Science for Service �Science for Policy �Science for Society�

Øystein Hov^*

Bergen 10 May 2023

^* Advisor, Norwegian Meteorological Institute 2017-

Director of Research Norwegian Meteorological Institute 2004-2016

President WMO Commission for Atmospheric Sciences 2013-2020

BOTH SIDEWAYS AND IN-DEPTH VIEW; CURIOSITY

Science for service, science for policy, Science for society:

A value cycle around discovery-translation-application. Judged by quality – relevance - impact

THE SCIENTIST’S ROLE

Enabling technologies

and enabling cultures

are required

3

Traditional relation between science and society �(One way and separated)

Thick wall

between disciplines

THE SCIENTIST’S ROLE

Roles of scientists vis-à-vis policy-makers�(according to Pielke)

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Pure Scientist: Communicates facts without paying any attention to the political context

Science Arbiter: Answers questions of policy-makers, but without providing further assistance (e.g.� on whether the question is the right one)

Issue Advocate: Uses research results to influence a political agenda, thus narrowing the scope of choices

(Honest Broker:)

Science Broker: Engages actively with policy-makers and societal sectors to solve particular problems by developing policy alternatives

Science advice for policy

over time

THE SCIENTIST’S ROLE

Technical University of Denmark

07 November 2016

Add Presentation Title �in Footer via ”Insert”; �”Header & Footer”

Couple to existing research-driven and user- �informed operational �observations, forecasts and reanalysis�

• Understand the leading operational activities for marine observations, forecasting, reanalysis and services
• Approach them to develop a framework to enhance their value chain “science for services and policy” and to advance research infrastructures.
• Recognize the significant value gain for science and applications in a strong interactive relationship with the operational flow of marine observations and calculations
• Lessons learnt from diagnostics, verification and application of operational model results indicate ways forward in the development and testing of models and observations.

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OPERATIONAL MARINE INFRASTRUCTURES FOR PHYSICS, DYNAMICS AND BIOGEOCHEMISTRY – SHOULD DEVELOP INTO THE RESEARCH INFRASTRUCTURE AS WELL

Earth system model (global or LAM) or system component model of physics, dynamics, biogeochemistry. Time scales:

Ocean forecasting, reanalysis Climate change

Days-months years-decades

Storage

4-d fields

Conti-nuously updated

Postprocessing

Shipping lanes

Fisheries

aquaculture

Search and rescue

Industry

Harbours

Seafloor

Pollution

Storm surges

Tides

Security

Sea level rise

CC adaptation

Data assimi-lation

Users:

specialized;

the public

Backend

Data policy

VALUE CYCLE : DISCOVERY – TRANSLATION - APPLICATION

Near real time

observations:

Remote sensing

Buoys

Argos floats

Field (research) experiments

Physical, dynamical, biogeochemical process description, numerical solution of equations of motion and energy, continuity equations for trace species, ensemble prediction systems for statistical properties of analysis and forecasts, BC

Parameter fields and diagnostics

Veri-fication

based on specia-lized obser-vations

RESEARCH GOALS – PROJECTS

A Strategic Framework for Research and Education

a) by developing the observational capability, the process understanding and the numerical modelling, improve the forecasting of physics, dynamics and biology of the ocean in an earth system framework, thereby reducing risk in current day’s marine operations including fisheries. b) address emerging patterns of climate, environmental, technological and socio-economic change. c) Frameworks: Copernicus CMEMS, C3S, Ocean Decade, High Level Ocean Panel. The Blue Economy

Research observations

Process studies, modelling

Integrated assessments

Frontend

NorKyst-800m

Science for service�is judged by

• scientific articles,
• scientific assessments and syntheses of topics that are in demand,
• technological innovations, including instruments, numerical models, methods, analysis tools, data management and interoperable and balanced earth system observations, especially where there is currently poor coverage and little or no redundancy,
• data that is "Findable, Interoperable, Accessible and Reusable (FAIR)",
• new or improved or advanced services for decision-making either by specialized users and businesses, or for broad societal groups, based on operational forecasts for and reanalyses of the Earth system as a whole or of one or more of its components - ocean, ice, atmosphere, water, land, biogeochemical cycles. Indirectly important for marine ecosystems and modeling of living resources.
• Clever communication of services and knowledge, so that user experience can give direction and content to what should be priority research questions going forward,
• ability to provide science-based advice - preferably as a "science broker": "one who actively engages with decision-makers and societal sectors to solve particular problems by developing policy options",
• a new generation of researchers and research training that connects science and services and thereby helps to close the knowledge and experience gap that often exists between the domain of research and the domain of practice,
• dissemination of research results and the role of research in complex social issues

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END

Remote sensing and insitu observations used in a 48h assimilation window (Nov. 3) in MET Norway’s coastal ocean modeling system (based on ROMS 4D-Var)

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CMEMS

NCEP

GFDL

ECMWF�UKMO

JMA

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AMAP

DATA ASSIMILATION IN A COASTAL MARINE MODEL 800M

MET NORWAY

Coastal modelling

11

NorFjords_CL

Plans: extended domain, new code, DA at 2.4 km resolution

dx ~ 160m

NorKyst-800m

SCIENCE FOR SERVICES, SCIENCE FOR POLICY

• Quality, Relevance and Impact
• User Interactions forces exploration of “What works”
• Seamless Earth system modelling across weather, water, environment, ocean, climate; interoperable observation systems of ES components

SCIENCE FOR POLICY - SCIENCE ADVICE FOR POLICY AS SCIENCE BROKER:

NATIONAL AND INTERNATIONAL NETWORKS, SOME «THICK» (CLRTAP/UNECE, EEA), OTHERS «THIN» (RCN AND EU PROJECTS) (IN IPCC THE SCIENCE ADVICE ROLE IS AS SCIENCE ARBITER)

https://ane4bf-datap1.s3-eu-west-1.amazonaws.com/wmocms/s3fs-public/ckeditor/files/The_meteorological_value_chain_and_the_role_of_observations.pdf?D2.itTqn948iUyhkS9e9wWIg4nfMjjB_

Operational ocean modelling, user informed and science driven

Analogue to the atmospheric case:

https://www.frontiersin.org/articles/10.3389/fmars.2019.00439/full

Global marine observation network of floats. In addition comes remote sensing, gliders, ships, opportunity field experiments and campaigns

https://ane4bf-datap1.s3-eu-west-1.amazonaws.com/wmocms/s3fs-public/ckeditor/files/The_meteorological_value_chain_and_the_role_of_observations.pdf?D2.itTqn948iUyhkS9e9wWIg4nfMjjB_

Operational infrastructures and research infrastructures in academia should overlap

User informed, research driven operational services («Science for service»)

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• Marine research institutes/agencies
• Coastal authorities
• Meteorological and hydrological institutions
• Operational observation systems, cruises, models, data flow FAIR
• Service providers coupled to users
• Governmental permanent funding

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«Research sector», «basic or fundamental research»

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• Academia, universities, independent research intitutions
• Intermittent cruises, experiments, field work, model runs
• Research projects funded nationally and internationally
• Usually weak legacy mechanisms

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Examples of joint research and operations:

• Copernicus for a blue ocean (Copernicus Marine Environmental Monitoring System CMEMS)
• GOOS Expert team Operational Ocean Forecasting Systems (ETOOFS)
• OceanPredict, the international network and science programme dedicated to operational oceanography.
• Interoperable, meta data governed, decentralized observations and model results (DestinE/ocean; ARGOS, Remote sensing/ESA and EUMETSAT  

Cycles of invention and discovery �Rethinking the Endless Frontier�Venkatesh Narayanamurti and Toluwalogo Odumosu, Harvard University Press 2016

• There is plenty of room for fundamental science within a value cycle framework
• «rather than being inimical to discovery research, it appears that having a goal and a well-defined mission (as the industrial laboratories all did) catalyzes research that leads to both inventions and discoveries»

Value cycle in marine sciences?

• The adaption and operation of the value cycle for the marine sciences is most advanced for the physical and dynamical matrix into which the marine biomass and living resources are embedded.
• The evolution of the understanding, modelling and the application for marine biology and living resources in a prognostic and reanalysis sense involves a range of organisations and academic disciplines.
• The physical and dynamical observational and modelling capability in the coupled operations and R&D mode is an essential requirement and building block for the modelling of the marine biological systems and living resources.

”Strong” vs ”weak” («thick» vs «thin») research infrastructures in weather- and ocean forecasting, climate and pollution

• The structure must be ”fit for purpose” in spatial and temporal scale/resolution:
• Contain in its spatial and temporal coverage the main driving forces and species
• Dynamical systems must be observed through a large number of realisations, requiring long term commitment in intellectual, institutional and financial resources.
• Societal importance (cost-benefit)
• Policy demand
• Industry interest /involvement/drive
• Governance
• Define expectations
• Grant power
• Verify perfomance
• Research infrastructure responsibility in institutions with a core mandate that fits with the use if the infrastructure, and where there is
• A significant research activity
• quality assurance protocols with real implementation
• Long-term commitment to quality of measurements, data storage, mining and retrieval
• Funding: Secure long term funding for the basic infrastructure operations.
• Dependence on short term projects for the funding of basic long term infrastructures gives ”weak structure”

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• Science interface
• Combine research and operational monitoring (multipurpose)
• Appropriate links to internal and external research interests
• Process oriented or other special studies can be project funded on a competeitive basis and over limited time periods
• The research infrastructure is shared with others (power sharing)
• The infrastructure should be international if its purpose warrants it (satellite remote sensing, aircraft, research vessel, observational network).
• National infrastructure when appropriate (weather radar network, surface monitoring sites); and/or linked up into an international network)
• Institutional infrastructure (super computing, storage facilities)
• Primary vs secondary infrastructure (system of systems)
• Flexibility in adapting to evolving user needs
• the coupling of atmospheric chemistry with climate or the cycling of reactive nitrogen (”forward looking”);
• formal structure/framework limiting in the long run?
• Open data policy (public good/private good, positive externalities)
• Learn from research infrastructures that functions well!

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Progress in subseasonal to seasonal prediction through a joint weather and climate community effort Annarita Mariotti, Paolo M. Ruti and Michel Rixen, Perspective, 26 March 2018, Climate and Atmospheric Science, www.nature.com/npjclimatsci

PREDICTABILITY IN EARTH SYSTEM COMPARTMENTS

Global risks landscape 2017��World Economic Forum

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RISK REDUCTION!

The Earth System: A Symbiotic Relationship

RISK REDUCTION – CORE THEMES