Using Remote Sensing to Understand Climate Variability

Friday, 19 December 2014: 8:30 AM
Julia Green, Columbia University of New York, Palisades, NY, United States and Pierre Gentine, Columbia University, New York, NY, United States
While a major source of uncertainty in global climate model predictions is due to the coarseness of their resolution, a significant amount of error is also generated due to the lack of information regarding the interactions between atmospheric and land parameters over time. When the behavior of a certain parameter is not clearly understood it is frequently estimated as one specific value while in reality it may vary with time and space. Remote sensing is allowing researchers to better estimate each of these parameters so one can see how they change with time.

This study is an effort to improve our knowledge of the inter-annual and seasonal variability in radiation, water and the carbon cycle using remote sensing products on a global scale. By examining monthly data over a multi-year period (data parameter and source are listed in Table 1) for fluorescence, groundwater, net radiation, vegetation indices, precipitation, soil moisture and evapotranspiration, we should be able to determine the behavior and interactions between these parameters and better understand how they vary together seasonally, annually and year to year. With this information it is our hope that global climate models can be improved to better understand what is occurring climatologically in the present as well as more accurately make predictions about future conditions.

Table 1. Parameters and Sources




Greenhouse gases Observing SATellite (GOSAT)1


Gravity Recovery and Climate Experiment (GRACE)

Net Radiation

Clouds and the Earth's Radiant Energy System (CERES)

Vegetation Indices

Moderate Resolution Imaging Spectroradiometer (MODIS)/ Multiangle Implementation of Atmospheric Correction (MAIAC)


Global Precipitation Climatology Project (GPCP)

Soil Moisture

Water Cycle Mutimission Observation Strategy (WACMOS)


Global Land-surface Evaporation: the Amsterdam Methodology (GLEAM)

1In future work, we hope to use fluorescence data from OCO-2 to perform a comparison with GOSAT.