This study attempts to quantify the relative contributions of vegetation greening in China due to climatic and human influences from multiple observational datasets. Satellite measured vegetation greenness, Normalized Difference Vegetation Index (NDVI), and relevant climate, land cover, and socioeconomic data since 1982 are analyzed using a multiple linear regression (MLR) method. A statistically significant positive trend of average growing-season (April–October) NDVI is found over more than 34% of the vegetated areas, mainly in North China, while significant decreases in NDVI are only seen in less than 5% of the areas. The relationships between vegetation and climate (temperature, precipitation, and radiation) vary by geographical location and vegetation type. We estimate the NDVI changes in association with the non-climatic effects by removing the climatic effects from the original NDVI time series using the MLR analysis. Our results indicate that land use change is the dominant factor driving the long-term changes in vegetation greenness. The significant greening in North China is due to the increase in crops, grasslands, and forests. The socioeconomic datasets provide consistent and supportive results for the non-climatic effects at the provincial level that afforestation and reduced fire events generally have a major contribution. This study provides a basis for quantifying the non-climatic effects due to possible human influences on the vegetation greening in China.

The influence of topography on east African climate is investigated using the International Centre for Theoretical Physics Regional Climate Model, with focus on October to December season. Results show that the mean rainfall (temperature) significantly reduces (increases) over the region when topography elevation is reduced. Based on the model, when topography over the selected region (KTU) is reduced to 25%, the mean rainfall (temperature) over east Africa is reduced (increased) by about 19% (1.4°C). The maximum rainfall (temperature) reduction (increase) is however observed around the region over which topography is reduced. The reduction in topography elevation resulted in an anomalous moisture divergence at low level and descending motion over the region. KTU topography enhances the surface heat flux over KTU region and tends to enhance convection over both KTU and the east African region. The topography also helps in the generation of the high frequency mesoscale and subsynoptic disturbances over the region. These disturbances produce precipitation over the region and may also enhance precipitation systems over remote areas due to propagation of the disturbances. The magnitude of the zonal wind speed at 850 hpa increases with the decrease in topography elevation.

Southeast Asia (SEA) is experiencing rapid warming, leading to more extreme heatwaves. Sustained compound heatwaves, with high temperatures during day and at night, pose profound threats in highly vulnerable regions, resulting in great stress on society. We estimated the changes in the compound heatwave characteristics and population exposure over SEA at the end of the 21st century based on the model outputs from the Coupled Model Intercomparison Project Phase 6. Results show that the projected compound heatwaves have significant intensified over SEA linked to increasing greenhouse gas emissions. Contemporary younger generations will face more potential risks than their parents' generation. A child born in the 2010s will experience 1,000 discrete heatwaves in their lifetime, a threefold increase compared with the 1980s. Climate change and population growth combine to drive increased population exposure. The climate effect accounts for 125% in the 0–24‐year‐old cohort, whereas the interaction effect accounts for 85% in the 75+ age group. The relative importance of effects evolves dynamically across age groups, gradually shifting from a predominance of climate effects to a synergy of the climate and population effects. Significant regional inequalities exist in the increased population exposure over SEA. The largest increase occurs in Indonesia, where the aggregate exposure ranges from 45 billion person‐days under the SSP1‐2.6 scenario to 81 and 108 billion person‐days, respectively, in the higher SSP2‐4.5 and SSP5‐8.5 scenarios. The study emphasizes the need for the SEA countries to focus on heat‐stress adaptation strategies, while also working toward fulfilling emission reduction commitments.

Solar influences on spatial patterns of Eurasian winter climate and possible mechanisms are investigated based on a multiple linear regression method and multisource observational and reanalysis data. Robust and significant solar signals are detected in Eurasian surface air temperature (SAT), and strong solar activity evidently warms most area of the continent. The spatial pattern of sea level pressure (SLP) responses to solar activity is similar but not identical to that of the North Atlantic Oscillation (NAO). Compared to the NAO, geographic distribution of solar‐induced SLP anomalies shifts eastward, with significantly enhanced influences over northern Eurasia. Relatively weaker solar signals were also found in mid‐to‐upper troposphere. The spatial pattern of 500 hPa geopotential anomalies resembles a negative Scandinavia teleconnection pattern, and the 200 hPa subtropical jet is weakened, while zonal wind at high latitudes is enhanced due to strong solar activity. The anomalous zonal circulations can be attributed to the “top‐down” mechanism. During high solar activity winters, an enhanced stratospheric zonal wind anomaly propagates downward, causing zonal wind anomalies in the troposphere. However, the “bottom‐up” mechanisms may provide more reasonable explanations of the distinct solar influences on Eurasian climate. Solar‐induced strong warm advection in lower atmosphere tends to increase SAT but decrease SLP, resulting in enhanced solar influences over northern Eurasia. Meanwhile, change in the land‐ocean thermal contrast (LOTC) could also amplify the circulation anomaly. Inhomogeneous surface heating caused by anomalous solar activity modifies LOTC, which probably enhances the solar‐induced circulation patterns. Such a positive feedback may potentially strengthen the solar influences.

Land use and cover change (LUCC) is an important anthropogenic forcing of the climate system. Previous studies have demonstrated that LUCC significantly impacts both mean and extreme temperatures. In this study, we explored the multimodel performance of simulating LUCC‐induced asymmetric effects on the different percentiles of maximum temperatures (T_max) as well as the possible reasons for these effects using results from the fifth phase of the Coupled Model Intercomparison Project (CMIP5). Four state‐of‐art Earth system models (which provide the necessary data) are selected for investigating this issue. In general, all the cases of the model from the Geophysical Fluid Dynamics Laboratory show robust asymmetric responses between the 90th (TX90P) and 10th percentiles (TX10P) of T_max, mainly due to cropland expansions, especially over India, the Sahel, and some parts of North America. However, weak and insignificant responses are shown for both the TX90P and TX10P in other models. The different changes in the T_max variability among the models are primarily responsible for the occurrence of asymmetric features. Furthermore, by decomposing the T_max changes over three typical regions, we analyze the potential causes for the inconsistencies among these models' results and find two crucial processes, that is, the repartitioning of the turbulent heat fluxes and the changes of the diurnal cycle variability due to LUCC. Whether these processes are pronounced determines the occurrence of the asymmetric T_max responses. Overall, this study provides a critical clue for reducing the uncertainties of the LUCC effects on temperature extremes, which should be evaluated against observations.

Recent studies have presented conflicting results regarding the 11-year solar cycle (SC) influences on winter climate over the North Atlantic/European region. Analyses of only the most recent decades suggest a synchronized North Atlantic Oscillation (NAO)-like response pattern to the SC. Analyses of long-term climate data sets dating back to the late 19th century, however, suggest a mslp response that lags the SC by 2-4 years in the southern node of the NAO (i.e. Azores region). To understand the conflicting nature and cause of these time dependencies in the SC surface response, the present study employs a lead/lag multi-linear regression technique with a sliding window of 44-years over the period 1751-2016. Results confirm previous analyses, in which the average response for the whole time period features a statistically significant 2-4-year lagged mslp response centered over the Azores region. Overall, the lagged nature of Azores mslp response is generally consistent in time, with stronger and statistically significant SC signals tend to appear in the periods when the SC forcing amplitudes are relatively larger. Individual month analysis indicates the consistent lagged response in December-January-February average arises primarily from early winter months (i.e. December and January), which is associated with ocean feedback processes that involve reinforcement by anomalies from the previous winter. Additional analysis suggests that the synchronous NAO-like response in recent decades arises primarily from the late winter month (February), possibly reflecting a result of strong internal noise.