This study investigates aerosol characteristics using ground-based measurements at two distinct regions, MohalKullu (31.9 degrees N, 77.12 degrees E; 1154 m amsl) and Kosi-Katarmal (29.64 degrees N, 79.62 degrees E; 1225 m amsl), from July 2019 to June 2022. The average Black Carbon (BC) concentrations were 1.5 f 1.0 mu g m- 3 at Mohal and 1.1 f 1.4 mu g m-3 at Katarmal. BC showed strong seasonal variability, with maxima during post-monsoon (2.6 f 1.0 mu g m- 3) and pre-monsoon (1.8 f 0.5 mu g m-3) seasons. The diurnal variation displayed distinct morning and evening peaks in all the seasons. High pre-monsoon AOD500 (0.30 f 0.06 to 0.54 f 0.08) and low values of & Aring;ngstrom exponent (0.67 f 0.10 to 0.95 f 0.30) indicated dominance of large particles, whereas lower AOD500 (0.21 f 0.07 to 0.25 f 0.03) in post-monsoon and winter, along with larger & Aring;ngstrom exponent (1.05 f 0.74 to 1.13 f 0.11), indicated smaller particles. Satellite-derived (OMI and MAIAC) AOD500 showed weak to moderate correlation with ground-based measurements at Mohal (R = 0.4639 for MAIAC, R = 0.1402 for OMI) and Katarmal (R = 0.3976 for MAIAC, R = 0.2980 for OMI). Using optical properties of aerosols and clouds (OPAC) and Santa Barbara discrete ordinate radiative transfer (SBDART) models, the short-wave aerosol radiative forcing (SWARF) was found negative at the surface and top of the atmosphere but positive in the atmosphere, suggesting significant surface cooling and atmospheric warming leading to high heating rates, respectively. Annual mean atmospheric radiative forcing was 27.36 f 6.00 Wm- 2 at Mohal and 21.87 f 7.26 Wm- 2 at Katarmal. These findings may have consequences for planning air pollution strategies and understanding the effects of regional climate change.

Snow cover variation significantly impacts alpine vegetation dynamics on the Tibetan Plateau (TP), yet this effect under climate change remains underexplored. This study uses a survival analysis model to assess the influence of snow on vegetation green-up dynamics, while controlling for key temperature and water availability factors. This analysis integrates multi-source data, including satellite-derived vegetation green-up dates (GUDs), snow depth, accumulated growing degree days (AGDD), downward shortwave radiation (SRAD), precipitation, and soil moisture. Our survival analysis model effectively simulated GUD on the TP, achieving an R of 0.62 (p < 0.01), a root mean square error (RMSE) of 11.20 days, and a bias of -1.41 days for 2020 GUD predictions. It outperformed both the model excluding snow depth and a linear regression model. By isolating snow's impact, we found it exerts a stronger influence on vegetation GUD than precipitation in snow-covered areas of the TP. Furthermore, snow depth effects varied seasonally: a 1-cm increase in preseason snow depth reduced green-up rates by 8.48% before 156(th) day but increased them by 4.74% afterward. This indicates that deeper preseason snow cover delays GUD before June, but advances it from June onward, rather than having a uniform effect. These findings highlight the critical role of snow and underscore the need to incorporate its distinct effects into vegetation phenology models in alpine regions.

Northeastern China (NEC) is the largest grain base in China. Improving understanding of the effect of climate change on grain production over NEC is conducive to providing immediate response strategies for grain production. In this study, the relationships of the maize production with the dry state during the different maize growth stage have been investigated using the year-to-year increment method. Results showed that the severe drought that occurred from the jointing to maturity period have exerted severe effects on the maize growth. Further analysis indicated that the sea surface temperature (SST) anomalies over North Atlantic and Maritime Continent in later spring are the important factors affecting the summer droughts over NEC. The late spring SST anomaly over North Atlantic can excite the Rossby waves from the western North Atlantic and propagate eastward to NEC. The snow anomaly over western Siberia in late spring and the soil moisture anomaly over NEC in summer are key factors linking the SST anomaly to drought over the NEC. On the other hand, the Maritime Continent SST anomaly in late spring can modulate the activity of the East Asian jet stream via the East AsiaPacific (EAP) teleconnection, which can provide the favorable conditions for the soil moisture reduction over NEC. Eventually, a predictive model for maize yield over NEC is successfully developed by using the predictive indices of the North Atlantic and the Maritime Continental SST during late spring. Both the cross-validation and independent sample tests show that the calibrated prediction model is robust and exhibits high skill in predicting maize yield over NEC.

Aerosols are an important factor leading to reduced visibility. In order to better comprehend the connection between visibility and aerosols, aerosol optical depth (AOD) and Angstrom exponent (AE) data from the Himawari-8 Advanced Himawari Imager (AHI) are used for validation in comparison with the data from the Aerosol Robotic Network (AERONET) observations in this paper, which amounted to 69,026 sets of data. The results indicate that the AOD of AHI is in good agreement with AERONET observations, but AE performs poorly. The correlation coefficients between the AOD of AHI and AERONET data increase with decreasing visibility and the root mean square error increase. The AE of AHI performs poorly in different visibility conditions. The conclusion drawn from further analysis of the correlation between aerosol products and meteorological factors is that the factor with the highest correlation with visibility. Mixed aerosols dominate at higher visibility and biomass burning/urban-industrial aerosols dominate at lower visibility. The visibility in a typical city (Beijing) has a strong negative correlation with AOD, a weak negative correlation with AE, and a strong correlation with aerosol radiative forcing. The reduction in visibility may be caused by the scattering and adsorption effects of aerosols. The results are important for the improvement and application of AHI aerosol products in regional pollution studies.

Particulate matter (PM) is a vital pollutant that severely impacts human health, ecosystem well-being, and climate systems. In this review, the importance of vertical profiling is considered for understanding PM behavior between different layers of the atmosphere, and it includes modern techniques used such as meteorological towers and building methods, unmanned aerial vehicles (UAVs), aircraft, and satellite-based aerosol optical depth measurements. A systematic review was conducted, sourcing 150 articles published between 2000 and 2023, using relevant keywords such as Particulate Matter, Vertical Profiling, Environmental Impacts, and Climate Change from databases like Web of Science, Scopus, and Google Scholar. Key findings illustrate the vertical variations in PM levels associated with interactions among urban environments, meteorology, and specific atmospheric processes such as cloud formation, radiative forcing, and long-distance transport of pollutants. PM's effects on biodiversity, nutrient cycles, and ecosystem stability are also discussed. The environmental impacts of PM deposition, including biodiversity loss, nutrient cycling disruption, and ecosystem destabilization, elucidate widespread chronic anthropogenic particulate causes of long-term ecological damage around the globe. The study also examines relevant regulatory frameworks, specifically air quality standards, and policies, underpinning mitigation strategies. This review discusses how PM pollution is an increasingly alarming health risk. It reiterates the importance of demanding effective regulations on the local and global levels to counteract detrimental environmental and climatic consequences. This review clearly shows the immediate threats of PM. It should form a wake-up call to develop more effective monitoring tools and stringent regulatory measures against this omnipresent pollutant.

In South Asia, our understanding of atmospheric aerosols and their optical properties is limited, posing a challenge to comprehending climate change dynamics. This study characterises aerosol optical properties, radiative properties, black carbon (BC) and ozone (O3) at seven South Asian locations, including Nam Co (Tibetan Plateau, TP), Dhaka, Bhola (Bangladesh), and Hanimaadhoo, Kashidhoo, Male' and Gan (Maldives). The study utilises columnar aerosol data from the Aerosol Robotic Network (AERONET) and reanalysis data from Modern-Era Retrospective Analysis for Research and Applications (MERRA-2) from 2001 to 2020. Notably, during the winter, the highest Aerosol optical depth (AOD) levels were observed in Dhaka (1.0 +/- 0.5) and Bhola (0.8 +/- 0.4) among these seven locations. BC concentrations in Dhaka ranged from 2.1 to 2.8 mu g m-3, while Bhola recorded concentrations between 1.4 and 2.1 mu g m-3. O3 levels across Maldives sites remained consistent, with values ranging between 314 and 345 dobson units (DU), surpassing those in Bangladesh and TP. The analysis shows a significant difference in the rate at which the atmosphere heats (HR) up due to aerosols. Higher heating rates were observed over Kashidhoo during the post-monsoon and winter seasons, while lower values were seen during the pre-monsoon and monsoon seasons, compared with Hanimaadhoo and Male'. It is important to note that Bangladesh had higher HR values than the Maldives. This study helps us better understand the impact of atmospheric aerosols on South Asia's climate and the different seasonal patterns.

A comprehensive global investigation on the impact of reduction (changes) in aerosol emissions due to Coronavirus disease-2019 (COVID-19) lockdowns on aerosol single scattering albedo (SSA) utilizing satellite observations and model simulations is conducted for the first time. The absolute change in Ozone Monitoring Instrument (OMI) retrieved, and two highly-spatially resolved models (Modern-Era Retrospective Analysis for Research and Applications-2 (MERRA-2) and Copernicus Atmosphere Monitoring Service (CAMS)) simulated SSA is <4% (<0.04-0.05) globally during COVID (2020) compared to normal (2015-2019) period. Change in SSA during COVID is not significantly different from long-term and year-to-year variability in SSA. A small change in SSA indicates that significant reduction in anthropogenic aerosol emissions during COVID-19 induced lockdowns has a negligible effect in changing the net contribution of aerosol scattering and/or absorption to total aerosol extinction. The changes in species-wise aerosol optical depth (AOD) are examined in detail to explain the observed changes in SSA. Model simulations show that total AOD decreased during COVID-19 lockdowns, consistent with satellite observations. The respective contributions of sulfate and black carbon (BC) to total AOD increased, which resulted in a negligible change in SSA during the spring and summer seasons of COVID over South Asia. Europe and North America experience a small increase in SSA (<2%) during the summer season of COVID due to a decrease in BC contribution. The change in SSA (2%) is the same for a small change in BC AOD contribution (3%), and for a significant change in sulfate AOD contribution (20%) to total AOD. Since, BC SSA is 5-times lower (higher absorption) than that of sulfate SSA, the change in SSA remains the same. For a significant change in SSA to occur, the BC AOD contribution needs to be changed significantly (4-5 times) compared to other aerosol species. A sensitivity analysis reveals that change in aerosol radiative forcing during COVID is primarily dependent on change in AOD rather than SSA. These quantitative findings can be useful to devise more suitable future global and regional mitigation strategies aimed at regulating aerosol emissions to reduce environmental impacts, air pollution, and public health risks.

Svalbards permafrost is thawing as a direct consequence of climate change. In the Low Arctic, vegetation has been shown to slow down and reduce the active layer thaw, yet it is unknown whether this also applies to High Arctic regions like Svalbard where vegetation is smaller, sparser, and thus likely less able to insulate the soil. Therefore, it remains unknown which components of High Arctic vegetation impact active layer thaw and at which temporal scale this insulation could be effective. Such knowledge is necessary to predict and understand future changes in active layer in a changing Arctic. In this study we used frost tubes placed in study grids located in Svalbard with known vegetation composition, to monitor the progression of active layer thaw and analyze the relationship between vegetation composition, vegetation structure and snow conditions, and active layer thaw early in summer. We found that moss thickness, shrub and forb height, and vascular vegetation cover delayed soil thaw immediately after snow melt. These insulating effects attenuated as thaw progressed, until no effect on thaw depth was present after 8 weeks. High Arctic mosses are expected to decline due to climate change, which could lead to a loss in insulating capacity, potentially accelerating early summer active layer thaw. This may have important repercussions for a wide range of ecosystem functions such as plant phenology and decomposition processes. Temperatures are rising in the Arctic, causing increased thaw of the layer of soil located above the permanently frozen ground. In Low Arctic regions vegetation cools the soil, which reduces the thawing. So far, we do not know whether the small plants growing in the High Arctic may be able to slow or reduce thaw. We measured soil thaw throughout the summer in High Arctic Svalbard in locations where vegetation composition is known. We also measured thickness of the moss layer, height of plants and snow depth. We found that moss thickness was the strongest factor in insulating the soil. Also the cover of plants, height of shrubs and forbs, and height of grass-like plants slowed soil thaw in the early summer. The insulating effects became less over time and no effects were found 8 weeks after onset of thaw. As climate change is causing changes in the Arctic vegetation, mosses and small shrubs are expected to decrease. As we found these to be the most important factors in insulating the soil, a future decrease in mosses and small shrubs may cause accelerated soil thaw at the start of summer. High Arctic vegetation slows active layer thaw in early summer after snow melt Mosses show a stronger negative relation with thaw depth than vascular vegetation Factors influencing active layer thaw change over time in early summer

Empirical orthogonal function (EOF) and correlation analyses were employed to investigate the winter and spring snow depth in Eurasia and its relationship with Eastern China precipitation based on the observed and reanalyzed data from 1980 to 2016. The results show that the winter and spring snow cover in Eurasia not only highlights a decreasing trend due to global warming (the first EOF mode, its variance accounted for 24.4% and 22.6% of the total variance) but also exhibits notable interdecadal variation (the second EOF mode, its variance accounted for 10.2% and 11.5% of the total variance). The second EOF mode of winter snow depth in Eurasia is characterized by a west-east dipole pattern. It was observed that the spatial correlation pattern between the EOF2 of Eurasian snow depth and summer precipitation in China closely resembles the meridional quadrupole structure of the third EOF mode of summer precipitation in China. This pattern is characterized by excessive rainfall in Northeast China and the lower-middle reaches of the Yangtze River, and less rainfall over the Yellow River basin and southern China. The EOF mode of spring snow depth not only reflects the declining trend but also regulates precipitation in Eastern China. The possible mechanisms by which snow depth causes changes in soil moisture and subsequently affects atmospheric circulation are then explored from the perspective of the hydrological effects of snow cover. Decreased (Increased) snow depth in Eurasia during the winter and spring directly leads to diminished (increased) soil moisture while increasing (decreasing) net radiation and sensible heat flux at the surface. The meridional distribution of surface temperature also exhibits a dipole pattern, leading to enhanced subtropical westerly jet in the upper troposphere. The Eurasian snow cover anomalies pattern triggered an anomalous mid-latitude Eurasian wave train, which strengthened significantly in the Western Siberian Plain. It then splits into two branches, one continuing to propagate eastward at high latitudes and the other shifting towards East Asia, thereby impacting precipitation in Eastern China. This work indicates that the second EOF mode of Eurasian snow cover can impact the precipitation variability in Eastern China during the same period and in summer on an interdecadal scale.

China is an important emitter of light-absorbing carbonaceous aerosols (LACs), including black carbon (BC) and brown carbon (BrC). Currently, there are large uncertainties in model-estimated direct radiative forcing (DRF) of LACs, partially due to the poor understanding of the emissions and optical properties of LACs. In this study, we estimated the DRF of LACs over China during the implementation of the Air Pollution Prevention and Control Action Plan (APPCAP) using the global chemical transport model (GEOS-Chem) coupled with the Rapid Radiative Transfer Model of GCMs (RRTMG). We updated the refractive index of BC, includedbiomass burning (BB) sources, biofuel (BF) and coal combustion (CC) sources in the residential sector as BrC emission sources and the optical properties were updated, which were not fully considered in the previous model studies. Our results showed that model could reasonably capture the spatial and temporal variations of LACs in China with the correlation coefficients between model simulated and Aerosol Robotic Network (AERONET) observed daily absorption aerosol optical depth (AAOD) of LACs at 440 nm above 0.63 and the corresponding values of the normalized mean bias within +/- 30%. The simulated annual mean LACs AAOD at 440 nm in China was 0.016 (0.021) in 2017 (2014) and BrC contributed about 20% (21%). The estimated annual mean clear-sky LACs DRF at the top of the atmosphere in China was 1.02 W m(-2) in 2017 and 1.38 W m(-2) in 2014, and the contribution of BrC was about 10% and 11%, respectively, which was dominated by the BF sources (46% in 2017 and 44% in 2014) and the BB sources (38% in 2017 and 43% in 2014), with CC sources being low (16% in 2017 and 13% in 2014). The annual mean AAOD and DRF of LACs in China decreased by 0.005 and 0.36 W m(-2) from 2014 to 2017, which were largely attributed to the reductions of anthropogenic emissions during the implementation of APPCAP. Our results would improve the understanding of the light absorption capacity and climate effects of LACs in China.