Bicheng Huang

Evaporation is a key component of air–sea coupling processes and understanding the uncertainty in its estimation is essential for climate research and prediction. Based on four widely used datasets (OAFlux, NCEP2, MERRA2 and ERA5), this study systematically analyzes the seasonal evolution of inter-dataset uncertainty in evaporation variation over the tropical Indian Ocean using an evaporation decomposition method. Our main contribution is to show that evaporation variation uncertainty is not seasonally uniform but organized into distinct seasonal regimes with different dominant controlling factors and sensitivity structures. The results reveal significant seasonal dependence of evaporation variation uncertainty: the uncertainty is relatively small in boreal spring and autumn but larger in boreal summer and winter. The evaporation variation is primarily controlled by the relative humidity term (RH^*) in boreal summer and by the wind speed term (U^*) in other seasons. More importantly, the sources of uncertainty differ fundamentally between seasons: the large uncertainty of RH^* in boreal summer mainly originates from the high and variable sensitivity of evaporation to relative humidity, whereas the large uncertainty of U^* in boreal winter primarily stems from substantial inter-dataset discrepancies in wind speed data itself. These findings reveal that evaporation variation uncertainty arises from both input data discrepancies and the nonlinear sensitivity of evaporation processes, with their relative contributions varying seasonally. This study provides a physically based explanation for evaporation uncertainty and offers a useful basis for evaporation dataset selection and climate model evaluation.

Based on four datasets (ERA5, JRA-55, MERRA-2, and OAFlux) and the evaporation decomposition method, this study examines the principal spatiotemporal characteristics of North Atlantic evaporation during the cold season (December–May) and warm season (June–November) from 1980 to 2015. The results indicate that during the cold season, all four datasets consistently exhibit a meridional triple pattern, driven primarily by the wind speed term (U*) and the stability term (S*). All datasets reveal a synchronous interdecadal shift around the late 1990s, underscoring the high reliability of cold season evaporation features. In contrast, the dominant evaporation modes during the warm season diverge significantly across datasets. ERA5 and JRA-55 display a dominant zonal triple pattern, whereas this pattern emerges only as a secondary mode in MERRA-2 and OAFlux, with notable discrepancies in both spatial structure and temporal evolution. Warm season patterns are mainly controlled by the relative humidity term (RH*), and the associated uncertainties can be attributed to differences in how the various datasets characterize RH* under global warming. This study demonstrates that the cold season evaporation characteristics over the North Atlantic are highly credible, offering a robust foundation for mechanistic studies. Conversely, warm season results exhibit sensitivity to dataset selection, necessitating rigorous uncertainty assessment in future studies. Our findings provide a scientific basis for data selection and seasonal differential analysis in related research.

Vegetation plays a critical role in land–atmosphere interactions by both responding to and influencing climate variability. In northern China, a climate-sensitive region, previous studies have mainly focused on relationship between temperature and vegetation growth, with limited attention to the biophysical mechanisms through which vegetation impacts surface temperature. This study assesses the vegetation dynamics and temperature changes in northern China during the growing seasons from 2000 to 2020, using the Normalized Difference Vegetation Index (NDVI). An Intrinsic Biophysical Mechanism Model (IBM) is used to investigate the influence of vegetation growth on surface temperature in three northern regions (Northeast China, North China, and Northwest China). Our findings reveal that greening vegetation accounts for 58.92% of the total area in northern China, with significant growth trends observed: 2.19 × 10 ⁻³ yr ⁻¹ in Northeast China, 2.61 × 10 ⁻³ yr ⁻¹ in North China, and 1.84 × 10 ⁻³ yr ⁻¹ in Northwest China. NDVI increases corresponded to relatively slower surface temperature rises, especially in the Northeast China, while notable surface temperature increases are observed in Northwest China and North China. Net shortwave radiation exhibited an increasing trend in Northwest and decreasing trends in Northeast China and North China. The combined effects of three pathways from vegetation change by IBM result in a net contribution of −0.0043 K yr ⁻¹ (0.0905 K yr ⁻¹ ), indicating that increased vegetation leads to a decrease (increase) in surface temperature in Northeast China (North China). Vegetation contributes 0.1420 K yr ⁻¹ to surface temperature changes in Northwest China, closely matching the observed 0.133 K yr ⁻¹ increase. This study highlights the complex feedback between vegetation dynamics and surface temperature in northern China.

Extreme precipitation plays an increasingly important role in shaping hydroclimatic risk under global warming, yet its long‐term contribution to total precipitation remains poorly understood. This study investigates the interdecadal changes in the Proportion of Extreme Precipitation to Total Precipitation (PEPTP) in South China from 1961 to 2024. Results show a significant increase in PEPTP since the early 1990s. The Indian Ocean capacitor effect dominate the earlier period, the post‐1992 phase is increasingly influenced by North Atlantic Zonal Dipole (NAZD). Notably, the Pacific Decadal Oscillation (PDO) modulates the NAZD–PEPTP relationship, with a significant positive correlation emerging during negative PDO phases. The recent enhancement of PEPTP coincides with a persistent negative PDO phase. These findings highlight the importance of multi‐oceanic modes and decadal variability in shaping extreme precipitation under climate change.

Drought plays a critical role in the terrestrial carbon cycle, influencing both ecosystem disturbances and carbon sink dynamics. Here, we assess the effects of future droughts on gross primary production (GPP) under different Shared Socioeconomic Pathways (SSPs). Results show a marked increase in drought frequency, especially extreme droughts by the end of the century. GPP is projected to reduce across most regions, except in high‐latitude areas. Extreme droughts cause the largest declines in GPP, particularly in the late century. GPP sensitivity to drought varies by vegetation type and climate zone. Evergreen Broadleaf Forests are particularly vulnerable to all drought intensities, with severe and extreme events showing intensifying impacts over time. Drought impacts are most pronounced in regions with aridity index near 0.15 (dry area) and around 0.8 (humid area). These findings underscore the importance of considering vegetation‐specific and climate‐sensitive responses to drought when designing future ecosystem mitigation strategies.

Vegetation plays a crucial role in maintaining the balance between nature, water and soil resources. However, understanding its impact mechanisms in arid and semi-arid areas remains limited. This study aims to analyze the spatial–temporal characteristics of the vegetation leaf area index (LAI) and climate elements in typical regions of northwest China and the correlations between LAI and climate elements; it also aims to explore the influence of regional vegetation growth on climate change. The results reveal significant correlations between LAI and various climate elements. Specifically, within the same region, surface temperature, precipitation, vegetation transpiration, and total evaporation show positive correlations with the LAI, whereas surface albedo shows a negative correlation. Vegetation may affect climate through both heat and water exchange between the land and atmosphere. Increased vegetation leads to the enhanced absorption of solar radiation by the land surface, elevating surface temperature. Increased levels of vegetation also increase vegetation transpiration and total evaporation, increasing the water vapor content in the atmosphere and thus leading to increased surface precipitation. Therefore, vegetation distribution plays a role in climate change, and ecological restoration projects in the northwest region hold significant potential for addressing ecological challenges in its arid and semi-arid areas.

Global warming weakened the summer monsoon and increased the evaporation, leading to more contribution of local evaporation moisture to the local precipitation for the monsoon areas. However, the descriptions of the contribution of the local moisture to the total precipitation and its characteristics have not been known very well. In this paper, taking the middle and lower Reaches of the Yangtze River (MLRYR) as a case and using the precipitation recycling process model, we analyzed the characteristics of the contribution of the local moisture to the total precipitation and the possible reasons. The results show that: the seasonal difference in precipitation recycling rates is obvious, the precipitation recycling rates in spring and summer are small (18.30% and 19.30%), the maximum in autumn is 30.50%, and the precipitation recycling rates in all seasons except summer show a significant upward trend (about 1.70%/10a). Additionally, the water vapor input into MLRYR from four boundaries significantly reduced except for the eastern boundary, and the water vapor contribution from the South and East borders is in summer, and the water vapor contribution from the North and West borders is in autumn, winter and spring. We suggest that the model of the precipitation recycling rate is important to evaluate the contribution of different water vapor sources, and help to further improve the ability of river water prediction in flood season.