Defining Climate Change Scenarios for Planning

Transcription

1 Defining Climate Change Scenarios for Planning Levi Brekke (Reclamation, Technical Service Center) Front Range Group meeting, Denver Water, Denver, CO, 20 March 2009

2 Framework for relating Climate Change Information to Planning 1) Survey Global Climate Projections, downscaled for the study region 2.a) Decide whether/how to cull the information. 2.b) Decide how retained information will be used. 3) Define supplies, demands, and/or operating constraints in terms of climate info from 2.b; characterize uncertainties Natural Systems Response Social Systems Response 4) Assess operations and dependent resource responses; characterize uncertainties

3 1) Information Survey Global Climate Projections Generated through World Climate Research Programme s Coupled Model Intercomparison Project Phase 3 Documented in IPCC AR4 (2007) Vary with choice of: global climate model (GCM), future emissions, & initial condition (end-of-20 th century) 100+ contemporary projections

4 1) Information Survey Bias-Corrected, Downscaled Climate Projections Developers Santa Clara University Reclamation LLNL Funding Reclamation, DOE NETL Contents 112 projections, produced by 16 GCMs, 3 emissions paths, 1 or more runs per combo Variables: T air and P, Spatial resolution & extent: 1/8, contiguous U.S. Time resolution & extent: monthly,

5 2.a) Do we cull the Info? How? In other words, Should all available downscaled projections be considered as equally plausible, or Should some of these projections be regarded as more credible? If the latter, how would we rate credibility?

6 Recent Reclamation work (Brekke et al. 2008) Philosophy: skill in simulating past implies credibility in projecting future GCM Data from CMIP3: Surveyed over study region (e.g., Northern CA in this study) 20 th Century Ensemble for climate model skill assessment 59 20C3M simulations, 17 models, multiple runs 21 st Century Ensemble for planning, affected by skill results 75 projections, same models, A2 and B1 paths, multiple runs Objectives: Fit projection distributions to change in 30-year climate norms Temperature (T), Precipitation (P), and joint {T, P}; mean-annual Assess with and without considering climate model skill Brekke, L.D., M.D. Dettinger, E.P. Maurer, M. Anderson, Significance of model credibility in estimating climate projection distributions for regional hydroclimatological risk assessments, Climatic Change, 89(3-4),

7 Take-away Messages Relative skill among current GCMs tends to be similar when enough metrics are considered (Brekke et al. 2008). Gleckler et al. JGR 2008, Reichler et al. BAMS 2008 showed similar results in globally focused studies. Even when half of available GCMs and their projections are culled, projection distributions don t necessarily change significantly. (Brekke et al. 2008)

8 2.b) How do we use the retained climate information? Decide how to represent projection uncertainty Need to consider ensemble of projections Decide what type of climate change you wish to reflect in your study Period Statistical change in the mean Period Statistical change in the distribution Period Frequencies change in lower frequencies drought spells/severities, interarrival times Period Frequencies change in higher frequencies flood/storm possibilities, sub-monthly variability

9 2.b) How do we use the retained climate information? Decide what type of climate change you wish to reflect in your study (continued) Consider two options: Delta method, w/ bracketing: Change in mean, range Ensemble Transient: Change in lower frequencies Approach depends on study purpose and goals E.g., Delta approach w/ bracketing may be useful for sensitivity analysis, response to a range of plausible climates E.g., transient approach may be useful to support adaptation planning relative to a time-developing climate Today s focus: Delta w/ bracketing

10 Projection Selection Factors for Bracketing purposes 1. Periods: historical and future (relates to our study s look-ahead ) 2. Metrics: for assessing change (relates to climate aspects relevant to our study ) 3. Location: for assessing spread (relevant to our study s geography and choice #2) 4. Change Range: portion of spread we wish to represent (depends on study goals )

11 Selection Factors Example #2: Hypothetical Application Lugert, OK 1. Periods: , (let s say your study is focused on conditions ~2040) 2. Metrics: Period Mean-Annual T air & P (let s say you re focused on mean-annual supplies) 3. Location: Lugert, OK (near Lake Altus) 4. Change Range: 10 to 90 %-tile T air, P (desire to represent broad set of possibilities)

12 Result: chosen projections spanning 10/90%-tile range of possible climate changes for the study

13 Bracketing Approach: Pros/Cons Pros: Easy way to explore system response Easy way to build analytical familiarity Cons: Less ideal for adaptation planning, where climate change timing matters Climate Change diagnosis: for T, more clear for P, less clear Role of GCM initial conditions? Where is multi-decadal variability in this diagnosis?

14 On Factor #1: Period Duration How much time do we need to define climate? period mean period distribution period lower frequencies period higher frequencies WMO traditionally uses 30-year period to define climate normals. What about multi-decadal variability?

15 Recall info on GCM initial conditions CMIP3 Stage 1: pre-industrial spin-up Continue until climate statistics are determined to be stable CMIP3 Stage 2: initiate 20 th century forcings Initial state (oceans) taken from end of Stage 1 CMIP3 Stage 3: initiate 21 st century projection Given chosen emissions path Initial state (oceans) taken from end of Stage 2 Take-away points: (1) Regional P assessments depend on CMIP3, where initial conditions varied. (2) Regional P depends (supposedly) on multi-decadal climatic influences. (3) The phasing of these influences matters and challenges our interpretation of P change when we implement the Bracketing Approach and diagnose change as 30-year mean-annual P.

16 Delta Application: (1) change in period mean T and P, (2) bracket spread, (3) relate to impacts Question: Are these possibilities climate change only or blend of climate change and sampled interdecadal variability? Vancouver

17 check observed multi-decadal variability Red Line: Obs. Annual P Blue Line: Obs. moving 25- Year Mean Annual P Western Washington (WestMap data): Given 25 year periods, center-separated 40 years, Deltas range from -2% to +11% Southern Idaho (WestMap data): Given 25 year periods, center-separated 40 years, Deltas range from -3% to +11%

18 Example #3 Delta Application: (1) compute change in period mean T and P, (2) identify 10/90%-tile spread 10%-tile P Delta: -3% 90%-tile P Delta: +11% Similar to historical range! Revisit the Question: Are these possibilities climate change only or blend of climate change and sampled interdecadal variability? (given that projections could be out of phase )

19 Remedy on the Diagnosis issue Option 1: Drop bracket, focus on central tendency Use 50%tile s rather than bracket of 10/90%tile s Pro: easy fix Con: still involves casting these Deltas as climate change only, but we re on more solid ground with this statement Con: communication risk we wouldn t be quantifying any impacts uncertainty (only impacts with centrally expected climate change) need to manage stakeholder interpretation of results Option 2: Use Smaller bracket Maybe use 25/75%tile s rather than 10/90%tile s Pro: easy fix Con: choosing any range is subjective and still requires statement that Deltas are climate change only

20 Selection Factors Example #2: Hypothetical Application Lugert, OK 1. Periods: , (let s say your study is focused on conditions ~2040) 2. Metrics: Period Mean-Annual T air & P (let s say you re focused on mean-annual supplies) 3. Location: Lugert, OK (near Lake Altus) 4. Change Range: 25 to 75 %-tile T air, P (desire to represent broad set of possibilities)

21 Result: chosen projections spanning 25/75%-tile range of possible climate changes for the study

22 Recommended Reading: Modified Delta Method CH2M-Hill (Armin Munevar), August 2008 Climate Change Study Report on Evaluation Methods and Climate Scenarios, Section 7.5 TX Lower Colorado River Auth., San Antonio Water System Summary of the Modification Old: Delta = shift in period-mean Modified: Delta = shift in period CDF Requires quantile mapping Similar to our climate projection bias-correction scheme That scheme was (observed historical, GCM historical). This scheme is (GCM historical, GCM future).

23 Extras

24 Selection Factors Example #1: Central Valley Project OCAP BA - CC Sensitivity Analysis 1. Periods: , (consultation horizon is through 2030) 2. Metrics: Period Mean-Annual T air & P (CVP performance trends with mean-annual supply) 3. Location: Above Folsom (interested in water supply changes; central to Sierra Nev. tribs) 4. Change Range: 10 to 90 %-tile T air, P (desire to represent broad set of possibilities)

25 Implementation Example #2: Hypothetical Application to Lugert, OK Step 1) Go to website, get projections 1a. From website, download monthly T air & P time series. 1b. Compute historical and future period climate metrics for every projection. 1c. Compute historical-to-future period changes in climate metric for every projection.

26 Step 2) Identify rank-threshold T air and P

27 Step 3) Overlay rank-threshold T air and P on scatter paired changes

28 2.b cont.) How do we use retained information? Assume we re still doing Delta w/ Bracketing We ve chosen our climate projections, now we have to relate them to changes in planning assumptions about supplies, demands, and constraints. Focus on Supplies changes for now Assess Runoff Response for given Climate Projection Relate Runoff Response to Supply Assumptions

29 Delta Method for Runoff Analysis Get hydrology model e.g., 1/8 VIC model of our basin Get "Base weather series for hydrology model (observations) e.g., 1/8 daily T, P, and wind speed (U) data needed by VIC We have these data for (Maurer et al. 2002) Create Delta weather series Same duration as Base ( , daily) assuming use of VIC: For monthly Deltas, do adjustment in month-specific portions Daily T climate change = Daily T Historical + T Delta Daily P climate change = Daily P Historical x P Delta Daily U climate change = Daily U Historical Do Simulations (Base, Delta) Apply Results Evaluate runoff impacts (changes in runoff, Delta minus Base) Make Runoff Deltas (Runoff Delta = Runoff climate change / Runoff Historical ) Use Runoff Deltas to scale inflows in reservoir operations model

30 Monthly T and P Deltas from 5 projections: a middle and four 10/90 bracketing projections

31 Monthly T and P Deltas from 5 projections: a middle and four 10/90 bracketing projections

32 Projection Selection Issues with Ensemble Transient approach Details not provided here Projection eligibility for ensemble membership: same as Delta w/ Bracketing Sizing the ensemble: reflect time-evolving spread of ensemble impacts analysis - computational capacity Presumption: stakeholders are used to looking at hydrologic ensembles often not the case!

33 Transient Application: How do projections of temperature & precipitation evolve over my region? Projected Precipitation Example*: Upper Missouri, and 39 A1b climate projections. Projected Temperature * Reclamation 2008 (DRAFT) Comparison of Long-Term Planning Hydrologies based on Different Blends of Instrumental Record, Paleoclimate, and Projected Climate Information

34 How do these climate projections translate into runoff projections? Precipitation Hydrologic Modeling** Model from NWS MBRFC. Requires time-disaggregation of archive monthly T & P into sixhourly T&P (Reclamation 2008). Temperature