A. Johannes Dolman

Recent decline in the global land evapotranspiration trend due to limited moisture supply

More than half of the solar energy absorbed by land surfaces is currently used to evaporate water. Climate change is expected to intensify the hydrological cycle and to alter evapotranspiration, with implications for ecosystem services and feedback to regional and global climate. Evapotranspiration changes may already be under way, but direct observational constraints are lacking at the global scale. Until such evidence is available, changes in the water cycle on land—a key diagnostic criterion of the effects of climate change and variability—remain uncertain. Here we provide a data-driven estimate of global land evapotranspiration from 1982 to 2008, compiled using a global monitoring network, meteorological and remote-sensing observations, and a machine-learning algorithm. In addition, we have assessed evapotranspiration variations over the same time period using an ensemble of process-based land-surface models. Our results suggest that global annual evapotranspiration increased on average by 7.1 ± 1.0 millimetres per year per decade from 1982 to 1997. After that, coincident with the last major El Niño event in 1998, the global evapotranspiration increase seems to have ceased until 2008. This change was driven primarily by moisture limitation in the Southern Hemisphere, particularly Africa and Australia. In these regions, microwave satellite observations indicate that soil moisture decreased from 1998 to 2008. Hence, increasing soil-moisture limitations on evapotranspiration largely explain the recent decline of the global land-evapotranspiration trend. Whether the changing behaviour of evapotranspiration is representative of natural climate variability or reflects a more permanent reorganization of the land water cycle is a key question for earth system science.

Land management and land-cover change have impacts of similar magnitude on surface temperature

The direct effects of land-cover change on surface climate are increasingly well understood, but fewer studies have investigated the consequences of the trend towards more intensive land management practices. Now, research investigating the biophysical effects of temperate land-management changes reveals a net warming effect of similar magnitude to that driven by changing land cover.

Stomatal and surface conductance of tropical rainforest

Abstract Although the absolute values of stomatal conductance of tropical rainforest vary greatly, there is some similarity in the response to humidity deficit and radiation. Stomatal conductance decreases downward through the canopy of Amazonian rainforest. Using a multi-layer approach and measured profiles of stomatal conductance and weather variables through the canopy, good agreement can be obtained between calculated and observed values of dry canopy evaporation. The relationship between the biological response of stomata to radiation at the leaf level and the response of surface conductance to radiation above the canopy is derived by relating the profile of stomatal conductance through the canopy to the attenuation of radiation. Simple use of responses derived at leaf level will greatly overestimate surface conductance if used with above-canopy radiation measurements. Three models of surface conductance of the same Amazonian forest, varying in their degree of complexity, were tested against measured evaporation data for the Reserva Ducke forest in Brazil. A simple model with surface conductance varying only with time of day was found to model the observed data slightly better than a more complex environmental model. Using a constant value of surface conductance gave a poorer fit to the data, although the average evaporation can be calculated accurately. It is recommended that the more complex environmental model be used when estimates of evaporation are required under any conditions substantially different from those of the central Amazonian forest where the data were collected.

Short communication: A three-dimensional gap filling method for large geophysical datasets: Application to global satellite soil moisture observations

The presence of data gaps is always a concern in geophysical records, creating not only difficulty in interpretation but, more importantly, also a large source of uncertainty in data analysis. Filling the data gaps is a necessity for use in statistical modeling. There are numerous approaches for this purpose. However, particularly challenging are the increasing number of very large spatio-temporal datasets such as those from Earth observations satellites. Here we introduce an efficient three-dimensional method based on discrete cosine transforms, which explicitly utilizes information from both time and space to predict the missing values. To analyze its performance, the method was applied to a global soil moisture product derived from satellite images. We also executed a validation by introducing synthetic gaps. It is shown this method is capable of filling data gaps in the global soil moisture dataset with very high accuracy.

Patch scale aggregation of heterogeneous land surface cover for mesoscale meteorological models

Theoretical studies dealing with aggregation of surface parameters at small scale are reviewed. Finding effective parameters for surface resistance is possible for most cases by taking simple geometric or arithmetic averages of the component resistances. The use of more sophisticated techniques such as the blending height improves the calculations. Resistances for heat and water vapour behave differently in heterogeneous terrain. A simple surface energy balance model is adapted to show the behaviour of the roughness length of heat and water vapour in heterogeneous terrain. It is suggested that this simple parameterization can adequately take into account the effect of variation in surface cover on the fluxes of heat and water vapour.

What eddy-covariance measurements tell us about prior land flux errors in CO2-flux inversion schemes

To guide the future development of CO2-atmospheric inversion modeling systems, we analyzed the errors arising from prior information about terrestrial ecosystem fluxes. We compared the surface fluxes calculated by a process-based terrestrial ecosystem model with daily averages of CO2 flux measurements at 156 sites across the world in the FLUXNET network. At the daily scale, the standard deviation of the model-data fit was 2.5 gC*m−2*d−1; temporal autocorrelations were significant at the weekly scale (>0.3 for lags less than four weeks), while spatial correlations were confined to within the first few hundred kilometers (<0.2 after 200 km). Separating out the plant functional types did not increase the spatial correlations, except for the deciduous broad-leaved forests. Using the statistics of the flux measurements as a proxy for the statistics of the prior flux errors was shown not to be a viable approach. A statistical model allowed us to upscale the site-level flux error statistics to the coarser spatial and temporal resolutions used in regional or global models. This approach allowed us to quantify how aggregation reduces error variances, while increasing correlations. As an example, for a typical inversion of grid point (300 km × 300 km) monthly fluxes, we found that the prior flux error follows an approximate e-folding correlation length of 500 km only, with correlations from one month to the next as large as 0.6.

Testing of vegetation parameter aggregation rules applicable to the Biosphere-Atmosphere Transfer Scheme (BATS) and the FIFE site

Abstract A realistic model of surface-atmosphere exchanges was created by coupling the Biosphere-Atmosphere Transfer Scheme (BATS) with an advanced, two-dimensional model of the atmospheric boundary layer. This was initiated and tested using data obtained from the First International Satellite Land Surface Climatology Project Field Experiment (FIFE). This model was used to investigate the acceptability of simple rules for defining the aggregate value of the parameters required to specify surface interactions, as applied to heterogeneous mixes of vegetation types allowed in BATS but appropriate to the FIFE site, namely short and long grass, mixed crops, and irrigated crops. Under the range of meteorological and surface conditions relevant to FIFE and as used in this study, these rules are shown to estimate aggregate parameters which give surface fluxes similar to those calculated with an explicit representation of separate vegetation patches, except in the particular case of artificially wetted (irrigated) patches set in an otherwise dry landscape.

Contribution of water-limited ecoregions to their own supply of rainfall

The occurrence of wet and dry growing seasons in water-limited regions remains poorly understood, partly due to the complex role that these regions play in the genesis of their own rainfall. This limits the predictability of global carbon and water budgets, and hinders the regional management of natural resources. Using novel satellite observations and atmospheric trajectory modelling, we unravel the origin and immediate drivers of growing-season precipitation, and the extent to which ecoregions themselves contribute to their own supply of rainfall. Results show that persistent anomalies in growing-season precipitation—and subsequent biomass anomalies—are caused by a complex interplay of land and ocean evaporation, air circulation and local atmospheric stability changes. For regions such as the Kalahari and Australia, the volumes of moisture recycling decline in dry years, providing a positive feedback that intensifies dry conditions. However, recycling ratios increase up to 40%, pointing to the crucial role of these regions in generating their own supply of rainfall; transpiration in periods of water stress allows vegetation to partly offset the decrease in regional precipitation. Findings highlight the need to adequately represent vegetation–atmosphere feedbacks in models to predict biomass changes and to simulate the fate of water-limited regions in our warming climate.