Alessandro Cescatti

Terrestrial Gross Carbon Dioxide Uptake: Global Distribution and Covariation with Climate

Carbon Cycle and Climate Change As climate change accelerates, it is important to know the likely impact of climate change on the carbon cycle (see the Perspective by Reich). Gross primary production (GPP) is a measure of the amount of CO2 removed from the atmosphere every year to fuel photosynthesis. Beer et al. (p. 834, published online 5 July) used a combination of observation and calculation to estimate that the total GPP by terrestrial plants is around 122 billion tons per year; in comparison, burning fossil fuels emits about 7 billion tons annually. Thirty-two percent of this uptake occurs in tropical forests, and precipitation controls carbon uptake in more than 40% of vegetated land. The temperature sensitivity (Q10) of ecosystem respiratory processes is a key determinant of the interaction between climate and the carbon cycle. Mahecha et al. (p. 838, published online 5 July) now show that the Q10 of ecosystem respiration is invariant with respect to mean annual temperature, independent of the analyzed ecosystem type, with a global mean value for Q10 of 1.6. This level of temperature sensitivity suggests a less-pronounced climate sensitivity of the carbon cycle than assumed by recent climate models. A combination of data and models provides an estimate of how much photosynthesis by all the world’s plants occurs each year. Terrestrial gross primary production (GPP) is the largest global CO2 flux driving several ecosystem functions. We provide an observation-based estimate of this flux at 123 ± 8 petagrams of carbon per year (Pg C year−1) using eddy covariance flux data and various diagnostic models. Tropical forests and savannahs account for 60%. GPP over 40% of the vegetated land is associated with precipitation. State-of-the-art process-oriented biosphere models used for climate predictions exhibit a large between-model variation of GPP’s latitudinal patterns and show higher spatial correlations between GPP and precipitation, suggesting the existence of missing processes or feedback mechanisms which attenuate the vegetation response to climate. Our estimates of spatially distributed GPP and its covariation with climate can help improve coupled climate–carbon cycle process models.

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.

Global patterns of land‐atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations

We upscaled FLUXNET observations of carbon dioxide, water, and energy fluxes to the global scale using the machine learning technique, model tree ensembles (MTE). We trained MTE to predict site-level gross primary productivity (GPP), terrestrial ecosystem respiration (TER), net ecosystem exchange (NEE), latent energy (LE), and sensible heat (H) based on remote sensing indices, climate and meteorological data, and information on land use. We applied the trained MTEs to generate global flux fields at a 0.5 degrees x 0.5 degrees spatial resolution and a monthly temporal resolution from 1982 to 2008. Cross-validation analyses revealed good performance of MTE in predicting among-site flux variability with modeling efficiencies (MEf) between 0.64 and 0.84, except for NEE (MEf = 0.32). Performance was also good for predicting seasonal patterns (MEf between 0.84 and 0.89, except for NEE (0.64)). By comparison, predictions of monthly anomalies were not as strong (MEf between 0.29 and 0.52). Improved accounting of disturbance and lagged environmental effects, along with improved characterization of errors in the training data set, would contribute most to further reducing uncertainties. Our global estimates of LE (158 +/- 7 J x 10(18) yr(-1)), H (164 +/- 15 J x 10(18) yr(-1)), and GPP (119 +/- 6 Pg C yr(-1)) were similar to independent estimates. Our global TER estimate (96 +/- 6 Pg C yr(-1)) was likely underestimated by 5-10%. Hot spot regions of interannual variability in carbon fluxes occurred in semiarid to semihumid regions and were controlled by moisture supply. Overall, GPP was more important to interannual variability in NEE than TER. Our empirically derived fluxes may be used for calibration and evaluation of land surface process models and for exploratory and diagnostic assessments of the biosphere.

An underestimated role of precipitation frequency in regulating summer soil moisture

Soil moisture induced droughts are expected to become more frequent under future global climate change. Precipitation has been previously assumed to be mainly responsible for variability in summer soil moisture. However, little is known about the impacts of precipitation frequency on summer soil moisture, either interannually or spatially. To better understand the temporal and spatial drivers of summer drought, 415 site yr measurements observed at 75 flux sites world wide were used to analyze the temporal and spatial relationships between summer soil water content (SWC) and the precipitation frequencies at various temporal scales, i.e., from half-hourly, 3, 6, 12 and 24 h measurements. Summer precipitation was found to be an indicator of interannual SWC variability with r of 0.49 (p < 0.001) for the overall dataset. However, interannual variability in summer SWC was also significantly correlated with the five precipitation frequencies and the sub-daily precipitation frequencies seemed to explain the interannual SWC variability better than the total of precipitation. Spatially, all these precipitation frequencies were better indicators of summer SWC than precipitation totals, but these better performances were only observed in non-forest ecosystems. Our results demonstrate that precipitation frequency may play an important role in regulating both interannual and spatial variations of summer SWC, which has probably been overlooked or underestimated. However, the spatial interpretation should carefully consider other factors, such as the plant functional types and soil characteristics of diverse ecoregions.

Global Convergence in the Temperature Sensitivity of Respiration at Ecosystem Level

Carbon Cycle and Climate Change As climate change accelerates, it is important to know the likely impact of climate change on the carbon cycle (see the Perspective by Reich). Gross primary production (GPP) is a measure of the amount of CO2 removed from the atmosphere every year to fuel photosynthesis. Beer et al. (p. 834, published online 5 July) used a combination of observation and calculation to estimate that the total GPP by terrestrial plants is around 122 billion tons per year; in comparison, burning fossil fuels emits about 7 billion tons annually. Thirty-two percent of this uptake occurs in tropical forests, and precipitation controls carbon uptake in more than 40% of vegetated land. The temperature sensitivity (Q10) of ecosystem respiratory processes is a key determinant of the interaction between climate and the carbon cycle. Mahecha et al. (p. 838, published online 5 July) now show that the Q10 of ecosystem respiration is invariant with respect to mean annual temperature, independent of the analyzed ecosystem type, with a global mean value for Q10 of 1.6. This level of temperature sensitivity suggests a less-pronounced climate sensitivity of the carbon cycle than assumed by recent climate models. The long-standing discrepancy between modeled and empirical measures of ecosystem temperature sensitivity is resolved. The respiratory release of carbon dioxide (CO2) from the land surface is a major flux in the global carbon cycle, antipodal to photosynthetic CO2 uptake. Understanding the sensitivity of respiratory processes to temperature is central for quantifying the climate–carbon cycle feedback. We approximated the sensitivity of terrestrial ecosystem respiration to air temperature (Q10) across 60 FLUXNET sites with the use of a methodology that circumvents confounding effects. Contrary to previous findings, our results suggest that Q10 is independent of mean annual temperature, does not differ among biomes, and is confined to values around 1.4 ± 0.1. The strong relation between photosynthesis and respiration, by contrast, is highly variable among sites. The results may partly explain a less pronounced climate–carbon cycle feedback than suggested by current carbon cycle climate models.

Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence.

Significance Global food and biofuel production and their vulnerability in a changing climate are of paramount societal importance. However, current global model predictions of crop photosynthesis are highly uncertain. Here we demonstrate that new space-based observations of chlorophyll fluorescence, an emission intrinsically linked to plant biochemistry, enable an accurate, global, and time-resolved measurement of crop photosynthesis, which is not possible from any other remote vegetation measurement. Our results show that chlorophyll fluorescence data can be used as a unique benchmark to improve our global models, thus providing more reliable projections of agricultural productivity and climate impact on crop yields. The enormous increase of the observational capabilities for fluorescence in the very near future strengthens the relevance of this study. 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-of-the-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.

Reconciling the temperature dependence of respiration across timescales and ecosystem types

Ecosystem respiration is the biotic conversion of organic carbon to carbon dioxide by all of the organisms in an ecosystem, including both consumers and primary producers. Respiration exhibits an exponential temperature dependence at the subcellular and individual levels, but at the ecosystem level respiration can be modified by many variables including community abundance and biomass, which vary substantially among ecosystems. Despite its importance for predicting the responses of the biosphere to climate change, it is as yet unknown whether the temperature dependence of ecosystem respiration varies systematically between aquatic and terrestrial environments. Here we use the largest database of respiratory measurements yet compiled to show that the sensitivity of ecosystem respiration to seasonal changes in temperature is remarkably similar for diverse environments encompassing lakes, rivers, estuaries, the open ocean and forested and non-forested terrestrial ecosystems, with an average activation energy similar to that of the respiratory complex (approximately 0.65 electronvolts (eV)). By contrast, annual ecosystem respiration shows a substantially greater temperature dependence across aquatic (approximately 0.65 eV) versus terrestrial ecosystems (approximately 0.32 eV) that span broad geographic gradients in temperature. Using a model derived from metabolic theory, these findings can be reconciled by similarities in the biochemical kinetics of metabolism at the subcellular level, and fundamental differences in the importance of other variables besides temperature—such as primary productivity and allochthonous carbon inputs—on the structure of aquatic and terrestrial biota at the community level.

Modelling the radiative transfer in discontinuous canopies of asymmetric crowns. I. Model structure and algorithms

Abstract A versatile model for mechanistic simulation of the radiative transfer in discontinuous canopies is presented. Canopy structure is described with arrays of asymmetric crown envelopes, adaptable to various tree geometries and based on the following parameters: total tree height, height at crown insertion and at the greatest width of the crown, crown radii in four orthogonal directions, and shape coefficients of vertical crown profiles. Unlike previous models, this canopy model can simulate the high level of asymmetry in crown shape and displacement typical of natural and semi-natural forests. Within an individual crown, the vertical distribution of leaf area density (LAD) is modelled using the Beta or Weibull equation. The effect of the spatial pattern of leaf area is taken into account by simulating random, regular or clumped distributions. Parameters related to the canopy architecture (vertical and spatial distribution of the leaf area, angular distribution of the leaf normal) are treated as species-specific, so that mixed forests can be represented. Light penetration is modelled by the ‘turbid medium hypothesis’; that is, by computing the beam path length and the LAD within single crowns with a high angular resolution (0.05–5 deg). Diffuse fluxes generated by reflection and transmission of intercepted radiation are simulated by the Adding method, on the basis of the leaf scattering coefficients of each plant species. Using this model, the distribution of radiation intensity and spectra beneath heterogeneous canopies may be analysed in time and space at the required resolution, and could complement research activities involving remote sensing, plant physiology and ecology. The software has been written in C + + with an object oriented approach, and it does not impose any limit to the number of trees or species in an experimental plot.

Estimation of leaf area index in open-canopy ponderosa pine forests at different successional stages and management regimes in Oregon

Leaf area and its spatial distribution are key parameters in describing canopy characteristics. They determine radiation regimes and influence mass and energy exchange with the atmosphere. The evaluation of leaf area in conifer stands is particularly challenging due to their open nature and clumping on the needle, shoot and tree scale. The overall objective of our study was to characterize leaf area index (LAI) (Lh ,m 2 half-surface area foliage m 2 ground) in the vicinity of our old-growth and 14-year-old ponderosa pine (Pinus ponderosa, var. Laws) eddy covariance flux sites, with future plans to scale from the flux sites to the pine region using ecosystem models and remote sensing. From the combination of optical and canopy geometry measurements, sapwood and litter-fall measurements, and one- and three-dimensional (3-D) models, we evaluated the variation in estimates of Lh in a mixed-age stand at the old-growth flux site. We also compared sapwood area estimates from a local allometric equation with LAI-2000 estimates that have been corrected for clumping and the interception of light by stems and branches (Lhc ,m 2 half-surface area m 2 ground) across a range of age classes and stand densities of ponderosa pine forests along a 15 km swath in Central Oregon that encompassed the flux sites. In the old-growth stand, the litter-fall and sapwood estimates tended to be higher than the optical and 3-D radiative transfer model estimates. Across the 15 km east‐west gradient from the crest of the Cascade Mountains, Lhc was typically lower than the sapwood estimates (Lhsw; slope 0.38). The Lhc data, as well as aboveground production estimates for the 17 pine plots will be useful for scaling flux measurements to the region using ecosystem models that have been validated with these data.

Biophysical climate impacts of recent changes in global forest cover

Its not only the carbon in the trees Forest loss affects climate not just because of the impacts it has on the carbon cycle, but also because of how it affects the fluxes of energy and water between the land and the atmosphere. Evaluating global impact is complicated because deforestation can produce different results in different climate zones, making it hard to determine large-scale trends rather than more local ones. Alkama and Cescatti conducted a global assessment of the biophysical effects of forest cover change. Forest loss amplifies diurnal temperature variations, increases mean and maximum air temperatures, and causes a significant amount of warming when compared to CO2 emission from land-use change. Science, this issue p. 600 The biophysical effects of recent global forest cover change have caused measurable warming. Changes in forest cover affect the local climate by modulating the land-atmosphere fluxes of energy and water. The magnitude of this biophysical effect is still debated in the scientific community and currently ignored in climate treaties. Here we present an observation-driven assessment of the climate impacts of recent forest losses and gains, based on Earth observations of global forest cover and land surface temperatures. Our results show that forest losses amplify the diurnal temperature variation and increase the mean and maximum air temperature, with the largest signal in arid zones, followed by temperate, tropical, and boreal zones. In the decade 2003–2012, variations of forest cover generated a mean biophysical warming on land corresponding to about 18% of the global biogeochemical signal due to CO2 emission from land-use change.