Abstract. The Tibetan Plateau (TP) region, often referred to as the Third Pole, is the world's highest plateau and exerts a considerable influence on regional and global climate. The state of the snowpack over the TP is a major research focus due to its great impact on the headwaters of a dozen major Asian rivers. While many studies have attempted to validate atmospheric reanalyses over the TP area in terms of temperature or precipitation, there have been – remarkably – no studies aimed at systematically comparing the snow depth or snow cover in global reanalyses with satellite and in situ data. Yet, snow in reanalyses provides critical surface information for forecast systems from the medium to sub-seasonal timescales. Here, snow depth and snow cover from four recent global reanalysis products, namely the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 and ERA-Interim reanalyses, the Japanese 55-year Reanalysis (JRA-55) and the NASA Modern-Era Retrospective analysis for Research and Applications (MERRA-2), are inter-compared over the TP region. The reanalyses are evaluated against a set of 33 in situ station observations, as well as against the Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover and a satellite microwave snow depth dataset. The high temporal correlation coefficient (0.78) between the IMS snow cover and the in situ observations provides confidence in the station data despite the relative paucity of in situ measurement sites and the harsh operating conditions. While several reanalyses show a systematic overestimation of the snow depth or snow cover, the reanalyses that assimilate local in situ observations or IMS snow cover are better capable of representing the shallow, transient snowpack over the TP region. The latter point is clearly demonstrated by examining the family of reanalyses from the ECMWF, of which only the older ERA-Interim assimilated IMS snow cover at high altitudes, while ERA5 did not consider IMS snow cover for high altitudes. We further tested the sensitivity of the ERA5-Land model in offline experiments, assessing the impact of blown snow sublimation, snow cover to snow depth conversion and, more importantly, excessive snowfall. These results suggest that excessive snowfall might be the primary factor for the large overestimation of snow depth and cover in ERA5 reanalysis. Pending a solution for this common model precipitation bias over the Himalayas and the TP, future snow reanalyses that optimally combine the use of satellite snow cover and in situ snow depth observations in the assimilation and analysis cycles have the potential to improve medium-range to sub-seasonal forecasts for water resources applications.

We investigate the possible causes of the weakening of northern East Asian summer monsoon (EASM) from 1954 to 2010. We found that the decreased intensity of northern EASM as measured by a circulation index (EASMI) is significantly correlated with the increase of the surface air temperature (SAT) averaged over the Lake Baikal region (45°–65°N, 80°–130°E) defined as SATI. Corresponding to increasing SATI, an anomalous low‐level anticyclone occurs with northeasterly prevailing over northern East Asia (30°–50°N,100°–130°E), resulting in a weakened southwesterly monsoon winds and drier climate in this region. Numerical experiments with the community atmosphere model version 3 (CAM3) show that the joint forcing induced by greenhouse gases (GHG), sea surface temperature (SST), solar radiance (SR), and volcano activity (VC) can replicate the observed trend of SATI and its related circulation anomalies, but without GHG forcing the model failed to simulate the warming trend of SATI after 1970s. This implies that the global warming is likely responsible for the local warming around the Lake Baikal, which in turn weakens the northern EASM in recent decades.

[1] Recent studies have shown that the spring dust storm frequency (DSF) in northern China exhibits an obvious downward trend over the past 50 years concurrently with the recent global warming. We found that the decline of DSF is significantly correlated with the increase of the surface air temperature (SAT) in the region of 70°E-130°E, 45°N-65°N around Lake Baikal, where anthropogenic forcing induces prominent warming in the recent decades. Corresponding to the SAT rise in this region, an anomalous dipole circulation pattern is found in the troposphere that consists of a warm anti-cyclone centered at 55°N and a cold cyclone centered around 30°N. The DSF is positively correlated to the activity of Mongolian cyclones. The warming trend around Lake Baikal possibly induces a weakening of the westerly jet stream and the atmospheric baroclinicity in northern China and Mongolian regions, which suppress the frequency of occurrence and the intensity of the Mongolian cyclones and result in the decreasing DSF in North China. This mechanism will likely further reduce the spring DSF in the future global warming scenario. Citation: Zhu, C., B. Wang, and W. Qian (2008), Why do dust storms decrease in northern China concurrently with the recent global warming?, Geophys. Res. Lett., 35, L18702,

In 2020, the Yangtze River (YR) suffered a long-persisting Meiyu season. The accumulated rainfall broke its record since 1961 and caused severe flooding and death in China. Our results show the sequential warm and cold Meiyu front regulated by the North Atlantic Oscillation (NAO) was responsible for this unexpected extreme Meiyu event. From 11 to 25 June with the positive NAO, the interaction between the South Asian High (SAH) and the western Pacific subtropical high maintained a warm front to strengthen the rainband north of the YR. Afterward, the coupling between SAH and midlatitude Mongolian Cyclone induced a cold front, which retreated the rainband to the south of YR from 30 June to 13 July with the negative NAO. Although the ECMWF S2S model successfully predicted the warm-front-related Meiyu rainband, it failed to forecast the Meiyu rainband in the cold-front period, suggesting a great challenge of S2S forecasting on Meiyu rainfall.

A new dataset of integrated and homogenized monthly surface air temperature over global land for the period since 1900 [China Meteorological Administration global Land Surface Air Temperature (CMA-LSAT)] is developed. In total, 14 sources have been collected and integrated into the newly developed dataset, including three global (CRUTEM4, GHCN, and BEST), three regional and eight national sources. Duplicate stations are identified, and those with the higher priority are chosen or spliced. Then, a consistency test and a climate outlier test are conducted to ensure that each station series is quality controlled. Next, two steps are adopted to assure the homogeneity of the station series: (1) homogenized station series in existing national datasets (by National Meteorological Services) are directly integrated into the dataset without any changes (50% of all stations), and (2) the inhomogeneities are detected and adjusted for in the remaining data series using a penalized maximal t test (50% of all stations). Based on the dataset, we re-assess the temperature changes in global and regional areas compared with GHCN-V3 and CRUTEM4, as well as the temperature changes during the three periods of 1900–2014, 1979–2014 and 1998–2014. The best estimates of warming trends and there 95% confidence ranges for 1900–2014 are approximately 0.102 ± 0.006 °C/decade for the whole year, and 0.104 ± 0.009, 0.112 ± 0.007, 0.090 ± 0.006, and 0.092 ± 0.007 °C/decade for the DJF (December, January, February), MAM, JJA, and SON seasons, respectively. MAM saw the most significant warming trend in both 1900–2014 and 1979–2014. For an even shorter and more recent period (1998–2014), MAM, JJA and SON show similar warming trends, while DJF shows opposite trends. The results show that the ability of CMA-LAST for describing the global temperature changes is similar with other existing products, while there are some differences when describing regional temperature changes