ground-based observations. This study analyzes spring and summer AOD variability using CAMSRA and MERRA-2 reanalyses, MODIS Terra and Aqua satellite observations, AERONET measurements, AEROSNOW retrievals, and GEOS-Chem model simulations. Results show that satellite-derived and satellite-assimilated reanalyses are far from capturing the expected seasonal Arctic Haze and Clean Air pattern, except at Bonanza Creek and Yakutsk, where anthropogenic pollution alters it. The inability of reanalyses to capture Arctic aerosol seasonality likely stems from the assimilation of satellite retrievals influenced by cloud contamination and surface reflection from snow and ice, as well as inherent biases in the underlying models used to generate these datasets. In contrast, AERONET observations and GEOS-Chem simulations consistently capture Arctic Haze in spring, driven by long-range transport, and Clean Air in summer, associated with efficient wet removal of aerosols. CAMSRA further underestimates emissions from Arctic forest fires and inadequately represents long-range pollution transport. These findings suggest that independent model simulations align more closely with ground-based observations than satellite products or reanalyses, and that adjusting wet-scavenging parameters to fit such reanalyses may misrepresent aerosol processes and their contribution to Arctic warming. Incorporating advanced retrieval algorithms like AEROSNOW into reanalyses offers a pathway to reduce these biases and improve representation of Arctic aerosol seasonality.

Abstract:

The Arctic is experiencing heightened precipitation, affected by aerosols impacting rainfall and snowfall. However, sparse aerosol observations in the central Arctic cryosphere contribute to uncertainties in simulating aerosol-precipitation two-way interaction. This study examines aerosol-precipitation co-variation in various climate models during the Arctic spring and summer seasons from 2003 to 2011, leveraging satellite-based aerosol data and various CMIP6 climate models. Findings reveal significant spatio-temporal biases between models and observations. Snowfall dominance occurs in models where total AOD surpasses the observation by 121% (57–186%, confidence interval), intensifying simulated snowfall by two times compared to rainfall during summer. Consequently, climate models tend to underestimate central Arctic rainfall to the total precipitation ratio, suggesting a positive bias towards snowfall dominance. This highlights the importance of constraining total AOD and associated aerosol schemes in climate models using satellite measurements, which potentially could lead to a substantial reduction in snowfall contribution to the total precipitation ratio in the central Arctic, contrary to current multi-model simulations across various spatiotemporal scales.