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Climate Variability & Predictability (CVP) logo

A Nudging and Ensemble Forecasting Approach to Identify and Correct Tropical Pacific Bias-Producing Processes in CESM

Current short-term tropical climate forecasts (e.g, of the Madden Julian Oscillation (MJO) and of El Niño/Southern Oscillation (ENSO) events) experience both a systematic error (climate drift) that results in sustained biases of the model tropical climatology and an error in representing the space-time scales of the transients (e.g., phase speed errors, etc.) We propose to identify the physical mechanisms that lead to the seasonal biases in the tropical Pacific by isolating the parameters and parameterizations that influence the development of biases in short-term climate forecasts. Our overarching scientific objective is to identify, explain, and correct the climate biases in the Pacific ocean that occur in the Community Earth System Model (CESM). We are currently using the Community Atmospheric Model (CAM3) in MJO forecast experiments and tests of convective parameterization improvements. We propose to extend these MJO forecast studies to include (a) the fully coupled CESM system, and (b) ENSO timescale forecasts.

We plan to study the spatiotemporal structures of bias development in CESM forecasts, launched from numerous initial states and during which random ENSO and MJO events occur, to determine the relative importance of poor mean-state representation versus the integrated impacts of the transient flows. This bias development will be studied as a function of season to account for significant changes in the background state of the coupled oceanatmosphere system in the tropical Pacific. We will also seek to ascribe these effects to wellknown physical processes for the specific climate modes of variability. We will test the sensitivity of the bias development to changes in coupled model resolution and model parameter selection. We will also implement nudging experiments (towards observations) to pinpoint where the worst parts of the biases develop apart from the nudged variables.

We expect this research to result in identification of the physical processes that lead to the mean biases in the model system and an improvement in parameterizations used in CESM and CAM for forecasts of the climate-scale processes in the tropical Pacific.

This proposal contributes to the CVP component of the NOAA ESS Program by attempting to improve our understanding of the processes contributing to tropical Pacific biases in global climate models (CESM, in this case). Specifically, our work involves (i) short-term forecast experiments, from weeks to a year, to isolate the time scales of bias development and the responsible processes, (ii) development of metrics for coupled GCMs that help to elucidate the main processes contributing to biases, (iii) atmosphere-only and ocean-only models to isolate the sources and amplifiers of biases, and (iv) intercomparison of model parameterizations within CESM. Our overall research will thereby contribute significantly to NOAA’s Next Generation Strategic Plan by improving our scientific understanding of the climate system that will result in better identifying potential climate impacts.

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