Use of Reanalysis for Detecting Climate Change Sensitivity to Land Types and Urbanization

Ming Cai¹, Young-Kwon Lim¹, and Eugenia Kalnay^2
1Department of Meteorology, Florida State University
²Department of Atmospheric and Oceanic Science, University of Maryland

In this paper, we will first present an analytic analysis showing that a reanalysis made with a frozen model can detect the warming trend due to an increase of greenhouse gases within the atmosphere at its full strength (at least 95% level) after a short transient (less than one hundred analysis cycles). The analytical proof is obtained by taking into consideration the following three possible deficiencies in the model used to create first guess fields: (1) the physical processes responsible for the observed trend (e.g., an increase of greenhouse gases) are completely absent from the model, (2) the first guess fields are affected by an initial drift due to the imbalance between the model equilibrium and the analysis that contains trends due to the observations, and (3) the model used in reanalysis has a constant model bias. Our analytic derivation shows that this systematic reduction can be very small (less than 5%) when the observations are available for twice daily assimilation. We next will present some evidence about the sensitivity of surface climate change to land types by subtracting the reanalysis from the observed surface temperature (OMR). The basis of this approach is that while reanalysis represents the large-scale climate changes due to greenhouse gases and atmospheric circulation, it is less sensitive to regional surface processes associated with land types. OMR trends derived from two independent reanalyses and two observations show similar dependence upon land types, suggesting the attribution of OMRs to different land types is robust.