Past Applications, Lessons Learned, Current Thinking

1. Past Applications, Lessons Learned, Current Thinking Levi Brekke (Reclamation, Research & Development Office) NCPP Quantitative Evaluation of Downscaling Workshop, Boulder, CO Panel “Panel discussion : Using downscaled data in the real world: Sharing experiences: Part II”, 15 August 2013

2. Traditional climate context in planning I. Choose Climate Context Instrumental Records: observed weather (T and P) and runoff (Q) II. Relate to Planning Assumptions Supply Variability Demand Variability Operating Constraints III. Conduct Planning Evaluations System Analysis, Evaluate Study Questions (related to Resource Management Objectives)

3. II. Climate Information Providers: “Here’s the info… use it wisely.” I. Decision-Makers: “Keep it simple.” III. Technical Practitioners (Ushers): “Keep it Manageable.”

4. Flow of Information:General View 1) Survey Future Climate Information over the study region 3) Assess climate change impacts on planning assumptions (e.g., supplies, demands, and or water management constraints). Analyses of Various Responses 2.a) Decide whether to cull information, and how… 4) Assess operations and dependent resource responses; characterize uncertainties 2.b) Decide how to use retained information…

5. Box 2.a) Climate Model Contest … everyone wants it, utility unclear Focusing on CA, Brekke et al. (2008) considered “historical” simulations from 17 GCMs, and found similar skill when enough metrics were considered. Focusing globally, Gleckler et al. 2008 and Reichler et al. 2008 found similar results. Focusing on CA, projection distributions didn’t change much when the GCM-skill assessment (Brekke et al. 2008) was used to reduce the set of 17 GCMs to a “better” set of 9 GCMs. Santer et al. PNAS 2009 – results from a global water vapor detection and attribution (D&A) study were largely insensitive to skill-based model weighting. Pierce et al. PNAS 2009 – results from western U.S. D&A study were more sensitive to ensemble size than skill-based model weighting.

6. Box 2.b) Two Method Classes (generally speaking) • Period-Change • prevalent among impacts studies • “perturbed historical” at some milestone future • Transient • time-evolving view, from past to future • prevalent in the climate science community (“climate projections”)

7. PNW Example: Three Fed agencies (BPA, USACE, Reclamation) adopting consensus scenarios 1) Used UW CIG HB2860 scenarios (Period-Change + Transient) 2) Selected smaller set of both scenario types Period-Change type was of most interest. Goal was to select set that spans the rest: LW = less warming, MW = more warming D = drier W = wetter C = central MC = minimal change

8. Logical Process, but there were surprises Scenarios selected for big-basin change… sub-basin changes didn’t always reflect the big-basin scenarios (e.g., Upper Snake is wetter if 5 of 6 scenarios)

9. Projection-specific approach: individual projections inform change definitions (e.g., black dot choices) Ensemble-informed approach: projections are grouped and their pooled information informs definitions Reclamation 2010 Oklahoma Reservoirs Yield Study

10. … another concern comes from portrayal of monthly impacts … projection-specific approaches can lead to serial monthly impacts that seem questionable … ensemble-informed approaches emphasize “consensus” changes from projections Two projection-specific methods One ensemble-informed method Reclamation 2010 Oklahoma Reservoirs Yield Study

11. Scoping with thought towards decision-making?

12. Most info. generation approaches have been science-centric. Science Application Decision-Making

13. Would we select the same approach using a decision-centric view? Science Application Decision-Making

14. We can approach from both views… Science-centric (What’s credible?) Decision-Support Information Decision-Centric (What’s relevant?)

15. Another way of looking at this… System Sensitivities to Climate Changes Global Climate Models’ Simulation Qualities What’s Relevant? What’s Reliable? Practical limitations? Tool Fitness, Project Resources What’s Applicable.

16. Scoping Questions… What’s relevant? What’s reliable? Practical limitations? What are the study decisions and level of interest in climate uncertainties? What types of regional future climate and hydrologic datasets are available? What modeling steps are required to assess system metrics? Which climate and/or hydrologic changes are projected well? What system metrics influence the study decisions? How does climate change influence each modeling step? What future climate/hydrologic assumptions should still be based on history? Which climate change influences are practical to represent? Which types of climate changes influence these metrics the most?

17. FY13-14 Project: Evaluating the Relevance, Reliability, and Applicability of CMIP5 Climate Projections for Water Resources and Environmental Planning • Goal • develop & demonstrate a framework for evaluating information relevance & reliability to guide judgment of applicability • Approach • Broadband quality evaluation of CMIP5 (what’s reliable?); serve results on web • System sensitivity analyses (what’s relevant?) • Applicability Pilots (observe process, characterize framework for use elsewhere) • Collaborators (& POCs) • Reclamation (Ian Ferguson, iferguson@usbr.gov) • USACE (Jeffrey Arnold) • NOAA ESRL (Mike Alexander) • NOAA CIRES (Jamie Scott)

18. Extras

19. Two Method Classes have emerged… • Period-Change • prevalent among impacts studies • “perturbed historical” at some milestone future • Transient • time-evolving view, from past to future • prevalent in the climate science community (“climate projections”)

20. Period-Change: Overview • Historical climate variability sequence is retained (space and time) • “Climate Change” Scenarios are defined for perturbing historical, where a change is diagnosed from a historical period to a future period • Studies typically feature an Historical scenario and multiple climate change scenarios in order to reveal impacts uncertainty • Several methods are available to define scenarios, differing by: • time (e.g., change in means, change in distributions) • space (e.g., change in regional condition, or change in spatilly disaggregated conditions), and • amount of information (e.g., single climate projection, or many projections)

21. Period-Change: Pros and Cons • Pros: • Retains familiar historical variability patterns • Simple frame for exploring system sensitivity • Permits “cautious” sampling of temporal aspects from climate projections (e.g., can be simple like change in annual mean, or complex like change in monthly distribution) • Cons: • Less ideal for adaptation planning; climate change timing matters • Diagnosing period “Climate Change” is not obvious (more of a problem for DP than for DT) • (when single-projections inform climate change scenarios) month-to-month changes may seem disorderly or noisy

22. Transient: Overview • Historical climate variability sequence not retained (but distribution may be retained through climate projection bias-correction…) • “Climate” Projections are selected to define an evolving envelope of climate possibility, representing simulated past to projected future • Monthly or daily time series projections typically used • Climate Projections may be developed using various methods, e.g.: • Time series outputs from a GCM simulation (or a GCM-RCM simulation) • … bias-corrected and spatially downscaled translations of these outputs • … stochastically resampled (resequenced) versions of these outputs, reflecting a different frequencies reference (observations, paleoproxies) • Studies need to feature a large ensemble of climate projections to adequately portray an envelope of climate possibility through time

23. Transient: Pros and Cons • Pros: • Avoids challenges of “Climate Change” diagnosis • Not discussed, but a key issue is “multi-decadal varaibility” in projections • Supports “master planning” for CC adaptation • schedule of adapations through time, including project triggers • Cons: • Projection historical sequences differ from experience • Requires “aggressive” sampling of temporal information from climate projections (frequencies vary by member, and may be questionable) • Information is more complex • Requires use of many projections, challenging analytical capacities, and requiring probabilistic discussion of results, evolving through time… requires learning phase

24. Legacy climate context for planning assumptions in water resources studies I. Choose Climate Context Instrumental Records: observed weather (T and P) and runoff (Q) II. Relate to Planning Assumptions Supply Variability Demand Variability Operating Constraints III. Conduct Planning Evaluations System Analysis, Evaluate Study Questions (related to Resource Management Objectives)

25. We’ve developed ways to blend climate change information into this context. I. Choose Climate Context Instrumental Records: observed weather (T and P) and runoff (Q) Global Climate Projections: Representing various GCMs, forcing  bias-correction, spatial downscaling e.g., Reclamation 2008, Mid-Pacific Region’s Central Valley Project – Operations Criteria and Plan, Biological Assessment watershed simulation Regional T and P II. Relate to Planning Assumptions Global T and P… Sea Level Rise Runoff Supply Variability Demand Variability Operating Constraints Delta Flow-Salinity Relationship Constraint on Upstream Operations III. Conduct Planning Evaluations Reservoir Operations Regional T Future Operations Portrayal for OCAP BA (flows, storage, deliveries, etc.) …Stream Water Temperature analyses

26. When using projected climate, future climate & hydrology assumptions typically reflect a blend of observed and projected information. I. Choose Climate Context Instrumental Records: observed weather (T and P) and runoff (Q) Global Climate Projections: Representing various GCMs, emissions  bias-correction, spatial downscaling Runoff Magnitudes watershed simulation Regional T and P II. Relate to Planning Assumptions Runoff Supply Variability Demand Variability Operating Constraints Reclamation 2010 Info: Levi Brekke (lbrekke@usbr.gov), Tom Pruitt (tpruitt@usbr.gov) III. Conduct Planning Evaluations System Analysis, Evaluate Study Questions (related to Resource Management Objectives)

27. … future climate & hydrology assumptions can also be based on blend of observed and paleoclimate information. I. Choose Climate Context Paleoclimate Proxies: reconstructed runoff (Q) Instrumental Records: observed weather (T and P) and runoff (Q) Runoff Magnitudes Yeartype Spells statistical modeling II. Relate to Planning Assumptions Runoff Supply Variability Demand Variability Operating Constraints http://www.usbr.gov/lc/region/programs/strategies.html III. Conduct Planning Evaluations System Analysis, Evaluate Study Questions (related to Resource Management Objectives)

28. … we can also possible blend all three. (Reclamation 2009, CRWAS 2011, others) I. Choose Climate Context Paleoclimate Proxies: reconstructed runoff (Q) Instrumental Records: observed weather (T and P) and runoff (Q) Instrumental Records: observed weather (T and P) and runoff (Q) Global Climate Projections: Representing various GCMs, emissions  bias-correction, spatial downscaling Runoff Magnitudes Yeartype Spells statistical modeling watershed simulation Regional T and P II. Relate to Planning Assumptions Runoff Supply Variability Demand Variability Operating Constraints III. Conduct Planning Evaluations http://www.usbr.gov/research/docs/2009_Hydrology-DiffClimateBlends.pdf System Analysis, Evaluate Study Questions (related to Resource Management Objectives)

29. Flow of Information: UW CIG HB 2860 Data … Considered 100+ current projections… 1) Survey Future Climate Information over the study region Made two types of Columbia Basin weather and hydrology 3) Assess climate change impacts on planning assumptions related to water supply and power demands. Hydrologic Simulation, Electricity Demand Modeling 2.a) Decide whether to cull information, and how… Decided to focus on 19 projections… 4) Assess operations response (Reclamation, USACE, and BPA systems & models) 2.b) Decide how to use retained information… http://warm.atmos.washington.edu/2860/ 860/

30. Flow of Information: … used by Fed PNW agencies Considered CIG’s 19 projections… 1) Survey Future Climate Information over the study region Decided to use both types of Columbia Basin weather and hydrology… 3) Assess climate change impacts on planning assumptions related to water supply and power demands. Hydrologic Response and Local Power Demand Response 2.a) Decide whether to cull information, and how… Decided to focus on smaller set… Assessing operations under both types of information… insights for planning applications 4) Assess operations and dependent resource responses; characterize uncertainties 2.b) Decide how to use retained information… http://www.usbr.gov/pn/programs/climatechange/reports/index.html