Leaf-level coordination principles propagate to the ecosystem scale.

Fundamental axes of variation in plant traits result from trade-offs between costs and benefits of resource-use strategies at the leaf scale. However, it is unclear whether similar trade-offs propagate to the ecosystem level. Here, we test whether trait correlation patterns predicted by three well-known leaf- and plant-level coordination theories - the leaf economics spectrum, the global spectrum of plant form and function, and the least-cost hypothesis - are also observed between community mean traits and ecosystem processes. We combined ecosystem functional properties from FLUXNET sites, vegetation properties, and community mean plant traits into three corresponding principal component analyses. We find that the leaf economics spectrum (90 sites), the global spectrum of plant form and function (89 sites), and the least-cost hypothesis (82 sites) all propagate at the ecosystem level. However, we also find evidence of additional scale-emergent properties. Evaluating the coordination of ecosystem functional properties may aid the development of more realistic global dynamic vegetation models with critical empirical data, reducing the uncertainty of climate change projections.

Global nature run data with realistic high-resolution carbon weather for the year of the Paris Agreement.

The CO2 Human Emissions project has generated realistic high-resolution 9 km global simulations for atmospheric carbon tracers referred to as nature runs to foster carbon-cycle research applications with current and planned satellite missions, as well as the surge of in situ observations. Realistic atmospheric CO2, CH4 and CO fields can provide a reference for assessing the impact of proposed designs of new satellites and in situ networks and to study atmospheric variability of the tracers modulated by the weather. The simulations spanning 2015 are based on the Copernicus Atmosphere Monitoring Service forecasts at the European Centre for Medium Range Weather Forecasts, with improvements in various model components and input data such as anthropogenic emissions, in preparation of a CO2 Monitoring and Verification Support system. The relative contribution of different emissions and natural fluxes towards observed atmospheric variability is diagnosed by additional tagged tracers in the simulations. The evaluation of such high-resolution model simulations can be used to identify model deficiencies and guide further model improvements.

A unified vegetation index for quantifying the terrestrial biosphere.

Empirical vegetation indices derived from spectral reflectance data are widely used in remote sensing of the biosphere, as they represent robust proxies for canopy structure, leaf pigment content, and, subsequently, plant photosynthetic potential. Here, we generalize the broad family of commonly used vegetation indices by exploiting all higher-order relations between the spectral channels involved. This results in a higher sensitivity to vegetation biophysical and physiological parameters. The presented nonlinear generalization of the celebrated normalized difference vegetation index (NDVI) consistently improves accuracy in monitoring key parameters, such as leaf area index, gross primary productivity, and sun-induced chlorophyll fluorescence. Results suggest that the statistical approach maximally exploits the spectral information and addresses long-standing problems in satellite Earth Observation of the terrestrial biosphere. The nonlinear NDVI will allow more accurate measures of terrestrial carbon source/sink dynamics and potentials for stabilizing atmospheric CO2 and mitigating global climate change.

Satellite chlorophyll fluorescence measurements reveal large-scale decoupling of photosynthesis and greenness dynamics in boreal evergreen forests.

Mid-to-high latitude forests play an important role in the terrestrial carbon cycle, but the representation of photosynthesis in boreal forests by current modelling and observational methods is still challenging. In particular, the applicability of existing satellite-based proxies of greenness to indicate photosynthetic activity is hindered by small annual changes in green biomass of the often evergreen tree population and by the confounding effects of background materials such as snow. As an alternative, satellite measurements of sun-induced chlorophyll fluorescence (SIF) can be used as a direct proxy of photosynthetic activity. In this study, the start and end of the photosynthetically active season of the main boreal forests are analysed using spaceborne SIF measurements retrieved from the GOME-2 instrument and compared to that of green biomass, proxied by vegetation indices including the Enhanced Vegetation Index (EVI) derived from MODIS data. We find that photosynthesis and greenness show a similar seasonality in deciduous forests. In high-latitude evergreen needleleaf forests, however, the length of the photosynthetically active period indicated by SIF is up to 6 weeks longer than the green biomass changing period proxied by EVI, with SIF showing a start-of-season of approximately 1 month earlier than EVI. On average, the photosynthetic spring recovery as signalled by SIF occurs as soon as air temperatures exceed the freezing point (2-3 °C) and when the snow on the ground has not yet completely melted. These findings are supported by model data of gross primary production and a number of other studies which evaluated in situ observations of CO2 fluxes, meteorology and the physiological state of the needles. Our results demonstrate the sensitivity of space-based SIF measurements to light-use efficiency of boreal forests and their potential for an unbiased detection of photosynthetic activity even under the challenging conditions interposed by evergreen boreal ecosystems.