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Abstract All provinces of China respond to the central government, predict future carbon dioxide emissions, and formulate action plans detailing how the province intends to fulfill its target of carbon emission peaking before 2030. Based on the bottom‐up energy consumption prediction and top‐down goal verification, this paper constructs a set of regional carbon dioxide emission prediction methods. Compared to the traditional bottom‐up prediction method, this method could simplify the parameters while improving the prediction accuracy. This model is used to predict and analyze the process of carbon dioxide emission peaking in Zhejiang. The results show that the mean absolute percentage error of the retrospective prediction value is only 1.56%. Zhejiang will reach carbon dioxide emission peaking around 2029–2030, and the peak value will be 569.7 million tons. Different factors have different effects on the process of carbon dioxide emission peaking. There is a strong correlation between the peak time of carbon dioxide emission and the production time of major energy‐consuming projects in Zhejiang. Meanwhile, if the 16 nuclear reactors are not put into operation, Zhejiang will not be able to achieve the goal of carbon dioxide emission peaking. Besides, the basic data used in this model is mainly from the local statistical departments of the region. Thus, it can be applied to other provinces and regions conveniently.

The upper-tropospheric carbonaceous aerosol layer (TCAL) represents the increase of aerosols in the upper-troposphere. It was first discovered over Asia but was found in this study to also occur over South America and Africa. The TCALs over three regions typically exist during the strong deep convection season, with the Asian, South American, and African TCALs showing peak intensity during July–August, October–December, and November–December, respectively. Over Asia, the TCAL has the highest altitude and widest spread due to strongest deep convection and upper-troposphere anticyclonic system. TCAL intensity is highest in South America maybe due to heaviest pollutant emissions. Anthropogenic pollution from India and western China produces two Asian TCAL centers, whereas widespread wildfires result in single centers over South America and Africa. TCAL radiative effect at the top of the atmosphere has warming effects over Asia (+0.23 W m ^−2 ), whereas cooling effects perform over South America (−0.54 W m ^−2 ) and Africa (−0.20 W m ^−2 ) owing to its altitude and the divergent strengths of black-carbon absorption and organic-carbon scattering.

Wildfire events have recently shown a rapid increase in frequency and scale due to the warmer present-day climate; however, their potential effects on the cryosphere are difficult to assess. Catastrophic wildfires in Australia during 2019–2020 emitted large amounts of light-absorbing particles (LAPs) to the atmosphere. Satellite observations indicate that these LAPs caused unprecedented snow-darkening of glaciers in New Zealand through long-range transport and deposition, with their effects lasting for up to three months in January–March 2020, influencing >90% of total glacier/snow and leading to a mean broadband snow-reflectance reduction of 0.08 ± 0.03. This snow darkening accelerated snowmelt by ~0.41 ± 0.2 cm day–1 during the southern summer, equivalent to that caused by a ~1.8 °C increase in air temperature. This indicates the significant impact of the 2019–2020 Australian wildfires on the hydrologic cycle in New Zealand, exceeding that of the local climate warming of ~1.5 °C since the preindustrial period. Wildfire-induced snow darkening is not limited to New Zealand. Future projections of wildfire incidence indicate widespread effects of snow darkening on the global cryosphere.

Abstract We quantified the combined effects of mineral dust nonsphericity and size on snow albedo reduction using the MOPSMAP (Modeled optical properties of ensembles of aerosol particles) package and SAMDS (Spectral Albedo Model for Dirty Snow) with the consideration of dust from Sahara, Greenland, San Juan Mountains, and Tibetan Plateau. Results indicate that the dust‐induced albedo reduction decreases by up to 30% as the effective radii of dust particles increase in 1–5 μm. Nonspherical dust enhances snow albedo reduction relative to spherical dust by up to 20%. Stronger enhancements are obtained for higher dust concentration and larger dust size. Furthermore, the dust nonsphericity‐induced enhancement of snow albedo reduction is more pronounced for more‐absorptive dust. Finally, we develop a new parameterization for quantifying the dependence of snow albedo reduction on dust nonsphericity and size, and provide a convenient way for assessing the climate impacts of dust in snow.

Light-absorbing particles (LAPs) deposited on snow can significantly reduce surface albedo and contribute to positive radiative forcing. This study firstly estimated and attributed the spatio-temporal variability in the radiative forcing (RF) of LAPs in snow over the northern hemisphere during the snow-covered period 2003–2018 by employing Moderate Resolution Imaging Spectroradiometer (MODIS) data, coupled with snow and atmospheric radiative transfer modelling. In general, the RF for the northern hemisphere shows a large spatial variability over the whole snow-covered areas and periods, with the highest value (12.7 W m−2) in northeastern China (NEC) and the lowest (1.9 W m−2) in Greenland (GRL). The concentration of LAPs in snow is the dominant contributor to spatial variability in RF in spring (~73%) while the joint spatial contributions of snow water equivalent (SWE) and solar irradiance (SI) are the most important (>50%) in winter. The average northern hemisphere RF gradually increases from 2.1 W m−2 in December to 4.1 W m−2 in May and the high-value area shifts gradually northwards from mid-altitude to high-latitude over the same period, which is primarily due to the seasonal variability of SI (~58%). More interestingly, our data reveal a significant decrease in RF over high-latitude Eurasia (HEUA) of −0.04 W m−2 a−1 and northeastern China (NEC) of −0.14 W m−2 a−1 from 2003 to 2018. By employing a sensitivity test, we find the concurrent decline in the concentration of LAPs in snow accounted for the primary responsibility for the decrease in RF over these two areas, which is further confirmed by in situ observations.

Black carbon in snow (BCS) has a significant impact on global climate and is an important component of Earth system modeling. Here, we provide a comprehensive evaluation of BCS simulations in the Coupled Model Intercomparison Project Phase 6 (CMIP6) and its radiative forcing on a global scale. Overall, the multi-model mean generally captures the characteristics of BCS spatial patterns, with maximum concentrations in East Asia and the Tibetan Plateau (~120 ng·g−1), and the lowest in Antarctica (~0.05 ng·g−1). The BCS concentrations in all CMIP6 multi-model mean and individual models generally exhibit a temporally increasing trend globally, with particularly large increases after the 1940s. In terms of seasonal cycles, individual models are generally consistent in most regions. Globally, BCS concentrations are highest around January and lowest in September. The albedo reduction in the Tibetan Plateau and East Asia simulated by the CMIP6 multi-model mean reached ~0.06 in 2014 and may influence climate more than expected.

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 remote38 sensing to monitor large-scale snow darkening. [ABSTRACT FROM AUTHOR]

Water-soluble organic carbon (WSOC) in the cryosphere has important impact on the biogeochemistry cycling and snow/ice surface energy balance through changes in the surface albedo. This work reports on chemical characterization of WSOC in 28 representative snowpack samples collected across regional area of northern Xinjiang, northwestern China. We employed multi-modal analytical chemistry techniques to investigate both bulk and molecular-level composition of WSOC and its optical properties, informing the follow-up radiative forcing (푅퐹) modeling estimates. Based on the geographic differences and proximity of emission sources, the snowpack collection sites were grouped as urban/industrial (U), rural/remote (R), and soil-influenced (S) sites, for which average WSOC total mass loadings were measured as 1968±953 ng g-1 (U), 885±328 ng g-1 (R), and 2082±1438 ng g-1 (S), respectively. The S sites showed the higher mass absorption coefficients at 365 nm (푀퐴퐶365) of 0.94±0.31 m² g-1 compared to those of U and R sites (0.39±0.11 m² g-1 and 0.38±0.12 m² g-1, respectively). Bulk composition of WSOC in the snowpack samples and its basic source apportionment was inferred from the Excitation-Emission Matrices and the Parallel Factor analysis featuring relative contributions of two humic-like (HULIS-1 and HULIS-2) and one protein-like (PRLIS) components with ratios specific to each of the S, U, and R sites. Additionally, a sample from site 120 showed unique pollutant concentrations and spectroscopic features remarkably different from all other U, R, and S samples. Molecular-level characterization of WSOC using high-resolution mass spectrometry (HRMS) provided further insights into chemical differences among four types of samples (U, R, S, and 120). Specifically, much more reduced S-containing species with high degree of unsaturation and aromaticity were uniquely identified in U samples, suggesting an anthropogenic source. Aliphatic/proteins like species showed highest contribution in R samples, indicating their biogenic origin. The WSOC components from S samples showed high oxygenation and saturation levels. A few of unique CHON and CHONS compounds with high molecular weight were detected in the 120 sample, which might be anthraquinone derivatives from plant debris. Modeling of the WSOC- induced 푅퐹 values showed warming effects of 0.04 to 0.59 W m-2 among different groups of sites, which contribute up to 16% of that caused by BC, demonstrating the important influences of WSOC on the snow energy budget. [ABSTRACT FROM AUTHOR]

Light-absorbing particles (LAPs) deposited on snow can decrease snow albedo and affect climate through the snow-albedo radiative forcing. In this study, we use MODIS observations combined with a snow albedo model (SNICAR) and a radiative transfer model (SBDART) to retrieve the radiative forcing by LAPs in snow (RFLapsMODIS) across Northeastern China (NEC) in January-February from 2003 to 2017. RFLapsMODIS presents distinct spatial variability, with the minimum (22.3Wm-2) in western NEC and the maximum (64.6Wm-2) near industrial areas in central NEC. The regional mean RFLapsMODIS is ~45.1±6.8Wm-2 in NEC. The positive (negative) uncertainties of retrieved RFLapsMODIS due to atmospheric correction range from 14% to 57% (-14% to -47%) and the uncertainty value basically decreased with the increased RFLapsMODIS. We attribute the variations of radiative forcing based on remote sensing and find that the spatial variance of RFLapsMODIS in NEC is 74.6% due to LAPs, while 21.2% and 4.2% due to snow grain size, and solar zenith angle. Furthermore, based on multiple linear regression, the BC dry and wet deposition and snowfall could totally explain 81% of the spatial variance of LAP contents, which confirms the reasonability of the spatial patterns of retrieved RFLapsMODIS in NEC. We validate RFLapsMODIS using in situ radiative forcing estimates. We find that the biases in RFLapsMODIS are negatively correlated with LAP concentrations and range from ~5% to ~350% in NEC. [ABSTRACT FROM AUTHOR]