An extreme biomass burning event occurred in the Amazonian rainforest from July through September 2019 due to the extensive wildfires used to clear the land, which allowed for more significant forest burning than previously occurred. In this study, we reclustered the clear-sky ambient aerosols to adapt the black carbon (BC) aerosol retrieval algorithm to Amazonia. This not only isolated the volumetric fraction of BC (f(bc)) from moderate-resolution imaging spectroradiometer (MODIS) aerosol data, but also facilitated the use of aerosol mixing and scattering models to estimate the absorption properties of smoke plumes. The retrieved MODIS aerosol dataset provided a space perspective on characterizing the aerosol changes and trends of the 2019 pollution event. A very high aerosol optical depth (AOD) was found to affect the source areas continuously, with higher and thus stronger aerosol absorption. These pollutants also affected the atmosphere downwind due to the transport of air masses. In addition, properties of aerosols emitted from the 2019 Amazonian wildfire events visualized a significant year-to-year enhancement, with the averaged AOD at 550 nm increased by 150%. A 200% increase in the aerosol-absorption optical depth (AAOD) at 550 nm was recognized due to the low single-scattering albedo (SSA) caused by the explosive BC emissions during the pollution peak. Further simulations of aerosol radiative forcing (ARF) showed that the biomass-burning aerosols emitted during the extreme Amazonian wildfires event in 2019 forced a significant change in the radiative balance, which not only produced greater heating of the atmospheric column through strong absorption of BC, but also reduced the radiation reaching the top-of-atmosphere (TOA) and surface level. The negative radiative forcing at the TOA and surface level, as well as the positive radiative forcing in the atmosphere, were elevated by similar to 30% across the whole of South America compared to 2018. These radiative effects of the absorbing aerosol could have the ability to accelerate the deterioration cycle of drought and fire over the Amazonian rainforest.

Recent climate change has induced widespread soil thawing and permafrost degradation in the Tibetan Plateau. Significant advances have been made in better characterizing Tibetan Plateau soil freeze/thaw dynamics, and their interaction with local-scale ecohydrological processes. However, factors such as sparse networks of in-situ sites and short observational period still limit our understanding of the Tibetan Plateau permafrost. Satellite-based optical and infrared remote sensing can provide information on land surface conditions at high spatial resolution, allowing for better representation of spatial heterogeneity in the Tibetan Plateau and further infer the related permafrost states. Being able to operate at all-weather conditions, microwave remote sensing has been widely used to retrieve surface soil moisture, freeze/thaw state, and surface deformation, that are critical to understand the Tibetan Plateau permafrost state and changes. However, coarse resolution (>10 km) of current passive microwave sensors can add large uncertainties to the above retrievals in the Tibetan Plateau area with high topographic relief. In addition, current microwave remote sensing methods are limited to detections in the upper soil layer within a few centimetres. On the other hand, algorithms that can link surface properties and soil freeze/thaw indices to permafrost properties at regional scale still need improvements. For example, most methods using InSAR (interferometric synthetic aperture radar) derived surface deformation to estimate active layer thickness either ignore the effects of vertical variability of soil water content and soil properties, or use site-specific soil moisture profiles. This can introduce non-negligible errors when upscaled to the broader Tibetan Plateau area. Integrating satellite remote sensing retrievals with process models will allow for more accurate representation of Tibetan Plateau permafrost conditions. However, such applications are still limiting due to a number of factors, including large uncertainties in current satellite products in the Tibetan Plateau area, and mismatch between model input data needs and information provided by current satellite sensors. Novel approaches to combine diverse datasets with models through model initialization, parameterization and data assimilation are needed to address the above challenges. Finally, we call for expansion of local-scale observational network, to obtain more information on deep soil temperature and moisture, soil organic carbon content, and ground ice content.

Accurate quantification of the distribution and characteristics of frozen soil is critical for evaluating the impacts of climate change on the ecological and hydrological systems in high-latitude and-altitude regions, such as the Tibetan Plateau (TP). However, field observations have been limited by the harsh climate and complex terrain on the plateau, which greatly restricts our ability to predict the existence of and variations in frozen soils, especially at the regional scale. Here, we present a study relying solely on satellite data to drive process-based simulation of soil freeze-thaw processes. Modifications are made to an existing process-based model (Geomorphology-Based Eco-Hydrological Model, GBEHM) such that the model is fully adaptable to remote sensing inputs. The developed model fed with a combination of MODIS, TRMM and AIRX3STD satellite products is applied in the upper Yellow River Basin (coverage of similar to 2.54 x 10(5) km(2)) in the northeast TP and validated against field observations of freezing and thawing front depths (D-ft) and soil temperature (T-soil) at 54 China Meteorological Administration (CMA) stations, as well as frozen-ground types at 22 boreholes. Results indicate that the developed model performs reasonably well in simulating D-ft (R-2 = 0.69; mean bias = -0.03 m) and T-soil (station averaged R-2 and mean bias range between 0.90-0.96 and -0.51 similar to -0.14 degrees C at eight observational depths, respectively), and outperforms the original GBEHM forced with ground-measured meteorological variables. The frozen-ground types are also (in general) accurately identified by the satellite-based approach, except for a few permafrost boreholes located near the permafrost boundary regions. Additionally, we also demonstrate the importance of considering dynamic soil water content in frozen soil simulation: We find that a static-soil-moisture assumption (as used in previous studies) would lead to biased soil temperature estimates by > 0.5 degrees C. Our study demonstrates the value of using satellite data in frozen-soil simulation over complex landscapes, potentially leading to a greater understanding of soil freeze-thaw processes at the regional scale.

This paper investigates snow albedo changes in the Sierra Nevada Mountain area associated with potential deposition of absorbing aerosols in spring by using the snow albedo, aerosol optical depth, land surface temperature, and other relevant parameters available from the Moderate-Resolution Imaging Spectroradiometer (MODIS) onboard the NASA Terra satellite during 2000-2009. Satellite pixels with 100% snow cover have been selected to derive the monthly mean snow albedo value, along with aerosol optical depth, surface temperature, and days after snowfall in March and April to perform multiple regression analysis. We show that aerosol optical depth, which generally includes dust and black carbon over the Sierra Nevada as a result of the transpacific transport from East Asia and local sources, represents a significant parameter affecting snow albedo variation, second only to the land surface temperature change. The regression analysis illustrates that a one standard deviation increase in land surface temperature (2.2 K) and aerosol optical depth (0.044) can lead to decreases in snow albedo by 0.038 and 0.026, respectively. This study also shows that approximately 26% of snow albedo reduction from March to April over the Sierra Nevada is caused by an increase in aerosol optical depth, which has a profound impact on available water resources in California. However, the results show that there are no significant trends for snow albedo, surface temperature, and aerosol optical depth of snow-covered areas over the Sierra Nevada Mountain area in this 10-year period. (C) 2012 Elsevier Ltd. All rights reserved.