Photosynthesis is the process by which plants harvest sunlight to produce sugars from carbon dioxide and water. It is the primary source of energy for all life on Earth; hence it is important to understand how this process responds to climate change and human impact. However, model-based estimates of gross primary production (GPP, output from photosynthesis) are highly uncertain, in particular over heavily managed agricultural areas. Recent advances in spectroscopy enable the space-based monitoring of sun-induced chlorophyll fluorescence (SIF) from terrestrial plants.Here we demonstrate that spaceborne SIF retrievals provide a direct measure of the GPP of cropland and grassland ecosystems. Such a strong link with crop photosynthesis is not evident for traditional remotely sensed vegetation indices, nor for more complex carbon cycle models. We use SIF observations to provide a global perspective on agricultural productivity. Our SIF-based crop GPP estimates are 50-75% higher than results from state-ofthe-art carbon cycle models over, for example, the US Corn Belt and the Indo-Gangetic Plain, implying that current models severely underestimate the role of management. Our results indicate that SIF data can help us improve our global models for more accurate projections of agricultural productivity and climate impact on crop yields. Extension of our approach to other ecosystems, along with increased observational capabilities for SIF in the near future, holds the prospect of reducing uncertainties in the modeling of the current and future carbon cycle.crop productivity | carbon fluxes | Earth observation | carbon modeling | spaceborne spectroscopy T he rapidly growing demand for food and biofuels constitutes one of the greatest challenges for humanity in coming decades (1). It is estimated that we must double world food production by 2050 to meet increasing demand (2), but the once rapid growth seen in the "green revolution" has stalled, and even past advances are threatened by climate change (3-5). Much of past yield improvement has focused on increases in the harvest index and resistance to pests. However, all else being equal, the quantity of photosynthesis places an upper limit on the supply of food and fuels from our agricultural systems.Ironically, we currently have very limited ability to assess photosynthesis of the breadbaskets of the world. Agricultural production inventories provide important information about crop productivity and yields (6-8), but these are difficult to compare between regions and lag actual production. Carbon cycle models, based on either process-oriented biogeochemistry or semiempirical data-driven approaches, have been used to understand the controls and variations of global gross primary production (GPP, equivalent to ecosystem gross photosynthesis) (9) and to investigate the climate impact on crop yields (10). However, uncertainty associated with inaccurate input data and much simplified process descriptions based on the plant functional type concept severely challenge the applicat...

Abstract. Globally mapped terrestrial chlorophyll fluorescence retrievals are of high interest because they can provide information on the functional status of vegetation including light-use efficiency and global primary productivity that can be used for global carbon cycle modeling and agricultural applications. Previous satellite retrievals of fluorescence have relied solely upon the filling-in of solar Fraunhofer lines that are not significantly affected by atmospheric absorption. Although these measurements provide near-global coverage on a monthly basis, they suffer from relatively low precision and sparse spatial sampling. Here, we describe a new methodology to retrieve global far-red fluorescence information; we use hyperspectral data with a simplified radiative transfer model to disentangle the spectral signatures of three basic components: atmospheric absorption, surface reflectance, and fluorescence radiance. An empirically based principal component analysis approach is employed, primarily using cloudy data over ocean, to model and solve for the atmospheric absorption. Through detailed simulations, we demonstrate the feasibility of the approach and show that moderate-spectral-resolution measurements with a relatively high signal-to-noise ratio can be used to retrieve far-red fluorescence information with good precision and accuracy. The method is then applied to data from the Global Ozone Monitoring Instrument 2 (GOME-2). The GOME-2 fluorescence retrievals display similar spatial structure as compared with those from a simpler technique applied to the Greenhouse gases Observing SATellite (GOSAT). GOME-2 enables global mapping of far-red fluorescence with higher precision over smaller spatial and temporal scales than is possible with GOSAT. Near-global coverage is provided within a few days. We are able to show clearly for the first time physically plausible variations in fluorescence over the course of a single month at a spatial resolution of 0.5 • × 0.5 • . We also show some significant differences between fluorescence and coincident normalized difference vegetation indices (NDVI) retrievals.

Philipp, "Estimation of vegetation photosynthetic capacity from space-based measurements of chlorophyll fluorescence for terrestrial biosphere models" (2014 Estimation of vegetation photosynthetic capacity from space-based measurements of chlorophyll fluorescence for terrestrial biosphere modelsInstitute for Space Sciences, Free University of Berlin, Berlin 12165, Germany, AbstractPhotosynthesis simulations by terrestrial biosphere models are usually based on the Farquhar's model, in which the maximum rate of carboxylation (V cmax ) is a key control parameter of photosynthetic capacity. Even though V cmax is known to vary substantially in space and time in response to environmental controls, it is typically parameterized in models with tabulated values associated to plant functional types. Remote sensing can be used to produce a spatially continuous and temporally resolved view on photosynthetic efficiency, but traditional vegetation observations based on spectral reflectance lack a direct link to plant photochemical processes. Alternatively, recent space-borne measurements of sun-induced chlorophyll fluorescence (SIF) can offer an observational constraint on photosynthesis simulations. Here, we show that top-of-canopy SIF measurements from space are sensitive to V cmax at the ecosystem level, and present an approach to invert V cmax from SIF data. We use the Soil-Canopy Observation of Photosynthesis and Energy (SCOPE) balance model to derive empirical relationships between seasonal V cmax and SIF which are used to solve the inverse problem. We evaluate our V cmax estimation method at six agricultural flux tower sites in the midwestern US using spaced-based SIF retrievals. Our Our results support the use of space-based SIF data as a proxy for photosynthetic capacity and suggest the potential for global, time-resolved estimates of V cmax .

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.

Globally mapped terrestrial chlorophyll fluorescence retrievals are of high interest because they can provide information on the functional status of vegetation including light-use efficiency and global primary productivity that can be used for global carbon cycle modeling and agricultural applications. In addition, fluorescence can contaminate photon path estimates from the O₂ A-band that has become an integral part of missions to accurately measure greenhouse gas concentrations. Global mapping of far-red (~ 755–770 nm) terrestrial vegetation solar-induced fluorescence from space has been accomplished using the high spectral resolution (ν/Δ ν > 35 000) interferometer on the Japanese Greenhouse gases Observing SATellite (GOSAT). These satellite retrievals of fluorescence rely solely upon the filling-in of solar Fraunhofer lines that are not significantly affected by atmospheric absorption. Although these measurements provide near global coverage on a monthly basis, they suffer from relatively low precision and sparse spatial sampling. Here, we describe a new methodology to retrieve global far-red fluorescence information; we use hyperspectral data to disentangle the spectral signatures of three basic components in and surrounding the O₂ A-band: atmospheric absorption, surface reflectance, and fluorescence radiance. Through detailed simulations, we demonstrate the feasibility of the approach and show that moderate spectral resolution measurements with a relatively high signal-to-noise ratio within and outside the O₂ A-band can be used to retrieve far-red fluorescence information with good precision and accuracy. The method is then applied to data from the Global Ozone Monitoring Instrument 2 (GOME-2). The GOME-2 fluorescence retrievals display similar spatial structure as compared with GOSAT. GOME-2 enables global mapping of far-red fluorescence with higher precision over smaller spatial and temporal scales than is possible with GOSAT. It should be noted that both GOME-2 and GOSAT were designed to make atmospheric trace gas measurements and were not optimized for fluorescence measurements. Our approach can be applied to other existing and future space-based instruments that provide moderate spectral resolution observations in the near-infrared region

Abstract. The properties of European windstorms under present climate conditions are estimated on the basis of surface wind forecasts from the European Centre for MediumRange Weather Forecast (ECMWF) Ensemble Prediction System (EPS). While the EPS is designed to provide forecast information of the range of possible weather developments starting from the observed state of weather, we use its archive in a climatological context. It provides a large number of modifications of observed storm events and includes storms that did not occur in reality. Thus it is possible to create a large sample of storm events, which entirely originate from a physically consistent model, whose ensemble spread represents feasible alternative storm realizations of the covered period. This paper shows that the huge amount of identifiable events in the EPS is applicable to reduce uncertainties in a wide range of fields of research focusing on winter storms. Windstorms are identified and tracked in this study over their lifetime using an algorithm based on the local exceedance of the 98th percentile of instantaneous 10 m wind speed, which is associated with a storm severity measure. After removing inhomogeneities in the data set arising from major modifications of the operational system, the distributions of storm severity, storm size, and storm duration are computed. The overall principal properties of the homogenized EPS storm data set are in good agreement with storms from the ERAInterim data set, making it suitable for climatological investigations of these extreme events. A demonstrated benefit in the climatological context by the EPS is presented. It gives clear evidence of a linear increase of maximum storm intensity and wind field size with storm duration. This relation is not recognizable from a sparse ERA-Interim sample for long-lasting events, as the number of events in the reanalysis is not sufficient to represent these characteristics.

No abstract

Abstract. Observational data during the past several decades show faster increase of hot temperature extremes over land than changes in mean temperature. Towards more extreme temperature is expected to affect terrestrial ecosystem function. However, the ecological impacts of hot extremes on vegetation production remain uncertain across biomes in natural climatic conditions. In this study, we investigated the effects of hot temperature extremes on aboveground net primary production (ANPP) by combining MODIS EVI dataset and in situ climatic records during 2000 to 2009 from 12 long-term experimental sites across biomes and climates. Our results showed that higher mean annual maximum temperatures (Tmax) greatly reduced grassland production, and yet enhanced forest production after removing the effects of precipitation. Relative decreases in ANPP were 16% for arid grassland and 7% for mesic grassland, and the increase were 5% for forest. We also observed a significant positive relationship between interannual ANPP and Tmax for forest biome (R2 = 0.79, P < 0.001). This line of evidence suggests that hot temperature extreme leads to contrasting ecosystem-level response of vegetation production to warming climate between grassland and forest. Given that many terrestrial ecosystem models use average daily temperature as input, predictions of ecosystem production should consider these contrasting responses to more hot temperature extreme regimes associated with climate change.