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Water‐soluble organic carbon (WSOC) deposited in ambient snowpack play key roles in regional carbon cycle and surface energy budget, but the impacts of photo‐induced processes on its optical and chemical properties are poorly understood yet. In this study, melted samples of the seasonal snow collected from northern Xinjiang, northwestern China, were exposed to ultraviolet (UV) radiation to investigate the photolytic transformations of WSOC. Molecular characteristics and chemical composition of WSOC and its brown carbon (BrC) constituents were investigated using high‐performance liquid chromatography interfaced with a photodiode array detector and a high‐resolution mass spectrometer. Upon illumination, formation of nitrogen‐ and sulfur‐containing species with high molecular weight was observed in snow samples influenced by soil‐ and plant‐derived organics. In contrast, the representative sample collected from remote region showed the lowest molecular diversity and photolytic reactivity among all samples, in which no identified BrC chromophores decomposed upon illumination. Approximately 65% of chromophores in urban samples endured UV irradiation. However, most of BrC composed of phenolic/lignin‐derived compounds and flavonoids disappeared in the illuminated samples containing WSOC from soil‐ and plant‐related sources. Effects of the photochemical degradation of WSOC on the potential modulation of snow albedo were estimated. Apparent half‐lives of WSOC estimated as albedo reduction in 300–400 nm indicated 0.1–0.4 atmospheric equivalent days, which are shorter than typical photolysis half‐lives of ambient biomass smoke aerosol. This study provides new insights into the roles of WSOC in snow photochemistry and snow surface energy balance. Plain Language Summary: Seasonal snow is one of the largest terrestrial cryosphere components, its water‐soluble organic carbon (WSOC) constituents are highly dynamic and significantly affect the snowpack energy balance and carbon cycles. In this study, photolysis experiments were conducted in the laboratory to investigate the photochemical characteristics of WSOC in snowmelt collected from northwestern China. Impacts of ultraviolet (UV) light on the molecular composition of WSOC, its brown carbon (BrC) constituents, and the radiative effects of WSOC were analyzed. The results showed that WSOC and its BrC components in the samples collected from remote areas were less affected by the UV irradiation compared to the urban samples. The samples influenced by soil and plants were more photo‐active, majority of the chromophores disappeared upon irradiation, along with significant formation of nitrogen‐ and sulfur‐containing organic compounds. Furthermore, we found that WSOC in snowmelts have shorter lifetimes than the biomass burning aerosol. This study showed that photo‐induced processes substantially influence the molecular composition and radiative properties of WSOC in snowpack across the arid regions of northwestern China, where seasonal snow is the primary fresh water resource. Key Points: S‐ and N‐containing species with high molecular weight formed in the illuminated snowmelt samples containing soil and plant organicsBrown carbon (BrC) in the remote snowpack are photo‐resistant, however, those in the samples influenced by soil and plant organics are photo‐labileThe photochemical half‐lives of snow water‐soluble organic carbon showed 0.1–0.4 atmospheric‐relevant days, which are shorter than that of biomass smoke aerosol [ABSTRACT FROM AUTHOR]

Black carbon in snow (BCS) is a crucial parameter in Earth System modeling, as it influences global radiative balance. Here, simulated BCS from Coupled Model Intercomparison Project Phase 5 and 6 (CMIP5 and CMIP6) that provided BCS as a model output were evaluated. In comparison with global BCS observations, CMIP5/6 models successfully reproduced long‐term historical trends linked to human activities, but struggled capturing decadal variability caused by natural climate variability. CMIP6 models NorESM2‐MM, NorESM2‐LM, and TaiESM1 yielded the most accurate simulations of BCS concentration with modest overestimation of <50%, while the four CESM2 models underestimated concentrations by up to ∼80%. These errors effectively balanced for the CMIP6 multi‐model ensemble mean (MME), which had a relative error (RE) of −37%. However, the CMIP5 MME was less reliable due to extreme overestimation by up to 8,000% in the three MIROC models. The significant BCS concentration errors in the MIROC and CESM2 models were linked mainly to errors in handling of BC in snow processes. Conversely, marked improvements in NorESM, the only common to both CMIP5 and CMIP6, were due to improved simulation of black carbon deposition. BCS errors significantly impacted radiative forcing estimates, particularly at the poles, where model errors reached several thousandfold. CMIP6 exhibited superior results compared to CMIP5, achieving global MME RE of −33% in radiative forcing estimates. However, it's worth noting BCS output is currently limited, with only seven models available for each of CMIP5 and CMIP6 here. Additional models simulating BCS are desirable in the next CMIP generations. Plain Language Summary: Black carbon in snow (BCS) has important implications for snow albedo reduction, solar radiation absorption, and snow melt acceleration on the Earth. However, model performance in BCS simulation remains unclear in the widely used Coupled Model Intercomparison Project phases 5 and 6 (CMIP5/6). In this study, CMIP5 and CMIP6 models providing BCS information were evaluated against a global data set of BCS observations from ice cores and snow samples. Results indicate that both CMIP generations successfully reproduce long‐term trends in BCS but fail to capture the decadal variability. Marked differences exist in modeled BCS concentrations, with the best‐performing models (NorESM2‐LM, NorESM2‐MM, and TaiESM1) overestimating concentrations by <50%, while the three worst‐performing MIROC models yield extreme overestimation. Such discrepancies stem primarily from the post‐depositional treatment of black carbon in snow, which influences the estimation of climate‐warming effects of BCS. Key Points: The global pattern of black carbon in snow (BCS) is effectively simulated by CMIP5/6 models, but large errors remainPost‐depositional snow processes contribute most to the large simulated‐BCS errorsBCS‐induced radiative forcing in Antarctica is poorly simulated by CMIP5/6 models with an overestimation of ∼200,000% and 144%, respectively [ABSTRACT FROM AUTHOR]

The Taklamakan Desert (TD) is a major source of mineral dust emissions into the atmosphere. These dust particles have the ability to darken the surface of snow on the surrounding high mountains after deposition, significantly impacting the regional radiation balance. However, previous field measurements have been unable to capture the effects of severe dust storms accurately, and their representation on regional scales has been inadequate. In this study, we propose a modified remote-sensing approach that combines data from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite and simulations from the Snow, Ice, and Aerosol Radiative (SNICAR) model. This approach allows us to detect and analyze the substantial snow darkening resulting from dust storm deposition. We focus on three typical dust events originating from the Taklamakan Desert and observe significant snow darkening over an area of ∼ 2160, ∼ 610, and ∼ 640 km 2 in the Tien Shan, Kunlun, and Qilian mountains, respectively. Our findings reveal that the impact of dust storms extends beyond the local high mountains, reaching mountains located approximately 1000 km away from the source. Furthermore, we observe that dust storms not only darken the snowpack during the spring but also in the summer and autumn seasons, leading to increased absorption of solar radiation. Specifically, the snow albedo reduction (radiative forcing) triggered by severe dust deposition is up to 0.028–0.079 (11–31.5 W m -2), 0.088–0.136 (31–49 W m -2), and 0.092–0.153 (22–38 W m -2) across the Tien Shan, Kunlun, and Qilian mountains, respectively. This further contributes to the aging of the snow, as evidenced by the growth of snow grain size. Comparatively, the impact of persistent but relatively slow dust deposition over several months during non-event periods is significantly lower than that of individual dust events. This highlights the necessity of giving more attention to the influence of extreme events on the regional radiation balance. This study provides a deeper understanding of how a single dust event can affect the extensive snowpack and demonstrates the potential of employing satellite remote sensing to monitor large-scale snow darkening. [ABSTRACT FROM AUTHOR]

The extensive snow cover across the Tibetan Plateau (TP) regions has a major influence on the climate and water supply for over one billion downstream inhabitants. However, an adequate evaluation of snow cover fraction (SCF) variability over the TP simulated by global multiple reanalysis datasets has yet to be undertaken. In this study, we examined eight global reanalysis SCF datasets using the Snow Property Inversion from Remote Sensing (SPIReS) product spanning the period 2001–2020. The results reveal that the HMASR generated the best SCF simulations because of its outstanding spatial and temporal accuracy. The GLDAS and CFSR demonstrated acceptable SCF accuracy with respect to spatial variability, but struggled to reproduce the annual trend. Pronounced SCF overestimations were found when using the ERA5, ERA5L, and JRA55, but SCF was underestimated by MERRA2, and CRAL generated poor spatial pattern. Overall biases were related to the combined effect of precipitation forcing, temperature forcing, snow data assimilation, and SCF parameterization methods, with the dominant factor changing across datasets. In ERA5 and ERA5L, temperature and snowfall bias exhibited significant correlations with SCF bias over most TP areas, therefore having a greater impact on the accuracy of SCF in terms of spatial variability and temporal evolution. On the other hand, the impact of snow assimilation was possibly more pronounced in MERRA2 and CRAL. Although parameterization methods can improve SCF simulation accuracy, their influence was weaker than those of other factors, except for JRA55. To further improve the accuracy of SCF simulation, an ensemble average method was developed. The ensemble based on HMASR and GLDAS generated the most accurate SCF spatial distribution, whereas the ensemble containing ERA5L, CFSR, CRAL, GLDAS, ERA5, and MERRA2 proved optimal for capturing the annual trend. [ABSTRACT FROM AUTHOR]

The Taklamakan Desert (TD) is a major source of mineral dust emissions into the atmosphere. These dust particles have the ability to darken the surface of snow on the surrounding high mountains after deposition, significantly impacting the regional radiation balance. However, previous field measurements have been unable to capture the effects of severe dust storms accurately, and their representation on regional scales has been inadequate. In this study, we propose a modified remote-sensing approach that combines data from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite and simulations from the Snow, Ice, and Aerosol Radiative (SNICAR) model. This approach allows us to detect and analyze the substantial snow darkening resulting from dust storm deposition. We focus on three typical dust events originating from the Taklamakan Desert and observe significant snow darkening over an area of >2100, >600, and >630 km2 in the Tien Shan, Kunlun, and Qilian Mountains, respectively. Our findings reveal that the impact of dust storms extends beyond the local high mountains, reaching mountains located approximately 1000 km away from the source. Furthermore, we observe that dust storms not only darken the snowpack during the spring but also in the summer and autumn seasons, leading to increased absorption of solar radiation. Specifically, the snow albedo reduction (radiative forcing) triggered by severe dust depositions is up to 0.028–0.079 (11–31.5 W m−2), 0.088–0.136 (31–49 W m−2), and 0.092–0.153 (22–38 W m−2) across the Tien Shan, Kunlun, and Qilian Mountains, respectively. This further contributes to the aging of the snow, as evidenced by the growth of snow grain size. Comparatively, the impact of persistent but relatively slow dust deposition over several months during non-event periods is significantly lower than that of individual dust event. This highlights the necessity of giving more attention to the influence of extreme events on the regional radiation balance. Through this study, we gain a deeper understanding of how a single dust event can affect the extensive snowpack and demonstrates the potential of employing satellite remote-sensing to monitor large-scale snow darkening. [ABSTRACT FROM AUTHOR]