Interactions between the biosphere and the atmosphere can be well characterized by fluxes between the two. In particular, carbon and energy fluxes play a major role in understanding biogeochemical ...processes on an ecosystem level or global scale. However, the fluxes can only be measured at individual sites, e.g., by eddy covariance towers, and an upscaling of these local observations is required to analyze global patterns. Previous work focused on upscaling monthly, 8-day, or daily average values, and global maps for each flux have been provided accordingly. In this paper, we raise the upscaling of carbon and energy fluxes between land and atmosphere to the next level by increasing the temporal resolution to subdaily timescales. We provide continuous half-hourly fluxes for the period from 2001 to 2014 at 0.5° spatial resolution, which allows for analyzing diurnal cycles globally. The data set contains four fluxes: gross primary production (GPP), net ecosystem exchange (NEE), latent heat (LE), and sensible heat (H). We propose two prediction approaches for the diurnal cycles based on large-scale regression models and compare them in extensive cross-validation experiments using different sets of predictor variables. We analyze the results for a set of FLUXNET tower sites showing the suitability of our approaches for this upscaling task. Finally, we have selected one approach to calculate the global half-hourly data products based on predictor variables from remote sensing and meteorology at daily resolution as well as half-hourly potential radiation. In addition, we provide a derived product that only contains monthly average diurnal cycles, which is a lightweight version in terms of data storage that still allows studying the important characteristics of diurnal patterns globally. We recommend to primarily use these monthly average diurnal cycles, because they are less affected by the impacts of day-to-day variation, observation noise, and short-term fluctuations on subdaily timescales compared to the full half-hourly flux products. The global half-hourly data products are available at https://doi.org/10.17871/BACI.224.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Abstract
The response of vegetation physiology to drought at large spatial scales is poorly understood due to a lack of direct observations. Here, we study vegetation drought responses related to ...photosynthesis, evaporation, and vegetation water content using remotely sensed data, and we isolate physiological responses using a machine learning technique. We find that vegetation functional decreases are largely driven by the downregulation of vegetation physiology such as stomatal conductance and light use efficiency, with the strongest downregulation in water-limited regions. Vegetation physiological decreases in wet regions also result in a discrepancy between functional and structural changes under severe drought. We find similar patterns of physiological drought response using simulations from a soil–plant–atmosphere continuum model coupled with a radiative transfer model. Observation-derived vegetation physiological responses to drought across space are mainly controlled by aridity and additionally modulated by abnormal hydro-meteorological conditions and vegetation types. Hence, isolating and quantifying vegetation physiological responses to drought enables a better understanding of ecosystem biogeochemical and biophysical feedback in modulating climate change.
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.
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DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The Budyko framework elegantly reduces the complex spatial patterns of actual evapotranspiration and runoff to a general function of two variables: mean annual precipitation (MAP) and net radiation. ...While the methodology has first‐order skill, departures from a globally averaged curve can be significant and may be usefully attributed to additional controls such as vegetation type. This paper explores the magnitude of such departures as detected from flux tower measurements of ecosystem‐scale evapotranspiration, and investigates their attribution to site characteristics (biome, seasonal rainfall distribution, and frozen precipitation). The global synthesis (based on 167 sites with 764 tower‐years) shows smooth transition from water‐limited to energy‐limited control, broadly consistent with catchment‐scale relations and explaining 62% of the across site variation in evaporative index (the fraction of MAP consumed by evapotranspiration). Climate and vegetation types act as additional controls, combining to explain an additional 13% of the variation in evaporative index. Warm temperate winter wet sites (Mediterranean) exhibit a reduced evaporative index, 9% lower than the average value expected based on dryness index, implying elevated runoff. Seasonal hydrologic surplus explains a small but significant fraction of variance in departures of evaporative index from that expected for a given dryness index. Surprisingly, grasslands on average have a higher evaporative index than forested landscapes, with 9% more annual precipitation consumed by annual evapotranspiration compared to forests. In sum, the simple framework of supply‐ or demand‐limited evapotranspiration is supported by global FLUXNET observations but climate type and vegetation type are seen to exert sizeable additional controls.
Key Points
Global FLUXNET data support Budyko hypothesis of surface water balance controls
Climate type, vegetation type exert additional control on evapotranspiration
Grasslands exhibit higher evaporative index than forests
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
In the northern high latitudes, alternative hypotheses with regards to how warming-related shifts in seasonality influence ecosystem productivity exist. Increased plant growth associated with a ...longer growing season may enhance ecosystem productivity, but shifts to earlier springs may also negatively influence soil moisture status and productivity during the peak of the growing season. Here, we analyzed nearly three decades (1982-2008) of observational records and derived products, including satellite microwave and optical imagery as well as upscaled ecosystem flux observations, to better understand how shifts in seasonality impact hydrology and productivity in the North American boreal forests. We identified a dominant adverse influence of earlier springs on peak summer forest greenness, actual evapotranspiration and productivity at interannual time scales across the drier western and central sections of the North American boreal forests. In the vast regions where this spring onset mechanism operates, ecosystem productivity gains from earlier springs during the early portion of the growing season are effectively cancelled through corresponding losses in the later portion. Our results also indicate that recent decadal shifts towards earlier springs and associated drying in the midst of the growing season over western North American boreal forests may have contributed to the reported declines in summer productivity and increases in tree mortality and fire activity. With projections of accelerated northern high-latitude warming and associated shifts to earlier springs, persistent soil moisture deficits in peak summer may be an effective mechanism for regional-scale boreal forest dieback through their strong influence on productivity, tree mortality and disturbance dynamics.
This paper introduces a modular processing chain to derive global high-resolution maps of leaf traits. In particular, we present global maps at 500 m resolution of specific leaf area, leaf dry matter ...content, leaf nitrogen and phosphorus content per dry mass, and leaf nitrogen/phosphorus ratio. The processing chain exploits machine learning techniques along with optical remote sensing data (MODIS/Landsat) and climate data for gap filling and up-scaling of in-situ measured leaf traits. The chain first uses random forests regression with surrogates to fill gaps in the database (> 45% of missing entries) and maximizes the global representativeness of the trait dataset. Plant species are then aggregated to Plant Functional Types (PFTs). Next, the spatial abundance of PFTs at MODIS resolution (500 m) is calculated using Landsat data (30 m). Based on these PFT abundances, representative trait values are calculated for MODIS pixels with nearby trait data. Finally, different regression algorithms are applied to globally predict trait estimates from these MODIS pixels using remote sensing and climate data. The methods were compared in terms of precision, robustness and efficiency. The best model (random forests regression) shows good precision (normalized RMSE≤ 20%) and goodness of fit (averaged Pearson's correlation R = 0.78) in any considered trait. Along with the estimated global maps of leaf traits, we provide associated uncertainty estimates derived from the regression models. The process chain is modular, and can easily accommodate new traits, data streams (traits databases and remote sensing data), and methods. The machine learning techniques applied allow attribution of information gain to data input and thus provide the opportunity to understand trait-environment relationships at the plant and ecosystem scales. The new data products – the gap-filled trait matrix, a global map of PFT abundance per MODIS gridcells and the high-resolution global leaf trait maps – are complementary to existing large-scale observations of the land surface and we therefore anticipate substantial contributions to advances in quantifying, understanding and prediction of the Earth system.
•Presented a modular process chain for plant trait mapping including local effects•High-resolution global maps of leaf traits by fusing measured trait data, LANDSAT and MODIS•Scope for testing and parameterizing trait-enabled Earth System models•Implications for land management and Earth system science applications
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Sun-induced fluorescence (SIF) in the far-red region provides a new noninvasive measurement approach that has the potential to quantify dynamic changes in light-use efficiency and gross primary ...production (GPP). However, the mechanistic link between GPP and SIF is not completely understood.
We analyzed the structural and functional factors controlling the emission of SIF at 760 nm (F760) in a Mediterranean grassland manipulated with nutrient addition of nitrogen (N), phosphorous (P) or nitrogen–phosphorous (NP). Using the soil–canopy observation of photosynthesis and energy (SCOPE) model, we investigated how nutrient-induced changes in canopy structure (i.e. changes in plant forms abundance that influence leaf inclination distribution function, LIDF) and functional traits (e.g. N content in dry mass of leaves, N%, Chlorophyll a+b concentration (Cab) and maximum carboxylation capacity (V
cmax)) affected the observed linear relationship between F760 and GPP.
We conclude that the addition of nutrients imposed a change in the abundance of different plant forms and biochemistry of the canopy that controls F760. Changes in canopy structure mainly control the GPP–F760 relationship, with a secondary effect of Cab and V
cmax.
In order to exploit F760 data to model GPP at the global/regional scale, canopy structural variability, biodiversity and functional traits are important factors that have to be considered.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NMLJ, NUK, OILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK
Plant leaves emit a broad spectrum of organic compounds that typically play multiple roles in plant protection. Furthermore, most of these compounds actively participate in tropospheric chemistry. ...There has been rapid progress in understanding how the emission of volatiles is regulated, mostly focusing on the biochemical controls over compound production. However, physicochemical characteristics such as low volatility or diffusion can also control the emissions and interact with physiological limitations. In particular, non-specific leaf storage of less volatile compounds smooths the emission responses to fluctuating environmental conditions, and diffusion through stomata leads to conspicuous emission bursts after stomatal opening and modifications of diurnal emission time courses. Because natural conditions always fluctuate, both physiological and physicochemical controls exert a major influence over plant volatile emissions.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
The carbon balance of terrestrial ecosystems is particularly sensitive to climatic changes in autumn and spring with spring and autumn temperatures over northern latitudes having risen by about 1.1 ...°C and 0.8 °C, respectively, over the past two decades. A simultaneous greening trend has also been observed, characterized by a longer growing season and greater photosynthetic activity. These observations have led to speculation that spring and autumn warming could enhance carbon sequestration and extend the period of net carbon uptake in the future. Here we analyse interannual variations in atmospheric carbon dioxide concentration data and ecosystem carbon dioxide fluxes. We find that atmospheric records from the past 20 years show a trend towards an earlier autumn-to-winter carbon dioxide build-up, suggesting a shorter net carbon uptake period. This trend cannot be explained by changes in atmospheric transport alone and, together with the ecosystem flux data, suggest increasing carbon losses in autumn. We use a process-based terrestrial biosphere model and satellite vegetation greenness index observations to investigate further the observed seasonal response of northern ecosystems to autumnal warming. We find that both photosynthesis and respiration increase during autumn warming, but the increase in respiration is greater. In contrast, warming increases photosynthesis more than respiration in spring. Our simulations and observations indicate that northern terrestrial ecosystems may currently lose carbon dioxide in response to autumn warming, with a sensitivity of about 0.2 PgC °C-1, offsetting 90% of the increased carbon dioxide uptake during spring. If future autumn warming occurs at a faster rate than in spring, the ability of northern ecosystems to sequester carbon may be diminished earlier than previously suggested.
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DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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