El Niño–Southern Oscillation (ENSO), as the strongest source of interannual variability in the tropics, has far-reaching impacts on global climate through teleconnections. As a key factor modulating the vegetation changes, the impact of ENSO has been studied over the past two decades using satellite observations. The paper aims to review results from the past 10–20 years and put together into a consistent picture of ENSO global impacts on vegetation. While ENSO affects vegetation worldwide, its impact varies regionally. Different ENSO flavors, Central Pacific and Eastern Pacific events, can have distinct impacts in the same regions. The underlying mechanisms involve ENSO-driven changes in precipitation and temperature, modulated by the background climate states, with varying response from vegetations of different types. However, the interactions between vegetation and ENSO remain largely unexplored, highlighting a critical gap for future research.

Observational data from the Global Precipitation Measurement (GPM) Core Observatory during four summers (2014–2017) has been used to investigate deep convection systems (DCSs) over the Tibetan Plateau (TP) and its south slope (SS). The frequency, geographical distribution diurnal variation, and vertical structure of DCSs over the TP and SS are compared among these two regions. The frequency of DCSs over the SS (0.98%) was far higher than over the TP (0.15%), suggesting that stronger DCSs occur to the east and south of the TP. The maximum number of DCS occurred in July and August. A clear diurnal variation in DCS was found over the whole region, DCSs over the TP and SS both have a greatest amplitude in the afternoon. The probability of DCSs from 1200 to 1800 local time (LT) was 76.3% and 44.1% over TP and SS respectively, whereas the probability of DCSs being generated from 2200 (LT) to 0600 on the next day LT was 0.03% and 33.1% over the TP and SS respectively. There was a very low frequency of DCSs over the TP during the night. Five special echo top heights were used to investigate the vertical structure of DCSs. DCSs over the TP were both weaker and smaller than those over the SS.

Using time‐slice experiments performed with version 4 of the Whole Atmosphere Community Climate Model (WACCM4) and satellite observations, we investigate hemispheric differences in, and the seasonal dependence of, water vapor transport into the stratosphere in response to Indo‐Pacific warm pool (IPWP) sea‐surface temperature (SST) variations. Specifically, the amplitude of the lower stratospheric water vapor (SWV) response to a warmer (cooler) IPWP (i.e., IPWP Niño [Niña]) occurs mainly in boreal winter when the IPWP SST forcing is identical for different seasons, and the response in the northern tropics (NT) is greater than that in the southern tropics (ST). The seasonality in and hemispheric contrasts of the SWV response to IPWP Niño and IPWP Niña are traced to the tropical cold point temperature, including its zonally symmetric pattern associated with tropical upwelling and its zonally asymmetric features that manifest as the morphology of the coldest point region. Together with the seasonal variations in topical upwelling response, the morphology of the coldest point region, which is affected by equatorial planetary waves and the summer monsoon, determines the seasonality and NT‐ST contrasts in the SWV response to IPWP SST forcing. Among these factors, the importance of the climatological displacement of convective activity from the equator to NT associated with the summer monsoon is highlighted.

The equatorial Atlantic zonal sea surface temperature (SST) gradient, which has significant climatic and biogeochemical effects, is closely associated with the equatorial Pacific zonal SST gradient through Walker circulation on seasonal and interannual time scales. However, discrepancies in current SST data sets mean that its long‐term trend is not well understood. Here, using multiple data sets, we find a robust weakening long‐term trend (i.e., greater warming in the east than west) in the equatorial Atlantic zonal SST gradient over the period 1900–2010 in all data sets. We also find that this weakening trend is closely linked to the tropical Pacific cold tongue mode (CTM), which corresponds to a strong increasing long‐term trend of zonal SST gradient along the equatorial Pacific (i.e., warming in the west and cooling in the east). Specifically, the long‐term cooling SST anomalies associated with the CTM modify the Walker circulation, and leads to weaker trade winds over the western equatorial Atlantic. These in turn deepen the thermocline in the eastern equatorial Atlantic, and cause the weakening long‐term trend of SST gradient along the equatorial Atlantic. The long‐term trend of the CTM is induced by ocean dynamical feedback in response to global warming, suggesting that global warming could affect the equatorial Atlantic zonal SST gradient via the CTM. Our results provide a novel explanation of the linkages between the long‐term trend of equatorial Atlantic zonal SST gradient and the CTM under global warming, which carries important implications for the relationship between global warming and the equatorial Atlantic zonal SST gradient.

Accurate site-specific estimations of surface wind speeds (SWS) would greatly aid clean energy development. The quality of estimation depends on the method of interpolating gridded SWS data to derive the wind speed at a given location. This work uses multiple machine learning (ML) and deep learning (DL) methods to estimate wind speeds at locations across eastern China using the gridded fifth-generation data from the European Centre for Medium-Range Weather Forecasts. The root-mean-square error (RMSE) of these models’ estimates for summer and winter are, respectively, reduced by 23% and 16% on average against simple linear interpolation. A deep convolution neural network (DCNN) consistently performs best among the considered models, reducing the RMSE by 26% and 23% for summer and winter data, respectively. We further examine the dependence of the models’ estimations on altitude, land use category, and local mean SWS. And found that the DCNN can reflect the nonlinear relationships among these variables and SWS. Threfore, it can be used for site-specific wind speed estimates over a large area like eastern China.