Permafrost carbon could produce a positive climate feedback. Until now, the ecosystem carbon budgets in the permafrost regions remain uncertain. Moreover, the frequently used models have some limitations especially regarding to the freeze-thaw process. Herein, we improved the IBIS model by incorporating an unfrozen water scheme and by specifying the parameters to estimate the present and future carbon budget of different land cover types (desert steppe, steppe, meadow, and wet meadow) in the permafrost regions. Incorporating an unfrozen water scheme reduced the mean errors in the soil temperature and soil water content by 25.2%, and the specifying leaf area parameters reduced the errors in the net primary productivity (NPP) by 79.9%. Further, the simulation results showed that steppes are carbon sources (39.16 gC/m(2)/a) and the meadows are carbon sinks (-63.42 gC/m(2)/a ). Under the climate warming scenarios of RCP 2.6, RCP 6.0, and RCP 8.5, the desert steppe and alpine steppe would assimilated more carbon, while the meadow and wet meadow were projected to shift from carbon sinks to carbon sources in 2071-2100, implying that the land cover type plays an important role in simulating the source/sink effects of permafrost ecosystem carbon in the IBIS model. The results highlight the importance of unfrozen water to the soil hydrothermal regime and specific leaf area for the growth of alpine vegetation, and present new insights on the difference of the responses of various permafrost ecosystems to climate warming.

The Black carbon (BC) and Brown carbon (BrC) concentration has been measured over Srinagar (Garhwal) in central Himalayas during October 2020 to September 2021 periods. The average BC mass was 2.59 +/- 1.96 mu g m- 3 and its absorption coefficients were abundant at shorter wavelength. BC seasonal variation exhibited a significant variability, with highest during winter (4.54 +/- 2.64 mu g m- 3) followed by pre-monsoon (2.69 +/- 2.04 mu g m- 3) and post-monsoon (1.93 +/- 0.91 mu g m- 3) while lowest was observed in the monsoon (1.05 +/- 0.54 mu g m- 3). Relatively high contribution of total spectral light absorption coefficient (Abs lambda) was observed (75.94 Mm-1) at 370 nm than longer wavelength (16.86 Mm-1) at 950 nm. The BrC contribution was higher at 370 nm (32.50 Mm-1) to the total babs (lambda), while at higher wavelengths it has extensively decreased (2.54 Mm-1 at 660 nm). Seasonally, the absorption coefficient of BC and BrC was greater in winter (83.99 and 68.37 Mm-1) while lowest in monsoon (19.38 and 9.27 Mm-1), respectively. The babs BrC/babs (t) ratio revealed higher contribution of BrC in winters. The secondary brown carbon (BrCsec) and primary brown carbon (BrCpri) contributed 43.16 % and 56.88 % towards the total BrC Abs (lambda) at 370 nm with higher in winter and lowest in monsoon, respectively. BrCsec and BrCprim has shown higher contribution in evening (18.00-20.00 h) and in morning (09.00-11.00 h) hours. The average radiative forcing (RF) of BC was 36.11 +/- 6.99 Wm-2, 2.19 +/- 1.22 Wm-2 and -33.92 +/- 5.96 Wm-2 at the atmosphere (ATM), Top of the Atmosphere (TOA), and at the Surface (SUR), respectively.

Tea is a vital agricultural product in Taiwan. Due to global warming, the increasing extreme weather events have disrupted tea garden conditions and caused economic losses in agriculture. To address these challenges, a comprehensive tea garden risk assessment model, a Bayesian network (BN), was developed by considering various factors, including meteorological data, disaster events, tea garden environment (location, altitude, tea tree age, and soil characteristics), farming practices, and farmer interviews, and constructed risk assessment indicators for tea gardens based on the climate change risk analysis concept from the Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC AR5). The results demonstrated an accuracy of over 92% in both validating and testing the model for tea tree damage and yield reduction. Sensitivity analysis revealed that tea tree damage and yield reduction were mutually influential, with weather, fertilization, and irrigation also impacting tea garden risk. Risk analysis under climate change scenarios from various global climate models (GCMs) indicated that droughts may pose the highest risk with up to 41% and 40% of serious tea tree growth damage and tea yield reduction, respectively, followed by cold events that most tea gardens may have less than 20% chances of serious impacts on tea tree growth and tea yield reduction. The impacts of heavy rains get the least concern because all five tea gardens may not be affected in terms of tea tree growth and tea yield with large chances of 67 to 85%. Comparing farming methods, natural farming showed lower disaster risk than conventional and organic approaches. The tea plantation risk assessment model can serve as a valuable resource for analyzing and offering recommendations for tea garden disaster management and is used to assess the impact of meteorological disasters on tea plantations in the future.

Decline in snow mass threatens the regional economy that critically depends on meltwater. However, the economic scale of snow mass loss is hardly understood, and its role in the vulnerability of future economic development is unclear. We investigate the current reserves of snow cover and the value of its loss. The result showed that the total annual snow mass in western China declines at a rate of 3.3 x 10(9) Pg per decade (p < 0.05), which accounts for approximately 0.46% of the mean of annual snow mass (7.2 x 10(11) Pg). Snow mass loss over the past 40 years in western China turns into an average loss value of CN0.1 billion (in the present value) every year ($1 = CN7). If the trend continues at the current rate, the accumulated loss value would rise to CN63 billion by 2040. Furthermore, subject to the combinations of RCPs and SSPs scenario, the future economic value of snow mass loss in western China appears to accelerate driven by both declining snowmelt resources and socioeconomic development demand. RCP26-SSP1 is the pathway among all to have the least economic cost in replacing the snowmelt loss, and the cost would be quadrupled in RCP80-SSP3 scenario by 2100. At a basin scale, the declining snow mass would turn the regional economy to be more vulnerable except Junggar and Ili endorheic basin. The Ertis river and Qaidam endorheic basins display to be most vulnerable. It highlights that the snowvalue can be economically important in the regions ofwest China and should be considered more properly in water resources management. (C) 2020 The Author(s). Published by Elsevier B.V.

Despite the importance of the Yellow River to China, climate change for the middle reaches of the Yellow River Basin (YRB) has been investigated far less than for other regions. This work focuses on future changes in mean and extreme climate of the YRB for the near-term (2021-2040), mid-term (2041-2060), and far-term (2081-2100) future, and assesses these with respect to the reference period (1986-2005) using the latest REgional MOdel (REMO) simulations, driven by three global climate models (GCMs) and assuming historical and future [Representative Concentration Pathway (RCP) 2.6 and 8.5] forcing scenarios, over the CORDEX East Asia domain at 0.22 degrees horizontal resolution. The results show that REMO reproduces the historical mean climate state and selected extreme climate indices reasonably well, although some cold and wet biases exist. Increases in mean temperature are strongest for the far-term in winter, with an average increase of 5.6 degrees C under RCP 8.5. As expected, the future temperatures of the warmest day (TXx) and coldest night (TNn) increase and the number of frost days (FD) declines considerably. Changes to mean temperature and FD depend on elevation, which could be explained by the snow-albedo feedback. A substantial increase in precipitation (34%) occurs in winter under RCP 8.5 for the far-term. Interannual variability in precipitation is projected to increase, indicating a future climate with more extreme events compared to that of today. Future daily precipitation intensity and maximum 5-day precipitation would increase and the number of consecutive dry days would decline under RCP 8.5. The results highlight that pronounced warming at high altitudes and more intense rainfall could cause increased future flood risk in the YRB, if a high GHG emission pathway is realized.

积雪深度的变化对地表水热平衡起着至关重要的作用。选用了国际耦合模式比较计划第六阶段（CMIP6）中目前情景比较齐全的五个全球气候模式，通过对比新疆地区1979—2014年积雪深度长时间序列数据集，评估了气候模式在新疆地区模拟积雪深度的模拟能力，接着预估了未来不同SSPs-RCPs情景下新疆地区在2021—2040年（近期）、2041—2060年（中期）、2081—2100年（末期）相对于基准期（1995—2014年）的积雪深度变化。气温和降水对积雪深度变化有着重要的影响，因此还分析了新疆地区到21世纪末期气温和降水的变化趋势。结果表明：订正后的气候模式模拟的积雪深度数据与观测数据的相关系数均达到0.8以上，其中1月至3月与观测数据的结果更为吻合。气候模式基本上能够反映积雪深度年内变化的基本特征，气候模式模拟的积雪深度空间分布和观测数据具有相似的特征。气温和降水在未来不同情景下均会波动上升，其中气温的增幅相对比较明显，达0.43℃·（10a）-1，而降水的增幅为0.63mm·（10a）-1，新疆未来的气候总体上呈现出变暖变湿的趋势。新疆地区的平均积雪深度在未来不同时...

积雪深度的变化对地表水热平衡起着至关重要的作用。选用了国际耦合模式比较计划第六阶段（CMIP6）中目前情景比较齐全的五个全球气候模式，通过对比新疆地区1979—2014年积雪深度长时间序列数据集，评估了气候模式在新疆地区模拟积雪深度的模拟能力，接着预估了未来不同SSPs-RCPs情景下新疆地区在2021—2040年（近期）、2041—2060年（中期）、2081—2100年（末期）相对于基准期（1995—2014年）的积雪深度变化。气温和降水对积雪深度变化有着重要的影响，因此还分析了新疆地区到21世纪末期气温和降水的变化趋势。结果表明：订正后的气候模式模拟的积雪深度数据与观测数据的相关系数均达到0.8以上，其中1月至3月与观测数据的结果更为吻合。气候模式基本上能够反映积雪深度年内变化的基本特征，气候模式模拟的积雪深度空间分布和观测数据具有相似的特征。气温和降水在未来不同情景下均会波动上升，其中气温的增幅相对比较明显，达0.43℃·（10a）-1，而降水的增幅为0.63mm·（10a）-1，新疆未来的气候总体上呈现出变暖变湿的趋势。新疆地区的平均积雪深度在未来不同时...

积雪深度的变化对地表水热平衡起着至关重要的作用。选用了国际耦合模式比较计划第六阶段（CMIP6）中目前情景比较齐全的五个全球气候模式，通过对比新疆地区1979—2014年积雪深度长时间序列数据集，评估了气候模式在新疆地区模拟积雪深度的模拟能力，接着预估了未来不同SSPs-RCPs情景下新疆地区在2021—2040年（近期）、2041—2060年（中期）、2081—2100年（末期）相对于基准期（1995—2014年）的积雪深度变化。气温和降水对积雪深度变化有着重要的影响，因此还分析了新疆地区到21世纪末期气温和降水的变化趋势。结果表明：订正后的气候模式模拟的积雪深度数据与观测数据的相关系数均达到0.8以上，其中1月至3月与观测数据的结果更为吻合。气候模式基本上能够反映积雪深度年内变化的基本特征，气候模式模拟的积雪深度空间分布和观测数据具有相似的特征。气温和降水在未来不同情景下均会波动上升，其中气温的增幅相对比较明显，达0.43℃·（10a）-1，而降水的增幅为0.63mm·（10a）-1，新疆未来的气候总体上呈现出变暖变湿的趋势。新疆地区的平均积雪深度在未来不同时...

Black carbon (BC), primary brown carbon (BrCpri), and secondary brown carbon (BrCsec) are important light-absorbing aerosol. BC and BrC from the surrounding area can reach the Tibetan Plateau (TP) and influence climate change and glacial melting. Here, we presented a study of the light absorption, radiative forcing, and potential source areas of BC and BrC over the northeastern, central, and southwestern TP. The higher light absorption was observed in the northeastern and southwestern sites compared to the central TP site. The major carbonaceous light-absorbing was attributed to BC with the percentages of 65%, 56%, and 82% in Ngari, Qinghai Lake, and Beiluhe, respectively. The heighten contribution of BrCsec to total light absorption indicated the importance of BrCsec in the TP, especially in the northeastern and southwestern areas. The BrCsec radiative forcings relative to BC were much higher than those of BrCpri. The potential BC and BrCpri source distributions were obtained.

We estimated the current (base years) and future (2021-2100) direct radiative forcing ( DRF) of four aerosol components (water-soluble, insoluble, black carbon (BC), and sea-salt) at urban (Yeonsan (Busan) and Gwangjin (Seoul)) and background sites (Aewol and Gosan (Jeju Island)), based on a modeling approach. The analysis for base years was conducted using PM2.5 samples measured at two urban and two background sites (Yeonsan and Gwangjin: 2016, Aewol and Gosan: 2014). The future DRFs were estimated according to changes in relative humidity (RH) of RCP8.5 climate change scenario at the same sites during four different periods (PI: 2021 similar to 2040, PII: 2041 similar to 2060, PIII: 2061 similar to 2080, and PIV: 2081 similar to 2100). In addition, we compared the differences between the DRFs of future (PI similar to PIV) and base years (2016 and 2014). Overall, the water-soluble component was predominant over all other components in terms of the concentrations, optical parameters (e.g., AOD), and DRFs, regardless of sites. For the base years, the monthly patterns of total DRFs for all components and the DRFs for the water-soluble component varied with sites, and months of their highest and lowest DRFs were different depending on sites. This might be due to the combined effect of the monthly patterns of the concentrations and RHs for each site. For the differences between the DRFs of future and base years, the highest future DRFs at Yeonsan and Aewol ranged from -59 to -63 W/m(2) increasing -20 (July in PII) to -28 W/m(2) (August in PIII) compared to the base years and from -73 to -74 W/m(2) increasing -31 (July in PII) to -41 W/m(2) (September in PIV), respectively. These DRFs at Gwangjin and Gosan ranged from -79 to -84 W/m(2) increasing -29 (June in PII and PIII) to -34 W/m(2) (June in PI) and from -58 to -92 W/m(2) increasing -14 (July in PII) to -26 W/m(2) (May in PI), respectively. The high heating rates at Yeonsan (up to 4.4 K/day in November) and Aewol (up to 3.7 K/day in February) of BC component might be caused by its strong radiative absorption.