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Pacific Northwest Hydrologic and Climate Change Scenarios for the 21st Century
A Brief Introduction to New Products and Overview of Downscaling Approaches
Climate Science in the Public Interest
a webinar sponsored by the
Climate Impacts GroupUniversity of Washington
March 24, 20101:00-2:00pm
Webinar Tips
• Mute phones to reduce background noise (*6 or “mute” button). Do not put call on “hold” if your organization has hold music.
• Type questions in the chat box (lower left). We will track the questions and answer as time permits at the end of the presentation.
• Additional opportunities for Q&A:• Post-webinar survey• Email CIG: cig@uw.edu
• Presentation will be posted on the webinar meeting page on the CIG web page (www.cses.uw.edu/cig)
Purpose
• Who is the Climate Impacts Group?
• Part 1: Introduction to the Columbia Basin Climate Change Scenarios Project products and statistical downscaling methods
• Part 2: A broader look at the role of dynamical regional climate modeling in scenario development at the CIG
• Future workshops, webinar opportunities
Collective expertise includes (but is not limited to):
• Statistical and dynamical downscaling of global climate model projections
• Macro and fine scale hydrologic modeling• Water resources impacts assessment• Terrestrial and aquatic ecosystems impacts
assessment • Adaptation planning and outreach
Objectives • Increase regional resilience to climate variability and change
• Produce science useful to (and used by!) the decision making community
An integrated research team studying the impacts of climate variability and climate change in the PNW and western US
The Climate Impacts GroupThe Climate Impacts Group
Part 1
Responding to Evolving Stakeholder Needs for 21st Century Hydrologic Scenarios:An Overview of the Columbia Basin Climate Change Scenarios Project
Alan F. HamletMarketa McGuire Elsner
CSES Climate Impacts GroupDepartment of Civil & Environmental Engineering
University of Washington
Climate Science in the Public Interest
Climate Change Planning Needs are Evolving
Stakeholder requests:1. Address diverse stakeholder planning needs (terrestrial and aquatic
ecosystems, water management, human health, energy, etc.)2. Provide comprehensive coverage over large geographic areas using
consistent methods3. Increase spatial resolution
-- address both large-scale and small-scale planning efforts in a consistent manner
4. Increase temporal resolution-- address changes at daily timescales and assess changes in hydrologic extremes
5. Quantify uncertainties in future projections
The Columbia Basin Climate Change Scenarios Project
This 2-year research project (finalizing Spring 2010) is designed to provide a comprehensive suite of 21st century hydroclimatological scenarios for the Columbia River basin and coastal drainages in OR and WA
Collaborative Partners:•WA State Dept. of Ecology (via HB 2860)•Bonneville Power Administration•Northwest Power and Conservation Council•Oregon Water Resources Department•BC Ministry of the Environment
297 297 streamflowstreamflow
sitessites
• Provide a wide range of products to address multiple stakeholder needs
• Increase spatial and temporal resolution
• Provide a large ensemble of climate scenarios to assess uncertainties
• Address hydrologic extremes (e.g. Q100 and 7Q10)
Project Goals and Objectives
http://www.hydro.washington.edu/2860/
Draft
Overview of
Methods
Primary Meteorological Stations Used to Create Spatial Dataset* (1915-2006)
*1/16 spatial resolution & daily timestep
Climate Impacts Group 2009, WA Assessment, Ch. 1http://cses.washington.edu/cig/res/ia/waccia.shtml
Climate Change Scenarios
Figure shows change compared with 1970 -1999 average
IPCC AR 4 Emissions Scenarios:
A1B Medium High
B1 Low
Projected change in mean annual T
Projected change in mean annual P
Downscaling Relates the “Large” to the “Small”
~200 km(~125 mi)resolution
~5 km(~3 mi)
resolution
Statistical Downscaling Approaches
• Simple, easy to explain “snapshot” of average future conditions• Incorporates realistic historical daily time series and spatial variability• Provides 91 years of historical variability combined with changes in T and P for
each future time frame and emissions scenario• Projections are easily relate to stakeholder knowledge of historical impacts
Composite Delta Method
Strengths:
Limitations:
• Incorporates only average changes in mean monthly T and P, not extremes• Changes are assumed to be the same throughout the region
Statistical Downscaling Approaches
• Incorporates more information from the monthly GCMs, including altered monthly T and P variability in space and time
• Produces a transient (i.e. continually varying) daily time series for 150 years (1950-2098+)
• Simulates rates of change in hydrologic variables• Offers flexible time period of analysis (one run gives all future time periods)
Bias Corrected and Spatially Downscaled (BCSD)
Strengths:
Limitations:• Quality of downscaled realizations is dependent on GCM performance – What
you get out is only as good as what you put in!• Ensemble analysis needed to account for decadal P variability due to relatively
small sample size in future time slices (~30 years)• Daily time series characteristics are not suitable for some kinds of analyses
(e.g. analysis of hydrologic extremes)
Statistical Downscaling Approaches
• Combines the strengths of the Composite Delta and BCSD methods, while avoiding most of the weaknesses of both
• Incorporates more information from the monthly GCMs, including altered spatial patterns of T and P changes
• Incorporates realistic historical daily time series and spatial variability• Provides 91 years of historical variability combined with changes in T and P for
each future time frame and emissions scenario• Arguably the best method for evaluating hydrologic extremes (floods and low
flows)
Hybrid Delta Method
Strengths:
Limitations:• Quality of downscaled realizations is dependent on GCM performance (spatial
patterns only)• Constrained by the time series behavior in the historic record
Available PNW Scenarios
2020s – mean 2010-2039; 2040s – mean 2030-2059; 2080s – mean 2070-2099
Downscaling Approach A1B Emissions Scenario
B1 Emissions Scenario
Hybrid Delta
hadcm cnrm_cm3 ccsm3 echam5 echo_g cgcm3.1_t47 pcm1 miroc_3.2 ipsl_cm4 hadgem1
2020s 10 10
2040s 10 10
2080s 10 10
Transient BCSD
hadcm cnrm_cm ccsm3 echam5 echo_g cgcm3.1_t47 pcm1
1950-2098+ 7 7
Delta Method
composite of 10
2020s 1 12040s 1 12080s 1 1
Snow Model
Schematic of VIC Hydrologic Model• Sophisticated, fully distributed,
physically based hydrologic model• Widely used globally in climate change
applications• 1/16 Degree Resolution
(~5km x 6km or ~ 3mi x 4mi)
General Model Schematic
Sample Results Using Different Statistical
Downscaling Approaches
http://www.hydro.washington.edu/2860/
HydrologicProducts
Draft
Changing Watershed Classifications:Transformation From Snow to Rain
• Based on Composite Delta Method scenarios (multimodel average change in T & P)
• Historical period includes 1916-2006 water years (Oct-Sep)Map: Rob Norheim
Trend (Days per Decade) * 50mm SWE Plotting Threshold
Linear trend for the ECHAM 5 A1B Scenario downscaled using the
transient BCSD Downscaling Method
Trends in Date of Peak SWE
http://www.hydro.washington.edu/2860/products/sites/?site=6092
Snow and Runoff Summary (Cispus River near Randle)
Scenario Ensembles Ensemble Mean Historical Mean
Draft study results (to be finalized Spring 2010) are already being used and evaluated by a wide range of stakeholders including:•USGS•Bonneville Power Administration•U.S. Bureau of Reclamation•U.S. Army Corps of Engineers•U.S. Forest Service•U.S. Fish and Wildlife Service•Boise Aquatic Research Laboratory•National Marine Fisheries Science Center
Who’s Using the Data?
Next steps…
Extending the approach to additional western U.S. watersheds in partnership with:
•US Forest Service•US Fish and Wildlife Service•Boise Aquatic Sciences Lab•Trout Unlimited
Eric Salathé
JISAO Climate Impacts GroupUniversity of Washington
Ruby Leung & Qian Fu PNNLYongxin Zhang CIG, NCARCliff Mass UW
Part 2
Regional Climate Modeling for Impacts Applications in the US Pacific Northwest
Downscaling and Regional Climate ModelingStatistical Downscaling
•Maps the climate change signal from a global model onto the observed patterns•Computationally efficient•Can tune to observed climate•Preserves uncertainty in Global Climate Models•Cannot represent fine-scale patterns of climate change
Regional Climate Models (“Dynamic Downscaling”)
•Extend the physical modeling of the climate system to finer spatial scales•Computationally demanding•Cannot correct bias in global model•Adds to uncertainty from Global Climate Models
12-50 km or~7-32 mi
Global ClimateModel
6-hourlyMonthly
100-200 km
6 km or~3.7 mi
150-km GCM
High resolution is needed for regional studies
Washington
Oregon
IdahoC
asca
de R
ange
Rocky M
ountains
Snake Plain
Olympics
Global models typically have 100-200 km (62-124 mi.) resolution
•Cannot distinguish Eastern WA from Western WA
•No Cascades
•No land cover differences
150-km GCM
Washington
Oregon
IdahoC
asca
de R
ange
Rocky M
ountains
Snake Plain
Olympics
Regional models typically have 12-50 km (7-32 mi) resolution
• 12 km WRF at UW/CIG
• Can represent major topographic features
• Can simulate small extreme weather systems
• Represent land surface effects at local scales
12-km WRF
High resolution is needed for regional studies, cont’d
Why do we want to simulate the regional climate?
Process studies Topographic effects on temperature and
precipitation Extreme weather Attribution of observed climate change Land-atmosphere interactions
Climate Impacts Applications Streamflow and flood statistics Water supply Ecosystems Human health Air Quality
Regional Climate Modeling at CIG
WRF Model (NOAH LSM) Resolution: 12 to 36 km
(~7- 32 mi)
ECHAM5 forcing
CCSM3 forcing
(A1B and A2 scenarios)
HadRM Resolution: 25 km
(~15 mi)
HadCM3 forcing
Statistical Downscaling CCSM3
Fall difference between 1990s and 2040s
Low spatial detail for climate change signal
°C %
Temperature (°C) Precipitation (% change)
WRF “Dynamic Downscaling” CCSM3
Fall difference between 1990s and 2040s
High spatial detail for climate change signal
%
Temperature (°C) Precipitation (% change)
°C
Regional Effects andApplications
Regional Effects andApplications
Land-Atmosphere Interactions
Snow Cover Change Temperature Change
Change in winter temperature (degrees C)Change in fraction of days with snow cover
Wintertime Change from 1990s to 2050s
Salathé et al. 2008
Land-Atmosphere Interactions
Wintertime Change from 1990s to 2050s
Salathé et al 2008
Solar Radiation
Example: Endangered Species
Pika
The American Pika depends on cold alpine climate
Loss of snow accelerates warming
Andrea J Ray, et al. Rapid-Response Climate Assessment to Support the FWS Status Review of the American Pika. U.S. Fish and Wildlife Service and the NOAA Earth System Research Laboratory
Projected Changes to 2040s in Spring Pacific Northwest Snowpack
Extreme Precipitation
Change from 1970-2000 to 2030-2060 in the percentage of total precipitation occurring when daily precipitation exceeds the 20th century 95th percentile
•Larger increase on windward slopes of Cascades, Columbia basin•Smaller increase or decrease along Cascade crest
Example: Stormwater Management
Rosenberg, E.A. et al, 2010. Climatic Change, in press.
After Bias Correction, WRF results were used to compute flow in Thornton Creek, Seattle, to project storm-water impacts
(David Hartley, Northwest Hydraulic Consultants)
Percent time exceedance of 50% of the peak 2-year flow for the period 1970–2000 using HSPF model with Bias-Corrected WRF simulations
Output Data for Applications Models
WRF:
Output provides the full hourly 3-D meteorological data needed as input for many applications models
•Air quality•Puget Sound circulation
Air Quality Modeling with WRF
WRF Meteorology
Air Quality Air Quality ModelModel
(CMAQ)(CMAQ)
AnthropogenicBiogenic
Emissions
Human Health Risk Assessment
Example: Summertime Ozone Mortality
King
1997-2006
King
2045-2055
Spokane
1997-2006
Spokane
2045-2055
Population 1,758,260 2,629,160 424,636 712,617
O3 20.7 26.5 35.5 41.6
Daily Mortality rate
0.026 0.033 0.058 0.068
Deaths 69 132 37 74
Jackson, JE. et al, 2010. Climatic Change, in press.
Ozone projections from WRF-CMAQ and population projections are used to assess the future risk of mortality due to ozone exposure
Ongoing and Future Directions
Ongoing and Future Directions
Model Validation and Improvement
1. RCMs add significant local gradients to climate projections
2. If the magnitude or placement of gradients is wrong, this can degrade the climate projections
• Model Validation‒ Both “forecast mode” and “climate mode”‒ Compare to station observations‒ Compare to satellite observations
• Model Improvement‒ Modified snow model‒ Optimizing model for accuracy and efficiency
WRF Ensemble Climate Projections
• To understand uncertainties in regional climate prediction
• Follows similar modeling and statistical analysis used in UW Ensemble Prediction System
• Goal: Use a large set of simulations to project probabilities for future changes
• WRF ensemble simulations underway now
– 4 members completed
– 12 km
– 100-year or 30-year duration
climateprediction.net Oxford University
climateprediction.net •Oxford University and Hadley Centre•Global Climate Experiments•Run by volunteers on personal computer
Regional climateprediction.net •Western US project at OCCRI and CIG•Super ensemble of regional simulations•Integrated global and regional modelling system•25 km grid•Monthly-mean and statistical results•Beta version soon•Results over the next year
The North American Regional Climate Change Assessment Program (NARCCAP)
• Exploration of multiple uncertainties in regional model and global climate model regional projections.
• Development of multiple 50-km regional climate scenarios for use in impacts assessments.
• Evaluation of regional model performance over North America.
www.narccap.ucar.edu
50-km Grid
GFDL CGCM3 HADCM3 CCSM
MM5 X X1
RegCM X1** X
CRCM X1** X
HADRM X X1
RSM X1 X
WRF X X1
Red = run completed
SummarySummary
Regional models can simulate unique local responses to climate change and improve on global models and statistical downscaling
High spatial resolution is necessary to provide added value
Many applications require information only available from a regional climate model
Some applications require additional bias correction or downscaling
Errors in regional models can amplify uncertaintyModel validation is essential
Large uncertainties remain due to Interannual variability Global climate projections Regional climate model differences
An ensemble regional modeling approach is required
More to Come
In the coming year, the CIG plans to host:
• Workshop(s) and/or webinars on the Columbia Basin Climate Change Scenarios Project
• A series of thematic webinars on global and regional climate variability and change, including webinars on specific sectors(e.g., water, forests) and planning for climate change
Announcements will be sent via CIG listserve: climateupdate@uw.edu (see CIG home page)
Thank You!
Climate Science in the Public Interest
Additional opportunities for Q&A:
• Post-webinar survey• Email CIG: cig@uw.edu
CIG Website: http://cses.uw.edu/cig/
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