Abstract
Gross primary productivity (GPP) of vegetation is affected by air temperature. Biogeochemical models use the optimum air temperature (Topt) parameter, which comes from biome-specific look-up tables (Topt−b−LT). Many studies have shown that plants have the capacity to adapt to changes in environmental conditions over time, which suggests that the static Topt−b−LT parameters in the biogeochemical models may poorly represent actual Topt and induce uncertainty in GPP estimates. Here, we estimated biome-specific, site-year-specific, and site-specific optimum air temperature using GPP data from eddy covariance (EC) flux tower sites (GPPEC) (Topt−b−EC, Topt−sy−EC, Topt−s−EC), the Enhanced Vegetation Index (EVI) from MODIS images (Topt−b−EVI, Topt−sy−EVI, Topt−s−EVI), and mean daytime air temperature (TDT). We evaluated the consistency among the four Topt parameters (Topt−b, Topt−sy, Topt−s and Topt−b−LT), and assessed how they affect satellite-based GPP estimates. We find that Topt parameters from MODIS EVI agree well with those from GPPEC, which indicates that EVI can be used as a variable to estimate Topt at individual pixels over large spatial domains. Topt−b, Topt−sy, and Topt−s differed significantly from Topt−b−LT. GPP estimates using Topt−b and Topt−sy were more consistent with GPPEC than when using Topt−b−LT for all the land cover types. Our use of Topt−sy substantially improved 8-day and annual GPP estimates across biomess (from 1% to 34%), especially for cropland, grassland, and open shrubland. Our simple calculation shows that global GPP estimates differ by up to 10 Pg C/yr when using our suggested Topt−sy−EVI instead of using the static Topt−b−LT. Our new approach on estimating Topt has the potential to improve estimates of GPP from satellite-based models, which could lead to better understanding of carbon-climate interactions.
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