Large Scale Data Analysis Using the CDS API – Indicators of Climate Change in MERRA

Wednesday, 17 December 2014
Joseph Clamp1, Daniel Duffy2, Dennis Lazar2, Denis Nadeau2, Caitlin Ross3, Glenn Tamkin2 and John H Thompson2, (1)Pennsylvania State University Main Campus, University Park, PA, United States, (2)NASA Center for Climate Simulation, Greenbelt, MD, United States, (3)Rensselaer Polytechnic Institute, Troy, NY, United States
Analyses of climate models have shown indications of climate change that are consistent with observations. This study focuses on the indications of climate change within the Modern-Era Retrospective Analysis for Research and Applications (MERRA) dataset. Specifically, an analysis was conducted to find climate change indicators within the global and northern polar region surface temperature data from 1980 to 2010. The expectation is that MERRA will show an overall temperature trend that agrees with observations during the thirty-year time period. The MERRA surface temperature data was gathered using the Climate Data Services (CDS) Application Programming Interface (API), which is a query based data retrieval interface developed by the NASA Center for Climate Simulation (NCCS) at Goddard Space Flight Center (GSFC). Since MERRA uses cylindrical projection, not all grid cells are equal in size. Spatial average areas, weighted by grid interval, were calculated to account for the non-linear grid spacing. Preliminary results of this spatial average indicate a thirty-year global temperature increase of 0.51K (0.017K/year). The study also finds that the northern polar region is warming at over double the global rate. This results in an increase of 1.20K (0.040K/year) over the thirty years. Observational data has been found to support these results. This support would lead to verifying that MERRA data does show a temperature trend that agrees with observational data. This poster will highlight the utilization of the CDS API, the results of the spatial average analysis, and the comparison of results with observational data.