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Suggested Citation:"Summary." National Academies of Sciences, Engineering, and Medicine. 2016. Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts. Washington, DC: The National Academies Press. doi: 10.17226/21873.
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Summary

The use of weather forecasts by governments, businesses, and individuals is ubiquitous in the United States: Should a school system be closed due to cold or snowy conditions on a given day? How much power should an electric utility plan to produce in order to meet demand for air conditioning during a summer week? Is a weather-sensitive military sortie likely to be effective on a particular afternoon? Making these and myriad other decisions across virtually all sectors of the economy has been transformed by the availability of skillful forecasts with lead times of a few hours to a few days. The value and importance of weather and other environmental forecasts will increase as the nation’s economic activities, security concerns, and stewardship of natural resources become increasingly complex, globally interrelated, and affected by longer-term climate changes.

Although short-term forecasts already play a vital role in shaping societal decision-making, many critical decisions must be made several weeks to months in advance of potentially favorable or disruptive environmental conditions. For example, it can take weeks or months to move emergency and disaster-relief supplies, but pre-staging resources to areas that are likely to experience extreme weather or an infectious disease outbreak could save lives and stretch the efficacy of limited resources. Similarly, emergency managers responding to unanticipated events such as nuclear power plant accidents or large oil spills face the task of communicating the ramifications of such events on timescales that stretch well beyond a few days. There are many more such examples: naval and commercial shipping planners designate shipping routes weeks in advance, seeking to stage assets strategically, avoid hazards, and/or take advantage of favorable conditions; with improved knowledge of the likelihood of precipitation or drought, farmers can purchase seed varieties that are most likely to increase yields and reduce costs; and depending on the year, water resource managers can face a multitude of decisions about reservoir levels in the weeks, months, and seasons ahead of eventual water consumption.

A frontier in forecasting involves extending the capability to skillfully predict environmental conditions and disruptive weather events to several weeks and months in advance, filling what has long been a gap between today’s short-term weather and ocean forecasting capabilities (within the next 14 days) and a growing ability to project the longer-term climate (on scales of years to decades or more). Seasonal—and more recently subseasonal—predictions (defined in Box S.1) have improved over the past

Suggested Citation:"Summary." National Academies of Sciences, Engineering, and Medicine. 2016. Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts. Washington, DC: The National Academies Press. doi: 10.17226/21873.
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decade, but there is great opportunity to further improve the skill of subseasonal to seasonal (S2S) forecasts, as well as the breadth of forecasted variables and routinely available forecast products. Doing so could dramatically increase the benefits of the environmental prediction enterprise: saving lives, protecting property, increasing economic vitality, protecting the environment, and informing policy choices.

Despite their large potential, Earth system predictions on S2S timescales remain challenging for researchers, modelers, and forecasters. Although it is increasingly recognized that many sources of predictability exist in the Earth system on S2S timescales, representing these sources of predictability in Earth system models is challenging. Models must adequately capture the initial states of the atmosphere, ocean, land surface and cryosphere, as well as the interactions, or coupling, of these different components. Furthermore, the longer lead times associated with S2S predictions make the representation of uncertainty and the verification process more challenging and more computationally intensive than numerical weather prediction. Nonetheless, potential advances both in technology—satellites, computing, etc.—and in science—model parameterizations, data assimilation techniques, etc.—make advances in S2S forecasting feasible within the next decade.

Another key challenge is making S2S forecasts more applicable to users. S2S forecasts are generally less skillful than shorter-term predictions, are issued at lower spatial and temporal resolutions, and may involve the communication of probabilistic information that is unfamiliar to many users. These barriers have the potential to be overcome through research about and engagement with users.

Given the opportunities associated with improved S2S forecasts, but also the many challenges associated with developing them, the Office of Naval Research (ONR), the National Aeronautics and Space Agency (NASA), and the Heising-Simons Foundation asked the National Academies of Sciences, Engineering, and Medicine to undertake a study to develop a 10-year U.S. research agenda to increase S2S research and model-

Suggested Citation:"Summary." National Academies of Sciences, Engineering, and Medicine. 2016. Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts. Washington, DC: The National Academies Press. doi: 10.17226/21873.
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ing capability, advance S2S forecasting, and aid in decision-making at medium and extended lead times (see Appendix A for the study’s Statement of Task). The Academies convened the Committee on Developing a U.S. Research Agenda to Advance Subseasonal to Seasonal Forecasting to meet this request.

VISION AND RESEARCH STRATEGIES FOR THE NEXT DECADE

The committee believes that there is great potential to advance S2S forecasting capability and rapidly increase the benefits of S2S predictions to many sectors in society. However, overcoming the challenges to developing S2S forecasting will take sustained effort and investment.

Encouraged by its sponsors to be bold, the committee puts forward a vision that S2S forecasts will be as widely used a decade from now as weather forecasts are today and identifies four research strategies and 16 recommendations to guide progress toward that vision. The research strategies for improving the use of S2S forecasts in the next decade (see Figure S.1) are as follows:

  1. Engage Users in the Process of Developing S2S Forecast Products
  2. Increase S2S Forecast Skill
  3. Improve Prediction of Extreme and Disruptive Events and Consequences of Unanticipated Forcing Events
  4. Include More Components of the Earth System in S2S Forecast Models

RECOMMENDED ELEMENTS OF A RESEARCH AGENDA

Implementing the four strategies above will require research in the physical and social sciences, as well as improved coordination among user, research, and operational forecast communities. The committee’s recommendations collectively constitute an S2S research agenda for the nation. Given the fluid technological, political, and financial environment in which the research agenda will be implemented, the committee decided it was more important to identify the most important areas where progress can be made without overly prescribing the sequence or priority in which they should be addressed. Although most recommendations support more than one research strategy, they are described in the following sections under the primary strategy with which they are associated.

To help agencies and others within the weather/climate enterprise select specific parts of the research agenda to pursue, Table S.1 and Table 8.1 provide additional

Suggested Citation:"Summary." National Academies of Sciences, Engineering, and Medicine. 2016. Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts. Washington, DC: The National Academies Press. doi: 10.17226/21873.
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Image
FIGURE S.1 Schematic of the relationship between the committee’s vision, strategies, and recommendations for advancing subseasonal to seasonal forecasting. NOTES: The committee’s vision (center) serves as the target for the research agenda. Four research strategies are intended to organize actions to advance toward the vision, but are not mutually exclusive (indicated by the white arrows). The outermost layer of the circle contains paraphrases of the individual recommendations for more research activities, aligned with the strategy that they most closely support (although recommendations can support more than one strategy—see Table S.1). The base of the circle shows activities necessary to support the research agenda.
Suggested Citation:"Summary." National Academies of Sciences, Engineering, and Medicine. 2016. Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts. Washington, DC: The National Academies Press. doi: 10.17226/21873.
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TABLE S.1 These 16 recommendations—lettered in the order that they appear in the report—comprise the committee’s research agenda (a similar table that also contains the more specific recommendations from the chapters is in Table 8.1). The second column indicates the research strategy that each recommendation primarily supports (colors are the same as in Figure S.1). Additional research strategies (1-4) supported by each recommendation are indicated by numbers. The committee specifically did not prioritize these recommendations. However, this table presents the committee’s opinion on whether each activity will involve mainly basic or applied research/operational activities, or both; whether a short-term return on investment is likely (≤ 5 years); and whether a new initiative or program, or a significant expansion of a program, may be necessary to implement each recommendation. The last column indicates recommendations for which the Committee believes that international collaboration and coordination is particularly important.

Recommendation Research Strategies Basic Research Applied Research/Operational Benefits Likely in the Short Term May Need New Initiative International Collab. Critical
Chapter 3
A: Develop a body of social science research that leads to more comprehensive and systematic understanding of the use and barriers to use of seasonal and subseasonal Earth system predictions. 1, 4 image
B: Establish an ongoing and iterative process in which stakeholders, social and behavioral scientists, and physical scientists codesign S2S forecast products, verification metrics, and decision-making tools. 1, 4 image
Chapter 4
C: Identify and characterize sources of S2S predictability, including natural modes of variability (e.g., ENSO, MJO, QBO), slowly varying processes (e.g., sea ice, soil moisture, and ocean eddies), and external forcing (e.g., aerosols), and correctly represent these sources of predictability, including their interactions, in S2S forecast systems. 2, 3
D: Focus predictability studies, process exploration, model development, and forecast skill advancements on high-impact S2S “forecasts of opportunity” that in particular target disruptive and extreme events. 3, 2
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 prediction and for discovering and modeling new sources of S2S predictability. 2, 3, 4 image
Suggested Citation:"Summary." National Academies of Sciences, Engineering, and Medicine. 2016. Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts. Washington, DC: The National Academies Press. doi: 10.17226/21873.
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Recommendation Research Strategies Basic Research Applied Research/Operational Benefits Likely in the Short Term May Need New Initiative International Collab. Critical
F: Determine priorities for observational systems and networks by developing and implementing observing system simulation experiments, observing system experiments, and other sensitivity studies using S2S forecast systems. 2, 3, 4 image
G: Invest in research that advances the development of strongly coupled data assimilation and quantifies the impact of such advances on operational S2S forecast systems. 2, 3, 4 image
H: Accelerate research to improve parameterization of unresolved (e.g., subgrid scale) processes, both within S2S system submodels and holistically across models, to better represent coupling in the Earth system. 2, 3, 4
I: Pursue next generation ocean, sea ice, wave, biogeochemistry, and land surface/hydrologic as well as atmospheric model capability in fully coupled Earth system models used in S2S forecast systems. 4, 2, 3 image
J: Pursue feature-based verification techniques to more readily capture limited predictability at S2S timescales as part of a larger effort to improve S2S forecast verification. 2, 1, 3 image
K: Explore systematically the impact of various S2S forecast system design elements on S2S forecast skill. This includes examining the value of model diversity, as well as the impact of various selections and combinations of model resolution, number of ensemble perturbations, length of lead, averaging period, length of retrospective forecasts, and options for coupled sub-models. 2, 3, 4 image
Chapter 6
L: Accelerate efforts to carefully design and create robust operational multi-model ensemble S2S forecast systems. 2, 3
M: Provide mechanisms for research and operations communities to collaborate, and aid in transitioning components and parameterizations from the research community into operational centers, by increasing researcher access to operational or operational mirror systems. 2, 1, 3, 4
N: Develop a national capability to forecast the consequences of unanticipated forcing events. 3, 1
Suggested Citation:"Summary." National Academies of Sciences, Engineering, and Medicine. 2016. Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts. Washington, DC: The National Academies Press. doi: 10.17226/21873.
×
Recommendation Research Strategies Basic Research Applied Research/Operational Benefits Likely in the Short Term May Need New Initiative International Collab. Critical
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 assimilation, operation of high-resolution coupled Earth system models, and storage and management of results. Supporting image
P: Pursue a collection of actions to address workforce development that removes barriers that exist across the entire workforce pipeline and increases the diversity of scientists and engineers involved in advancing S2S forecasting and the component and coupled systems. Supporting

detail about the recommendations: 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. The chapters contain additional recommended activities that fall under each main recommendation, which add further specificity and breadth to the research agenda. Although it might not be possible to pursue all of these actions simultaneously, the more that is done to implement these recommendations, the more advances in S2S forecasting can be made.

Research Strategy 1: Engage Users in the Process of Developing S2S Forecast Products

Many barriers hinder the use of existing S2S forecast information, including increasing demand for a wider variety of forecast variables and formats that are not readily available. An important first step in providing more actionable S2S forecast information is to develop a body of social and behavioral sciences research that leads to more comprehensive understanding of the current use and barriers to use of S2S predictions (Recommendation A). This will involve research to uncover the specific aspects of products—forecast variables, spatial and temporal resolutions, necessary levels of skill, etc.—that make S2S products more useful to decision-makers across multiple sectors.

Suggested Citation:"Summary." National Academies of Sciences, Engineering, and Medicine. 2016. Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts. Washington, DC: The National Academies Press. doi: 10.17226/21873.
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Beyond such assessments, engaging the S2S research and operational prediction community in an iterative dialogue with user communities is necessary to help ensure that forecast systems, forecast products, and other model output, are designed from the outset to be useful for decision-making (Recommendation B). Ongoing efforts will be needed to match what is scientifically predictable and technologically feasible at S2S timescales with what users find actionable, as both scientific skill and user needs continually evolve. Launching such a dialogue requires bringing decision-makers into the research and development process sooner rather than later. Private industry and “boundary organizations” within academia and the public sector (such as the National Oceanic and Atmospheric Agency’s [NOAA] Regional Integrated Sciences and Assessments program and the International Research Institute for Climate and Society at Columbia University, and many others) have already started such discussions. Leveraging the entire weather and climate enterprise—not just the public sector—will be necessary for further developing such an iterative approach to the development of S2S products and services.

Research Strategy 2: Increase S2S Forecast Skill

The skill (i.e., the quality) of S2S forecasts has been increasing, but is still limited, even for traditional weather and climate variables (e.g., temperature, precipitation). Improving the skill of S2S forecasts is fundamental to increasing their value to society. Enhancing skill begins with understanding sources of and limits to S2S predictability within the Earth system. Current research indicates that a large portion of S2S predictability originates from

  • Natural modes of variability (e.g., El Niño-Southern Oscillation [ENSO], the Madden-Julian Oscillation [MJO], and the Quasi Biennial Oscillation [QBO]—see Box 1.3);
  • Slowly-varying processes (e.g., involving soil moisture, snow pack and other aspects of the land surface, ocean heat content, currents and eddy positions, and sea ice); and
  • Elements of external forcing (e.g., aerosols, greenhouse gasses) that can result in a systematic and predictable evolution of the Earth system.

Basic research on these phenomena and their interactions is fundamental to identifying and understanding the processes that must be included in Earth system models in order to increase S2S forecast skill (Recommendation C).

In addition to extending knowledge about sources of S2S predictability, efforts are needed across every part of the forecast system, including improved observations and

Suggested Citation:"Summary." National Academies of Sciences, Engineering, and Medicine. 2016. Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts. Washington, DC: The National Academies Press. doi: 10.17226/21873.
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data assimilation methods, advances in Earth system models, and improved methods for uncertainty quantification, calibration, and verification.

Observations

Routine observations are essential for accurately initializing models, validating model output, and improving understanding of the physical system and its predictability. The ocean, land surface, and cryosphere remain significantly under-observed compared to the atmosphere, despite being major sources of S2S predictability. Maintaining and in some cases bolstering the network of observations across all components of the Earth system is critical to advancing S2S prediction skill (Recommendation E).

Although it would be beneficial to expand the geographic coverage and resolution of many types of observations, cost and logistics will continue to demand an identification of the most critical priorities. Observing system simulation experiments (OSSEs) and other sensitivity studies are powerful tools for exploring the importance of specific observations on estimation of the state of the Earth system and overall model performance, and could be better used to prioritize improvements to observation networks for S2S prediction systems (Recommendation F).

Data Assimilation

Data assimilation is the process of initializing and updating Earth system models with observations and is important for uncertainty quantification, calibration, and validation of forecasts. Integration of tens of millions of observations into the different components of an Earth system model presents many challenges, including ensuring that initializations are dynamically consistent and minimize the growth of errors. Given that coupling between the multiple, dynamic components of the Earth system (e.g., atmosphere, ocean, ice, land) is central to S2S prediction, developing and implementing coupled data assimilation methods is at the forefront of S2S model development. “Weakly coupled” data assimilation is one existing method that is increasingly implemented in weather prediction and holds promise for improving S2S prediction systems.“Strongly coupled” data assimilation allows observations within one component of the Earth system to affect state estimates in other components (with constraints). This technique is still in its infancy but has the potential to spur a more dramatic leap forward. Realizing the method’s potential will require significant research and testing that should be explored while continuing to pursue weakly coupled methods (Recommendation G).

Suggested Citation:"Summary." National Academies of Sciences, Engineering, and Medicine. 2016. Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts. Washington, DC: The National Academies Press. doi: 10.17226/21873.
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Models

Systematic errors are numerous within the Earth system models used for S2S forecasting—many global models produce an unrealistically strong Pacific equatorial cold tongue, a spurious double Inter Tropical Convergence Zone (ITCZ), wet or dry biases in rainfall in many parts of the world, among other issues. These model errors can be large compared to the predictable signals targeted by S2S forecasts. Thus taking steps to reduce systematic errors within coupled Earth system models is one of the most important steps in improving the skill of S2S predictions.

Modest increases in model resolutions hold potential for reducing model errors, and such improvements should continue to be studied. However, given the computational costs of increasing model resolution, many critical Earth system processes will need to be parameterized (i.e., represented using simplified physics schemes rather than being explicitly resolved in models) for the foreseeable future. Thus improving physical parameterizations will remain fundamental to reducing model errors and increasing S2S forecast skill, even as the capability to resolve more and more processes expands (Recommendation H). Coordinated, coupled field campaigns, process-targeted satellite missions, and long-term collaborations between research and operational scientists are essential for developing the understanding required to improve models and model parameterizations.

Calibration, Combination, Verification, and Optimization of S2S forecasts

Some model errors will remain even with major improvements in models and increased resolution. Using multi-model ensembles (MMEs) is likely to remain critical for S2S prediction as one of the most promising ways to account for errors associated with Earth system model formulation. However, current MMEs are largely systems of opportunity (i.e., basing the MME design on expediency). Research is required to more systematically develop MME forecast systems. Careful optimization of the configurations of a multi-model prediction system will include systematic exploration of the benefits and costs of adding unique models to an MME and evaluation of other S2S forecast system design elements (“trade space”), including calibration methods, model resolution, number of ensemble members, averaging period, lengths of lead and retrospective forecasts, and options for coupled sub-models (Recommendation K). Exploring this trade space will be a complicated and expensive endeavor, but determining how performance depends on system configuration is a key task in the S2S research agenda.

Suggested Citation:"Summary." National Academies of Sciences, Engineering, and Medicine. 2016. Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts. Washington, DC: The National Academies Press. doi: 10.17226/21873.
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Verification metrics are important for tracking and comparing model improvements, and are a critical part of building user trust in S2S forecasts. Improving verification should be done in collaboration with user groups, along with research on feature-based and two-step verification methods and 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 (Recommendation J).

Moving Research to Operations

Finally, transitioning new ideas, tools, and other technology between the S2S research community and operational centers is challenging but essential to translating research discoveries into better informed decision-making. The use of MMEs in research and demonstration settings, for example the North American Multi-model Ensemble (NMME) program, has demonstrated the potential for improving the skill of S2S forecasts and has produced many lessons for developing an operational MME. Fully operationalizing the current NMME, which relies on nonoperational institutions supported by research funding, may be challenging. However, there would be great value in the development of a fully operational MME forecast system that includes the operational centers of the United States (Recommendation L).

To make the rapid improvements to operational S2S prediction systems that the committee envisions, it will be generally important to speed the flow of information between scientists with research and operational foci (Recommendation M). This includes promoting and expanding existing mechanisms to facilitate knowledge transfer—such as NOAA’s Climate Process Teams—and developing new mechanisms to enhance researcher access to operational forecast data, including access to archives of ensemble forecasts, retrospective forecasts, and initialization data. Additionally, allowing researchers to conduct or request specific experiments on operational systems would provide an additional boost to the flow of discoveries and technical advances.

Research Strategy 3: Improve Prediction of Extreme and Disruptive Events and of the Consequences of Unanticipated Forcing Events

To improve the overall skill of S2S forecasts and provide more actionable information to users, the committee identifies two areas that deserve special attention and promotes them to the third and fourth Research Strategies. Research Strategy 3 involves

Suggested Citation:"Summary." National Academies of Sciences, Engineering, and Medicine. 2016. Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts. Washington, DC: The National Academies Press. doi: 10.17226/21873.
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an increased focus on discrete events and includes two sets of recommendations. The first is to emphasize the prediction of weather, climate, and other Earth system events that disrupt society’s normal functioning (e.g., major winter storms, excessive rainfall events, monsoon onset and breaks, tropical storms, heat waves). Thus, in contrast to the forecasts of specific weather events on a scale of days, improved S2S forecasts would identify situations with high probabilities of disruptive consequences, especially for subseasonal forecast ranges (approximately 2-12 weeks). A coordinated effort to improve the forecasting of these events could allow communities more time to plan for these events and mitigate damages. Improved forecasting of disruptive events may also involve developing “forecasts of opportunity”—identifying windows in time when expected skill is higher than usual at a particular place because of the presence of certain features in the Earth system, certain phases of large-scale climate patterns (e.g., seasonal cycle, ENSO, or MJO), or certain interactions of these modes, slowly varying processes, and external forcing. Studying these interactions and ensuring they are represented in models will be important for S2S prediction and for identifying forecasts of opportunity (Recommendation D).

The second part of this research strategy involves using S2S forecast systems to predict the consequences of disruptive events caused by outside forces. Such outside forces include volcanoes, meteor impacts, and human actions (e.g., aerosols, widespread fires, large oil spills, certain acts of war, or climate intervention). Even though these events themselves are not predictable, their consequences may be—in particular the consequences on S2S timescales. A national system for projecting the consequences from these unanticipated events on S2S timescales would aid emergency response and disaster planning (Recommendation N). With improved coordination between government agencies and academics, it would be possible to assist in recovery efforts by quickly generating S2S forecasts of the consequences of such unanticipated events shortly after they take place.

Research Strategy 4: Include More Components of the Earth System in S2S Forecast Models

The other area that the committee believes needs more focused attention is the utilization and further development of advanced Earth system model components beyond the troposphere, which has been the traditional focus of numerical weather prediction. The S2S prediction problem is inherently a problem of capturing the coupled processes operating at the interface between various components of the Earth system, including the troposphere, stratosphere, ocean, cryosphere, biosphere, and land surface.

Suggested Citation:"Summary." National Academies of Sciences, Engineering, and Medicine. 2016. Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts. Washington, DC: The National Academies Press. doi: 10.17226/21873.
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Progress in recent decades has extended the coupling of more model components and more comprehensive representation of processes within these components in operational S2S forecast systems (see also Research Strategy 2). However, there is an increasing need to accelerate the development of model components outside the troposphere and to improve their coupling within S2S forecast systems. In particular, it will be important to rapidly advance toward next generation ocean, sea ice, and land surface modeling capability within coupled Earth system models, in addition to preparing for cloud-resolving capability in atmospheric models. This will include moving toward eddy-resolving resolutions in the ocean, inclusion of ocean surface wave effects, and developing better representation of sea ice, land surface, and surface hydrological processes. Other strong candidates for improvements to existing practices for operational S2S forecasting systems include advancing prediction capabilities of aerosols and air quality, soil-state and seasonal vegetation growth, and aquatic and marine ecosystems. Research is also required to better understand which added components have significant interactions with the weather and climate system as a whole, pointing to the need for dynamic integration into operational forecasting systems (Recommendation I).

Improving these model components may also be important for better predicting a wider array of Earth system variables on S2S timescales (e.g., sea ice, ocean productivity, hydrology, air quality), even if they do not feedback strongly to the coupled system. Iterative interaction with forecast users (Research Strategy 1) can help determine what processes and variables are most important to include in coupled S2S systems as these systems evolve.

Supporting the S2S Forecasting Enterprise

The research strategies outlined in the report will require advances in computational infrastructure to support S2S forecasting, and the development and maintenance of a workforce ready to realize potential advances in S2S forecasting. These challenges are not unique to the S2S enterprise—they are also important in the weather prediction and climate modeling communities, among other technical enterprises.

Similar to weather forecasting and climate modeling, S2S prediction systems test the limits of current cyber-infrastructure. The volume of observational data, data assimilation steps, model outputs, and reanalysis and retrospective forecasts involved in S2S forecasting means that the S2S modeling process is extremely data intensive. Advances in S2S forecast models (such as higher resolutions, increased complexity, the generation and retention of long retrospective forecasts) will require dramatic increases (likely 1,000-fold) in computing capacities, together with similar expansions

Suggested Citation:"Summary." National Academies of Sciences, Engineering, and Medicine. 2016. Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts. Washington, DC: The National Academies Press. doi: 10.17226/21873.
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in storage and data transport. Earth system models are not taking full advantage of the complexity of current computing architectures, and improving their performance will likely require new algorithms that process more data locally and new algorithms to exploit even more parallelism. The transition over the next decade to new computing hardware and software that is not necessarily faster, but is more complex, will be highly disruptive. Future storage technology will also be more complex and varied than it is today, and leveraging these innovations will require fundamental software changes. Facing these challenges and uncertainties about the future, the United States would benefit from developing a national plan and investment strategy 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 (Recommendation O).

There are numerous barriers to training and retaining talented workers in the S2S enterprise. S2S is complex and involves working across computing and traditional Earth science disciplinary boundaries to develop and improve S2S models, and across science-user decision boundaries to better design and communicate forecast products. From the limited workforce data available, the Committee surmises that the pipeline of workers for the S2S enterprise is not growing robustly in the United States and is not keeping pace with this rapidly evolving field. Given the importance of S2S predictions to the nation, a concerted effort is needed to entrain, develop, and retain S2S professionals. This involves gathering quantitative information about workforce requirements and the expertise base to support S2S forecasting, improving incentives and funding to support existing professionals and attract new professionals, and expanding interdisciplinary programs to train a more robust and diverse workforce to employ in boundary organizations that fill the space between S2S modelers and forecast user communities (Recommendation P).

CONCLUSION

This report envisions a substantial improvement in S2S prediction capability, and the committee expects valuable benefits to flow from these improvements to a wide range of public and private activities. It sets forth a research agenda that describes what must be done—observations, basic research, data management, and interactions with users—to advance prediction capability and improve societal benefits. Despite the specificity in recommending what should be done, the report does not address the challenging issues of how the agenda should actually be pursued—who will do what and how the work will be supported financially. Because this research agenda significantly expands the scope of the current S2S efforts, the committee believes

Suggested Citation:"Summary." National Academies of Sciences, Engineering, and Medicine. 2016. Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts. Washington, DC: The National Academies Press. doi: 10.17226/21873.
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that some progress can be made with current levels of support and within current organizational structures, but achieving even a considerable fraction of the S2S vision will likely require additional resources for basic and applied research, observations, forecast operations, and user engagement. The scope of the research agenda will also require closer collaboration between federal agencies and international partners, better flow of ideas and data between the research and operational forecasting communities, and engagement of the entire weather and climate enterprise.

Again, the committee acknowledges that addressing the challenge of dramatically improving the skill and use of S2S forecasts will require many different actions, but the committee reiterates that these are the actions that will need to be pursued to achieve the full potential for S2S forecasting. The more that can be pursued within this research agenda, the closer the nation can be toward realizing the full potential of S2S forecasting and the more benefits can be produced for a wide range of users and the nation as a whole.

Suggested Citation:"Summary." National Academies of Sciences, Engineering, and Medicine. 2016. Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts. Washington, DC: The National Academies Press. doi: 10.17226/21873.
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Suggested Citation:"Summary." National Academies of Sciences, Engineering, and Medicine. 2016. Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts. Washington, DC: The National Academies Press. doi: 10.17226/21873.
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Suggested Citation:"Summary." National Academies of Sciences, Engineering, and Medicine. 2016. Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts. Washington, DC: The National Academies Press. doi: 10.17226/21873.
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Suggested Citation:"Summary." National Academies of Sciences, Engineering, and Medicine. 2016. Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts. Washington, DC: The National Academies Press. doi: 10.17226/21873.
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Suggested Citation:"Summary." National Academies of Sciences, Engineering, and Medicine. 2016. Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts. Washington, DC: The National Academies Press. doi: 10.17226/21873.
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Suggested Citation:"Summary." National Academies of Sciences, Engineering, and Medicine. 2016. Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts. Washington, DC: The National Academies Press. doi: 10.17226/21873.
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Suggested Citation:"Summary." National Academies of Sciences, Engineering, and Medicine. 2016. Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts. Washington, DC: The National Academies Press. doi: 10.17226/21873.
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Suggested Citation:"Summary." National Academies of Sciences, Engineering, and Medicine. 2016. Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts. Washington, DC: The National Academies Press. doi: 10.17226/21873.
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Suggested Citation:"Summary." National Academies of Sciences, Engineering, and Medicine. 2016. Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts. Washington, DC: The National Academies Press. doi: 10.17226/21873.
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Suggested Citation:"Summary." National Academies of Sciences, Engineering, and Medicine. 2016. Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts. Washington, DC: The National Academies Press. doi: 10.17226/21873.
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Page 9
Suggested Citation:"Summary." National Academies of Sciences, Engineering, and Medicine. 2016. Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts. Washington, DC: The National Academies Press. doi: 10.17226/21873.
×
Page 10
Suggested Citation:"Summary." National Academies of Sciences, Engineering, and Medicine. 2016. Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts. Washington, DC: The National Academies Press. doi: 10.17226/21873.
×
Page 11
Suggested Citation:"Summary." National Academies of Sciences, Engineering, and Medicine. 2016. Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts. Washington, DC: The National Academies Press. doi: 10.17226/21873.
×
Page 12
Suggested Citation:"Summary." National Academies of Sciences, Engineering, and Medicine. 2016. Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts. Washington, DC: The National Academies Press. doi: 10.17226/21873.
×
Page 13
Suggested Citation:"Summary." National Academies of Sciences, Engineering, and Medicine. 2016. Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts. Washington, DC: The National Academies Press. doi: 10.17226/21873.
×
Page 14
Suggested Citation:"Summary." National Academies of Sciences, Engineering, and Medicine. 2016. Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts. Washington, DC: The National Academies Press. doi: 10.17226/21873.
×
Page 15
Suggested Citation:"Summary." National Academies of Sciences, Engineering, and Medicine. 2016. Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts. Washington, DC: The National Academies Press. doi: 10.17226/21873.
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Page 16
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Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts Get This Book
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As the nation’s economic activities, security concerns, and stewardship of natural resources become increasingly complex and globally interrelated, they become ever more sensitive to adverse impacts from weather, climate, and other natural phenomena. For several decades, forecasts with lead times of a few days for weather and other environmental phenomena have yielded valuable information to improve decision-making across all sectors of society. Developing the capability to forecast environmental conditions and disruptive events several weeks and months in advance could dramatically increase the value and benefit of environmental predictions, saving lives, protecting property, increasing economic vitality, protecting the environment, and informing policy choices.

Over the past decade, the ability to forecast weather and climate conditions on subseasonal to seasonal (S2S) timescales, i.e., two to fifty-two weeks in advance, has improved substantially. Although significant progress has been made, much work remains to make S2S predictions skillful enough, as well as optimally tailored and communicated, to enable widespread use. Next Generation Earth System Predictions presents a ten-year U.S. research agenda that increases the nation’s S2S research and modeling capability, advances S2S forecasting, and aids in decision making at medium and extended lead times.

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