S50 Fourier Analysis of Monthly Differences in Temperature Trends in Observation data sets and Climate Models

Sunday, 7 January 2018
Exhibit Hall 5 (ACC) (Austin, Texas)
Alexa Zabaske, Saint. Cloud State University, Clearwater, MN; and J. Nielsen-Gammon

The purpose of this project is to compare warming trends in the global monthly mean temperature represented by observation data sets and CMIP5 climate model output. The discrepancies of the warming trends seen in observations and in models are examined with multiple ensemble members, in different latitude bands, for land points vs. sea points, and over time. The warming trends of interest are calculated by averaging the global mean temperature per month for one time period, doing this again for another time period, and then taking the difference between the two. The differences in global monthly mean temperature can be approximated by a sinusoidal function with a period of one year, thus these monthly warming trends can be quantified with a Fourier analysis.

Our results quantify the discrepancies of the warming trends in observational and model data sets not being in phase with one another. Each observational data set has the same general trend of phase and amplitude with both time and within specific latitude. Various individual climate models within the CMIP5 were found to have a much larger range of phase and amplitude. When the observations are averaged and compared with the model data, there is notable disagreement in both phase and amplitude, over time and per latitude.

Because the explanation for the discrepancy was not apparent over time or in latitude analysis, further research is necessary. This includes a land vs. ocean comparison, and a categorization of models with similar warming trends which allows for parameterization investigation.

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner