Diagnosing CFS2 forecast skill using empirical models of climate prediction and predictability from sub-seasonal to multi-decadal scales

We propose to employ a seamless empirical dynamical modeling approach to construct a state-of-the-art benchmark probabilistic forecast system and to evaluate factors impacting predictability at forecast lead times ranging from weeks to decades in the CFS2 forecast system, including climate drift. The model used, a linear inverse model (LIM) derived from observed simultaneous and time-lag correlation statistics of oceanic and atmospheric variables, can also be used to make forecasts whose skill is competitive with current coupled global forecast GCMs. The geographical and temporal variations of forecast skill are also generally similar for the LIM and CGCMs. This makes the much simpler LIM an attractive tool for assessing and diagnosing climate predictability, including determining factors that contribute to climate prediction skill as well as those that limit it, and also for diagnosing the predictability of climate modes such as the MJO, ENSO, PDO, and AMO. We will use the LIM to diagnose where CGCM improvements should be targeted to yield the most significant gains in forecast skill. The LIM formalism also allows determination of which climate regimes have particularly high or low predictability, and how this affects the predictability of different system variables. This suggests that an important “best practices” aspect of climate forecasts on all time scales should be the issuance of both a forecast and a “forecast of forecast skill”, of which the LIM would be an important component. The proposed work addresses NOAA’s long-term goal of climate adaptation and mitigation (see NOAA’s Next-Generation Strategic Plan) as well as the goals of MAPP in several ways. It projects strongly on NOAA’s fundamental mission responsibilities To understand and predict changes in climate, weather, oceans, and coasts and To share that knowledge and information with others. Its contribution to NOAA science includes discoveries and ever new understanding of the oceans and atmosphere, and the application of this understanding to such issues as the causes and consequences of climate change, the physical dynamics of high-impact weather events, ... and the ability to model and predict the future states of these systems. The proposed work will also improve scientific understanding of the changing climate system and its impacts by elucidating the sources of climate predictability and suggesting directions for improvement of their representation in models used in the IPCC assessments. The performance of these tasks will also contribute both directly and indirectly to a climate literate public that makes informed decisions through the direct release of information to the general public by means of web products. Developing an Optimum Multimodel ENSO Prediction This work falls under MAPP priority area 1 in the area of achieving an objective comparative evaluation of climate prediction skill from dynamical, statistical, and hybrid or consolidated systems to assess optimal prediction methodologies for specific applications. Principal Investigator: Anthony G. Barnston, Lead Forecaster The Earth Institute, Columbia University, Lamont Campus Co-PIs: Michael K. Tippett, Huug M. van den Dool 1 International Research Institute for Climate and Society (IRI), Columbia University 2 National Oceanic and Atmospheric Administration, Climate Prediction Center