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Over‐estimation of summer precipitation over the Tibetan Plateau (TP) is a well‐known and persistent problem in most climate models. This study demonstrates the impact of a Gaussian Probability Density Function cloud fraction scheme on rainfall simulations using the Weather Research and Forecasting model. It is found that this scheme in both 0.1° and 0.05° resolutions significantly reduces the wet bias through both local feedbacks and large‐scale dynamic process. Specifically, increased cloud water/ice content with this scheme reduces surface shortwave radiation, and consequently surface heat fluxes and evapotranspiration. This, in turn, dampens the large‐scale thermal effect of the TP and weakens the exaggerated monsoon circulation and low‐level moisture convergence. It is this large‐scale dynamic process that contributes the most (∼70%) to the wet bias reduction. Although this paper presents a modeling study, it highlights the cloud radiative feedback to the large‐scale dynamics and precipitation over the TP. Plain Language Summary: Despite numerous attempts to correct the overestimation of summer precipitation over the Tibetan Plateau (TP) in current global and regional climate models, the issue persists. This study applies the Gaussian Probability Density Function (GPDF) cloud fraction scheme in the Weather Research and Forecasting model at two different resolutions (0.1° and 0.05°) during a summer over the TP. The results show that the GPDF scheme significantly mitigates the precipitation overestimation, particularly in the high‐resolution modeling. We explored the physical processes, both local and remote, that contribute to this improvement. Specifically, an increase in cloudiness reduces the amount of radiation reaching the land surface. This decrease in surface radiative heating not only reduces local evaporation but also weakens the thermal effect of the TP. The latter is a major driver of the South Asian monsoon that conveys moisture to the TP, and its weakening reduces moisture convergence over the TP. Both the decreases in local evaporation and remote moisture convergence contribute to the alleviation of the precipitation overestimation, and the latter plays a dominant role. These findings provide a unique perspective for reducing the wet bias over the TP, focusing on the surface available energy and associated remote moisture processes. Key Points: The use of the Gaussian Probability Density Function cloud fraction scheme in high resolution greatly reduces wet bias over the Tibetan Plateau (TP) during summerMore cloud water/ice with the scheme lessens TP's thermal effect, causing a weaker South Asian monsoon and moisture convergenceWet bias reduction is mainly governed by the decrease in remote moisture rather than local evapotranspiration [ABSTRACT FROM AUTHOR]

The cloud classification algorithm widely used in the International Satellite Cloud Climatology Project (ISCCP) tends to underestimate low clouds over the Tibetan Plateau (TP), often mistaking water clouds for high‐level clouds. To address this issue, we propose a new algorithm based on cloud‐top temperature and optical thickness, which we apply to TP using Advanced Himawari Imager (AHI) geostationary satellite data. Compared with Clouds and the Earth's Radiant Energy System cloud‐type products and ISCCP results obtained from AHI data, this new algorithm markedly improved low‐cloud detection accuracy and better aligned with cloud phase results. Validation with lidar cloud‐type products further confirmed the superiority of this new algorithm. Diurnal cloud variations over the TP show morning dominance shifting to afternoon high clouds and evening mid‐level clouds. Winter is dominated by high clouds, summer by mid‐level clouds, spring by daytime low clouds and nighttime high clouds, and autumn by low and mid‐level clouds. Plain Language Summary: The accurate identification of low clouds over the Tibetan Plateau (TP) is crucial for climate regulation, ecosystems, aviation safety, research, and modeling. However, satellite‐based methods often miss these clouds, misclassifying them as high‐level clouds. To remedy this, we developed a new algorithm using cloud‐top temperature and optical thickness, applied to Advanced Himawari Imager data. This significantly improves low‐cloud detection, better aligning with actual cloud phases. Simultaneously, we analyzed diurnal cloud variations over the TP with the new algorithm. Cloud types at different altitudes in the TP exhibit strong seasonality. The dominant cloud types in winter and summer are high and mid‐level, respectively. In spring, low clouds dominate during the day (2:00–10:00 UTC), transitioning to high clouds at night (10:00–18:00 UTC), with mid‐level clouds prevailing at other times. In autumn, low clouds dominate during the day, transitioning to mid‐level clouds at other times, with fewer occurrences of high clouds. Key Points: Employing cloud‐top temperature instead of pressure resolves classification‐phase inconsistencies for clouds in the Tibetan Plateau (TP)Lidar validation shows new algorithm's low cloud detection outperforms the conventional International Satellite Cloud Climatology Project algorithm for both TP and plainsThe study reveals significant diurnal and seasonal variations in low clouds over the TP [ABSTRACT FROM AUTHOR]

With the rapid global warming in recent decades, the Tibetan Plateau (TP) has suffered severe impacts, such as glacier retreat, glacial lake expansion, and permafrost degradation, which threaten the lives and properties of the local and downstream populations. Regional Reanalysis (RR) is vital for TP due to the limitations of observations. In this work, a 62-year (1961–2022) long atmospheric regional reanalysis with spatial resolution of 9 km (convective gray-zone scale) and temporal resolution of 1 hour over the TP (TPRR) was developed using the Weather Research and Forecasting (WRF) model, combined with re-initialization method, spectral nudging (SN), and several optimizations. TPRR is forced by ERA5 at hourly intervals. TPRR outperforms ERA5, realistically capturing climatological characteristics and seasonal variations of precipitation and T2m (air temperature at 2m above ground level). Moreover, TPRR better reproduces the frequency and intensity of precipitation, as well as the diurnal cycle of precipitation. This study also quantifies the wetting trend of 0.0071 mm/year over the TP amid global warming using TPRR. [ABSTRACT FROM AUTHOR]

The effect of topography on shortwave downward radiation (SWDR) is interest in the geoscience. However, such effects are rarely quantiatively and systematically evalulated, especially over the Tibetan Plateau region. With the geostationaly satellite measurements and topographic radiation model, this study reveals a heightened significance of topography on SWDR with increasing slope. Particularly in abrupt terrain (slopes >15°) the impact becomes pronounced, wherein the topographic radiative forcing (TRF) contributes 9.5% of the annual‐average SWDR. And the ratio of TRF to SWDR reaches a peak during winter, exceeding 150%. In annual‐average scales, the SWDR is 169 ± 38.4 W/m2 and the corresponding TRF is 16.2 ± 22.6 W/m2. Seasonal variations manifest on northern and southern slopes, with the sourthern slopes significant in summer, while the northern ones significant in winter. Notably, topographic effects persist across spatial scales and remain evident at 5 km resolution, emphasizing the necessity of considering topography in SWDR product utilization. Plain Language Summary: Shortwave downward radiation is the main source of surface energy. In mountainous areas, the terrain significantly alters the amount of received SWDR. This study comrephensively examines the topographic influence on SWDR across the Tibetan Plateau for the first time. We investigate the influence of diverse topographic factors on the distribution of surface shortwave radiation in mountainous terrains. With the slope increasing, the impact of topography on SWDR becomes more and more significant. The topographic impact of northern and sourthern slopes behaves obvious seasonal variations, with the sourthern slopes significant in summer, while the northern ones significant in winter. With the increasing of spatial scale, the topographic effect gradually decreases and tends to be stable, but it can never disappear. As remote sensing data resolution coarsens, topographic radiative forcing diminishes, but even at 5 km resolution, terrain significantly affects SWDR distribution. Key Points: In abrupt slopes, the annual‐average proportion of topographic radiative forcing to shortwave downward radiation (SWDR) can reach up to 9.5%The proportion of topographic radiative forcing to SWDR exceeding 150% in winter on the northern slopesDespite decreasing influence with coarser spatial resolutions, topographic effects persist even at 5 km [ABSTRACT FROM AUTHOR]

The Qinghai–Tibetan Plateau (QTP), which has a unique and severe environment, suffers from the absence of rainfall gauges in western arid land. Using different precipitation products in this region would easily lead to contradictory results. To evaluate nine fine-resolution precipitation products in the QTP, we propose a "down to top" methodology, based on water balance and drought chain, by forecasting two accuracy assessment indices—multi-year precipitation bias and precipitation correlation. We assessed the biases of all products in the Jinsha–Yalong, Yellow, Heihe, Yangtze, Yarlung Zangbo catchments and interior drainage areas. And we assessed gauge-based correlation of precipitation products, based on the correlations between precipitation product-based effective drought index (EDI), Soil Moisture Active Passive (SMAP)-based soil moisture anomaly, and the moderate-resolution imaging spectroradiometer (MODIS)-based normalized difference vegetation index (NDVI) anomaly (R = 0.712, R = 0.36, and R = 0.785, respectively) for cross-sectional rainfall observations on the Tibetan Plateau in 2018. The results showed that ERA5-Land and IMERG merged precipitation dataset (EIMD) can efficiently close the water budget at the catchment scale. Moreover, the EIMD-based EDI exhibited the best performance in correlation with both the SMAP-based soil moisture anomaly and MODIS-based NDVI anomaly for the three main herbaceous species areas—Kobresia pygmaea meadow, Stipa purpurea steppe, and Carex moorcroftii steppe. Overall, we find that EIMD is the most accurate among the nine products. The annual average precipitation (2001–2018) was determined to be 568.16 mm in the QTP. Our assessment methodology has a remote sensing basis with low cost and can be used for other arid lands in the future. [ABSTRACT FROM AUTHOR]

To reduce the uncertainty estimation of clouds and improve the forecast of surface shortwave radiation (SSR) over the Tibetan Plateau, a new cloud assimilation system is proposed which is the first attempt to directly apply the four-dimensional local ensemble transform Kalman filter method to assimilate the cloud optical thickness (COT). The high-resolution spatial and temporal data assimilated from the next-generation geostationary satellite Himawari-8, with the high-assimilation frequency, realized an accurate estimation of the clouds and radiation forecasting. The COT and SSR were significantly improved after the assimilation by independent verification. The correlation coefficient (CORR) of the SSR was increased by 11.3%, and the root-mean-square error (RMSE) and mean bias error (MBE) were decreased by 28.5% and 58.9%, respectively. The 2-h cycle assimilation forecast results show that the overestimation of SSR has been effectively reduced using the assimilation system. These findings demonstrate the high potential of this assimilation technique in forecasting of SSR in numerical weather prediction. The ultimate goal that to improve the model forecast through the assimilation of cloud properties requires further studies to achieve. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP) is a vital and vulnerable water tower that supports the livelihoods of billions of people. The use of a data-driven recharge–storage–runoff perspective enables a more comprehensive estimation of multiple aspects of the water cycle. Through an analysis of the diagnostic net water flux from ERA5, water storage changes (dS/dt) from GRACE, runoff estimations (R) from the land–atmosphere water balance, and river discharge measurements (Rd), the annual cycle of recharge–storage–runoff has been studied over the TP and its basins. The net water flux determines a recharge of 326 mm yr−1 over the TP. Recharge in coupled storages, leading to an increase in water mass (dS/dt > 0) and runoff (R > 0) during the wet season, is considered the fast response and is measured using the ratio of runoff to net water flux (r1). Conversely, the slow response determined by the water storage release (dS/dt < 0) during the dry season is quantified by the ratio of storage release to runoff (r2). The ratios of r1 and r2 are influenced by climatic and terrain drivers, indicating specific characteristics of recharge–storage–runoff at the basin scale. Small r1 values and large r2 values suggest high buffer capacity, while the basin of Amu Darya (Salween) is characterized by the highest (lowest) buffer capacity over the TP. However, measurements of river discharge at Amu Darya suggest an uncoupled recharge–storage–runoff. The imbalance between river discharge and runoff estimation was most severe in the first decade of the twenty-first century but has been mitigated since 2012. River discharge at Amu Darya is likely constrained by energy during summer. [ABSTRACT FROM AUTHOR]

Most climate models show systematic cold biases during snow‐covered period over the Tibetan Plateau (TP), which is associated with snow and surface albedo overestimations. In this work, a snow cover fraction (SCF) scheme and a recently developed albedo scheme for shallow snow are implemented in the Noah‐MP land surface model coupled with the Weather Research and Forecasting (WRF) model. The SCF scheme introduces subgrid orographic variability to reduce the SCF, and the shallow‐snow albedo scheme parameterizes the fresh‐snow albedo as a function of the snow depth (SD). Evaluations by remote sensing data show that both schemes can effectively alleviate the overestimation of the simulated surface albedo, SCF, snow water equivalent, and SD over the TP. The reductions in the modeled SCF and snow albedo directly lead to lower surface albedo values and thus more surface solar radiation absorption, which accelerates snow melting and causes surface warming effects. Further comparisons with Moderate Resolution Imaging Spectroradiometer data and station observations show that both schemes can significantly reduce the cold biases in the surface skin temperature (from −4.39°C to 0.19°C for the TP mean) and 2‐m air temperature (from −4.48°C to −1.05°C for the station mean) during the cold season (October to May of next year) in the study region. This work provides guidance for advancing the snow‐related physics in climate models and the improved WRF model could facilitate weather forecasting and climate prediction for the plateau region. Plain Language Summary: The cold bias of the Weather Research and Forecasting model over the Tibetan Plateau is significantly reduced by implementing a snow coverage‐topography relationship and a fresh snow albedo scheme. With the introduction of the subgrid orographic variability in parameterizing the snow cover fraction and a shallow‐snow albedo scheme in parameterizing the fresh‐snow albedo, less snow and a lower surface albedo are simulated. Thus, more solar radiation is absorbed by the land surface, leading to a surface warming effect. As a result, the cold biases in the surface skin temperature and 2‐m air temperature are significantly reduced when evaluated by Moderate Resolution Imaging Spectroradiometer data and station observations. Key Points: A snow coverage‐topography relationship and a fresh snow albedo scheme are implemented in Weather Research and Forecasting and applied to the Tibetan Plateau (TP)The overestimation in the simulated snow cover, snow depth (SD) and albedo over the TP is significantly alleviatedThe modeled cold biases over the TP are significantly reduced due to the enhanced surface net solar radiation induced by albedo reduction [ABSTRACT FROM AUTHOR]

There has been a statement that the satellite and reanalyses significantly overestimate snow depths (SDs) as compared with in-situ observations over the Tibetan Plateau (TP). The inconsistency may be partly due to representations related to different spatial resolutions. To further clarify matters of scale, this work estimates and compares the fractional snow cover (FSC) from MODIS (500 m), SDs from Sentinel-1 C-band SAR (1 km), ERA5-Land (9 km) and ERA5 (31 km), together with ground observations from GHCN-D and at 6 newly-established stations in the Namco watershed. SD and FSC from MERRA-2 (50 km) are also discussed in comparison with ERA5s. Results indicate that SD from fine resolution reanalysis has better consistency to in-situ observations over the TP. SD of ERA5-Land matches in-situ measurements better than ERA5 and MERRA-2. Overestimates of SDs in reanalyses are likely for shallow snowpack. However, underestimates are found for deep snow particularly late in the snow season. Improvements are displayed in ERA5-Land, while increased SD is attributed by altitude in fine resolution. Overall SDs of ERA5 and ERA5-Land have similar spatial distributions and annual cycle patterns over the TP, consistent with satellite-based datasets. A notable defect of ERA5s is related to delayed ablation of deep snowpack during spring and early summer. It causes cold temperature biases at surface that may impact on the land-atmosphere interaction. Snow analysis that combines the information of FSC into SD has been demonstrated in MERRA-2 data. Snow analysis has the potential to improve SDs of ERA5s over the TP. [ABSTRACT FROM AUTHOR]

An observation‐based parameterization of thermal roughness length (TRP) for bare ground has been implemented in the Weather Research and Forecasting model. This scheme introduces a temperature scale to account for the impact of atmospheric stability. The effect of the TRP scheme is evaluated by comparing simulations with the default and with the implemented one for the Tibetan Plateau (TP) region. We find that the newly implemented scheme generally shows remarkable improvement in simulating the nighttime near‐surface 2m air temperature (T2) and daytime surface skin temperature (TSK), with a reduction in cold biases. The newly implemented scheme directly leads to weakened sensible heat release to the atmosphere in the daytime, thereby increasing energy storage in the land. These changes enhance snow melting in spring and reduce snow accumulation in autumn. Due to reduced snow cover and decreased albedo in the two transitional seasons, more solar radiation is absorbed by the land surface, leading to higher TSK and T2. In summer, the snow albedo feedback plays a minor role when there is minimal snow cover. In winter, the solar radiation is weak, and the atmosphere is relatively stable. Consequently, the warming effect due to the newly implemented scheme is more remarkable in the transitional seasons than in summer and winter. This work demonstrates the importance of the TRP in simulating T2 and TSK in the coupled atmosphere‐land model, and reveals that the influence of this scheme on T2 and TSK over the TP region is mainly associated with snow‐albedo‐shortwave radiation processes. Key Points: The parameterization of thermal roughness length (TRP) is important for simulating the 2m air temperature (T2) and skin temperature (TSK)The influence of the TRP on T2 and TSK is more significant during transitional seasons than in summer and winterAt seasonal scale, the TRP influences T2 and TSK mainly through snow‐albedo‐shortwave radiation processes [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP) has important influences on regional and global climate change. Here, we perform an in‐depth study of the relationship between cloud parameters and meteorological factors over the TP. The spatiotemporal variations in cloud cover and cloud optical thickness over the TP during the daytime from 2003 to 2015 are analysed using the Aqua‐MODIS level 2 atmospheric product data MYD06. Results show that the annual average cloud cover over the TP decreases from the southeast to the northwest. The cloud cover of the western TP is highest in spring and lowest in autumn, while the cloud cover of the eastern TP is higher in spring and summer. The cloud covers in most areas of the TP exceed 30% in spring and summer, and the cloud optical thickness in the southeastern TP exceeds 10 in summer, with substantial cloud cover and cloud optical thickness changes. Compared to other seasons, the cooling effect of the near surface cloud net radiative forcing is greater during daytime in summer, which is likely associated with thick cloud optical thickness or large cloud cover. The results of the analysis of relationships among cloud cover, water vapour and air temperature show that positive correlations exist between cloud cover and water vapour, and that significantly negative correlations exist between cloud cover and air temperature over the TP. Combined with the analysis of variation features in the cloud parameters and meteorological factors, we find that water vapour variations during the daytime over the TP lead to cloud cover changes, which affect the air temperature variations over the TP by the cooling effects of clouds, especially in summer. The cloud cover of the western Tibetan Plateau (TP) is highest in spring and lowest in autumn, while the cloud cover of the eastern TP is higher in spring and summer. Combined with the analysis of variation features in the cloud parameters and meteorological factors, we find that water vapour variations during the daytime over the TP lead to cloud cover changes, which affect the air temperature variations over the TP by the cooling effects of clouds, especially in summer. [ABSTRACT FROM AUTHOR]

Long-term highly accurate surface soil moisture data of TP (Tibetan Plateau) are important to the research of Asian monsoon and global atmospheric circulation. However, due to the sparse in-situ networks, the lack of soil moisture observations has seriously hindered the progress of climate change researches of TP. Based on the Dual-Channel soil moisture retrieval algorithm and the satellite observation data of AMSR-E (Advanced Microwave Scanning Radiometer for EOS), we have produced the surface soil moisture data of TP from 2003 to 2010 and analyzed the seasonal characteristic of the soil moisture spatial distribution and its multi-year changing trend in area of TP. Compared to the in-situ observations, the accuracy of the soil moisture retrieved by the proposed algorithm is evaluated. The evaluation result shows that the new soil moisture product has a better accuracy in the TP region than the official product of AMSR-E. The spatial distribution of the annual mean values of soil moisture and the seasonal variations of the monthly-averaged soil moisture are analyzed. The results show that the soil moisture variations in space and time are consistent with the precipitation distribution and the water vapor transmission path in TP. Based on the new soil moisture product, we also analyzed the spatial distribution of the changing trend of multi-year soil moisture in TP. From the comparisons with the precipitation changing trend obtained from the meteorological observation sites in TP, we found that the spatial pattern of the changing trend of soil moisture coincides with the precipitation as a whole. [ABSTRACT FROM AUTHOR]

The Qinghai-Tibet Plateau plays a very important role in studying severe weather in China and around the globe because of its unique characteristics. Moreover, the surface emissivities of the Qinghai-Tibet Plateau are also important for retrieving surface and atmospheric parameters. In the current study, a retrieval algorithm was developed to retrieve the surface emissivities of the Qinghai-Tibet Plateau. The developed algorithm was derived from the radiative transfer model and was first validated using simulated data from a one-dimensional microwave simulator. The simulated results show good precision. Then, the surface emissivities of the Qinghai-Tibet Plateau were retrieved using brightness temperatures from the advanced microwave-scanning radiometer and atmospheric profile data from the moderate resolution imaging spectroradiometer. Finally, the features of the time and space distribution of the retrieved results were analyzed. In terms of spatial characteristics, a spatial distribution consistency was found between the retrieved results and surface coverage types of the Qinghai-Tibet Plateau. In terms of time characteristics, the changes in emissivity, which were within 0.01 for every day, were not evident within a one-month time scale. In addition, surface emissivities are sensitive to rainfall. The reasonability of the retrieved results indicates that the algorithm is feasible. A time-series surface emissivity database on the Qinghai-Tibet Plateau can be built using the developed algorithm, and then other surface or atmospheric parameters would have high retrieval precision to support related geological research on the Qinghai-Tibet Plateau. [ABSTRACT FROM AUTHOR]

Abstract: In this work, an algorithm for automatic subpixel snow mapping was developed for Moderate Resolution Imaging Spectroradiometer (MODIS) data of the Qinghai–Tibet Plateau. The algorithm consists of two parts: cloud removal and snow mapping. An approach to remove cloud was presented and it was demonstrated to remove thick and thin clouds well from MODIS images. Multiple-endmember spectral mixture analysis was used in the subpixel snow mapping of the Qinghai–Tibet Plateau, and multiple indices (including the normalized difference vegetation index and normalized difference snow index) were introduced to automatically select the endmembers. Additionally, the combined use of typical and neighboring endmembers was introduced into the unmixing of mixed pixels. Finally, highly accurate snow-cover data of the Qinghai–Tibet Plateau obtained with this algorithm were stored in a spatio-temporal database. The results of the subpixel snow mapping were validated with Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data recorded at the same time as corresponding MODIS data. According to the validation results, the correlation coefficients of the MODIS results versus the ASTER data exceed 0.9, and the root-mean-square errors are less than 0.2. [Copyright &y& Elsevier]

[Display omitted] • Hydrological ensemble modeling is set up with multiple remote sensing precipitation products. • An acceleration of the hydrological cycle in TP is evidenced with long-term water budgets. • High-yield runoff is detected in southern TP because of the large amount of precipitation and glacier mass loss. Global warming potentially increases precipitation and intensifies water exchange, thereby accelerating the hydrological cycle. The Tibetan Plateau (TP) is an Asian water tower in which the water budget varies and its anomaly exerts stress on resource availability. Few studies have quantified long-term water budgets across TP owing to scarcity of ground-based observations and uncertainties in remote sensing data. In this study, water budget components (i.e., precipitation, glacial melting [GM], evapotranspiration [ET], runoff, and soil moisture [SM] state) in TP are synthetically estimated for the past three decades. The water budget estimation benefits from a GM-coupled hydrological ensemble modeling, which is forced by nine precipitation products with seven from satellite methods. The results show that the ensemble modeling effectively captures the dynamics of runoff, ET, and terrestrial water storage. The long-term average annual water input (sum of precipitation and GM) was approximately 438 mm, with ∼4 % contribution from GM, for which the annual ET and runoff take away was approximately 263 and 173 mm, respectively. From 1984 to 2015, the four water fluxes significantly increased with varying rates (2.3 mm/yr, precipitation; 0.9 mm/yr, GM; 1.5 mm/yr, ET; 1.1 mm/yr, runoff), which suggested an accelerating hydrological cycle. Particularly, increasing GM (∼5.8 mm/yr) in the Nyainqentanglha Mountains in southern TP induced high-yield runoff (>800 mm). These estimations aid in yielding robust solutions for water management in TP and neighboring regions. The accelerated hydrological cycle implies potential flooding risk and vulnerability of the hydrological system under climate change. [ABSTRACT FROM AUTHOR]