A key challenge in constraining uncertainty in land carbon dynamics is how to represent the vertical and horizontal spatial heterogeneity and community structure of vegetation canopies in dynamic global vegetation models (DGVMs) that couple with climate models.   DGVMs that represent demography of vegetation communities -- the distribution of plants of different sizes -- offer a promising, still emerging computational framework.  Satellite lidar data are now also emerging as a means to quantify observed vertical canopy structure.  Our group pursues studies to incorporate such data into a demographic DGVM coupled to the NASA GISS Model Earth System Model.  We use satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS), the  Global Ecosystem Dynamics Investigation (GEDI), and other Earth observing missions. 

Postdoctoral fellows are invited to propose research incorporating lidar observations of vegetation at the site, regional and/or global scales.  Studies could involve, but are not limited to:   

(1) radiative transfer and albedo 

(2)  plant allometry to constrain canopy structure, vegetation biomass, and radiative transfer metrics 

(3) development of new satellite lidar data products estimating vegetation demography or landscape structure 

(5) coupled climate-carbon cycle simulation experiments that may incorporate any of the above, including climate change, seasonality, fire dynamics. 

Successful applicants should have experience with mathematical modeling, computer programming, and analyzing field and remote sensing datasets. Background in the following is highly desirable: Earth, Physical, and/or Computing Sciences, Carbon Cycle Science, advanced statistics, programming in a compiled language like C or FORTRAN95, and scripting with python or R.