[1] Results are presented from the multi-institution partnership to develop a real-time and retrospective North American Land Data Assimilation System (NLDAS). NLDAS consists of (1) four land models executing in parallel in uncoupled mode, (2) common hourly surface forcing, and (3) common streamflow routing: all using a 1/8°grid over the continental United States. The initiative is largely sponsored by the Global Energy and Water Cycle Experiment (GEWEX) Continental-Scale International Project (GCIP). As the overview for nine NLDAS papers, this paper describes and evaluates the 3-year NLDAS execution of 1 October 1996 to 30 September 1999, a period rich in observations for validation. The validation emphasizes (1) the land states, fluxes, and input forcing of four land models, (2) the application of new GCIP-sponsored products, and (3) a multiscale approach. The validation includes (1) mesoscale observing networks of land surface forcing, fluxes, and states, (2) regional snowpack measurements, (3) daily streamflow measurements, and (4) satellite-based retrievals of snow cover, land surface skin temperature (LST), and surface insolation. The results show substantial intermodel differences in surface evaporation and runoff (especially over nonsparse vegetation), soil moisture storage, snowpack, and LST. Owing to surprisingly large intermodel differences in aerodynamic conductance, intermodel differences in midday summer LST were unlike those expected from the intermodel differences in Bowen ratio. Last, anticipating future assimilation of LST, an NLDAS effort unique to this overview paper assesses geostationary-satellite-derived LST, determines the latter to be of good quality, and applies the latter to validate modeled LST.

We present Multi-Source Weighted-Ensemble Precipitation, version 2 (MSWEP V2), a gridded precipitation P dataset spanning 1979–2017. MSWEP V2 is unique in several aspects: i) full global coverage (all land and oceans); ii) high spatial (0.1°) and temporal (3 hourly) resolution; iii) optimal merging of P estimates based on gauges [WorldClim, Global Historical Climatology Network-Daily (GHCN-D), Global Summary of the Day (GSOD), Global Precipitation Climatology Centre (GPCC), and others], satellites [Climate Prediction Center morphing technique (CMORPH), Gridded Satellite (GridSat), Global Satellite Mapping of Precipitation (GSMaP), and Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42RT)], and reanalyses [European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) and Japanese 55-year Reanalysis (JRA-55)]; iv) distributional bias corrections, mainly to improve the P frequency; v) correction of systematic terrestrial P biases using river discharge Q observations from 13,762 stations across the globe; vi) incorporation of daily observations from 76,747 gauges worldwide; and vii) correction for regional differences in gauge reporting times. MSWEP V2 compares substantially better with Stage IV gauge–radar P data than other state-of-the-art P datasets for the United States, demonstrating the effectiveness of the MSWEP V2 methodology. Global comparisons suggest that MSWEP V2 exhibits more realistic spatial patterns in mean, magnitude, and frequency. Long-term mean P estimates for the global, land, and ocean domains based on MSWEP V2 are 955, 781, and 1,025 mm yr−1, respectively. Other P datasets consistently underestimate P amounts in mountainous regions. Using MSWEP V2, P was estimated to occur 15.5%, 12.3%, and 16.9% of the time on average for the global, land, and ocean domains, respectively. MSWEP V2 provides unique opportunities to explore spatiotemporal variations in P, improve our understanding of hydrological processes and their parameterization, and enhance hydrological model performance.

Abstract. We undertook a comprehensive evaluation of 22 gridded (quasi-)global (sub-)daily precipitation (P ) datasets for the period 2000-2016. Thirteen non-gauge-corrected P datasets were evaluated using daily P gauge observations from 76 086 gauges worldwide. Another nine gaugecorrected datasets were evaluated using hydrological modeling, by calibrating the HBV conceptual model against streamflow records for each of 9053 small to mediumsized ( < 50 000 km 2 ) catchments worldwide, and comparing the resulting performance. Marked differences in spatiotemporal patterns and accuracy were found among the datasets. Among the uncorrected P datasets, the satellite-and reanalysis-based MSWEP-ng V1.2 and V2.0 datasets generally showed the best temporal correlations with the gauge observations, followed by the reanalyses (ERA-Interim, JRA-55, and NCEP-CFSR) and the satellite-and reanalysis-based CHIRP V2.0 dataset, the estimates based primarily on passive microwave remote sensing of rainfall (CMORPH V1.0, GSMaP V5/6, and TMPA 3B42RT V7) or near-surface soil moisture (SM2RAIN-ASCAT), and finally, estimates based primarily on thermal infrared imagery (GridSat V1.0, PER-SIANN, and PERSIANN-CCS). Two of the three reanalyses (ERA-Interim and JRA-55) unexpectedly obtained lower trend errors than the satellite datasets. Among the corrected P datasets, the ones directly incorporating daily gauge data (CPC Unified, and MSWEP V1.2 and V2.0) generally provided the best calibration scores, although the good performance of the fully gauge-based CPC Unified is unlikely to translate to sparsely or ungauged regions. Next best results were obtained with P estimates directly incorporating temporally coarser gauge data (CHIRPS V2.0, GPCP-1DD V1.2, TMPA 3B42 V7, and WFDEI-CRU), which in turn outperformed the one indirectly incorporating gauge data through another multi-source dataset (PERSIANN-CDR V1R1). Our results highlight large differences in estimation accuracy, and hence the importance of P dataset selection in both research and operational applications. The good performance of MSWEP emphasizes that careful data merging can exploit the complementary strengths of gauge-, satellite-, and reanalysis-based P estimates.

An understanding of how facets of a nanocrystal develop is critical for controlling nanocrystal shape and designing novel functional materials. However, the atomic pathways of nanocrystal facet development are mostly unknown because of the lack of direct observation. We report the imaging of platinum nanocube growth in a liquid cell using transmission electron microscopy with high spatial and temporal resolution. The growth rates of all low index facets are similar until the {100} facets stop growth. The continuous growth of the rest facets leads to a nanocube. Our calculation shows that the much lower ligand mobility on the {100} facets is responsible for the arresting of {100} growing facets. These findings shed light on nanocrystal shape-control mechanisms and future design of nanomaterials.

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...