Dataset Information

Sun-induced fluorescence and gross primary productivity during a heat wave.

ABSTRACT: Remote sensing of sun-induced chlorophyll fluorescence (SIF) has been suggested as a promising approach for probing changes in global terrestrial gross primary productivity (GPP). To date, however, most studies were conducted in situations when/where changes in both SIF and GPP were driven by large changes in the absorbed photosynthetically active radiation (APAR) and phenology. Here we quantified SIF and GPP during a short-term intense heat wave at a Mediterranean pine forest, during which changes in APAR were negligible. GPP decreased linearly during the course of the heat wave, while SIF declined slightly initially and then dropped dramatically during the peak of the heat wave, temporally coinciding with a biochemical impairment of photosynthesis inferred from the increase in the uptake ratio of carbonyl sulfide to carbon dioxide. SIF thus accounted for less than 35% of the variability in GPP and, even though it responded to the impairment of photosynthesis, appears to offer limited potential for quantitatively monitoring GPP during heat waves in the absence of large changes in APAR.

SUBMITTER: Wohlfahrt G

PROVIDER: S-EPMC6155073 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Sun-induced fluorescence and gross primary productivity during a heat wave.

Wohlfahrt G G Gerdel K K Migliavacca M M Rotenberg E E Tatarinov F F Müller J J Hammerle A A Julitta T T Spielmann F M FM Yakir D D

Scientific reports 20180921 1

Remote sensing of sun-induced chlorophyll fluorescence (SIF) has been suggested as a promising approach for probing changes in global terrestrial gross primary productivity (GPP). To date, however, most studies were conducted in situations when/where changes in both SIF and GPP were driven by large changes in the absorbed photosynthetically active radiation (APAR) and phenology. Here we quantified SIF and GPP during a short-term intense heat wave at a Mediterranean pine forest, during which chan ...[more]

PMID: 30242255

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Project description:Data analysis and model-data comparisons in the environmental sciences require diagnostic measures that quantify time series dynamics and structure, and are robust to noise in observational data. This paper investigates the temporal dynamics of environmental time series using measures quantifying their information content and complexity. The measures are used to classify natural processes on one hand, and to compare models with observations on the other. The present analysis focuses on the global carbon cycle as an area of research in which model-data integration and comparisons are key to improving our understanding of natural phenomena. We investigate the dynamics of observed and simulated time series of Gross Primary Productivity (GPP), a key variable in terrestrial ecosystems that quantifies ecosystem carbon uptake. However, the dynamics, patterns and magnitudes of GPP time series, both observed and simulated, vary substantially on different temporal and spatial scales. We demonstrate here that information content and complexity, or Information Theory Quantifiers (ITQ) for short, serve as robust and efficient data-analytical and model benchmarking tools for evaluating the temporal structure and dynamical properties of simulated or observed time series at various spatial scales. At continental scale, we compare GPP time series simulated with two models and an observations-based product. This analysis reveals qualitative differences between model evaluation based on ITQ compared to traditional model performance metrics, indicating that good model performance in terms of absolute or relative error does not imply that the dynamics of the observations is captured well. Furthermore, we show, using an ensemble of site-scale measurements obtained from the FLUXNET archive in the Mediterranean, that model-data or model-model mismatches as indicated by ITQ can be attributed to and interpreted as differences in the temporal structure of the respective ecological time series. At global scale, our understanding of C fluxes relies on the use of consistently applied land models. Here, we use ITQ to evaluate model structure: The measures are largely insensitive to climatic scenarios, land use and atmospheric gas concentrations used to drive them, but clearly separate the structure of 13 different land models taken from the CMIP5 archive and an observations-based product. In conclusion, diagnostic measures of this kind provide data-analytical tools that distinguish different types of natural processes based solely on their dynamics, and are thus highly suitable for environmental science applications such as model structural diagnostics.

Dataset Information

Sun-induced fluorescence and gross primary productivity during a heat wave.

Publications

Sun-induced fluorescence and gross primary productivity during a heat wave.

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