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Climate model projections using offline aridity and/or drought indices predict substantial terrestrial drying over the twentyfirst century. However, these same models also predict an increased runoff. This contradiction has been linked to an absence of vegetation responses to an elevated atmospheric CO2 concentration [CO2] in offline impact models12,14,16,17. Here we report a close and consistent relationship between changes in surface resistance (rs) and [CO2] across 16 CMIP5 models. Attributing evapotranspiration changes under nonwater-limited conditions shows that an increase in evapotranspiration caused by a warming-induced vapour pressure deficit increase18 is almost entirely offset by a decrease in evapotranspiration caused by increased rs driven by rising [CO2]. This indicates that climate models do not actually project increased vegetation water use under an elevated [CO2], which counters the perception that ‘warming leads to drying’ in many previous studies1–11. Moreover, we show that the hydrologic information in CMIP5 models can be satisfactorily recovered using an offline hydrologic model that incorporates the [CO2] effect on rs in calculating potential evapotranspiration (EP). This offers an effective, physically-based yet relatively simple way to account for the vegetation response to elevated [CO2] in offline impact models.

Drought, generally characterized by below-average water supply, propagates through thehydrologic system with consequent ecological and societal impacts. Compared with other drought aspects,the recovery of drought especially in the hydrological components, which directly relates to the recovery ofwater resources for agricultural, ecological and human needs, is less-understood. Here, taking the Millen-nium drought in southeast Australia ( 1997–2009) as an illustrating case, we comprehensively examinedmultiple aspects of the meteorological (i.e., precipitation) and hydrological (i.e., streamflow and base flow)droughts across 130 unimpaired catchments using long-term hydro-meteorological observations. Resultsshow that the duration and intensity of the meteorological drought are both lengthened and amplified inthe hydrological drought, suggesting a nonstationarity in the rainfall-runoff relationship during a prolongeddrought. Additionally, we find a time lag commonly exists between the end of the meteorological droughtsand the end of the hydrological drought, with the recovery of base flow showing a longer lag than therecovery of streamflow. The recovery rate of precipitation after drought was found to be the dominant fac-tor that controls the recovery of hydrological droughts while catchment landscape (i.e., valley bottom flat-ness) plays an important but secondary role in controlling the lags in the hydrological recovery. Otherhydro-climatic factors and catchment properties appear to have only minor influences governing hydrologi-cal drought recovery. Our findings highlight a delayed response in the terrestrial components of the hydro-logical cycle to precipitation after prolonged drought, and provide valuable scientific guidance to waterresources management and water security assessment in regions facing future droughts

Vegetation captures carbon from the atmosphere through photosynthesis, the rate of which varies across space, through time and is determined by both physical and biological factors. Methods for estimating photosynthesis (A) vary in their complexity and in which driving processes they capture. Whilst the effect of diffuse shortwave irradiance on A is well understood, few models have explicitly incorporated the diffuse effect into estimates of A. Here we present the DIFFUSE model, a simple, generic, diffuse-light-based method for estimating A at the monthly time scale. This model is based on the assumption that, at the monthly time scale, the majority of variability in A can be explained by the variability in total and diffuse irradiance and in the fraction of shortwave irradiance absorbed by foliage (f). Comparison of model estimates to eddy flux tower-derived monthly A showed that the majority (83%) of variability in observed A could be explained by the DIFFUSE model. The diffuse fraction contributed 5 to 10% of the model's accuracy across many of Australia's coastal regions, but contributed up to 50% in the monsoonal north. Various aspects of the DIFFUSE model were tested including its performance relative to an example of the more commonly used "stress-scalar" type of photosynthesis model. In all tests, the DIFFUSE model performed at least as well as more complex alternative models, and often outperformed them. The strengths of DIFFUSE are its physical basis, its simplicity and transparency, and its minimalist data requirements - all of which are expected to make it useful to a wide variety of contexts and applications.

Satellite observations reveal a greening of the globe over recent decades. The role in this greening of the "CO2 fertilization" effect-the enhancement of photosynthesis due to rising CO2 levels-is yet to be established. The direct CO2 effect on vegetation

In a globally warming climate, observed rates of atmospheric evaporative demand have declined over recent decades. Several recent studies have shown that declining rates of evaporative demand are primarily governed by trends in the aerodynamic component (primarily being the combination of the effects of wind speed (u) and atmospheric humidity) and secondarily by changes in the radiative component. A number of these studies also show that declining rates of observed near-surface u (termed 'stilling') is the primary factor contributing to declining rates of evaporative demand. One objective of this paper was to review and synthesise the literature to assess whether stilling is a globally widespread phenomenon. We analysed 148 studies reporting terrestrial u trends from across the globe (with uneven and incomplete spatial distribution and differing periods of measurement) and found that the average trend was -0.014ms -1a -1 for studies with more than 30 sites observing data for more than 30years, which confirmed that stilling was widespread. Assuming a linear trend this constitutes a -0.7ms -1 change in u over 50years. A second objective was to confirm the declining rates of evaporative demand by reviewing papers reporting trends in measured pan evaporation (E pan) and estimated crop reference evapotranspiration (ET o); average trends were -3.19mma -2 (n=55) and -1.31mma -2 (n=26), respectively. A third objective was to assess the contribution to evaporative demand trends that the four primary meteorological variables (being u; atmospheric humidity; radiation; and air temperature) made. The results from 36 studies highlighted the importance of u trends. We also quantified the sensitivity of rates of evaporative demand to changes in u and how the relative contributions of the aerodynamic and radiative components change seasonally over the globe. Our review: (i) shows that terrestrial stilling is widespread across the globe; (ii) confirms declining rates of evaporative d

Why, in a globally warming climate, have observed rates of atmospheric evaporative demand (AED) declined at many sites over recent decades? The answer is that the evaporative process is primarily driven by radiative and aerodynamic components (the latter

Near-surface wind speeds (u) measured by terrestrial anemometers show declines (a 'stilling') at a range of midlatitude sites, but two gridded u datasets (a NCEP/NCAR reanalysis output and a surface-pressure-based u model) have not reproduced the stilling observed at Australian stations. We developed Australia-wide 0.01° resolution daily u grids by interpolating measurements from an expanded anemometer network for 1975-2006. These new grids represented the magnitude and spatial-variability of observed u trends, whereas grids from reanalysis systems (NCEP/NCAR, NCEP/DOE and ERA40) essentially did not, even when minimising the sea-breeze impact. For these new grids, the Australian-averaged u trend for 1975-2006 was -0.009 rn s-1 a-1 (agreeing with earlier site-based studies) with stilling over 88% of the land-surface. This new dataset can be used in numerous environmental applications, including benchmarking general circulation models to improve the representation of key parameters that govern u estimation. The methodology implemented here can be applied globally.