The response of tropical forests to droughts is highly uncertain 1 . During the dry season, canopy photosynthesis of some tropical forests can decline, whereas in others it can be maintained at the same or a higher level than during the wet season 2 . However, it remains uncertain to what extent water availability is responsible for productivity declines of tropical forests during the dry season 2,3 . Here we use global satellite observations of two independent measures of vegetation photosynthetic properties (enhanced vegetation index from 2002 to 2012 and solar-induced chlorophyll fluorescence from 2007 to 2012) to investigate links between hydroclimate and tropical forest productivity. We find that above an annual rainfall threshold of approximately 2,000 mm yr −1 , the evergreen state is sustained during the dry season in tropical rainforests worldwide, whereas below that threshold, this is not the case. Through a water-budget analysis of precipitation, potential evapotranspiration and satellite measurements of water storage change, we demonstrate that this threshold determines whether the supply of seasonally redistributed subsurface water storage from the wet season can satisfy plant water demands in the subsequent dry season. We conclude that water availability exerts a first-order control on vegetation seasonality in tropical forests globally. Our framework can also help identify where tropical forests may be vulnerable or resilient to future hydroclimatic changes.Photosynthetic metabolism in tropical forests controls ecosystem carbon uptake from the atmosphere, and it also influences critical ecosystem services, including carbon storage 4 , freshwater delivery 5 , maintenance of biodiversity 5 , and regulation of regional and global climate 6 . The photosynthetic metabolism of many tropical forests exhibits a recurring seasonality 2,7 . Understanding how climate influences these seasonal dynamics is an essential prerequisite for realistically predicting tropical forest responses to inter-annual and longer-term climate variation and change 3 . In particular, with a wide spectrum of varying total annual precipitation and dry-season length in the tropics ( Supplementary Fig. 7), the extent to which seasonality of vegetation productivity in tropical forests responds to water limitation remains unclear 2,3 . Although tropical forest seasonal dynamics have been studied at site and regional scales using eddy flux-tower networks and/or satellite remote sensing in Amazonia 2,8,9 , Insular Southeast (SE) Asia 7 and Africa 10 , a globally consistent functional inter-comparison of tropical forests is lacking. Thus, in this paper we address the following questions: What is the extent to which the seasonality of vegetation photosynthesis is limited by water availability in global tropical forests? Are there critical environmental thresholds that explain these seasonal variations? If so, what are the underlying physical mechanisms? What are the implications of such mechanisms on the future of tropical forests under cli...

[1] A series of hydrologic forecasts with lead times up to 6 months are conducted to investigate the relative contributions of atmospheric forcing and hydrologic initial conditions (IC) to the overall errors in hydrologic forecasting during cold and warm seasons. These experiments are known as the ensemble streamflow prediction (ESP) and the reverse-ESP (R-ESP). Analysis of these hindcasts suggests that IC uncertainties outweigh forcing uncertainties thus dominating forecast errors in a short lead time up to about 1 month; at longer lead times, forcing uncertainties become a more important contributor. Further investigation shows that forecast errors at short lead times due to uncertain ICs are mainly determined by the prescribed IC variability, while the evolution of forecast errors due to imperfect atmospheric forcings mainly corresponds to the interannual variability of precipitation. With respect to difference in forecasts initialized in winter and summer times, ICs tend to have longer impacts on warm season forecasts than on cold season ones, due mainly to drier initial moisture state in the summer time. As far as the basin size is concerned, we find that the larger the basin, the stronger the impacts from ICs at short lead times. Small basins are more sensitive to forcing fields. Regardless of basin size, forcing uncertainties dominate relative forecast errors for long lead times. In order to see whether statistically downscaled forcing fields from dynamic climate models are more skillful than traditional ESP, we conducted additional ESP-type experiments using the statistically downscaled climate forecast system (CFS) fields to drive the hydrological model. In comparison to traditional ESP, the IC errors show a larger impact on the forecasts when forced by the CFS fields, which suggests that the latter contains more skill than the traditional ESP approach.

Global and regional climate change assessments rely heavily on the general circulation model (GCM) outputs such as provided by the Coupled Model Intercomparison Project phase 5 (CMIP5). Here we evaluate the ability of 25 CMIP5 GCMs to simulate historical precipitation and temperature over central Africa and assess their future projections in the context of historical performance and intermodel and future emission scenario uncertainties. We then apply a statistical bias correction technique to the monthly climate fields and develop monthly downscaled fields for the period of 1948–2099. The bias‐corrected and downscaled data set is constructed by combining a suite of global observation and reanalysis‐based data sets, with the monthly GCM outputs for the 20th century, and 21st century projections for the medium mitigation (representative concentration pathway (RCP)45) and high emission (RCP85) scenarios. Overall, the CMIP5 models simulate temperature better than precipitation, but substantial spatial heterogeneity exists. Many models show limited skill in simulating the seasonality, spatial patterns, and magnitude of precipitation. Temperature projections by the end of the 21st century (2070–2099) show a robust warming between 2 and 4°C across models, whereas precipitation projections vary across models in the sign and magnitude of change (−9% to 27%). Projected increase in precipitation for a subset of models (single model ensemble (SME)) identified based on performance metrics and causal mechanisms are slightly higher compared to the full multimodel ensemble (MME) mean; however, temperature projections are similar between the two ensemble means. For the near‐term (2021–2050), neither the historical performance nor choice of models is related to the precipitation projections, indicating that natural variability dominated any signal. With fewer models, the “blind” MME approach will have larger uncertainties in future precipitation projections compared to projections by the SME models. We propose the latter a better approach in regions that lack quality climate observations. Our analyses also show that the choice of model and emission scenario dominate the uncertainty in precipitation projections, whereas the emission scenario dominates the temperature projections. Although our analyses are done for central Africa, the final Bias‐Corrected Spatially Downscaled data set is available for global land areas. The framework for climate change assessment and the data will be useful for a variety of climate assessment, impact, and adaptation studies.

Using 19 yr of Chinese soil moisture data from 1981 to 1999, the authors evaluate soil moisture in three reanalysis outputs: the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) ReAnalysis (ERA-40); the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) reanalysis 1 (R-1); and the NCEP-Department of Energy (DOE) reanalysis 2 (R-2) over China. R-2 shows improved interannual variability and better seasonal patterns of soil moisture than R-1 as the result of the incorporation of observed precipitation, but not for all stations. ERA-40 produces a better mean value of soil moisture for most Chinese stations and good interannual variability. Limited observations in the spring indicate a spring soil moisture peak for most of the stations. ERA-40 generally reproduced this event, while R-1 or R-2 generally did not capture this feature, either because the soil was already saturated or the deep soil layer was too thick and damped such a response. ERA-40 and R-1 have a temporal time scale comparable to observations, but R-2 has a memory of nearly 5 months for the growing season, about twice the temporal scale of the observations. The cold season tends to prolong soil moisture memory by about 3 months for R-2 and 1 month for ERA-40. The unrealistic long temporal scale of R-2 can be attributed to the deep layer of the land surface model, which is too thick and dominates the soil moisture variability. R-1 has the same land surface scheme as R-2, but shows a temporal scale close to observations, which is actually because of soil moisture nudging to a fixed climatology. This new long time series of observed soil moisture will prove valuable for other studies of climate change, remote sensing, and model evaluation.

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