In the atmosphere-ocean system, the global distribution of climate variables such as sea surface temperature, pressure, and precipitation fluctuate from interannual to decadal timescales. This naturally-occurring fluctuation is a form of climate variability. The climate variability in different parts of the globe is orchestrated by one or a combination of several climate modes, driven by complex physical processes. Therefore, the regional climate variability and extremes are strongly influenced by modes of climate variability. For adaptation to climate change, it is essential to understand and model the natural variability of climate.
The natural variations due to modes of climate variability are a major source of uncertainty in climate model projections. The climate model researchers around the world have been upgrading the climate models to exhibit the important modes of variability more realistically since the beginning of modeling. For example, the representation of the Madden-Julian Oscillation (MJO) and patterns associated with the El Niño-Southern Oscillation (ENSO) have greatly improved just over the past few years. A thorough understanding of the mechanisms underlying the improvements in the model skill on the modes could assist the evaluation of model performance and the credibility of climate projections.
In a recently published article in Journal of Climate, authors Clara Orbe, Luke Van Roekel, Ángel F. Adames. Amin Dezfuli. John Fasullo, Peter J. Gleckler, Jiwoo Lee, Wei Li, Larissa Nazarenko, Gavin A. Schmidt, Kenneth R. Sperber, and Ming Zhao, made an in-depth comparison across six U.S. climate modeling centers by accessing the model performance with respect to multiple modes of variability. Systematic improvement was found in the Madden-Julian Oscillation (MJO) and the Quasi-Biennial Oscillation (QBO), and in the teleconnection patterns associated with the Pacific Decadal Oscillation (PDO) and the El Niño-Southern Oscillation (ENSO). The authors also explored the processes driving the improvement, including the higher resolution and better process representation, especially for the coupled and extra-tropical modes. Their analysis further suggested possible better subseasonal forecasting with improved QBO. This study was partially funded by the MAPP program, and resulted from the National Climate Model Summit, an initiative of the US Global Change Research Program which the MAPP program helped initiate and plan.
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The Modeling, Analysis, Predictions, and Projections (MAPP) Program is a competitive research program in NOAA Research's Climate Program Office. MAPP's mission is to enhance the Nation's and NOAA's capability to understand, predict, and project variability and long-term changes in Earth's system and mitigate human and economic impacts. To achieve its mission, MAPP supports foundational research, transition of research to applications, and engagement across other parts of NOAA, among partner agencies, and with the external research community. MAPP plays a crucial role in enabling national preparedness for extreme events like drought and longer-term climate changes. For more information, please visit www.cpo.noaa.gov/MAPP.