Earth System Predictability Research and Development Proceedings of a Workshop–in Brief (2020) / Chapter Skim
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Earth System Predictability Research and Development: Proceedings of a Workshop - in Brief
Earth System Predictability Research and Development: Proceedings of a Workshop - in Brief
Pages 1-12
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Past research into Earth system predictability has led to profound insights into the Earth system and has facilitated improved predictions, said James Hurrell, Colorado State University. However, he continued, accelerating progress in providing practicable predictions across a broader set of phenomena will require deep and sustained interactions with user communities, understanding the theoretical limits of predictability and the sources of predictability, improvements in modeling, targeted observations, and infrastructure such as computing power and supporting workforce focused specifically on the science and applications of Earth system predictability research.
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The specific workshop themes were informed by an earlier community roundtable discussion2 and designed to explore opportunities for key research and development activities that would be most valuable with regard to understanding fundamental, theoretical limits of Earth system predictability. The Workshop on Earth System Predictability Research and Development was held on June 4-5, 2020, by the National Academies of Sciences, Engineering, and Medicine.
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Reprinted with permission; copyright 2019, American Meteorological Society. Jones concluded by making the case that advancing estimates of seamless Earth system predictability from minutes to centuries to meet societal needs can be done more effectively through a value cycle approach that focuses on users' needs (Figure 1)
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Session chair James Hurrell, Colorado State University, noted that current understanding of predictability limits is based on imperfect models and incomplete understanding and representation of critical processes, such as those linking the atmosphere to the ocean or land surface, which evolve more slowly. The panelists addressed the targeted research required to improve the understanding of Earth system predictability limits.
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For example, if the goal is to improve longer lead-time predictions of precipitation, then a research focus on surface hydrology or ground water, which have longer memory, could allow more improvements in predictability. EXPLORING PREDICTABILITY THROUGH NEW METHODOLOGIES AND TECHNOLOGIES This session focused on technological advances and other new methodologies and approaches -- from machine learning to coupled data assimilation -- that can accelerate progress on theoretical understanding of predictability and inform the development of models that more accurately represent the coupled Earth system, as noted by session chair, Jeanine Jones, California Department of Water Resources.
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He said that this is an exciting time in his field as there are many new opportunities to bring land surface data into a data assimilation framework as a way to improve Earth system prediction. Dietze emphasized the need to support open and scalable cyberinfrastructure to reduce redundant efforts, improve research quality, and improve accessibility of system models to a larger fraction of the community.
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While the current Argo14 ocean profiling float fleet is sufficient to support decadal predictability, a more nuanced and local understanding of the upper ocean mixed layer is needed for advancing weather and climate predictability on 10-day to 3-year timescales. The recent OceanObs'19 activity brought together scientists to develop recommendations for observation systems that could address these challenges related to air-sea fluxes.15 Characterizing the ocean mixed layer requires understanding of surface fluxes (e.g., heat, freshwater, momentum, gas)
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, is key to improving predictability.17 Randerson also noted opportunities to merge statistical and machine learning approaches with predictions from the National Multi Model Ensemble to improve predictability on these longer timescales.18 Peter Neilley, The Weather Company, discussed the need for a holistic optimized observational network that goes beyond geophysical observations to optimize use of predictions for society. Neilley explained that predictions of the Earth system are increasingly middleware toward the end goal of predictions of impacts on society.
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Bitz explained that improved representation of the fine-scale exchanges between sea ice and the ocean could lead to improved estimates of predictability. Developing these parameterizations requires detailed observations of the processes, assimilation of observations into models using coupled and multivariate methods to produce more complete data sets based on dynamical and statistical relationships, and application of machine learning approaches to develop simulation approaches that are less computationally expensive.
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A NEW RESEARCH FRAMEWORK FOR PRACTICABLE EARTH SYSTEM PREDICTABILITY Development of a national approach and strategy to knit together predictability-focused theoretical work with observational, modeling, and technology research is an imperative for advancing practicable prediction, said session chair Jenni Evans, The Pennsylvania State University. This session explored opportunities to break down compartmentalization of communities.
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The convergence of advances in computing capability, access to new observations, incorporation of more components in Earth system models, and the application of machine learning and other data analytic techniques all point to the potential to extend predictability to longer timescale and to a much broader range of decision contexts. Workshop participants discussed a number of cross-cutting challenges related to realizing those benefits.
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Colman, The Climate Corporation, Gabriele Pfister, National Center for Atmospheric Research. SPONSORS: The workshop was supported by the US Global Change Research Program, funded through the National Aeronautics and Space Administration.
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