Downscaling for the World of Water

Downscaling for the World of Water

NCPP WorkshopQuantitative Evaluation of Downscaling

12-16 August, 2013Boulder, CO

E.P. MaurerSanta Clara University, Civil Engineering Dept.

Extending hydrology to include climate change seems easy…

Extending hydrology to include climate change seems easy…

Estimating Climate Impacts to Water

Estimating Climate Impacts to Water

1. GHG Emissions Scenario

Adapted from Cayan and Knowles, SCRIPPS/USGS, 2003

2. Global Climate Model4. Land surface

(Hydrology) Model

3. “Downscaling”

5. Operations/impac

ts Models

Downscaling: bringing global signals to regional scale

Downscaling: bringing global signals to regional scale

• GCM scale and processes at too coarse a scale

• Resolved by:- Bias Correction- Spatial Downscaling

Figure: Wilks, 1995

IPCC CMIP3 GCM SimulationsIPCC CMIP3 GCM Simulations

20th century through 2100 and beyond >20 GCMs Multiple Future Emissions Scenarios

http://www-pcmdi.llnl.gov/

Impact Probabilities for PlanningImpact Probabilities for Planning

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Point at:120ºW, 38ºN

2/3 chance that loss will be at least 40% by mid century, 70% by end of century

•Combine many future scenarios, models, since we don’t know which path we’ll follow (22 futures here)

•Choose appropriate level of risk

Incorporating probabilistic projections into water planning

Incorporating probabilistic projections into water planning

• Overdesign for present• Can represent declining return periods for

extreme events

Mailhot and Sophie Duchesne, JWRPM, 2010

Das et al, 2012

Demand for downscaled dataDemand for downscaled data

Monthly downscaled dataMonthly downscaled data

• PCMDI CMIP3 archive of global projections

• 16 GCMs, 3 Emissions• 112 GCM runs• Allows quick analysis of

multi-model ensembles• gdo4.ucllnl.org/

downscaled_cmip3_projections

Use of U.S. Data ArchiveUse of U.S. Data Archive

• Thousands of users downloaded >20 TB of data• Uses for Research (R), Management & Planning

(MP), Education (E)

Downscaling for Extreme EventsDownscaling for Extreme Events

• Some impacts due to changes at short time scales– Heat waves– Flood events

• Daily GCM output limited for CMIP3, more plentiful for CMIP5

• Downscaling adapted for modeling extremes

Archive expansionArchive expansion

• Daily downscaled data (BCCA)

• Hydrology model output

• CMIP5

What was missing from the archive?What was missing from the archive?

Online Analysis and Download withhttp://ClimateWizard.org

Online Analysis and Download withhttp://ClimateWizard.org

• Global and US data sets• Country and US state

boundaries defined• Spatial and time series

analysis• Ensemble quick view• Upload of custom shapefiles

Girvetz et al., PLoS, 2009

http://ClimateKnowledgePortal.ClimateWizard.org

http://ClimateKnowledgePortal.ClimateWizard.org

Information Overload (practitioner’s dilemma)Information Overload

(practitioner’s dilemma)Little guidance in selection of:

EmissionsGCMsDownscaling methods

Hundreds of downscaled GCM runsMany impacts studies cannot use all of them

How much information is really useful?

Selecting 4 Specific GCM RunsSelecting 4 Specific GCM Runs

Bivariate probability plot shows correlation between T, P

Identify Change Range: 10 to 90 %-tile T, P

Select bounds based on:• risk attitude• interest in breadth of

changes• number of simulations

desired

Brekke et al., 2009

Selecting GCMs for Impact StudiesSelecting GCMs for Impact Studies• Ensemble mean provides better skill• Little advantage to weighting GCMs according to skill• Most important to have “ensembles of runs with enough

realizations to reduce the effects of natural internal climate variability” [Pierce et al., 2009]

• Maybe 10-14 GCMs is enough?

Brekke et al., 2008Gleckler, Taylor, and Doutriaux, Journal of Geophysical Research (2008) as adapted by B. Santer

Discard CMIP3 in favor of CMIP5?Discard CMIP3 in favor of CMIP5?

• Ensemble average changes comparable• RCP8.5 and SRES A2 comparable

Is BCSD method valid?Is BCSD method valid?• At each grid cell for “training” period,

develop monthly CDFs of P, T for– GCM– Observations (aggregated to GCM scale)– Obs are from Maurer et al. [2002]

Wood et al., BAMS 2006

• Use quantile mapping to ensure monthly statistics (at GCM scale) match obs

• Apply same quantile mapping to “projected” period

Are biases time-invariant?Are biases time-invariant?

Biases vary in time, space, at quantiles

R index: compares size of bias variability to average GCM bias

Time-invariant enough…Time-invariant enough…• On average, time-invariant

enough where bias correction works

• But for small ensembles maybe not (locations where biases vary with time differ for GCMs)

Bias correction changes trends!Bias correction changes trends!

• Changes in mean annual precipitation: 1970-1999 to 2040-2069

• Similar effect in CMIP3 and CMIP5

• Some more intense wettening in Rockies for CMIP5

Raw GCM Bias Corrected

What happens?What happens?

Contrived Example (Precipitation): •Gamma distributions

•asymmetry•Obs: mean=30•GCM Historic has −30% bias in SD

•GCM future has +40% change in mean

What is effect of trend/shift?What is effect of trend/shift?Quantile mapping changes shift in mean

+40% becomes +56%-40% becomes -39%5% decrease changes to ~2% increase

But is changing the trend bad?But is changing the trend bad?

• estimated change in precip from 1916-1945 and 1976-2005.

• Where index is >1 BC worsens• Ensemble avg shows trend

modification not consistently bad. Ensemble Avg

Challenges in providing downscaled data for water-resources communityChallenges in providing downscaled data for water-resources community• With >500 projections at 1 archive (of many),

need to provide users with better information for:– Selecting concentration pathways– Assembling an ensemble of GCMs– Using data downscaled appropriately– Interpreting results with uncertainty

• Despite known issues with every step, useful information can produced by this process.

• Bottom line for data users: Use an ensemble. Don’t worry. Revisions happen.

Global BCSDGlobal BCSD• Similar to US archive, but ½-degree• Publicly available since 2009• Captures variability among GCMs• www.engr.scu.edu/~emaurer/global_data/• Data accessed by users in all 50 States

and 99 countries (11 months only)

Source: Girvetz et al, PloS, 2009

A1B Scenario

Visits from 3 Nov 2011 to 27 Sep 2012