Assessing the potential predictability of seasonal means in present and future climates
Feng, X., Houser P., Assessing the potential predictability of seasonal means in present and future climates NASA-ROSES-2012 A13: MAP: Modeling, Analysis, and Prediction (NNH12ZDA001N-MAP), $258,782, 0.72 mo/yr (GMU), 01/01/2013 – 12/31/2014, Sponsor POC: David Considine (202/358-2277, david.b.considine@nasa.gov).
Summary: Climate variations on seasonal time scale have tremendously socioeconomic impact impacts in the face of growing human demand and environment stress. Accurate seasonal predictions could help the public and decision makers better anticipate and mitigate potential effects of seasonal climate variability. Evaluating the performance of climate models is a useful way to improve climate seasonal prediction and better understand the possible impact of natural climate variability and human-induced climate change. The proposed project will explore the realism of seasonal potential predictability in the National Aeronautics and Space Administration (NASA) Goddard Institute for Space Studies (GISS) Model E2 model simulations that are participated in the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment report (IPCC AR5). We will reveal model’s potential in generating realistic predictable component of seasonal variability on global and region scales in current climate conditions. By doing so, we hopefully would provide insight on clues to areas of the model’s representation of physical and dynamical processes that need further development and improvement. We will also assess the change of seasonal predictability in projected future climate, which are useful for policy makers to assess different strategies for adaptation and mitigation.
The method for estimating the seasonal predictability is previously proposed by the PIs, which is based on the analysis of covariance (ANOCOVA). The ANOCOVA has the advantage of not only taking into account the temporal dependence in the daily time series but also accounting for uncertainty in the estimated parameters. We will improve the current version of ANOCOVA by implementing a new functionality, that is to separate the potential predictability due to boundary forcings into sea surface temperature associated with the interannual variability of ENSO and local soil moisture effect.
This proposal work is submitted to NASA Research Opportunities in Space and Earth Science (ROSES) Modeling, Analysis and Prediction (MAP) Program in response to Funding Opportunity for FY2012.