Next Generation Earth System Prediction Strategies for Subseasonal to Seasonal Forecasts (2016) / Chapter Skim
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8 Vision and Way Forward for S2S Earth System Prediction
8 Vision and Way Forward for S2S Earth System Prediction
Pages 239-278
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From page 239... ...
This chapter draws from the text, findings, and recommendations presented in previous chapters to develop a vision to serve as an inspirational yet possible target for a desired future state of S2S prediction in the next 10 years, and a set of research strategies to guide actions that are necessary to move toward that vision. All of the recommendations from previous chapters are organized within these strategies and together serve as the committee's comprehensive research agenda for S2S forecasting over the next decade.
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From page 240... ...
Along with an enhanced focus on developing predictions of extreme and other disruptive events, such iterative engagement with forecast users has the potential to foster a stronger culture of planning across S2S and longer timescales, including adaptation and resilience to climate change. This could provide social and economic benefits that amplify and transcend the direct benefits of S2S forecasts themselves.
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Increase S2S Forecast Skill 3. Improve Prediction of Extreme and Disruptive Events and Consequences of Unanticipated Forcing Events 4.
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Although the main recommendations are placed under the research strategy or supporting activity that they primarily support, implementing each recommendation will often help to advance multiple strategies.
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Engage Users Improve VISION Include Prediction S2S Forecasts Will More Be as Widely Used of a Decade from Now as Earth Disruptive Weather Forecasts System Events Are Today Components Increase S2S Forecast Skill Build Cyberinfrastructure and Workforce FIGURE 8.1 Relationship between the four research strategies and supporting activities outlined in this report for advancing subseasonal to seasonal forecasting over the next decade, which all contribute to the overarching vision. NOTE: The white arrows indicate that the four research strategies interact and are not mutually exclusive.
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From page 244... ...
Such a process can help to further prioritize the development of specific forecast variables and metrics, and ensure that data and resource-intensive retrospective forecasts, as well as the operational forecasts themselves, retain and exploit parameters that are most critical to user decision-making. In order to maximize benefits of investments into improving S2S forecasts over time, there should be an ongoing effort to codesign forecast products on S2S timescales that match what is scientifically feasible with what users can make actionable.
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Recommendation A: Develop a body of social science research that leads to more comprehensive understanding of the use and barriers to use of seasonal and subseasonal Earth system predictions. Specifically: • Characterize current and potential users of S2S forecasts and their decision making contexts, and identify key commonalities and differences in needs (e.g., variables, temporal and spatial scale, lead times, and forecast skill)
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• Support boundary organizations and private-sector enterprises that act as interfaces between forecast producers and users. Research Strategy 2: Increase S2S Forecast Skill Operational weather and ocean forecasts have steadily increased in accuracy and lead time over the past few decades.
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This includes sustaining and improving the network of observations used to study predictability and to initialize models, developing improved techniques for data assimilation and uncertainty quantification in coupled Earth system models, and importantly, reducing Earth system model errors through a combination of increases in model resolution and the development of better model parameterizations to represent subgrid processes. Research to spur the development of new methods for probabilistic forecasting and probabilistic skill verification and calibration are also necessary.
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Research to advance under tanding of sources and s limits of predictability for specific Earth system phenomena will be critical to improving the fidelity of S2S Earth system models, as well as to improving the ability to forecast extreme or other disruptive events with longer lead times (Research Strategy 3)
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From page 249... ...
The procedure for solving for sea ice thickness needs to be efficient enough to be ready in about a day, so such measurements can contribute to initialization of S2S forecasts. Land observations are critical for modeling large-scale land surface-atmosphere feedbacks and for predictions of the terrestrial water cycle.
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In summary, observations of the atmosphere, ocean, land surface, and cryosphere play a critical role in building, calibrating, initializing, and evaluating the coupled Earth system models that are used to generate S2S forecasts. Better representing slow- aryingv processes in the Earth system -- such as the ocean, cryosphere, and land surface h ydrology -- and their coupling to the atmosphere, as well as developing observations to inform deep convection and storm formation, are important to capturing S2S predictability, but they represent the largest gaps in the current observing network.
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that are important for informing fluxes between the component interfaces, including but not limited to land surface observations of temperature, moisture, and snow depth; marine surface observations from tropical moored buoys; and ocean observations of near-surface currents, temperature, salinity, ocean heat content, mixed-layer depth, and sea ice conditions. • Apply autonomous and other new observing technologies to expand the spatial and temporal coverage of observation networks, and support the con tinued development of these observational methodologies.
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Fundamental research is needed to explore and realize the potential benefits to more advanced but expensive strongly coupled data assimilation, while continuing to pursue and implement weakly coupled methods in current systems. Efforts to improve the skill of S2S predictions will also benefit from more realistic representation of the uncertainty and statistical properties of observations and model output.
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• Foster interactions among the growing number of science and engineering communities involved in data assimilation, Bayesian inference, and uncertainty quantification. Systematic errors are numerous within the Earth system models used for S2S forecasting.
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In addition to contributing to Research Strategy 2, reducing model errors also contributes to Strategies 3 and 4. Recommendation H: Accelerate research to improve parameterization of unresolved (e.g., subgrid scale)
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. Improving verification should also involve continued research on feature-based and two-step verification methods, along with consideration of how the design of retrospective forecasts and reanalyses can influence the ability of some users to directly evaluate the consequences of acting on forecasts at various predicted probabilities.
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As such, this exploration would benefit tremendously from a central, coordinating a uthority and central funding, as well. Exploring the "trade space" will be important for increasing forecast skill, advancing the prediction of events (Research Strategy 3)
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• Evaluate calibration methods and ascertain whether some methods offer clear advantage for certain applications over others, as part of studies of the opti mum configurations of S2S models. • Explore systematically how many unique models in an MME are required to predict useful S2S parameters, and whether those models require unique data assimilation, physical parameterizations, or atmosphere, ocean, land, and ice components (see also Recommendation L)
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New mechanisms should also be developed especially to enhance researcher ccess a to operational forecast data, including access to archives of ensemble forecasts themselves, retrospective forecasts, and initialization data. There are data storage challenges with such an endeavor, but it would facilitate further analyses of sources of S2S predictability and efforts to diagnose skill, among other benefits.
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. Research Strategy 3: Improve Prediction of Extreme and Disruptive Events and Consequences of Unanticipated Forcing Events Within the efforts to improve the overall skill of S2S forecasts and to provide more actionable information to users, there are two areas that the committee believes deserve special attention (Research Strategies 3 and 4)
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, or may be contingent on interactions between these modes and other slowly varying processes. Moreover, skillful prediction of the probabilities of some types of disruptive events will be possible at these timescales, whereas others may not.
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• Investigate and estimate the predictability and prediction skill of disrup tive and extreme events through utilization and further development of forecast and retrospective forecast databases, such as those from the S2S Project and NMME. The second part of this research strategy involves using S2S forecast systems to predict the consequences of disruptive events caused by an unusual Earth system event, such as a volcanic eruption or a major oil spill.
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Research Strategy 4: Include More Components of the Earth System in S2S Forecast Models The other area that the committee believes requires more focused attention is accelerating the development of Earth system model components outside the roposphere -- t Research Strategy 4. As mentioned above, representing oceans, sea ice, land surface and hydrology, and biogeochemical cycles (including aerosol and air quality)
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Recommendations Improving the representation of more components and variables of the Earth system in S2S forecasts, including the ocean, sea ice, biogeochemistry, and land surface, will produce information applicable to a new and wider range of decisions. Iterative interaction with forecast users (Research Strategy 1)
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Additional strong candidates for improvements to existing practice for operational S2S forecasting systems include advancing the observations, modeling, data assimilation, and integrated prediction capabilities of aerosols and air quality, and aquatic and marine ecosystems. Beyond advancing the representation of the land surface, hydrology, stratosphere, sea ice, ocean, and biogeochemical models and translating these advancements to the coupled Earth system models used for S2S forecasting, efforts are needed to pave the way toward global cloud-/eddy-resolving atmosphere-ocean-land-sea ice coupled models, which will one day become operational for S2S prediction.
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a tmosphere-ocean-land-sea ice coupled models to operations, including s trategies for new parameterization schemes, data assimilation procedures, and multi-model ensembles. Supporting the S2S Forecasting Enterprise It is essential to highlight two specific cross-cutting challenges that must be met to support the four research strategies for reaching the committee's vision for S2S prediction.
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Recommendation O: Develop a national plan and investment strategy for S2S prediction to take better advantage of current hardware and software and to meet the challenges in the evolution of new hardware and software for all stages of the prediction process, including data assimilation, operation of high-resolution coupled Earth system models, and storage and management of results. Specifically: • Redesign and recode S2S models and data assimilation systems so that they will be capable of exploiting current and future massively parallel computa tional capabilities; this will require a significant and long-term investment in computer scientists, software engineers, applied mathematicians, and statistics researchers in partnership with the S2S researchers.
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S2S is complex and involves working across computing–Earth science boundaries to develop and improve S2S models and working across science–user decision boundaries to better design and communicate forecast products and decision tools. From the limited data available, it appears that the cadre of trained S2S modelers is not growing robustly in the United States and is not keeping pace with this rapidly evolving field (Chapter 7)
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• Provide more graduate and postgraduate training opportunities, enhanced professional recognition and career advancement, and adequate incentives to encourage top students in relevant scientific and computer programming disciplines to choose S2S model development and research as a career. CONCLUSION This report envisions a substantial improvement in S2S prediction capability and expects valuable benefits to flow from these improvements to a wide range of public and private activities.
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To help agencies and other actors within the weather/climate enterprise select specific parts of the research agenda to pursue, Table 8.1 provides additional details about both the main recommendations and more specific or related activities that the committee envisions to be part of implementing each main recommendation: whether they involve basic or applied research; which are expected to have short-term benefits; which might require a new initiative; and which have a scope that calls for international collaboration to leverage U.S. effort.
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Characterize current and potential users of S2S forecasts and their decision-making contexts, and identify key commonalities and differences in needs (e.g., variables, temporal and spatial scale, lead times, 1, 4 ¡ ¡ and forecast skill) across multiple sectors.
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, slowly varying processes (e.g., sea ice, soil moisture, and ocean eddies) , and external forcing (e.g., aerosols)
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Investigate and estimate the predictability and prediction skill of disruptive and extreme events through utilization and further development of forecast and retrospective forecast databases, such as those 3, 2 ¡ ¡ ¡ from the S2S Project and the NMME. Chapter 5 E: Maintain continuity of critical observations, and expand the temporal and spatial coverage of in situ and remotely sensed observations for Earth system variables that are beneficial for operational S2S 2, 3, 4 ¡__________¡ ¡ ¡ ¡ prediction and for discovering and modeling new sources of S2S predictability.
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Continue to invest in observations (both in situ and remotely sensed) that are important for informing fluxes between the component interfaces, including but not limited to land surface observations of temperature, moisture, and snow depth; marine surface observations 2, 3, 4 ¡__________¡ ¡ ¡ from tropical moored buoys; ocean observations of near-surface currents, temperature, salinity, ocean heat content, mixed-layer depth, and sea ice conditions.
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parameterization schemes in a holistic manner. Continue to investigate the potential for reducing model errors through increases in horizontal and vertical resolutions in the atmosphere and other model components, ideally in a coupled model framework (see 2, 3, 4 ¡ ¡ also Recommendation L)
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3, 1, 2 ¡ ¡ Consider the benefits of producing more frequent reanalyses using coupled S2S forecast systems in order for the initial conditions of retrospective forecasts to be more consistent with the real time forecasts, as 2, 1 ¡ ¡ ¡ well as for the purposes of predictability studies. K: Explore systematically the impact of various S2S forecast system design elements on S2S forecast skill.
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2, 3, 4 ¡__________¡ Evaluate calibration methods and ascertain whether some methods offer clear advantage over others for certain applications, as part of studies of the optimum 2, 3, 4 ¡__________¡ ¡ configurations of S2S models. Explore systematically how many unique models in a multi-model ensemble are required to predict useful S2S parameters, and whether those models require unique data assimilation, physical parameterizations, 2, 3, 4 ¡__________¡ ¡ ¡ or atmosphere, ocean, land, and ice components (see also Recommendation L)
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3, 1 ¡__________¡ Chapter 7 O: Develop a national plan and investment strategy for S2S prediction to take better advantage of current hardware and software and to meet the challenges in the evolution of new hardware and software for all stages of the prediction process, including data Supporting ¡__________¡ ¡ ¡ assimilation, operation of high-resolution coupled Earth system models, and storage and management of results. Redesign and recode S2S models and data assimilation systems so they will be capable of exploiting current and future massively parallel computational capabilities; this will require a significant and long-term investment in computer scientists, software engineers, applied Supporting ¡__________¡ ¡ mathematicians, and statistics researchers in partnership with the S2S researchers.
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Provide more graduate and postgraduate training opportunities, enhanced professional recognition and career advancement, and adequate incentives to encourage top students in relevant scientific and Supporting ¡ ¡ computer programming disciplines to choose S2S model development and research as a career.
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