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Climate Test Bed

  e-Newsletter Vol. 2  •  No. 3  •  2016

Climate Model Development Task Force (CMDTF) Teleconferences

April 19, 2016

    I) GFDL’s ocean biogeochemical (O-BGC) modeling activities and plan for prediction

   II) NCEP/EMC's O-BGC modeling and statistical approaches

The topic of the April CMDTF telecon was ocean biogeochemical (O-BGC) modeling. The invited talk by Dr. Charles Stock gave a brief overview on NOAA GFDL’s biogeochemical and marine ecosystem modeling efforts for seasonal to decadal prediction and projection of the ecosystem. The Carbon, Ocean Biogeochemistry and Lower Trophics (COBALT) planktonic ecosystem model developed in GFDL established a robust baseline, which quantitatively captured interactions between climate and ocean biogeochemistry with a more resolved, mechanistic representation of the plankton ecosystem dynamics, as well as between climate and the flow of carbon and energy through the planktonic food web to fisheries and other living marine resources. Progresses had been made in ESM4 integration of COBALT with MOM6 for CMIP6 (and beyond), the prototype high-resolution ESMs, and the regional Earth System downscaling across US. Promising initial results suggested predictability of biogeochemical signals and capturing of biogeochemical variability modes underlay the predictability of the model. There were needs to integrate biogeochemistry with data assimilative physics used in prediction systems, and to assess how the global biogeochemical observing system refined model-driven initial condition estimates.

Following up on the O-BGC topic, Dr. Sudhir Nadiga of NCEP/EMC presented a simple statistical (Neural Network) plugin for the missing of ocean biology component in CFSv3. His investigation using hybrid coupled models demonstrated that ocean biology-induced feedback modulated ENSO amplitude and period, and the ocean biology acted to counteract effects of interannual variability in freshwater fluxes on ENSO simulation. Neural Network technique, an easy and computationally cheap method, was able to reproduce monthly chl-a that compared well with satellite observations over long periods in seasonal forecast models.

March 10, 2016

    I) NCAR/CESM hierarchical modeling framework

   II) CLIVAR and WCRP plans for advancing decadal climate prediction and connections to

seasonal prediction

The March CMDTF telecon focused discussions on hierarchical modeling framework. Prof. Amy Clement of University of Miami gave an introduction on NCAR/Community Earth System Model (CESM) hierarchical modeling framework, a set of simple models for a university branch of CESM, to bridge the gap between simulation and understanding in climate modeling, and meanwhile promote collaborative contributions by national labs and universities. In discussions, the critical importance of interactive process with full operational model system was emphasized for success. Next, Prof. Yochanan Kushnir of Columbia University talked on WCRP plans for advancing decadal climate prediction and connections to seasonal prediction. A grand challenge was elaborated with R&D needs to address the objectives of (1) improving multi-year to decadal climate predictions by addressing existing issues with initialization, model biases and model drift, (2) collecting, collating, and synthesizing the prediction output and tailoring climate information (including assessments of uncertainty) to form the basis of service that addresses stakeholders’ needs, (3) developing organizational and technical processes, including international coordination to underpin the future routine provision of scientifically-sound, prediction services.

The CMDTF is an initiative of CPO/MAPP Program in partnership with CTB.

NOAA Center for Weather and Climate Prediction

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