Oceanic climate changes threaten the sustainability of Asia's water tower.

Water resources sustainability in High Mountain Asia (HMA) surrounding the Tibetan Plateau (TP)-known as Asia's water tower-has triggered widespread concerns because HMA protects millions of people against water stress1,2. However, the mechanisms behind the heterogeneous trends observed in terrestrial water storage (TWS) over the TP remain poorly understood. Here we use a Lagrangian particle dispersion model and satellite observations to attribute about 1 Gt of monthly TWS decline in the southern TP during 2003-2016 to westerlies-carried deficit in precipitation minus evaporation (PME) from the southeast North Atlantic. We further show that HMA blocks the propagation of PME deficit into the central TP, causing a monthly TWS increase by about 0.5 Gt. Furthermore, warming-induced snow and glacial melt as well as drying-induced TWS depletion in HMA weaken the blocking of HMA's mountains, causing persistent northward expansion of the TP's TWS deficit since 2009. Future projections under two emissions scenarios verified by satellite observations during 2020-2021 indicate that, by the end of the twenty-first century, up to 84% (for scenario SSP245) and 97% (for scenario SSP585) of the TP could be afflicted by TWS deficits. Our findings indicate a trajectory towards unsustainable water systems in HMA that could exacerbate downstream water stress.

Drying in the low-latitude Atlantic Ocean contributed to terrestrial water storage depletion across Eurasia.

Eurasia, home to ~70% of global population, is characterized by (semi-)arid climate. Water scarcity in the mid-latitude Eurasia (MLE) has been exacerbated by a consistent decline in terrestrial water storage (TWS), attributed primarily to human activities. However, the atmospheric mechanisms behind such TWS decline remain unclear. Here, we investigate teleconnections between drying in low-latitude North Atlantic Ocean (LNATO) and TWS depletions across MLE. We elucidate mechanistic linkages and detecte high correlations between decreased TWS in MLE and the decreased precipitation-minus-evapotranspiration (PME) in LNATO. TWS in MLE declines by ~257% during 2003-2017 due to northeastward propagation of PME deficit following two distinct seasonal landfalling routes during January-May and June-January. The same mechanism reduces TWS during 2031-2050 by ~107% and ~447% under scenarios SSP245 and SSP585, respectively. Our findings highlight the risk of increased future water scarcity across MLE caused by large-scale climatic drivers, compounding the impacts of human activities.

Increased moist heat stress risk across China under warming climate.

Heatwaves have afflicted human health, ecosystem, and socioeconomy and are expected to intensify under warming climate. However, few efforts have been directed to moist heat stress (MHS) considering relative humidity and wind speed, and moist heat stress risk (MHSR) considering exposure and vulnerability. Here we showed MHS and MHSR variations across China during 1998-2100 using China Meteorological Administration Land Data Assimilation System datasets, the 6th Coupled Model Intercomparison Project (CMIP6) merged datasets, Gross Domestic Product, population and leaf area index. We detected increased MHS across China under different Shared Socioeconomic Pathways (SSPs). Specifically, the historical MHS occurred mostly during mid-July to mid-August. We found increasing trends of 0.08%/year, 0.249%/year, and 0.669%/year in the MHS-affected areas under SSP126, SSP245, and SSP585, respectively. Furthermore, we observed the highest increasing rate of MHSR in Northwest and Southwest China, while the MHSR across Northeast and North China under SSP126 shifted from increasing to decreasing trends. Noteworthy is that the increasing trend of MHSR under SSP585 is 1.5-2.6 times larger than that under SSP245, especially in North and South China. This study highlights spatiotemporal evolutions of MHS and MHSR and mitigation to moisture heat stress in a warming climate.

Double increase in precipitation extremes across China in a 1.5 °C/2.0 °C warmer climate.

Paris Agreement's 1.5 °C or 2.0 °C global warming targets call for human concerns on warming climate on human society and environment in general. Here we analyzed spatiotemporal patterns and related impacts of precipitation extremes on human society across China using NEX-GDDP (NASA Earth Exchange Global Daily Downscaled Projections) dataset. We found increasing trends of almost all extreme precipitation indices except consecutive dry duration (CDD). Additional 0.5 °C warmer climate from 1.5 °C to 2.0 °C global warming targets can double increase of extreme precipitation indices. Specifically, the increase of Rx5day (Max 5-day precipitation amount) is from 3.98% to 7.63%, the increase of R95pTOT (precipitation in very wet days) is from 19.41% to 34.42% and the increase of PRCPTOT (annual total wet-day precipitation) is from 3.89% to 8.23%, showing that additional 0.5 °C warmer climate can potentially increase flood risks across China. While, we also found regional differences in responses of extreme precipitation to warming climate. Extreme precipitation in the Qinghai Tibet Plateau, the Western Arid and semiarid zone and in the lower Yangtze River basin is in higher sensitivity to warming climate. Constraint of temperature increase of below 1.5 °C but not 2.0 °C will avoid 4.34% to 73.96% impacts of extreme precipitation on human society. It is particularly important for China since that more than half of territory of China is under exposure to high flood and drought disasters.

A new statistical downscaling approach for global evaluation of the CMIP5 precipitation outputs: Model development and application.

Outputs of the Coupled Model Intercomparison Project Phase 5 (CMIP5) models have been widely used in studies of climate changes related to scenarios at global and regional scales. However, CMIP5 outputs cannot be used directly in analysis of climate changes due to coarse spatial resolution. Here, we proposed a new statistical downscaling method for the downscaling practice of the CMIP5 outputs, i.e. Bias-corrected and station-based Non-linear Regression Downscaling method based on Randomly-Moving Points (BNRD). And up to now, there are only two global downscaled CMIP5 precipitation datasets, i.e. NASA daily downscaled CMIP5 precipitation product and BCSD-based (Bias Correction Spatial Disaggregation) monthly downscaled CMIP5 precipitation product available online, which are both based on BCSD downscaling method. Hence, we evaluated downscaling performance of BNRD by comparing it with the downscaled CMIP5 outputs using the BCSD method in this current study. The results indicate that: (1) during the period for development of the model (1964-2005), the error between downscaled CMIP5 precipitation and GPCC ranges between -50 mm-50 mm at monthly scale. When compared to BCSD-downscaled CMIP5 precipitation, BNRD-downscaled CMIP5 precipitation well reduces errors and avoids underestimation and overestimation of GPCC by BCSD-downscaled CMIP5 precipitation; (2) during period for verification of the downscaling models (2006-2013), the maximum (182 mm), minimum (15 mm) and average (68 mm) RMSEs between BNRD-downscaled CMIP5 precipitation and GPCC are all lower than those between BCSD-downscaled CMIP5 precipitation and GPCC at continental scales. Besides, from the average precipitation viewpoint, BNRD-downscaled CMIP5 precipitation is in higher correlation (around 0.75) with GPCC than BCSD-downscaled CMIP5 precipitation under RCP4.5 and RCP8.5 scenarios at continental scales; (3) BNRD resolved the negative relation to GPCC in the areas near equator, including north part of the South America, southern Africa, northern Australia. In all, BNRD downscaling method developed in this study performs better in describing GPCC changes in both space and time when compared to BCSD and can be used for downscaling practice of CMIP5 and even potentially CMIP6 precipitation outputs over the globe.

Spatiotemporal impact of soil moisture on air temperature across the Tibet Plateau.

The Himalayan Tibet Plateau (HTP) is regarded as the third pole of the globe and is highly sensitive to global climate change. The hydrothermal properties of HTP greatly impacts the water cycle of the HTP and climate change in its surrounding regions. Using the NCEP-CFSR dataset, this study investigated the spatiotemporal pattern of soil moisture (SM) during different seasons considering vegetation types. The response of the evaporation fraction (EF) to SM and the impact of SM on air temperature through evapotranspiration were analyzed. Results showed that the spatial distribution of SM across the HTP was persistent during different seasons. A decreasing SM trend was observed from southeastern to northwestern HTP. Further, results of this study indicated a wetting tendency in past thirty years, espcially in desert region. In addition, the majority of the HTP regions were dominated by persistent transitional SM conditions which could be identified in the Himalayas and the southeastern HTP, whereas a persistent SM deficit in the Qaidam basin. The sensitivity of temperature response to EF was the strongest during spring and summer. Moreover, the spatial distribution of sensitivity was highly consistent with the vegetation regionalization, indicating the remarkable impact of vegetation type on the sensitivity of temperature to EF changes in summer.