Water stress is a major environmental factor limiting vegetation productivity, however, large interactions between atmospheric and soil dryness still hinder a complete understanding of the main cause of the widespread drought-related decreased vegetation productivity. In this study, we investigated inter-annual changes in gross primary productivity during 1982 to 1998 and 1998 to 2018 using two independent remote sensing products. We also analyzed the impacts of temperature, soil moisture, and vapor pressure deficit on gross primary productivity trends during 1982 to 1998 and 1998 to 2018 to explore the causes of gross primary productivity declines during recent decades. Results show that gross primary productivity trend during 1998 to 2018 tends to stall after the year 1998 concurrent with a significant enhancement of a positive vapor pressure deficit trend during 1998 to 2018, particularly in forests, grasslands, and warmer regions. In the Northern Hemisphere, whilst increasing vapor pressure deficit plays a dominant role in weakening the gross primary productivity trend from 1998 to 2018, changing soil moisture and temperature also influences the trends as identified in different regional responses. In addition, results from 8 dynamic global vegetation models showed that the dynamic vegetation models fail to capture the inter-annual changes in gross primary productivity, likely due to an overestimation of gross primary productivity responses to soil water.

Extensive research has been conducted on daily scale precipitation the Hengduan Mountain.The hourly scale data, which provides additional insights such as diurnal patterns, remains largely unexplored.Therefore, a fundamental study of the characteristics of extreme precipitation at the hourly scale is essential in the Hengduan Mountain.In this study, we analyzed the spatial and temporal distribution characteristics of hourly extreme precipitation (95th percentile, R95) and very extreme precipitation (99th percentile, R99) using Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) half-hourly satellite precipitation data from 2001 to 2020.Furthermore, we explored further the contribution to the whole year of rainy and non-rainy seasons periods.The results showed that the distribution pattern of hourly extreme precipitation and hourly very extreme precipitation thresholds, amount, intensity, and duration were decreasing from southeast to northwest in the Hengduan Mountain, and the number was high in the east and west but less in the center.Hourly extreme precipitation and hourly very extreme precipitation amounts occupied 29.58% and 10.15% of the annual total amount, respectively.In the analysis of interannual variability, an increasing trend in the amount, number and intensity were found for hourly extreme precipitation and hourly very extreme precipitation, while the duration is generally showed a decreasing trend, with an increase in the amount of 4.93 and 2.83 mm annually, respectively.The amount experienced a significant increase in the northeastern part of the Hengduan Mountain.In terms of diurnal variations, the hourly extreme precipitation and hourly very extreme precipitation amount and number were concentrated at nighttime, with two peaks during 17:00 (Beijing Time, same as after) -21:00 and 00:00 -02:00.A discrepancy in the distribution of the maximum occurrence time were found in the northern and southern regions in the Hengduan Mountain.The increasing trend in the hourly extreme precipitation amount was primarily associated with an increase in number, which was more pronounced in the hourly very extreme precipitation.During the rainy season, the amount and frequency of hourly extreme precipitation and hourly very extreme precipitation accounted for over 90% and significantly influenced the annual pattern of characteristics.In contrast, during the non-rainy season, the maximum occurrence time of amount and number were delayed by 1 hour in the main region compared to the annual pattern.

Climate change has led to increased drought stress in Southwest China (SWC), which used to have abundant precipitation and a humid climate. The increasing drought disaster significantly threatens the ecological security of SWC, accounting for 1/5 carbon stock of China. However, there is a lack of research regarding the response of vegetation growth (Leaf Area Index (LAI), Gross Primary Productivity (GPP)) to various extreme drought events. This study utilized multi-source remote sensing data sets and the Community Land Model version5 (CLM5.0) to examine the response of vegetation growth to all the three extreme drought events during 2001-2016. We found that areas that experienced the most drought events did not suffer the most severe vegetation losses. The longest-lasting (14 months) drought in 2009 caused the most severe immediate impacts (40.1% and 29.2% of SWC experienced LAI and GPP reductions) but not the strongest lagged effects due to faster soil water recovery. All data sets showed that drought caused more severe losses in LAI (29.61% of SWC experienced reductions) than in GPP (24.36%), and GPP exhibited higher resistance (Rt approximate to 27.18) than LAI (Rt approximate to 17.74) across all droughts. Moreover, CLM5.0 simulations revealed that trees with deeper roots displayed higher drought resistance but lower resilience from slower soil water recovery than grass. Our findings highlight the necessity of using multiple data sets and metrics to accurately reveal the complex impacts of different drought events. Future studies should develop ecosystem models with more accurate parameterization settings to better predict drought-caused ecological losses.

The Hengduan Mountains are susceptible to hydrological disasters, with precipitation representing a significant risk factor. For effective disaster mitigation strategies, accurate rainfall simulation is essential, typically achieved through the use of numerical models. Some research has indicated that using a convection-permitting model (CPM) at high resolution (< 4 km) could provide more precise rainfall estimates than traditional cumulus parameterization schemes (CPs) at lower resolutions, but CPM demands substantial computational resources. Therefore, to assess whether CPM maintains superior simulation accuracy, this study employed the Weather Research and Forecasting (WRF) model to simulate summer precipitation over the Hengduan Mountains in 2009, comparing CPM (4 km) and CPs (10 km) resolutions. The simulations were evaluated against satellite observations to quantify their performance differences. The results showed that all simulations overestimated amounts and frequency. The CPM outperformed most CPs, except the Tiedtke scheme, which exhibited Root Mean Square Errors (RMSEs) of 2.51 mm·day−1 for amount and 5.63

This study aims to investigate the microphysical structure and hydrometeor conversion processes of convective clouds in the Yushu region of the Tibetan Plateau （referred to as the Plateau）.Using the WRF mesoscale numerical forecast model combined with observational data from the Yushu region in Qinghai during the summer of 2019， we analyzed a summer convective precipitation event in the Yushu area.The results show： （1） The 24-hour cumulative precipitation simulated by WRF is similar to the observed precipitation at the Yushu station.The spatial and temporal distribution of simulated precipitation echoes is generally consistent with Ka-band millimeter-wave cloud radar detection results， indicating the reliability of the simulation results.（2） Particles of different phases in precipitation clouds show distinct vertical distribution structures.The maximum centers of solid hydrometeors are all at relatively high altitudes， with cloud ice's maximum center being the highest at around 200 hPa.The maximum center of liquid hydrometeors is at 500 hPa.Water vapor's maximum center is at the lowest height， below 500 hPa， and its maximum value appears earlier than other particles.（3） In cloud microphysical conversion processes， cloud water makes the largest contribution to precipitation.Water vapor forms snow， graupel， and other hydrometeors through deposition.Ice-phase particles transform into graupel and snow particles through processes such as aggregation， Bergeron process， collection， and collision-coalescence.As they descend， ice-phase particles melt and combine with cloud water， accelerating the conversion of cloud water to rainwater.

As a unique alpine peat swamp wetland situated on the Tibetan Plateau,climate change in the Zoige ecological region not only impacts its own fragile ecological environment,but also exerts influence over the cli-mate dynamics of the upper reaches of the Yellow River.Moreover,it plays crucial role in the climate stabiliza-tion of the western China.To investigate the simulation ability of the current high-resolution climate models in this area and to project the possible changes of the climate in this area in the future,this paper uses the four high-resolution climate models in the Coupled Model Intercomparison Program Phase 6(CMIP6),AWI-CM-1-1-MR,EC-Earth3,EC-Earth3-CC,MPI-ESM1-2-HR,and MPI-ESM1-2-HR,compared with the CN05.1 obser-vation dataset provided by the National Climate Center,to evaluate the simulation capability of CMIP6 high-res-olution models on the temperature and precipitation of the Zoige ecological region,and to make future tempera-ture and precipitation projection under four different Shared Socioeconomic Pathway(SSP)scenarios.The re-sults show that all the high-resolution CMIP6 models can simulate the distribution pattern and trend of tempera-ture in the Zoige ecological region,but all of them have the underestimation of temperature,especially in the western Zoige ecological region,where the correlation coefficient of multi-model ensemble(MME)with the an-nual average time series of the observed data is 0.75,and the MME is lower than the multi-year average of the observed data by 0.75℃.For the precipitation simulation,there is an obvious simulation overestimation in all models as well as MME,with an overestimation of 1.45 mm·d-1 in MME,and the correlation coefficient be-tween MME and the observed data is 0.21 for the less satisfactory simulation of precipitation trend compared with the temperature.In 2100,the SSP scenarios with low to high emission concentrations of SSP1-2.6,SSP2-4.5,SSP3-7.0,and SSP5-8.5 scenarios are expected to have warming increases of about 1.8,3.2,5.2 and 5.8 ℃,respectively,relative to the historical baseline period;the precipitation in the Zoige ecological region is expected to increase the most in the low SSP1-2.6 scenario compared with the historical period.The increase in 2100 was about 0.4 mm·d-1 compared to the historical period,while the medium to high concentration SSP2-4.5,SSP3-7.0 and SSP5-8.5 scenarios showed a slight increase and smaller difference in precipitation changes by the end of the 21st century,with increases ranging from 0.1 mm·d-1 to 0.2 mm·d-1.The results of the study can provide scientific basis for water resource management and climate change adaptation research in the upper reaches of the Yellow River,and are also of great significance to the ecological protection of Zoige wetland.

The diurnal temperature range (DTR) serves as a vital indicator reflecting both natural climate variability and anthropogenic climate change. This study investigates the historical and projected multitemporal DTR variations over the Tibetan Plateau. It assesses 23 climate models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) using CN05.1 observational data as validation, evaluating their ability to simulate DTR over the Tibetan Plateau. Then, the evolution of DTR over the Tibetan Plateau under different shared socioeconomic pathway (SSP) scenarios for the near, middle, and long term of future projection are analyzed using 11 selected robustly performing models. Key findings reveal: (1) Among the models examined, BCC-CSM2-MR, EC-Earth3, EC-Earth3-CC, EC-Earth3-Veg, EC-Earth3-Veg-LR, FGOALS-g3, FIO-ESM-2-0, GFDL-ESM4, MPI-ESM1-2-HR, MPI- ESM1-2-LR, and INM-CM5-0 exhibit superior integrated simulation capability for capturing the spatiotemporal variability of DTR over the Tibetan Plateau. (2) Projection indicates a slightly increasing trend in DTR on the Tibetan Plateau in the SSP1-2.6 scenario, and decreasing trends in the SSP2-4.5, SSP3-7.0, and SPP5-8.5 scenarios. In certain areas, such as the southeastern edge of the Tibetan Plateau, western hinterland of the Tibetan Plateau, southern Kunlun, and the Qaidam basins, the changes in DTR are relatively large. (3) Notably, the warming rate of maximum temperature under SSP2-4.5, SSP3-7.0, and SPP5-8.5 is slower compared to that of minimum temperature, and it emerges as the primary contributor to the projected decrease in DTR over the Tibetan Plateau in the future.

Global gridded crop models (GGCMs) have been broadly applied to assess the impacts of climate and environmental change and adaptation on agricultural production. China is a major grain producing country, but thus far only a few studies have assessed the performance of GGCMs in China, and these studies mainly focused on the average and interannual variability of national and regional yields. Here, a systematic national- and provincial-scale evaluation of the simulations by 13 GGCMs [12 from the GGCM Intercomparison (GGCMI) project, phase 1, and CLM5-crop] of the yields of four crops (wheat, maize, rice, and soybean) in China during 1980–2009 was carried out through comparison with crop yield statistics collected from the National Bureau of Statistics of China. Results showed that GGCMI models generally underestimate the national yield of rice but overestimate it for the other three crops, while CLM5-crop can reproduce the national yields of wheat, maize, and rice well. Most GGCMs struggle to simulate the spatial patterns of crop yields. In terms of temporal variability, GGCMI models generally fail to capture the observed significant increases, but some can skillfully simulate the interannual variability. Conversely, CLM5-crop can represent the increases in wheat, maize, and rice, but works less well in simulating the interannual variability. At least one model can skillfully reproduce the temporal variability of yields in the top-10 producing provinces in China, albeit with a few exceptions. This study, for the first time, provides a complete picture of GGCM performance in China, which is important for GGCM development and understanding the reliability and uncertainty of national- and provincial-scale crop yield prediction in China.

In the context of rising temperatures and increasing humidity in Northwest China, substantial gaps remain in understanding the mechanisms of land–atmosphere cloud–precipitation coupling across the northeastern Tibetan Plateau (TP), Loess Plateau (LP), and Huangshui Valley (HV). This study addresses these gaps by investigating cloud properties and precipitation patterns utilizing the Fengyun-4 Satellite Quantitative Precipitation Estimation Product (FY4A-QPE) and ERA5 datasets. We specifically focus on Lanzhou, a pivotal city within the LP, and Xining, which epitomizes the HV. Our findings reveal that diurnal variations in precipitation are significantly less pronounced in the eastern regions compared to northeastern TP. This discrepancy is attributed to marked diurnal fluctuations in convective available potential energy (CAPE) and wind shear between 200 and 500 hPa. While both cities share similar wind shear patterns and moisture transport directions, Xining benefits from enhanced snowmelt and effective water retention in surrounding mountains, resulting in higher precipitation levels. Conversely, Lanzhou suffers from moisture deficits, with dry, hot winds exacerbating the situation. Notably, precipitation in Xining is strongly correlated with CAPE, influenced by diurnal variability, and intensified by valley and lake–land breezes, which drive afternoon convection. In contrast, Lanzhou’s precipitation exhibits a weak relationship with CAPE, as even elevated values fail to generate significant cloud formation due to insufficient moisture. The ongoing trends of warming and humidification may lead to improved precipitation patterns, especially in the HV, with potential ecological benefits. However, concentrated rainfall during summer afternoons and midnights raises concerns regarding extreme weather events, highlighting the susceptibility of the HV to geological hazards. This research underscores the need to further explore the uncertainties inherent in precipitation dynamics in these regions.

The warming climate driven by global change has great potential in altering regional and global hydrologic cycles, thus leading to considerable changes in spatial variability and temporal pattern of precipitation. Northwest China (NW) has witnessed a significant wetting trend over the past decades, while the persistence of this wetting trend and potential changes in precipitation under future climate impacts remains elusive. In this study, long-term meteorological observations were used to probe historical variations of precipitation from 1951 to 2020, and the WRF model was employed as a regional climate model to examine future precipitation patterns over NW. Two 9-year downscaled WRF simulations were conducted comprising of historical (WRF-HIST; 2012–2020) and future climate change scenarios (WRF-SSP585; 2047–2055) using bias-corrected global climate model outputs from Coupled Model Intercomparison Project Phase 6 (CMIP6). Compared with ground observations, the WRF model exhibited strong capability in capturing the spatial pattern and temporal variations of precipitation across the NW. Intense precipitation was mainly found in stations located at northern NW and southeastern NW. Summertime precipitation substantially contributed to annual precipitation over the study region. Future precipitation projections suggest significant decreases of precipitation across the southern and eastern NW, with a stronger reduction magnitude in summer. Further, extreme precipitation events were projected to decrease in spring and summer, suggesting that the NW may become drier and the wetting trend may shift to another pattern in the 2050s under the SSP585 climate scenario. Overall, this study reveals historical and future potential changes in precipitation over NW through a high-resolution, dynamically downscaled dataset from WRF modeling, which in turn will help inform regional mitigation and adaption on potential impacts of future climate change on NW.

The remote sensing products showed significant vegetation greening during 1980-2010 under the “warm-humid” climate changes over the Tibetan Plateau. Several previous studies showed such significant increasing of vegetation NDVI and LAI resulted an overall cooling effects on climate over the Tibetan Plateau. Our field survey in 2008 and 2018 at 36 alpine grassland sites showed that aboveground biomass increased for legumes and forbs, but decreased for grasses and sedges, resulting in no overall change in the aboveground biomass during the 10-year period. Such hiatus of Tibetan Plateau vegetation greening was also found in three remote sensing products (GLASS, Globmap, GIMMS). We run WRF4.0 model to quantify the recent vegetation impact on climate during 2008-2018 and found the recent hiatus of Tibetan Plateau vegetation greening mainly showed warming effects due to the increasing of the daily minimum air temperature. Such warming effects also increased of the active layer depth and annual thawed fraction over the seasonal permafrost regions. Although the latent heat flux was also increased, the increasing water vapor showed insignificant impact on precipitation except on the cumulus precipitation in Fall.

Accurate characterization of land use and land cover changes (LULCC) is essential for numerical models to capture LULCC-induced effects on regional meteorology and air quality, while outdated LULC dataset largely limits model capability in reproducing land surface parameters, particularly for complex terrain. In this study, we incorporate land cover data from MODIS in 2019 into the Weather Research and Forecasting (WRF) model to simulate the impacts of LULC on meteorological parameters over the Sichuan Basin (SCB). Further, we conduct Community Multiscale Air Quality (CMAQ) simulations with WRF default LULC and MODIS 2019 to probe the effects on regional air quality. Despite consistency found between meteorological observations and WRF-CMAQ simulations, the default WRF land cover data does not accurately capture rapid urbanization over time compared with MODIS. Modeling results indicate that magnitude changes trigged by LULCC are highly varied across SCB and the impacts of LULCC are more pronounced over extended metropolitan areas due to alteration by urbanization, featured by elevating 2-m temperature up to 2°C and increased planetary boundary layer height (PBLH) up to 400 m. For air quality implications, it is found that LULCC leads to basin-wide O3 enhancements with maximum reaching 21.6 μg/m3 and 57.2 μg/m3 in the daytime and nighttime, respectively, which is mainly attributed to weakening NOx titration effects at night. This work contributes modeling insights into quantitative assessment for impacts of LULCC on regional meteorology and air quality which pinpoints optimization of the meteorology-air quality model.

The Sichuan Basin (SCB) of China is known for excessive ozone (O-3) pollution owing to high anthropogenic emissions combined with terrain-induced poor ventilation and weak wind fields against the surrounding mountains. While O-3 pollution has emerged as a prominent concern in southwestern China yet variations in O-3 levels during 2013-2020 are still unclear and the dominant factor in explaining the long-term O-3 trend throughout the SCB remains elusive due to uncertainties in emission inventory and variability associated with meteorological conditions. Here, we use extensive basin-wide ambient measurements to examine the spatial pattern and trend of O-3 and leverage OMI and TROPOMI satellites in conjunction with MEIC emission inventory to track emission changes. Sensitivity simulations are conducted by using WRF-CMAQ model to investigate the impacts of meteorological variability and emission changes on O-3 changes over 2013-2020. O-3 concentrations exhibit obvious interannual increases during 2013-2019 and a slight decrease in 2020. Both decreases in the MEIC emission inventory (-2.9% yr(-1)) and OMI NO2 column density (-3.1% yr(-1)) reflects the declining trend in NOx emissions over 2013-2020, while anthropogenic VOCs were not adequately regulated during 2013-2017, which explained the majority of deteriorated O-3 pollution from 2013 to 2017. Furthermore, attribution analysis based on CMAQ simulations indicate that the unexpected aggravated O-3 levels in 2019 is not only modulated by disproportional reductions in VOCs and NOx emissions, but also associated with unfavorable meteorological conditions featured by profound heatwaves and frequent stagnant conditions. In 2020, the abnormal meteorological conditions in May leads to substantial increase of O-3 by 26.8 mu gm(-3) as compared to May 2019, while the considerable enhancement was fully offset by low O-3 levels over the whole period which attributes to substantial emission reductions. This study reveals the long-term trend of O-3 levels and precursor emissions and highlights the effects of meteorological variability and emission changes on O-3 pollution over the SCB, with strong implications for designing effective O-3 control measures.

Changes in vegetation dynamics play a critical role in terrestrial ecosystems and environments. Remote sensing products and dynamic global vegetation models (DGVMs) are useful for studying vegetation dynamics. In this study, we revised the Community Land Surface Biogeochemical Dynamic Vegetation Model (referred to as the BGCDV_CTL experiment) and validated it for the Tibetan Plateau (TP) by comparing vegetation distribution and carbon flux simulations against observations. Then, seasonal–deciduous phenology parameterization was adopted according to the observed parameters (referred to as the BGCDV_NEW experiment). Compared to the observed parameters, monthly variations in gross primary productivity (GPP) showed that the BGCDV_NEW experiment had the best performance against the in situ observations on the TP. The climatology from the remote sensing and simulated GPPs showed similar patterns, with GPP increasing from northwest to southeast, although the BGCDV_NEW experiment overestimated GPP in the semi-arid and arid regions of the TP. The results show that temperature warming was the dominant factor resulting in the increase in GPP based on the remote sensing products, while precipitation enhancement was the reason for the GPP increase in the model simulation.

The Three Rivers Source Region (TRSR), the headwater region of the Yellow River, the Mekong River, and the Yangtze River, plays a significant role in water resources, food security, economy, and society in the downstream areas. This study applied a series of offline regional simulations of the Community Land Model (CLM5.0) over the TRSR to evaluate the impacts of regional climate and vegetation change on runoff. Firstly, we evaluated the performance of runoff depth using CLM5.0, the Nash–Sutcliffe efficiency between the simulated and observed runoff of TNH and ZMD gage stations are 0.56 and 0.51, respectively. The climate on the TRSR shows a warming and wetting trend, with the fastest warming rate in DJF (December, January, and February) and the fastest wetting rate in JJA (June, July, and August). Runoff increases in most of the TRSR with increased precipitation and decreases in the southeast of the Yellow River Source Region (YRSR). With increasing temperature, the simulated runoff shows a decreasing trend, while runoff tends to increase with precipitation enhancement over the TRSR. The results indicated that precipitation is the dominant factor affecting evapotranspiration (ET) and runoff, whilst the contribution of increasing temperature to runoff is 12

The Tibetan Plateau is the most extensive high-elevation grassland on Earth, with the largest expanse of high-elevation permafrost. It is experiencing climate warming that is projected to continue at rates above the global mean, potentially jeopardizing ecosystem functioning. We conducted a broad-scale resampling project in the permafrost region of Tibet to examine if plant production and diversity had changed over time. We recorded vascular plant species occurrences and harvested aboveground biomass at 36 alpine grassland sites in 2008 and 2018. Our results show that aboveground biomass increased for legumes and forbs, but decreased for grasses and sedges, resulting in no overall change in the aboveground biomass during the 10-year period. Our results indicate that functional group abundance may shift from grasses and sedges toward more legumes and forbs, and that species composition is becoming more similar between grassland types, and thus, beta diversity is decreasing in the permafrost region of Tibet.

The diurnal variation in precipitation and cloud parameters and their influencing factors during summer over the Tibetan Plateau (TP) and Sichuan Basin (SB) were investigated using the Hydro-Estimator satellite rainfall estimates, ground observations, and ERA5 dataset. The precipitation and cloud parameters show diurnal propagation over the SB during the mei-yu period in contrast to such parameters over the TP. The diurnal maximum precipitation from the Hydro-Estimator satellite and cloud ice and liquid water content (cloud LWC and IWC) from the ERA5 dataset are concentrated in the early evening, while their diurnal minimums manifest in the morning. Cloud LWC accounts for more than 60% of the total water during almost the entire diurnal cycle over the inner TP and SB during the mei-yu period. The IWC accounts for more than 60% of the total water in the late afternoon over the edge of the SB and TP. The cloud base height (CBH) above ground level (AGL), the lifting condensation level (LCL) AGL, and the zero degree level AGL are almost equal over the TP during the summer period. The zero degree level AGL over the SB is higher than that over the TP because the air temperature lapse rate over the TP is larger. The thickness of liquid water cloud over the SB is larger than that over the TP. The correlation analysis shows that the CBH AGL and LCL AGL over the TP are related to the dewpoint spread, but less so over the SB because of the stronger turbulence and lower air density over the TP than the SB. Convective available potential energy has a larger impact on precipitation over the TP than the SB. The cloud LWC makes a larger contribution to the precipitation over the SB than over the TP, which is related to the mean zonal wind and diurnal cycle of low-level winds. The precipitation at the edge of the TP and SB (i.e., the steep downstream slope) is largely influenced by the ice water contained within clouds owing to the convergence rising motion over the slopes.

Sustainable management of grasslands has always been an urgent issue for policy-makers. The three rivers source region (TRSR) contains widely distributed natural grasslands and is sensitive to climate warming. To enable the sustainable development of the human-nature system in the TRSR, we propose a novel indicator based on the allocation of aboveground net primary production (ANPP). The indicator we proposed is the ANPP that can be used for human activities (UANPP). In the study, we simulated the spatial and temporal patterns of the UANPP in the alpine grasslands in the TRSR during 1979–2016 and explored the main driving factors of the UANPP. The results revealed that (a) the annual total UANPP in the TRSR was 13.22 TgC, approximately accounting for 47% of total ANPP. (b) The areas with negative UANPP values accounted for 16% of the entire TRSR, and they were primarily located within the Nature Reserve of the Yangtze and Yellow river source regions, while three-quarters of the area exhibited improvement trends. (c) The regional mean UANPP significantly increased during 1979–2016, at a rate of 0.28 gC m −2 yr −1 ( p < 0.01). In the entire TRSR, 87% of the area exhibited increasing trends. (d) The UANPP in most areas of the TRSR was strongly correlated with precipitation, and the effect of human activities on the UANNP increased slightly during the 38 year study period. The UANPP represents the upper limit of human use of nature. These findings provide a reference for policy-makers to make decisions toward human-nature system sustainability while meeting human needs for grassland resources. ANPP allocation between nature and human system is a potentially important tool from the standpoint of sustainable development.

Based on the field observation and WRF-CLM model, the effects of Gyaring and Ngoring lakes on the short-term climate over the Yellow River source area during May to September have been studied through two experiments with and without the lakes. A backward water vapor transfer model was also employed to investigate the contribution of water vapor evapotranspiration from the Gyaring and Ngoring lakes and various surface types to the local precipitation. The results show that without the Gyaring and Ngoring lakes, the sensible heat is increased by 120%, whereas the latent heat is decreased by 58.5%, and the height of atmospheric boundary layer increases from 500 to 1,500–2,000 m during daytime over the lake area. The sum of sensible and latent heat fluxes in the lake area simulated by the experiment with and without the lakes is 185.8 and 130.3 W m−2, respectively. The precipitation amount over the lake area is significantly increased without considering the lake effect, generally by more than 20–40 mm. About 63.8% of the total precipitation in Gyaring and Ngoring lakes is contributed by the external water vapor sources. The evapotranspiration from the grassland is the secondary water vapor source for the precipitation in the Yellow River source area, and 25.2% of the total precipitation is contributed by this source. Around 4.2% of the total precipitation in the lake area is contributed by the evaporation from the Gyaring and Ngoring lakes.

The “warm-humid” climate change across the Tibetan Plateau (TP) has promoted grassland growth and an overall greening trend has been observed by remote sensing products. Many of the current generations of Earth System Models (ESMs) incorporate advanced process-based vegetation growth in the land surface module that can simulate vegetation growth, but the evaluation of their performance has not received much attention, especially over hot spots where projections of the future climate and vegetation growth are greatly needed. In this study, we compare the leaf area index (LAI) simulations of 35 ESMs that participated in CMIP6 to a remote-sensing-derived LAI product (GLASS LAI). The results show that about 40% of the models overestimated the Tibetan Plateau’s greening, 48% of the models underestimated the greening, and 11% of the models showed a declining LAI trend. The CMIP6 models generally produced poor simulations of the spatial distribution of LAI trend, and overestimated the LAI trend of alpine vegetation, grassland, and forest, but underestimated meadow and shrub. Compared with other vegetation types, simulations of the forest LAI trend were the worst, the declining trend in forest pixels on the TP was generally underestimated, and the greening of the meadow was underestimated as well. However, the greening of the grassland, was greatly overestimated. For the Tibetan Plateau’s averaged LAI, more than 70% of the models overestimated this during the growing seasons of 1981–2014. Similar to the forest LAI trend, the performance of the forest LAI simulation was the worst among the different vegetation types, and the forest LAI was underestimated as well.