Sunday, February 26, 2017



Climate observations are required for research and modeling, which are then required for forecasts, predictions, and projections. A strong research foundation, along with a sustained observational network and modeling system, provides the basis for building scenarios and assessments of future conditions and of developing process-related understanding. Over time, research assessments will feed back information on how information on research and modeling capabilities is critically needed by society, such as assessing whether mitigation actions result in documented changes in greenhouse gasses. Moreover, findings from weather, oceans and coastal R&D will help define and refine the scales at which climate information is most useful in decision-making.

The Healthy Oceans goal depends on inputs such as climate observations, assessments, and training to incorporate climate considerations into fishery and protected resource decisions as well as IEA programs, while providing the Climate R&D with LMR-specific impacts of climate change. In order to make fisheries sustainable objective, we must increase our understanding of climate change and ocean acidification impacts on global and regional scales and manage our resources accordingly. The future of sustainable fisheries is very dependent on Climate R&D. Coastal R&D also relies on sea level magnitude and impacts information as it relates to observing, modeling research (integration of climate and coastal models to enable down-scaling and up-scaling of sea level and change predictions), training, and products. Climate R&D is also required for the regional climate services development and delivery system across sea level and ecosystems societal challenges, and the Sentinel Site Program.


The goal of building a Weather Ready Nation is highly dependent on research that informs how the public consumes weather forecasts, warnings and information and applies it to actionable decisions. Thus, while improvements to the accuracy of environmental data and predictive models are of fundamental importance to producing more reliable weather information, the improvement of the communication and use of that information is equally necessary to making our nation “weather ready.” Much of the technology development to make the nation weather ready is conducted at NOAA’s National Weather Service, building on, and transitioning into NWS operations the work of its NOAA (and other) research partners, especially NOAA's Office of Oceanic and Atmospheric Research. R&D at NOAA’s National Environmental Satellite Data and Information Service serves to improve remote sensing systems that yield meteorological observations. Advancing interactive and complex NOAA decision support services for public sector stakeholders must be based on a combination of social and behavioral research, real-world experience, test bed activities and proving ground demonstrations. Weather Ready Nation is critically dependent on a robust IT infrastructure that can be quickly adapted to changing dissemination technologies, trends in social media, and new environmental forecast models.


To meet the R&D objectives for Healthy Oceans, NOAA programs rely on a diverse set of internal capabilities coupled with agreements with other Federal and state agencies, non-governmental and academic programs, and marine businesses, users, and stakeholders. The future success of ecosystem-based management (EBM) will depend on a diverse and complex set of interconnected information sources, hardware managers, and data and model developers. R&D to integrate models supports the incorporation of data derived from a suite of vessels, buoys, satellites, and human observations. These models integrate a broad spectrum of observational data, from higher spatial resolution climate information, weather forecasts delivered at increasing rates, physical and chemical parameters incorporated into biological data streams almost instantaneously, and socio-economic information looped as feedback into assessment outputs. Moreover, through data management R&D, data from these models is served to a multiplicity of users meeting diverse requirements and supporting a variety of temporal and spatial scales. In addition, R&D for Healthy Oceans is very dependent upon R&D that will improve regional climate predictions to understand climate-ecosystem interactions and their effect on ecosystem services.


As the current ecological forecasting portfolio increases its regional coverage for Harmful Algal Bloom forecasts to develop a nationwide capability and expands to include other topics, such as pathogen proliferation on beaches and in shellfish beds, there will be increased requirements for standardized and modular data integration, low-cost and high-throughput in situ monitoring systems, spatial coverage and skill assessment for modeling water and particle transport, and effective dissemination of information to resource managers and other stakeholders. The recently produced Ecological Forecasting Roadmap (September 2012) for enhanced development and delivery of a wide variety of ecological products and services at NOAA will be instrumental in setting priorities and achieving cost-efficiencies in developing new and enhanced forecasts for Harmful Algal Blooms, hypoxia, pathogens, sea level change impacts on coastal ecosystems and communities, and assessing impacts of land-based pollution on coastal ecosystems.

Stakeholder Engagement

Improved engagement with NOAA’s stakeholders depends not on the natural sciences and engineering, but on the social and behavioral sciences (to understand the actions and values of human beings), as well as on the arts and humanities (to craft communications and design media). For instance, the Climate Program Office within OAR needs such expertise to better understand the users of climate information, as well as their needs, to determine how and why they use (or don’t use) NOAA climate products, how decision makers could better incorporate climate information into their resource management routines, and how NOAA could better convey climate forecasts and information to decision makers. In-house, federal and contract staff are the cornerstone of NOAA’s capacity in the social sciences, arts and humanities, but they are by no means the whole story. NOAA’s social science capacity also includes those outside the Agency who are supported by contracts and grants, partnerships, Cooperative Institutes, and inter-agency agreements.

Observing Systems

R&D in support of observing systems significantly depends on the requirements established by the R&D and operations supporting the Mission goals. This dependency focuses on what needs to be measured to support each goal, both operationally and for R&D. Much of this focus is on how to effectively and efficiently measure new types of observations, as well as how to improve the accuracy, coverage, resolution, effectiveness, and cost of measuring existing parameters. The analysis and modeling requirements of each mission goal drive R&D on optimizing the observing systems for analysis and predictive modeling. The focus of such R&D is on where, when, and with what fidelity to observe. NOAA’s observing system design depends on modeling for configuration optimization with respect to requirements, priorities, and resources. In turn, modeling research and operations depend on R&D quantifying observing system uncertainty.

Environmental Modeling

Each goal and enterprise objective has a dependency on modeling R&D. As with R&D for NOAA’s observing system, R&D for environmental modeling depends on the requirements established by the Mission goals for analyses and projections/predictions. Environmental modeling ultimately depends on the R&D conducted in the interest of the goals for the science on underlying processes and relationships, critical elements necessary for establishing representative modeling. Improved representativeness, predictive skill, and the understanding and quantification of uncertainty depend on the R&D conducted in the interest of the goals and enterprise objective for Improved, Reliable Data. The R&D of models, particularly those for operational application, drives R&D for capabilities such as model linking and coupling, nesting, and data assimilation.


Holistic Understanding

Attention to the interdependence of NOAA’s varied R&D endeavors is also the key to the agency’s premiere R&D objective: a holistic understanding of the Earth system.  The R&D discussed in this plan will undoubtedly contribute to greater understanding of the Earth system; scientific activities always yield new knowledge, often leading future inquiries in unexpected directions.   It should also contribute to an understanding that is more holistic.

In popular language, “holistic” simply means that the whole is more than the sum of its parts.  In a scientific context, the word is a more specific reference to the perspective of general system theory,  in which the properties of systems are distinguished from those of the components of systems.  Systems’ properties are often “emergent,” that is, they cannot be deduced from knowledge of the properties of components.  Understanding the Pacific Ocean, for example, is different from understanding water, fish, and boats.  A more holistic understanding is, therefore, not a more fundamental understanding (i.e., reducing phenomena to smaller elements and general principles), nor a more precise understanding (i.e., increasing the detail with which we understand phenomena), nor is it a more comprehensive understanding (i.e., understanding more aspects of more elements of phenomena).

As an Earth science agency, NOAA contends with complex challenges that span many scientific and technical domains.  The natural world does not abide by the distinctions that humans make among disciplines, organizations, nor even among the strategic goals, questions, and objectives outlined in this plan.  In aiming for a holistic understanding of the Earth system, then, NOAA commits itself to integrating the diverse perspectives and professional expertise required by the Agency’s mission, and also to connecting the dots among otherwise separate R&D endeavors.

For NOAA’s ecosystem research, a holistic understanding demands that we study ecology as distinct from individual species and their habitats.  For research on climate systems, it is the bridging of phenomena at different spatial and temporal scales.  For the development of observation systems, it is the architecture of interwoven technologies to collect and manipulate data.  For the orchestration of NOAA’s entire innovation system, it is the translation of knowledge and technologies across the institutional boundaries of research and applications.   In all these cases, we are connecting the dots, not collecting the dots.