Abstract The middle–lower valley of the Yangtze River (MLY), located in the middle of eastern China, has been one of the largest economic centers of China since ancient times. Winter precipitation variability over the MLY is important for China because of its significant influence on the local economy. However, few studies have focused on the long-term variability of winter precipitation over the MLY. This study reports a significant wetting trend over the MLY in winter during the three decades since the late 1970s, forming a “mid-east-China winter wetting” pattern, which has become an important feature of precipitation change under the weakening East Asian winter monsoon. This wetting trend in the MLY also implies the poleward extension of the precipitation belts of southern China. Further investigation reveals that the increasing sea surface temperature (SST) in the tropical Indian Ocean (TIO) is the dominant factor responsible for recent increases in precipitation over the MLY. The thermal forcing driven by warming of the TIO SST gives rise to an anomalous cyclonic circulation along the coast of eastern China. This transports more water vapor onto the Chinese mainland, shifts and causes anomalous convergence over the MLY, and generates the increase in precipitation there. As such, the increasing SST in the TIO induces over 80% of the observed wetting trend over the MLY. This mechanism was verified by results obtained from two sets of sensitivity experiments using a numerical spectral atmospheric general circulation model. Thus, increasing SST in the TIO has made a dominant contribution to the recent winter precipitation increase over the MLY.
Abstract We investigate the global distribution of hourly precipitation and its connections with the El Niño–Southern Oscillation (ENSO) using both satellite precipitation estimates and the global sub-daily rainfall gauge dataset. Despite limited moisture availability over continental surfaces, we find that the highest mean and extreme hourly precipitation intensity (HPI) values are mainly located over continents rather than over oceans, a feature that is not evident in daily or coarser resolution data. After decomposing the total precipitation into the product of the number of wet hours (NWH) and HPI, we find that ENSO modulates total precipitation mainly through the NWH, while its effects on HPI are more limited. The contrasting responses to ENSO in NWH and HPI is particularly apparent at the rising branches of the Pacific and Atlantic Walker Circulations, and is also notable over land-based gauges in Australia, Malaysia, the USA, Japan and Europe across the whole distribution of hourly precipitation (i.e. extreme, moderate and light precipitation). These results provide new insights into the global precipitation distribution and its response to ENSO forcing.
The "Karakoram Vortex" (KV), hereafter also referred to as the "Western Tibetan Vortex" (WTV), has recently been recognized as a large-scale atmospheric circulation system related to warmer (cooler) near-surface and mid-lower troposphere temperatures above the Karakoram in the western Tibetan Plateau (TP). It is characterized by a deep, anti-cyclonic (cyclonic) wind anomaly associated with higher (lower) geopotential height in the troposphere, during winter and summer seasons. In this study, we further investigate the seasonality and basic features of the WTV in all four seasons, and explore its year-to-year variability and influence on regional climate. We find the WTV accounts for the majority of year-to-year circulation variability over the WTP as it can explain over 50% ( $${R^2} \geqslant 0.5$$ ) variance of the WTP circulation on multiple levels throughout the troposphere, which declines towards the eastern side of the TP in most seasons. The WTV is not only more (less) active but also has a bigger (smaller) domain area, with a deeper (shallower) structure, in winter and spring (summer and autumn). We find that the WTV is sensitive to both the location and intensity of the Subtropical Westerly Jet (SWJ), but the relationship is highly dependent on the climatological mean location of SWJ axes relative to the TP in different seasons. We also show that the WTV significantly modulates surface and stratospheric air temperatures, north–south precipitation patterns and total column ozone surrounding the western TP. As such, the WTV has important implications for the understanding of atmospheric, hydrological and glaciological variability over the TP.
This study developed a machine-learning-based model to extract the rainfall information from C-band synthetic aperture radar (SAR) images acquired in dual-polarization (VV/VH) over hurricane conditions. The model is based on a back-propagation neural network (BPNN) tuned by 1,1762 pairs of samples from collocations of Sentinel-1 data and Stepped Frequency Microwave Radiometer (SFMR) measurements. The model inputs include four SAR measured physical parameters and one morphological feature related to the hurricane. Several comparative tests show that the selection of different SAR inputs has a significant impact on the performance of the models for rainfall estimation. For example, compared to the SFMR measurements, the root mean square error and correlation coefficient of rain rate obtained by BPNN with all five inputs reach the best, which are 3.61 mm/hr and 0.87, respectively.
Abstract. The Arctic sea ice is suffering dramatic retreating in summer and fall, which has far-reaching consequences on the global climate and commercial activities. Accurate seasonal sea ice predictions are significant in inferring climate change and planning commercial activities. However, seasonally predicting the summer sea ice encounters a significant obstacle known as the spring predictability barrier (SPB): predictions made later than May demonstrate good skill in predicting summer sea ice, while predictions made on or earlier than May exhibit considerably lower skill. This study develops a transformer-based deep-learning model, SICNetseason (V1.0), to predict the Arctic sea ice concentration on a seasonal scale. Including spring sea ice thickness (SIT) data in the model significantly improves the prediction skill at the SPB point. A 20-year (2000–2019) testing demonstrates that the detrended anomaly correlation coefficient (ACC) of Sep. sea ice extent (sea ice concentration > 15 %) predicted by our model at May/Apr. is improved by 7.7 %/10.61 % over the ACC predicted by the state-of-the-art dynamic model from the European Centre for Medium-Range Weather Forecasts (ECMWF). Compared with the anomaly persistence benchmark, the mentioned improvement is 41.02 %/36.33 %. Our deep learning model significantly reduces prediction errors of Sep.'s sea ice concentration on seasonal scales compared to ECMWF and Persistence. The spring SIT data is key in optimizing the SPB, contributing to a more than 20 % ACC enhancement in Sep.'s SIE at four to five months lead predictions. Our model achieves good generalization in predicting the Sep. SIE of 2020–2023.
Abstract The Western Tibetan Vortex (WTV) is a large-scale circulation pattern identified from year-to-year circulation variability, which was used to understand the causal mechanisms for slowdown of the glacier melting over the western Tibetan Plateau (TP). A recent argument has suggested the WTV is the set of wind field anomalies resulting from variability in near-surface air temperatures over the western TP (above 1500 m), which, in turn, is likely driven by the surface net radiation. This study thereby evaluates the above putative thermal-direct mechanism. By conducting numerical sensitivity experiments using a global atmospheric circulation model, SAMIL, we find a WTV-like structure cannot be generated from a surface thermal forcing imposed on the western TP. A thermally-direct circulation generated by the surface or near surface heating is expect to cause upward motions and a baroclinic structure above it. In contrast, downward motions and a quasi-barotropic are observed in the vertical structure of the WTV. Besides, we find variability of the surface net radiation (sum of the surface shortwave and longwave net radiation) over the western TP can be traced back to the WTV variability based on ERA5 data. The anticyclonic (cyclonic) WTV reduces (increases) the cloudiness through the anomalous downward (upward) motions, causes more (less) input shortwave net radiation and thereby more (less) surface net radiations, resulting in the warmer (cooler) surface and near-surface air temperature over the western TP. The argument is constructive in encouraging examination of the radiative balance processes that complements previous studies.
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