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In this study, the performance of 33 Coupled Model Intercomparison Project 5 (CMIP5) global climate models (GCMs) in simulating precipitation over the Tibetan Plateau (TP) was assessed using data from 1961 to 2005 by an improved score-based method, which adopts multiple criteria to achieve a comprehensive evaluation. The future precipitation change was also estimated based on the Delta method by selecting the submultiple model ensemble (SMME) in the near-term and far future (2051-2095) periods under Representative Concentration Pathways (RCP) scenarios RCP4.5 and RCP8.5. The results showed that most GCMs can reasonably simulate the precipitation pattern of an annual cycle; however, all GCMs overestimated the precipitation over TP, especially in spring and summer. The GCMs generally provide good simulations of the temporal characteristics of precipitation, while they did not perform as well in reproducing its spatial distributions. Different assessment criteria lead to inconsistent results; however, the improved rank score method, which adopts multiple criteria, provided a robust assessment of GCMs performance. The future annual precipitation was projected to increase by~6% in the near-term with respect to the period 1961-2005, whereas increases of 12.3% and 16.7% are expected in the far future under RCP4.5 and RCP8.5 scenarios, respectively. Similar spatial distributions of future precipitation changes can be seen in the near-term and far future periods under the two scenarios, and indicate that the most predominant increases occurred in the north of TP. The results of this study are expected to provide valuable information on climate change, and for water resources and agricultural management in TP.

We present an analysis of the global annual mean surface temperature anomaly in 2014, 2015, and 2016 based on five datasets of historical observational records of surface temperature. These three years are the three warmest on record in all but one of the datasets. The largest warming occurred over land, especially at high latitudes. Since the strong El Niño event that occurred in 2015/2016 was similar to the 1997/1998 El Niño, we compared the 2014–2016 period with 1998, the warmest year in the 20th century. The contribution to the annual mean surface temperature anomaly of climate variations at different time scales was assessed using ensemble empirical mode decomposition. Results based on the HadCRUT4 dataset suggest that the interannual component may have contributed an anomaly of −0.01°C in 2014, 0.12°C in 2015, and 0.06°C in 2016. These values are substantially lower than the contribution in 1998 (0.18°C). In comparison, the combined contribution of the decadal‐to‐multidecadal (DM) component and the long‐term warming trend was 0.64°C in 2014, 0.70°C in 2015, and 0.77°C in 2016, which are substantially greater than that in 1998 (0.41°C). Similar results were obtained using the other four datasets. The larger contribution from the DM component and the long‐term warming trend implies that warmer years like 2014–2016 may occur more frequently in the near future. We conclude that the so‐called warming hiatus has faded away.

The occurrence of the 2017/2018 La Niña, following a weak‐to‐neutral La Niña in boreal winter 2016/2017, was surprising. Based on observational records and multiple linear regression analysis for the Pacific zonal wind tendency (dU/dt), this study investigates possible reasons why the La Niña condition suddenly happened in late 2017. Similar to previous four double‐peaked La Niña events (1983–1985, 1998–2000, 2007–2009, and 2010–2012), we find that the multiyearly persistent easterly anomaly in the central equatorial Pacific is a key condition to the development of the second La Niña. The occurrence of the 2017/2018 La Niña results from large warm sea surface temperature (SST) anomalies in the tropical Indian and Atlantic Oceans that act to force the persistent easterly anomaly in the Pacific via modifying the Walker Circulations. About 24% of the variance of the Pacific dU/dt can be statistically explained by the tropical Indian Ocean and Atlantic SST anomalies.

Based on observational data analyses and idealized modeling experiments, we investigated the distinctive impacts of central Pacific (CP-) El Niño and eastern Pacific (EP-) El Niño on the Antarctic sea ice concentration (SIC) in austral spring (September to November). The tropical heat sources associated with EP-El Niño and the co-occurred positive phase of Indian Ocean Dipole (IOD) excite two branches of Rossby wave trains that propagate southeastward, causing an anomalous anticyclone over the eastern Ross-Amundsen-Bellingshausen Seas. Anomalous northerly (southerly) wind west (east) of the anomalous anticyclone favor poleward (offshore) movements of sea ice, resulting in a sea ice loss (growth) in the eastern Ross-Amundsen Seas (the Bellingshausen-Weddell Seas). Meanwhile, the anomalous northerly (southerly) wind also advected warmer and wetter (colder and drier) air into the eastern Ross-Amundsen Seas (the Bellingshausen-Weddell Seas), causing surface warming (cooling) through the enhanced (reduced) surface heat fluxes and thus contributing to the sea ice melting (growth). CP-El Niño, however, forces a Rossby wave train that generates an anomalous anticyclone in the eastern Ross-Amundsen Seas, 20° west of that caused by EP-El Niño. Consequently, a positive SIC anomaly occurs in the Bellingshausen Sea. A dry version of the Princeton atmospheric general circulation model was applied to verify the roles of anomalous heating in the tropics. The result showed that EP-El Niño can remotely induce an anomalous anticyclone and associated dipole temperature pattern in the Antarctic region, whereas CP-El Niño generates a similar anticyclone pattern with its location shift westward by 20° in longitudes.

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Low-altitude atmospheric ducts are abnormal atmospheric phenomena in the troposphere, impacting the operation of microwave or ultrashort wave radio systems. Therefore, the real-time acquisition of low-altitude atmospheric duct parameters is essential to ensure the successful operation of radio systems. Remote sensing methods based on deep learning are, presently, the most important tools to infer duct parameters. In a traditional deep learning loss function, different duct parameters adopt the same weight coefficient. This study establishes a weight loss function and proposes a method for determining the weight coefficient based on the extended Fourier amplitude sensitivity test method. Based on Global Navigation Satellite System (GNSS) occultation signals, the cooperative inversion model of atmospheric duct parameters is established. Test results show that our proposed loss function was feasible, effective, and yielded a higher inversion accuracy than the traditional loss function.

Previous works extensively investigated the influences of the winter-spring Tibetan Plateau snow cover (TP, TPSC) on climate variability over the East Asia. The present work documents an interdecadal-changed impacts of different spring TPSC anomaly (TPSCA) patterns on spring precipitation over eastern China (SPEC) around the early 1990s. It is found that the correlation of eastern and western TPSCA shifts from negative to positive around 1990. The empirical orthogonal function (EOF) analysis applying onto the spring TPSCA during 1970–1989 (P1) and during 1991–2017 (P2) adds additional support for such interdecadal change in the relationship between the eastern and western TPSCA. Specifically, the leading EOF (EOF1) mode in P1 shows an out-of-phase pattern with opposite signals lying over the eastern and western TP, while the counterpart in P2 is characterized by an in-phase pattern over the entire TP. Corresponding to more (less) snow cover in the eastern (western) TP in P1, a significant TP cold cyclone (TPCC) and a downstream anticyclone over the western North Pacific are observed. Anomalous southerly flow prevailing east to TPCC could bring the warm-wet air from tropics to the coast of East Asian, which largely enhances the spring precipitation south to Yangtze River Valley (YRV). By contrast, regarding more snow cover both in the eastern and western TP in P2, a relatively northward-displaced and wider TPCC sweeps over the entire TP compared with the TPSC-induced TPCC in P1. Moreover, there are significant sinking anomalies observed in the downstream YRV-HRV region, which leads to suppressed spring precipitation over there via the dry-cold advection process. Hence, these discrepancies of local and downstream atmospheric circulation induced by the out-of-phase and in-phase TPSCA patterns in two epochs play an important role in resulting in the interdecadal shift of the SPEC anomaly pattern around 1990.