Comparison of dynamic vegetation model and satellite remote sensing derived terrestrial primary productivity
Justin
Fisk, University of New Hampshire, justin.fisk@unh.edu
(Presenting)
George
Hurtt, University of New Hampshire, george.hurtt@unh.edu
Elena
Shevliakova, NOAA Geophysical Fluid Dynamics Laboratory, elena@princeton.edu
Sergey
Malyshev, NOAA Geophysical Fluid Dynamics Laboratory, malyshev@princeton.edu
Quantifying primary productivity is of key importance to understanding the earth system. Heterogeneity of the climate and land surface makes scaling direct measurements difficult; however, multiple approaches have been developed to model net and gross primary productivity (NPP/GPP) at the global scale. We compare two approaches, the MODIS GPP/NPP product, MOD17, and the Geophysical Fluid Dynamics Laboratory (GFDL) land model, LM3V. Both approaches have strengths and limitations. MOD17 uses satellite remote sensing observations to provide knowledge of the state of the land surface at high spatial resolution, but is limited by observation quality and the short time period of available observations. LM3V uses mechanistic principles rather than observations and thus can be run historically and prognosticly; however, it offers much lower spatial resolution and is less constrained by land surface changes. Neither model can be validated globally against direct measurements; therefore, a comparison can provide insights into areas of strong agreement and areas where further research is needed. The comparison has already led to improvements of LM3V. In this poster, we present the preliminary results of our comparison of spatial and seasonal patterns and annual global totals from the two approaches.
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