0000000001308040
AUTHOR
Trevor F. Keenan
Partitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks
Abstract The eddy covariance (EC) technique is used to measure the net ecosystem exchange (NEE) of CO2 between ecosystems and the atmosphere, offering a unique opportunity to study ecosystem responses to climate change. NEE is the difference between the total CO2 release due to all respiration processes (RECO), and the gross carbon uptake by photosynthesis (GPP). These two gross CO2 fluxes are derived from EC measurements by applying partitioning methods that rely on physiologically based functional relationships with a limited number of environmental drivers. However, the partitioning methods applied in the global FLUXNET network of EC observations do not account for the multiple co‐acting…
The impact of the 2015/2016 El Niño on global photosynthesis using satellite remote sensing
The El Niño-Southern Oscillation exerts a large influence on global climate regimes and on the global carbon cycle. Although El Niño is known to be associated with a reduction of the global total land carbon sink, results based on prognostic models or measurements disagree over the relative contribution of photosynthesis to the reduced sink. Here, we provide an independent remote sensing-based analysis on the impact of the 2015–2016 El Niño on global photosynthesis using six global satellite-based photosynthesis products and a global solar-induced fluorescence (SIF) dataset. An ensemble of satellite-based photosynthesis products showed a negative anomaly of −0.7 ± 1.2 PgC in 2015, but a sli…
Figure S2 from The impact of the 2015–2016 El Niño on global photosynthesis using satellite remote sensing
The NEP anomalies and the detrended NEP anomalies from 2000 to 2016. NEP is calculated as the net residual land CO2 sink, estimated by the Global Carbon Project (GCP).
Figure S1 from The impact of the 2015–2016 El Niño on global photosynthesis using satellite remote sensing
The detrended RS GPP and SIF anomalies from 2000 to 2016, using the detrended time-average GPP(SIF) of the same period as the baseline.
Figure S3 from The impact of the 2015–2016 El Niño on global photosynthesis using satellite remote sensing
Latitudinal distribution of ensembles of air temperature (Tair), precipitation (PP), photosynthetic active radiation (PAR), vegetation indices (VI) and vapor pressure deficit (VPD) in 2015 and 2016, using the linear trends of variables from 2000 to 2016 as the baselines. The ensemble of Tair is consisted of CRU, CRU-NCEP, NCEP Reanalysis II, ERAI and MERRA2; the ensemble of PP is consisted of CRU, CRU-NCEP, NCEP Reanalysis II, ERAI, MERRA2 and TRMM; the ensemble of PAR is consisted of CRU, CRU-NCEP and ERAI; the ensemble of VI is consisted of MODIS NDVI, MODIS EVI (only 2015) and AVHRR fAPAR; the ensemble of VPD is consisted of CRU, CRU-NCEP and ERAI. The shadings indicate the inter-dataset…
Figure S4 from The impact of the 2015–2016 El Niño on global photosynthesis using satellite remote sensing
Uncertainty of GOMEA SIF trend. Blue line is the baseline of GOMEA SIF we used in this study. (a) first two data points were dropped to fit the line; (b) the last two data points were dropped to fit the line; (c) the first and the last data points were dropped to fit the line; (d) One or two data points were randomly dropped in 400 tests to fit the line. In 98.3% of the tests there was a negative detrended SIF anomaly in 2015 and a positive detrended SIF anomaly in 2016.