Significant land greening in the northern extratropical latitudes (NEL) has been documented through satellite observations during the past three decades 1-5 . This enhanced vegetation growth has broad implications for surface energy, water and carbon budgets, and ecosystem services across multiple scales 6-8 . Discernible human impacts on the Earth's climate system have been revealed by using statistical frameworks of detection-attribution 9-11 . These impacts, however, were not previously identified on the NEL greening signal, owing to the lack of long-term observational records, possible bias of satellite data, di erent algorithms used to calculate vegetation greenness, and the lack of suitable simulations from coupled Earth system models (ESMs). Here we have overcome these challenges to attribute recent changes in NEL vegetation activity. We used two 30-year-long remote-sensing-based leaf area index (LAI) data sets 12,13 , simulations from 19 coupled ESMs with interactive vegetation, and a formal detection and attribution algorithm 14,15 . Our findings reveal that the observed greening record is consistent with an assumption of anthropogenic forcings, where greenhouse gases play a dominant role, but is not consistent with simulations that include only natural forcings and internal climate variability. These results provide the first clear evidence of a discernible human fingerprint on physiological vegetation changes other than phenology and range shifts 11 .This study examines the growing season LAI over the NEL (30-75 • N). The LAI is a measurable biophysical parameter using satellite observation, an archived prognostic variable of the Coupled Model Intercomparison Project Phase 5 (CMIP5) ESMs, and a direct indicator of the leaf surface per unit ground area that exchanges energy, water, carbon dioxide and momentum with the planetary boundary layer. We employed the recently published LAI3g data set 12 and the GEOLAND2 LAI data 13 , both of which were quality-controlled over the NEL region for the 1982-2011 period ( Supplementary Information 1). We compared the observed changes of LAI to simulated variations from multi-model results obtained from the CMIP5 archive (Supplementary Information 2 and Supplementary Table 1). These ensemble simulations comprise ALL, with historical anthropogenic and natural forcings, GHG, with greenhouse gases forcing only, NAT, with natural forcing only, CTL, with internal variability (IV) only, esmFixClim2, with CO 2 physiological effects, and esmFdbk2, with greenhouse gases radiative effects. Beyond the standard comparison of time series and patterns of trends, two methods were applied to detect and attribute changes in observed LAI, including a formal 'optimal fingerprint' analysis (Methods).From 1982 to 2011, LAI3g, GEOLAND2 and their mean exhibited greening trends over the NEL vegetated area (85.3%, 69.5% and 80.6%, respectively), except across a narrow latitudinal band over Canada and Alaska, and in a few spots over Eurasia (Fig. 1a-c). The largest positive increase is observ...

Plants are expected to face increasing water stress under future climate change. Most land surface models, including Noah-MP, employ an idealized "big-leaf" concept to regulate water and carbon fluxes in response to soil moisture stress through empirical soil hydraulics schemes (SHSs). However, such schemes have been shown to cause significant uncertainties in carbon and water simulations. In this paper, we present a novel plant hydraulics scheme (PHS) for Noah-MP (hereafter, Noah-MP-PHS), which employs a big-tree rather than big-leaf concept, wherein the whole-plant hydraulic strategy is considered, including root-level soil water acquisition, stem-level hydraulic conductance and capacitance, and leaflevel anisohydricity and hydraulic capacitance. Evaluated against plot-level observations from a mature, mixed hardwood forest at the University of Michigan Biological Station and compared with the default Noah-MP, Noah-MP-PHS better represents plant water stress and improves water and carbon simulations, especially during periods of dry soil conditions. Noah-MP-PHS also improves the asymmetrical diel simulation of gross primary production under low soil moisture conditions. Noah-MP-PHS is able to reproduce different patterns of transpiration, stem water storage and root water uptake during a 2-week dry-down period for two species with contrasting plant hydraulic behaviors, i.e., the "cavitation riskaverse" red maple and the "cavitation risk-prone" red oak. Sensitivity experiments with plant hydraulic capacitance show that the stem water storage enables nocturnal plant water recharge, affects plant water use efficiency, and provides an important buffer to relieve xylem hydraulic stress during dry soil conditions.Plain Language Summary Plants regulate transpiration dynamically through the stomatal aperture, which, in many cases, is governed by plant water status and hydraulic properties. Plant hydraulics describes the mechanics of water movement through plant vascular systems, which is the culmination of emergent phenotypical hydraulic functional traits at the leaf, stem, and root levels. Such physiological mechanisms are excluded in most land surface models, which typically represent plant water stress through empirical soil hydraulics schemes (SHSs) based on either soil water content or soil water potential. In this study, we present a novel plant hydraulics scheme (PHS) to represent plant water stress and the regulation of stomatal conductance for use in the Noah-MP land surface model. Our results show Noah-MP-PHS performs better in its water and carbon simulations than the default Noah-MP with traditional SHSs, especially under dry soil conditions. Noah-MP-PHS also successfully captures the different plant hydraulic behaviors between the "cavitation risk-averse" red maple and the "cavitation risk-prone" red oak. Sensitivity experiments also highlight the vital role played by plant water storage in water and carbon simulations in terms of buffering xylem hydraulic stress during soil moisture dry-down periods. The ...

Hydraulic redistribution is the process of soil water transport through the low-resistance pathway provided by plant roots. It has been observed in field studies and proposed to be one of the processes that enable the Amazon rainforest to resist periodical dry spells without experiencing water limitations. How and to what extent hydraulic redistribution may increase vegetation resistance to longer or more severe droughts than seasonal dryness have not been investigated yet, which is the focus of this study. The artificially prolonged drought produced by the rainfall exclusion experiment is used as an example of long drought, and the 2005 drought is used as a severe drought. The parameterization of hydraulic redistribution proposed by Ryel et al. (2002) was incorporated into the Community Land Model version 4 (CLM4). Three paired numerical experiments were conducted, one set using the default model (CTL) and the other using the model with considerations of hydraulic redistribution (HR). Results show that the vegetation response (including evapotranspiration, biomass, and leaf area index (LAI)) to dryness of all the three types is better captured with hydraulic redistribution incorporated. Plants are more resistant to dryness when hydraulic redistribution increases plant water availability and thus facilitates their growth. When a drought is long lasting, the vegetation response is delayed by hydraulic redistribution. Therefore, if a drought ends earlier than permanent damage is made, the magnitude of vegetation response will be lowered by this mechanism, i.e., the vegetation will be more resistant to dryness.

The Earth's water cycle involves energy exchange and mass movement in the hydrosphere and thus sustains the dynamic balance of global hydrologic cycle. All water cycle variables on the Earth are closely interconnected with each other through the process of energy and water circulation. Observing, understanding and predicting the storage, movement, and quality of water remains a grand challenge for contemporary water science and technology, especially for researches across different spatio-temporal scales. The remote sensing observing platform has a unique advantage in acquiring complex water information and has already greatly improved observing, understanding, and predicting ability of the water cycle. Methods of obtaining comprehensive water cycle data are also expanded by new remote sensing techniques, and the vast amount of data has become increasingly available and thus accelerated a new Era: the Remote Sensing Big Data Study of Global Water Cycle. The element inversion, time and space reconstruction, and scale conversion are three key scientific issues for remote sensing water cycle in such Era. Moreover, it also presents a huge opportunity of capitalizing the combinations of Remote Sensing and Big Data to advance and improve the global hydrology and water security research and development, and uncork the new bottlenecks.

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