This page's content and links are no longer actively maintained. It is available for reference purposes only.
ICP Website Curator: Robert B. Schmunk — NASA Official: Gavin A. Schmidt


Ocean & Climate Modeling: Evaluating the NASA GISS GCM

Link to Introduction section.
Link to Methods section.
Link to Results section.
Link to Discussion section.


Leon Abbo, The Bronx H.S. of Science
Marquise McGraw, The Bronx H.S. of Science
Akinwale Olaleye, York College, CUNY
Angela Padilla, Hunter College, CUNY


Meghan Conk, Bayside High School
Mitchell Fox The Bronx H.S. of Science

Science Advisors

Ron Miller, NASA GISS
Gavin Schmidt, NASA GISS


This preliminary investigation evaluated the performance of three versions of the NASA Goddard Institute for Space Studies' recently updated General Circulation Model E (GCM). This effort became necessary when certain Fortran code was rewritten to speed up processing and to better represent some of the interactions (feedbacks) of climate variables in the model. For example, the representation of clouds in the model was made to agree more with the satellite observational data thus affecting the albedo feedback mechanism. The versions of the GCM studied vary in their treatments of the ocean. In the first version, the Fixed-SST, the sea surface temperatures are prescribed from the obsevered seasonal cycle and the atmospheric response is calculated by the model. The second, the Q-Flux model, computes the SST and its response to atmospheric changes, but assumes the transport of heat by ocean currents is constant. The third treatment, called a coupled GCM (CGCM) is a version where an ocean model is used to simulate the entire ocean state including SST and ocean currents, and their interaction with the atmosphere. Various datasets were obtained from satellite, ground-based and sea observations. Observed and simulated climatologies of surface air temperature sea level pressure (SLP) total cloud cover (TCC), precipitation (mm/day), and others were produced. These were analyzed for general global patterns and for regional discrepancies when compared to each other. In addition, difference maps of observed climatologies compared to simulated climatologies (model minus observed) and for different versions of the model (model version minus other model version) were prepared to better focus on discrepant areas and regions. T-tests were utilized to reveal significant differences found between the different treatments of the model. It was found that the model represented global patterns well (e.g. ITCZ, mid-latitude storm tracks, and seasonal monsoons). Divergence in the model from observations increased with the introduction of more feedbacks (fewer prescribed variables) progressing from the Fixed–SST, to the coupled model. The model had problems representing variables in geographic areas of sea ice, thick vegetation, low clouds and high relief. It was hypothesized that these problems arose from the way the model calculates the effects of vegetation, sea ice and cloud cover. The problem with relief stems from the model’s coarse resolution. These results have implications for modeling climate change based on global warming scenarios. The model will lead to better understanding of climate change and the further development of predictive capability. As a direct result of this research, the representation of cloud cover in the model has been brought into agreement with the satellite observations by using radiance measured at a particular wavelength instead of saturation.