“Community Climate Models: Is a New Paradigm of Model Development Possible?”

1. “Community Climate Models: Is a New Paradigm of Model Development Possible?” Richard B. Rood Cell: 301-526-8572 2525 Space Research Building (North Campus) rbrood@umich.edu http://aoss.engin.umich.edu/people/rbrood November 17, 2010 Supercomputing 2010, New Orleans, LA

2. Opportunity • Climate projections offer us historical opportunity. We have, at least for some part of our future, a credible vision of what the future will be like. It is up to us whether or not we take advantage of this opportunity. An essay, perhaps.

3. An Interesting Problem (motivator 1) • A couple of years ago one of my students brought me the following problem • Client wants to build a set of desalination plants in western Africa. • Purpose: Irrigation of Sahara • He has read that hurricanes are somehow affected by the Sahara. • Can he get some value for this? • Does he incur liability?

4. Outline • Motivating Examples (2) • How do we address problems of climate variability and change? • Comparing weather and climate • Reframing, perhaps • What stands in our way • A final remark • “I'm up on the tight wire, one side's ice and one is fire …” Leon Russell

5. Tight wire 1 • I will be talking about “open.” • Climate is a political issue, and there is a lot of talk about the “openness” of the climate community. There is, for example, discussion about how to make IPCC assessments more open. • I am not, here, talking about the political issues and a political flavor of inclusivity and transparency.

6. Another interesting problem (motivator 2) • I want to protect New Orleans from a Category 5 hurricane, that occurs during a 100 year flood, in 2075. • Some key things I need to know • What is sea level rise? • What is land subsidence? • How has the coast been engineered? • What do I need to protect? • How much money do I have?

7. Think about these motivating problems • They make sense. • For less than $200.00 an hour someone will answer them for you. • What knowledge base will they use? • What knowledge base “should” they use? • There is stunning complexity • Built environment • Land-use policy • Environmental engineering projects • Wealth • Local environment • Climate change • There exists a substantial knowledge base across many disciplines

8. Two paths to an answerPath 1 • I call a national lab and say I have an interesting problem. I develop a relationship with a scientist who agrees it is interesting. The scientist takes the idea forward and tries to convince colleagues that it is a scientifically interesting problem. Management reluctantly agrees that scientist can pursue the problem as a side interest, if scientist can find some funding. But no “special” experiments will be run. • Sponsor has funding: How does lab accept funding from Dubai Industries? • Sponsor does not have funding: Write proposals

9. Two paths to an answerPath 2 • Find a smart consultant who has a copy of • Ecological Climatology, Gordon Bonan • Use MAGICC/SCENGEN: It has been widely used and will have a branded level of credibility. • (http://www.cgd.ucar.edu/cas/wigley/magicc/ ) • Augment MAGICC/SCENGEN analysis with localized information from Climate Wizard • http://www.climatewizard.org/

10. Resolution • Which solution path is more likely?

11. Consider the West African Desalinization Motivator • The question makes sense? • Regional scale alteration of the land-surface to address a fundamental issue of a consuming population. • Surely will have climate consequences outside of the immediate region • Global? • Many similar scale energy projects and water engineering projects are being discussed • What is the climate impact? • Positive and negative • Risk and liability

12. Doesn’t this make sense? • Access to a validated climate model with a configurable land-surface to perform climate impact assessments. • Offers a path to direct evaluation of the questions being posed. • Access to expert interpretation and translation.

13. Let’s think about weather for some moments How often do we ask, I want to change the surface of the Earth or the composition of the atmosphere to see how it would change the weather forecast? Access here mostly scientists and software engineers. From a prediction point of view, here is the product. What goes in here is mostly settled.

14. Same approach for climate prediction and projection? Maybe … Surface will change appreciably over course of projection. Composition will change. We might decide to change the surface or composition to impact the climate? Or need to know the climate impact of major changes? Access here is needed to answer these questions. There may not be a scientist gatekeeper. From a prediction point of view, here is the product. What goes in here is not settled.

15. Uncertainty: Weather and Climate • Weather • Ability to quantify uncertainty based on many forecasts and many instantiations of observed state • Uncertainties related to known model – observation shortcomings • Climate • Limited ability for quantitative analysis of forecasts • Modes of variability not all known or modeled (atm-ocn-lnd-ice-life) • Forcing is changing • Dependent on human activity and policy • Processes not included What is the meaning of a climate projection? Scientists perception of useful. Users perception of usability.

16. Users of Climate Projections Scientific Investigation Model Projections Applications Some would like to think of a nice linear path like this. Perhaps would like to think scientific investigation is more or less done, and that we can hand this off to application’s experts. Perhaps create a separation between research and operations. But we have made this mistake, a dichotomy between research and operations, many times before, and we are not required to do it again.

17. Perhaps a more realistic flow nM, nS, nDC NM “product” Scientific Investigation Model Projections “uncertainty” NS “product” Tailoring 1 (e.g. downscaling) Applications “uncertainty” NT1 NA “product” The Point: Enormous complexity. NA >> NM, and NM is unmanageable. Tailoring 2  n (e.g. downscaling) “uncertainty” NTn

18. Fundamental Flaws in this Analysis • There is implied, here, a push of information from climate models to applications. • But this is naïve, • And the numbers don’t work. • Too large, Too slow • There is, still, implied a barrier between research and applications, with research limited to, perhaps, “climate science.” • There is implied a one-way communication of information, from climate-knowledge providers to climate-knowledge customers. • There is implied a hierarchical structure, with one action leading to the next action down a definable decision tree.

19. Re-framing • We have many of the ingredients that are needed to provide both useful and usable knowledge-based climate information. • We have some successful experiences. • To scale these successful experiences, to utilize these ingredients more effectively, to build that which is missing, we must re-frame how we view the end-to-end system necessary for climate-change problem solving.

20. An attempt at re-framing (1) • Push of climate information: There is great intellectual capital in the applications community, and we need to provide foundational climate knowledge in a way that this community can co-evolve with pulls on the knowledge base and incorporation of communities expertise and knowledge back into the knowledge base. We need to co-generate solutions. • Complexity: In a traditional institutional manner, we cannot manage, control or even provide the knowledge. We need to strive for self-organizing, self-correcting communities that form structures and advance from an experiential base.

21. An attempt at re-framing (2) • Implied one-way communication: There must be equivalent communication from the application side back to the climate-science side. Shared problem-solving language needs to be developed. • Hierarchical Structure: The relation between science-based knowledge and it’s use in applications is complex - and it changes. It is strongly influenced by externalities that render the role of climate information as subordinate. • Rather than thinking of hierarchical relations, perhaps, biological relations are more useful, objects that interact in different ways at different times for different purposes (this challenges the org chart). • Attention to interactions and interfaces

22. Open Climate • The importance of utilizing the information provided by climate projections in concert with the complexity, including the breadth, depth, and distribution of intellectual resources, frames the need for open community approaches that allow the emergence of solution paths and development of tools to accelerate and improve our ability to use climate projections to plan for climate change.

23. Open Communities, Open Source • An attempt to define a little • Open source means different things to different people. • To some it means that code is available • To some it means a certain type of license • In this case it represents a culture

24. Open Communities, Open Source, Open Innovation • “The open source model includes the concept of concurrent yet different agendas and differing approaches in production, in contrast with more centralized models of development such as those typically used in commercial software companies.A main principle and practice of open source software development is peer production by bartering and collaboration, with the end-product, source-material, "blueprints" and documentation available at no cost to the public” (Wikipedia, 20101117) The Cathedral and the Bazaar

25. Focus on climate models

26. The problems we face require tailoring here. nM, nS, nDC NM “product” Scientific Investigation Model Projections “uncertainty” NS “product” Tailoring 1 (e.g. downscaling) Applications “uncertainty” NT1 NA “product” The Point: Enormous complexity. NA >> NM, and NM is unmanageable. Tailoring 2  n (e.g. downscaling) “uncertainty” NTn

27. Tight wire 2 • Isn’t the climate community the archetype for community software? Haven’t we done that problem? • i.e. Community Earth Science Model • Yes, but …. Paul Edwards: A Vast Machine An excellent history of weather and climate as prototype community.

28. Tight wire 2 • Yes, but …. • This form of community evolved when the problem was smaller and resources more centralized. • Models for the community • Models with community participation • Stunningly successful • Still, fundamentally, institutional • Institutional and cultural inertia Paul Edwards: A Vast Machine An excellent history of weather and climate as prototype community.

29. There is a need • We have made the case that the surface of the planet will warm, ice will melt, sea level will rise, and the weather will change. • We cannot, then say, wait until the models are “good enough” by our unknown standards, then we will provide numbers for your use. • The role of institutions and gate keeping must change.

30. What stands in the way? • From Free and Open Source Software for Geospatial (FOSS4G) Conference: • “Professor (Andy) Pitman made an impassioned plea for more involvement by open source developers in climate models …” (link)  open source is not part of the climate modeling culture. • Something special about climate model software? • Easterbrook on Climate Model Software

31. What does stand in the way • Culture and History • Belief that climate software is special? • Misunderstanding of how “science” fits into software development and problem solving. • Good software • Transparent tuning and model validation • Governance of communities • How are decisions made? • …

32. A moment with tuning and validation • When talking with scientists, we always evolve or devolve to a conversation of model validation and tuning. That this is impossible without experts and institutional control. This comes off as a, de facto necessary, arcane process that cannot be explained, much less codified. • Is this true? • If yes … then think of the implications of a “yes” answer.

33. Addressing Validation and Uncertainty Description • Deconstruct the problem of validation • Monitoring, Quality Assessment, Forecasting, Systems Validation, Scientific Validation • Write a validation plan, including how to determine when a model is validated • Separate development and validation • Make it scientifically robust • Deconstruct uncertainty • Describe uncertainty • Relate uncertainty to validation • Relate uncertainty and validation to next development cycle.

34. In the end • Development of open innovation, open source, open … is critical to accelerate, to improve, and to develop the use of state-of-the-art climate information. • If this is not done, then the scientific investigation will be marginalized. • There are many successes and assets, some of which have been moving towards open communities. Often, still, anchored in the community of scientists with the idea of providing “science.” • This is too restrictive. • Can models be built by communities rather than for the community? • Cultural transformation • Have I been pushed of the tight wire?

35. Some Basic References • Intergovernmental Panel on Climate Change • IPCC (2007) Working Group 1: Summary for Policy Makers • Spencer Weart: The Discovery of Global Warming • Paul Edwards: A Vast Machine • Rood • Rood Climate Change Class • Reference list from course • Rood Blog Data Base