Land evaporation (ET) plays a crucial role in the hydrological and energy cycle. However, the widely used model-based products, even though helpful, are still subject to great uncertainties due to imperfect model parameterizations and forcing data. The lack of available observed data has further complicated estimation. Hence, there is an urgency to define the global proxy land ET with lower uncertainties for climate-induced hydrology and energy change. This study has combined three existing model-based products – the fifth-generation ECMWF reanalysis (ERA5), Global Land Data Assimilation System Version 2 (GLDAS2), and the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) – to obtain a single framework of a long-term (1980–2017) daily ET product at a spatial resolution of 0.25^∘. Here, we use the reliability ensemble averaging (REA) method, which minimizes errors using reference data, to combine the three products over regions with high consistencies between the products using the coefficient of variation (CV). The Global Land Evaporation Amsterdam Model Version 3.2a (GLEAM3.2a) and flux tower observation data were selected as the data for reference and evaluation, respectively. The results showed that the merged product performed well over a range of vegetation cover scenarios. The merged product also captured the trend of land evaporation over different areas well, showing the significant decreasing trend in the Amazon Plain in South America and Congo Basin in central Africa and the increasing trend in the east of North America, west of Europe, south of Asia and north of Oceania. In addition to demonstrating a good performance, the REA method also successfully converged the models based on the reliability of the inputs. The resulting REA data can be accessed at https://doi.org/10.5281/zenodo.4595941 (Lu et al., 2021).

Actual evapotranspiration (ET) is an essential variable in the hydrological process, linking carbon, water, and energy cycles. Global ET has significantly changed in the warming climate. Although the increasing vapor pressure deficit (VPD) enhances atmospheric water demand due to global warming, it remains unclear how the dynamics of ET are affected. In this study, using multiple datasets, we disentangled the relative contributions of precipitation, net radiation, air temperature (T₁), VPD, and wind speed on the annual ET linear trend using an advanced separation method that considers the Budyko framework. We found that the precipitation variability dominantly controls global ET in the dry climates, while the net radiation has substantial control over ET in the tropical regions, and VPD impacts ET trends in the boreal mid-latitude climate. The critical role of VPD in controlling ET trends is particularly emphasized due to its influence in controlling the carbon–water–energy cycle.

This study evaluated the historical precipitation simulations of 49 global climate models (GCMs) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) in reproducing annual and seasonal precipitation climatology, linear trends, and their spatial correlation with global SST across Africa and the Arabian Peninsula during the period of 1980–2014, using Global Precipitation Climatology Centre (GPCP) data as a reference. Taylor’s diagram was used to quantify the strengths and weaknesses of the models in simulating precipitation. The CMIP6 multi-mean ensemble (MME) and the majority of the GCMs replicated the dominant features of the spatial and temporal variations reasonably well. The CMIP6 MME outperformed the majority of the individual models. The spatial variation of the CMIP6 MME closely matched the observation. The results showed that at annual and seasonal scales, the GPCP and CMIP6 MME reproduced a coherent spatial pattern in terms of the magnitude of precipitation. The humid region received >300 mm and the arid region received <50 mm across Africa and the Arabian Peninsula. The models from the same modeling centers replicated the precipitation levels across different seasons and regions. The CMIP6 MME and the majority of the individual models overestimate (underestimate) in humid (arid and semi-arid)-climate zones. The annual and pre-monsoon seasons (i.e., DJFMA) were better replicated in the CMIP6 GCMs than in the monsoon-precipitation model (MJJASON). The CMIP6 MME (GPCP) showed stronger wetting (drying) trends in the northern hemisphere. In contrast, a strong drying trend in the CMIP6 MME and a weak wetting trend in the GPCP were shown in the Southern Hemisphere. The CMIP6 MME captures the spatial pattern of linear trends better than individual models across different climate zones and regions. The relationship between precipitation and sea-surface temperature (SST) exhibited a high spatial correlation (−0.80 and 0.80) with large variability across different regions and climate zones. The GPCP (CMIP6 MME) exhibited a heterogenous (homogeneous) spatial pattern, with higher correlation coefficients recorded in the CMIP6 MME than in the GPCP in all cases. Individual models from the same modeling centers showed spatial homogeneity in correlation values. The differences exhibited by the individual GCMs highlight the significance of each model’s unique dynamics and physics; however, model selection should be considered for specific applications.

Understanding vegetation seasonality and its driving mechanisms improves decision-making in the management of ecological systems in a warming global climate. Using multiple statistical methods (i.e., trend analysis, abrupt changes, and partial correlation analysis), this study analyzed the spatiotemporal variations in the Normalized Difference Vegetation Index (NDVI) in the Equatorial Africa (EQA) region and their responses to climate factors from 1982 to 2021. The NDVI values declined at a rate of 0.00023 year⁻¹, while the precipitation (P) and mean temperature (TMEAN) values increased at rates of 0.22 mm year⁻¹ and 0.22 °C year⁻¹, respectively. The mean minimum temperature (TMIN) had a higher rate of 0.2 °C year⁻¹ than the mean maximum temperature (TMAX) at 0.02 °C year⁻¹. An abrupt change analysis showed that the TMAX, P, and NDVI breakpoints occurred in 2000, 2002, and 2009, respectively; TMEAN and TMIN breakpoints occurred in 2001. The NDVI trends declined in forest and cropland areas but increased in shrubland and grassland areas. The summer NDVI trends declined for all vegetation types and were reversed in the winter season. The NDVI positively correlated with the P (r = 0.50) and TMEAN (r = 0.60). All seasonal analyses varied across four seasons. A temporal analysis was conducted using partial correlation analysis (PCR), and the results revealed that TMIN had a greater impact on the NDVI (PCR = −0.45), followed by the TMAX (PCR = 0.31) and then the P (PCR = −0.19). The annual trend showed that areas with significant greening were consistent with stronger wetter and weaker warming trends. Both precipitation and temperature showed a positive relationship with vegetation in semi-arid and arid regions but a negative relationship with humid regions. Our findings improve our insight into scientific knowledge on ecological conservation.

A reliable estimate of the gross primary productivity (GPP) is crucial for understanding the global carbon balance and accurately assessing the ability of terrestrial ecosystems to support the sustainable development of human society. However, there are inconsistencies in variations and trends in current GPP products. To improve the estimation accuracy of GPP, a deep learning method has been adopted to merge 23 CMIP6 data to generate a monthly GPP merged product with high precision and a spatial resolution of 0.25°, covering a time range of 1850–2100 under four climate scenarios. Multi-model ensemble mean and the merged GPP (CMIP6_DL GPP) have been compared, taking GLASS GPP as the benchmark. Compared with the multi-model ensemble mean, the coefficient of determination between CMIP6_DL GPP and GLASS GPP was increased from 0.66 to 0.86, with the RMSD being reduced from 1.77 gCm⁻²d⁻¹ to 0.77 gCm⁻²d⁻¹, which significantly reduced the random error. Merged GPP can better capture long-term trends, especially in regions with dense vegetation along the southeast coast. Under the climate change scenarios, the regional average annual GPP shows an upward trend over China, and the variation trend intensifies with the increase in radiation forcing levels. The results contribute to a scientific understanding of the potential impact of climate change on GPP in China.

This study investigated the actual evapotranspiration (AET) and potential evapotranspiration (PET) seasonality, trends, abrupt changes, and driving mechanisms with global sea surface temperature (SST) and atmospheric circulation patterns over Equatorial Africa (EQA) during 1980–2020. The spatiotemporal characteristics of mean ET were computed based on a 40-year average at annual and seasonal scales. The Mann-Kendall statistical test, the Sen slope test, and the Bayesian test were used to analyze trends and detect abrupt changes. The results showed that the mean annual PET (AET) for 1980–2020 was 110 (70) mm. Seasonal mean PET (AET) values were 112 (72) in summer, 110 (85) in autumn, 109 (84) in winter, and 110 (58) in spring. The MK test showed an increasing (decreasing) rate, and the Sen slope identified upward (downward) at a rate of 0.35 (0.05) mm yr⁻¹⁰. The PET and AET abrupt change points were observed to happen in 1995 and 2000. Both dry and wet regions showed observed weak (strong) correlation coefficient values of 0.3 (0.8) between PET/AET and climate factors, but significant spatiotemporal differences existed. Generally, air temperature, soil moisture, and relative humidity best explain ET dynamics rather than precipitation and wind speed. The regional atmospheric circulation patterns are directly linked to ET but vary significantly in space and time. From a policy perspective, these findings may have implications for future water resource management.