Assessing the potential predictability of seasonal means in present and future climates

May 25, 2012 | By | Add a Comment

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,

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.

Filed in: Pending Support

Dr. Paul R. Houser

About the Author (Author Profile)

Dr. Houser in an internationally recognized expert in local to global land surface-atmospheric remote sensing, in-situ observation and numerical simulation, development and application of hydrologic data assimilation methods, scientific integrity and policy, and global water and energy cycling. He received his B.S. and Ph.D. degrees in Hydrology and Water Resources from the University of Arizona in 1992 and 1996 respectively. Dr. Houser's previous experience includes internships at the U.S. Geological Survey and at Los Alamos National Laboratory. Dr. Houser joined the NASA-GSFC Hydrological Sciences Branch and the Data Assimilation Office (DAO/GMAO) in 1997, served as manager of NASA’s Land Surface Hydrology Program, and served as branch head of the Hydrological Science Branch. In 2005, he joined the George Mason University Climate Dynamics Program and the Geography and Geoinformation Sciences Department as Professor of Global Hydrology, and formed CREW (the Center for Research for Environment and Water). Dr. Houser has also teamed with groundwater development and exploration companies (EarthWater Global and Geovesi) and has served as Science Advisor to the U.S. Bureau of Reclamation. Dr. Houser has led numerous scientific contributions, including the development of Land Data Assimilation Systems (LDAS), the Hydrospheric States Mission (Hydros/SMAP), the Land Information System (LIS), the NASA Energy and Water cycle Study (NEWS), and the Water Cycle Solutions Network (WaterNet).

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