05 July 2016

An Observationally Based Global Band-by-Band Surface Emissivity Dataset for Climate and Weather Simulations

Global surface band-by-band emissivity dataset for GCMs


This study constructed and validated the first-ever observaiton-based band-by-band surface emissivity data base for the entire globe and for the entire longwave spectrum range. The impact of such realistic surface emissivity on the TOA radiation budget is also assessed.


This data set provides the global modeling community with a realistic data set for surface emissivity instead of the blackbody surface assumption commonly used in the GCM study. It can help remove biases represention of surface emission in the GCM and thus reduce uncertainties in the GCM simulation. Moreover, it contains the far-IR surface emissivity, a pieice of information can be crucial to the cryosphere change in the high latitudes.


Due to the enormous compleixity involved in the modeling of earth system, there are many possible compensating errors and biases in the model. To faithfully model a given physical process can reduce the possible compensating biases results from the process. Currently majority of GCMs still treats the surface as blackbody surface int he Atmospheric GCM. This study started from the first-principle calculation using index of refraction of different substances, anchorred with NASA satellite observation, and validated against ESA (European Space Angency) observation to develop a global surface band-by-band emissivity data set that can be used in the climate models and NWP models. It is first-eve such data set has been constructed for the entire LW spectrum. Moreover, its impact on radiation budget has also been assessed in the study.

Xianglei Huang
University of Michigan
2016.  "Journal of the Atmospheric Sciences."  Journal of the Atmospheric Sciences, doi:10.1175/JAS-D-15-0355.1.