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In the face of a mounting diversity of experimental, satellite and ground-based observations, it is becoming necessary to simulate all changes associated with vegetation (phenological, structural, physiological and biochemical) and to understand the links between them. In this respect, global land-surface models are an indispensible tool. These models require, above all, a temporally and spatially explicit parameterisation of light- and Rubisco-limited photosynthetic capacity in order to simulate photosynthesis accurately. The current study carries out a novel retrieval of these quantities by combining the standard satellite products of Leaf Area Index (LAI), from the Moderate Resolution Imaging Spectroradiometer (MODIS), with a hyperspectral index of total canopy chlorophyll concentration from the MEdium Resolution Imaging Spectrometer (MERIS). Monthly values of photosynthetic capacity are retrieved for the period 2002–2012 for global 0.5° landpoints and made available to the community. We examine the decadal trends in both photosynthetic capacity and LAI in order to ascertain biochemical and structural responses of vegetation to environmental change. The main conclusion is that these trends, if sustained, are of a sufficient magnitude to vie in importance with other environmental factors which affect vegetation productivity and carbon uptake (e.g. CO 2 fertilisation and climate). The decadal trends for Rubisco-limited photosynthetic capacity, which tend to be negative, depend more on plant functional type than latitude, suggesting that biochemical change, like physiological response (e.g. owing to CO 2 fertilisation), might best be monitored in terms of vegetation type rather than climate zone. We record an LAI trend which, globally, is flat (−0.2 ± 0.4% per decade) and, for the (mid-)northern latitudes, is much smaller (1.5–2.7% per decade) than that inferred by previous authors for Normalised Difference Vegetation Index (NDVI) during the 1980s (9–13%). [ABSTRACT FROM AUTHOR]

Process-based ecophysiological models, which simulate carbon exchange at the land-surface, are powerful and indispensable tools for understanding how vegetation behaves under present and future climate. However, these models are necessarily complex, containing numerous biophysical parameters which are often poorly defined. The current study develops a novel retrieval of Rubisco-limited top-of-canopy photosynthetic capacity (i.e. maximum carboxylation rate, V cmax 25 , toc ), which is one of the most critical parameters in the calculation of Gross Primary Productivity (GPP). The retrieval combines standard remote sensing satellite products of Leaf Area Index (LAI), from the Moderate Resolution Imaging Spectroradiometer (MODIS), with a hyperspectral index of total canopy chlorophyll concentration from the MEdium Resolution Imaging Spectrometer (MERIS). Monthly values of V cmax 25 , toc are determined over a 9 year period for 296 global FLUXNET sites (catalogue made available online) and 8 Plant Functional Types (PFTs). PFT averages agree favourably with compilations of field-based measurements. However, according to a Monte Carlo analysis, our method is still currently subject to large systematic uncertainties (25–30%), much of which arises from the empirical relationship between maximum electron transport and leaf chlorophyll content. For all 8 PFTs, except tropical broadleaf forest, V cmax 25 , toc varies considerably across the season (generally a factor of 1.6). Similarly, variability between sites of the same PFT is significant (interquartile range is 40% of the median). This suggests an important additional role for satellites in the spatial and temporal parameterisation of carbon models. Inclusion of this temporal and spatial variability in a process-based ecophysiological model produces, respectively, an impact of 11% and 12% on simulated GPP. [ABSTRACT FROM AUTHOR]

The current trend in land-surface and carbon modelling development is largely dichotomous: simple algorithms which minimise the number of biophysical parameters and meteorological drivers versus complex ecophysiologically based models which do not. Understanding the sensitivity of both types of approach to current uncertainties in Leaf Area Index (LAI) and meteorological forcing is an important step in producing accurate model predictions of land–atmosphere carbon exchange. We force two quite disparate models (the Moderate Resolution Imaging Spectroradiometer (MODIS) Light-Use Efficiency (LUE) algorithm and the ecophysiological model JULES-SF) with two LAI forcings (satellite and site-normalised) and two meteorologies (tower-based and reanalysis). Simulations are conducted for 67 sites and 10 vegetation classes. The sensitivity of modelled Gross Primary Productivity (GPP) to both LAI and meteorological forcing, thus derived, is compared with model bias against observed carbon fluxes. Our most novel findings are as follows: uncertainty in model formulation (LUE versus ecophysiological) is at least as important (20% change in simulated GPP) as that pertaining to LAI and meteorological forcing (10–20% change). However, all these uncertainties are modest compared to both model bias (≤30%) and inconsistencies between observational datasets used for model calibration (45%). The ecophysiological model is more sensitive to meteorology (20% change in simulated GPP) than the LUE algorithm (10%) owing to the former's reliance on precipitation and shortwave radiation to calculate, respectively, the internal balances of water and energy. [ABSTRACT FROM AUTHOR]

Understanding the response of plants to soil moisture stress is important given a future climate subject to greater extremes, including drought. Nevertheless, major discrepancies still exist between observed and simulated seasonal carbon, water and energy fluxes at the vegetated land-surface. For tropical forest, these discrepancies have been reduced, especially during the dry season, by taking account of tap roots and hydraulic redistribution. The expanding FLUXNET open-access archive allows the current study to extend the investigation of seasonal drought-stress to ten different vegetation types. A state-of-the-art land-surface model is enhanced to take account of tap roots and hydraulic redistribution in order to compare with traditional simulations. Carbon fluxes and fractional soil water content are simulated and compared against observations. We find that a traditional approach, by neglecting tap roots, simulates a seasonal drought for trees and shrubs which is generally too severe compared to observed net carbon flux. The introduction of a tap root benefits tropical broadleaf forest and other ecosystems with high annual potential evapotranspiration in reducing observation-model discrepancies. Our simulations suggest a minor role for hydraulic redistribution, modifying weekly soil moisture rather than substantially changing seasonal water flux totals. [ABSTRACT FROM AUTHOR]

Highlights: [•] Inconsistencies between measured fluxes over different spatial scales. [•] 30% reduction in site evaporation to agree with global river discharge. [•] Site fluxes needed in underrepresented tropical forest and C4 grassland. [•] Site fluxes needed in underrepresented climate zones (tundra). [•] Land-surface models require field measurements of tap roots. [Copyright &y& Elsevier]

Abstract: Several recent studies suggest that a simple productivity efficiency model (PEM), based on daily or weekly light-use efficiency, is sufficient to represent the exchange of carbon, water and energy at the land-surface. At the same time, many global land-surface models are becoming more process-based, simulating at high temporal resolution the interception of direct and diffuse sunlight at different depths within the canopy. Quantifying the accuracy and limitations of both types of model has become of great importance. The current study compares a PEM with a more complex (and computationally expensive) multilayer model operating at timesteps as short as 30min (JULES-SF). Each model is optimised against observed fluxes (net carbon exchange, latent heat, sensible heat and net radiation) within the FLUXNET archive for an unprecedented number of sites (30) and site-years (71). Our main finding is that, after optimisation, the process-based multilayer model performs significantly better than the PEM on all timescales (daily and seasonal). However, the difference in model performance appears to diminish with an increase in measurement timescale. Thus, on average, the modelling efficiency increases from 0.32 (daily) to 0.46 (seasonal) using the PEM approach (0.71), whilst it remains close to 0.6 for JULES-SF on both timescales (0.75). We find that the maximum number of biophysical parameters that can be tuned against site fluxes (4 observables) is quite limited (typically 3–4). This inference applies to both models despite their considerable difference in complexity. [Copyright &y& Elsevier]

Abstract: The task of parameterising global Land-Surface Models (LSMs) is rendered difficult by both the limited number of parameters that can be retrieved from eddy-covariance fluxes and the requirement for some physical processes to be modelled in a spatially explicit manner. Fortunately, some of the important biophysical parameters influencing energy exchange can be usefully constrained by satellite measurements of surface reflectance. In this study, we retrieve ground (soil) albedo and vegetation absorptance from the Moderate Resolution Imaging Spectroradiometer (MODIS) 1-arcdegree albedo product. This information is used to parameterise a revised global simulation of carbon, water and energy exchange at the land-surface. The new biophysical parameters produce significant modifications (5–30%) to predicted net radiation and sensible heat exchange, particularly for the northern sub-tropics and the boreal and northern tundra zones. We infer systematic differences in vegetation absorptance between 5 plant functional types (broadleaf forest, needleleaf forest, C3 grass, C4 grass and shrubland). The dispersion in values is fairly moderate within any given plant functional type (PFT), suggesting that a small number of PFTs is sufficient for setting albedo parameters within a global LSM. [Copyright &y& Elsevier]

Abstract: The assumed concentration and distribution of active leaf nitrogen (N) within vegetation canopies has a major influence on the gross primary product (GPP) predicted by land-surface models. We couple a Monte Carlo, three-dimensional (3D), ray-tracing simulation to the land-surface model JULES in order to compare the vertical profiles of light and active leaf-N in three forest stands of diverse composition and structure. Our simulations, which are driven by real climate data, strengthen the view that tree canopies are only partially light-acclimated. Notably, our computation demonstrates the importance of both diffuse sunlight and the manner in which the light profile is quantified. For example, when our temperate, broadleaf stand is subjected to diffuse sunlight, the mean leaf irradiance declines steeply in the upper third of crown (k ext ≳1, where k ext is the exponential extinction coefficient) but is relatively shallow below that height (k ext ≤0.75). Under direct sunlight, the foliage divides into sunlit and shaded fractions and a more appropriate measure of the light environment is probably the mode irradiance rather than the mean irradiance. Under direct sunlight, the optimal vertical distribution of Rubisco (i.e. that which maximises GPP for a fixed amount of active leaf-N in the canopy) is calculated to be shallow in both the upper and lower portions of the canopy (k rub <0.27, where k rub, is the exponential N-allocation parameter). Furthermore, an abrupt step, from high to low photosynthetic capacity, is predicted in the upper third of the crown. The theoretical gain in GPP for a stand that is fully light-acclimated compared to one that is only partially light-acclimated (i.e. with k rub =0.15, as measured in real tree canopies) is moderately important (8–13%). Our results are relatively insensitive to canopy architecture, i.e. crown structure, leaf-clumping and the leaf angle distribution. [Copyright &y& Elsevier]

• Global productivity dominated by tropical/needleleaf forest & C3 grass/crops. • Well sampled PFTs (e.g. needleleaf forest) sampled at wrong latitude & climate • (semi-)arid (MAP < 400 mm yr−1) & sparse (LAI ≤ 2 m2m−2) vegetation undersampled. • Each PFT requires > 50 climate-representative sites to determine its productivity. One of the major uncertainties in estimating global Net Primary Productivity (NPP) and Gross Primary Productivity (GPP) is the ability of carbon-monitoring sites to represent the climate and canopy-density of global vegetation ("representativeness"). These sites are used for empirical upscaling and calibration of global land-surface models. The current study determines the representativeness of two important carbon-monitoring networks – FLUXNET2015 and the Ecosystem Model-Data Intercomparison (EMDI) – by calculating the euclidian distance in climate-canopy space between each global 0.5∘ cell and all carbon-monitoring sites of the same biome or Plant Functional Type (PFT). Reliance on the single (most similar) site has been adopted in the past. A straightforward weighted upscaling, using inverse euclidian distance, identifies which PFTs contribute most to global primary productivity in the context of how well they are represented in carbon-monitoring networks. Some vegetation types, which are numerically well-represented within the network, are sampled at the 'wrong' latitude and in more temperate climes than their global distribution. This includes non-mediterranean needleleaf forest which is one of the main vegetation types contributing to global GPP and NPP. (Semi-)arid regions (mean annual precipitation < 400 mm yr−1) are undersampled as well as the sparse vegetation that tends to characterise them. These regions include the tundra and the northern half of the boreal forest where growth is disproportionately affected by climate change. We find a large spread in NPP and GPP recorded at sites of the same PFT (standard deviation is 56% mean). Consequently, our bootstrap error analysis indicates that a minimum of 50 climate-representative sites per PFT is required to quantify adequately (2% precision) the primary productivity of each global vegetation type. Selecting unchartered climate-canopy space for new sites appears to be more important than a simple increase in site numbers. [ABSTRACT FROM AUTHOR]