Development of GCM Based Climate Scenarios

Richard Palmer, Kathleen King, Courtney O’Neill,

Austin Polebitski, and Lee Traynham

Department of Civil and Environmental EngineeringUniversity of Washington

December 13, 2006

Objective

Develop Future Climate Variable Database for Consistent Evaluation in the Region

Approach Take Global Climate Model output and

refine to local scale through a downscaling method

Downscaling Method Requirements Maintain local characteristics while

acknowledging changes in larger scale state The downscaling method must account for:

Effects of underlying climate trends (ie., warming) Effects of interannual variability (consecutive years

can be very different)

Seeking method that: Preserves full range of historic observed variability Creates steady-state representation of future climate

3 Stages to Develop Local Climate Variables

1) Downscale climate variables from the GCM scale grid to a regional scale grid

2) Bias-correct a single regional grid cell to an individual station location

3) Expand the station scale transient scenario into multiple, quasi-steady-state time scenarios with the full historic variability

Downscaling in a Nutshell

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Stage 1

Downscale climate variables from the GCM scale grid to a regional scale grid

Downscale from GCM to Regional Scale

Downscaling takes us from 107 km2 to a regional scale of 104 km2

Overview of Stage 1 Downscaling Process

Bias-correctionDownscale

CDFTransfer Function

RegionalGlobal

Quantile Mapping

Develop Transfer Functions from Historic Climate Simulation from GCM and Historic Observed Data

Use Transfer Functions developed to Bias-Correct Future Climate Output

Downscale Bias-Corrected GCM output to finer scale

CDF – cumulative distribution function,

Develop Transfer Functions and Bias-Correction Monthly temperature and precipitation

CDF calculated for same historic periodEach grid cell in GCM Each grid cell at regional scaleThe GCM and regional scale CDFs are used

to derive a set of transformation functions The process of relating the CDFs is

generically referred to as “Quantile Mapping”

Develop Transfer Functions and Bias-Correct Quantile mapping method is based on a bias correction

scheme for downscaling climate model output Assumes that shifts in climate variables occur with different

magnitudes at different points along the distribution Temperature and precipitation simulated by the climate

model are then bias corrected using the transfer function

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Downscale The bias-corrected model is downscaled and

disaggregated The bias-corrected model data are sampled onto the

1/8° grid The mean difference between the bias-corrected model

and the 1/8° data for each calendar month during the time period (1950-2000) is computed to form a perturbation factor

The factor is added to the monthly simulated variable of the simulated scenario Temperature Precipitation

Stage 1 Output

The output from Stage 1 is transient, monthly time-series at the 1/8° scale of GCM simulated climate

The daily, transient, regional climate grid is then be used as forcings in regional scale hydrologic models or further downscaled to specific locations

Stage 2

Bias-correct a single regional grid cell to an individual station location

Transformation from regional grid to station locations Data from the regional grid can be further downscaled to

individual weather station locations by an additional application of the Quantile Mapping method

Downscale to Location

The monthly transformation relationships are defined Historic climate CDFs from the regional cell to CDFs

from the observed station data Future regional scale climate data is downscaled

to the station location though use of developed relationships The difference (bias) between the regional gridcell

value and the station record tends to be considerably smaller than the bias seen when comparing GCM scale cells to regional cells

Stage 2 Output

A bias-corrected, transient, monthly GCM time series for each station location of interest

The output from this process is used to:Examine climate trends at the station scale Examine transient hydrologic phenomena

generated using a high resolution hydrologic model

Create Quasi-Steady-State Long-term Time Series

Stage 3

Create Expanded Time Series

Expanding Transient Time Series into Quasi-Steady-State Time Series Climate is defined as the average condition of the

weather over a period of time Assumes that the climate state being defined is stationary (Long

term average does not change over time) These averages do change

Influenced by the range of time Range of natural variability is often greater than the

magnitude of change expected over several decades This is NOT to imply that climate change impacts are

insignificant Need to include the full range of potential variability in

any estimate of future climate change

Extreme Events

Extreme events are the defining events when describing the sustainability of a water resource

It is important to include these events in any representation of potential future climate

Steady-state vs. Transient

By using a steady state approach to estimate climate conditions, it is likely that a significant amount of potential variability will be excluded

If a transient scenario is used (examining the entire time series) then it becomes hard to see the potential impacts of climate change at a specific point in time Each simulation is only a single realization of the

infinite number of possible combinations of events

Solution

Incorporate a step into the downscaling process that expands the climate time series so that it includes the full range of observed historic variability by creating an expanded time series

Expanded Time Series

Uses a quantile relationship similar to quantile mapping to develop transfer functions

Combines the climate variable distributions derived from one data subset with time series of events from different subset

Allows use of a shorter period to define the climate state, yet maintains the variability of the full historic record

Creating an Expanded Time Series

1. A 31-yr slice, centered on the Year of Investigation is extracted from the transient GCM data

These 31 years are considered indicative of the average climate for that period, so the climate of 2050 would be described by the years 2035-2065

2. Bias-corrected, transient, monthly GCM time series is divided by climate variable and by month into 24 (12 months x 2 variables) climate progressions

Creating an Expanded Time Series

3. Create CDFs of extracted climate data and aggregated historic observed data. Develop Quantile Maps between historic observed and GCM CDFs.

4. Output from mapping is historic time series shifted by GCM based climate. This is compared to historic monthly CDFs.

The differences in temperature and precipitation are computed as the difference in temperature (dT) and the quotient of the precipitation (dP).

The result is a full time series of monthly dT and dP values

Creating an Expanded Time Series

5. The monthly dT, dP time series is applied to the daily station level time series

dT values are added to temperatures dP values are multiplied by daily precipitation6. The output of this step is a daily time series of

temperature and precipitation that has the range of variability seen in the historic record, but also has the long-term climate properties of the GCM

Expanded Time Series

This procedure captures the climate change signal from the GCM with the shifts in the climate variable CDFs, while also creating a series that contains all of the extreme events in the observed record

Advantages of an Expanded Time Series The long-term climate trends from the GCM

are removed so that the station scale data set contains a long climatic sequence that is not complicated by the presence of an underlying trend

Instead it is a steady-state approximation of the climate during a window of time that contains the full range of potential variability

Climate Change

Changes in climate are unlikely to occur as a uniform shift in values In fact- they are highly non-linear

Current impact assessments use delta methodThese rely only on changes in the means of

climate variables to fully describe the range of potential impacts

New Method Improves Upon the Delta Method Allows for differential shifts in climate

variables at different rates of change at the extremes of climate distributionMonthly climate means simulated by GCMsProbability of extreme events

Why Use the Expanded Time Series Approach? The examination of climate change impacts to

water resources must be targeted to specific future periods Difficult due to combined effects of a constantly

shifting underlying climate trend and large year to year variability

System impacts are best described using a long time series that incorporates the full range of potential variability and represents a steady state approximation of climate as defined for a chosen future period

Why Use the Expanded Time Series Approach? The quantile mapping process used with

an expanded historic time series reproduces the desired statistics of the target time period while providing the length and variability of record needed for most system reliability assessments

Conclusion

This method is most appropriate for application to a water resources evaluation where: Natural variability can strongly affect system

performance Small changes in extreme events can have a

much larger impact than changes in the long-term means

Future Climate Variable Database

Goals of Website:

Disseminate Climate Data:Access to historic climate dataAccess to projected data from GCMsAbility to view trends from projected GCM

data graphically through simple manipulations on website

Data Sets – Overview Regions:

WRIA 7,8,9,and 10 Models:

IPSL A2 (‘Pessimistic’) GISS B1 (‘Optimistic’) ECHAM A2 (‘Average’)

Years 2000 2025 2050 2075

Climate Variables Temperature (Daily Minimum and Maximum) Precipitation Data (Daily Total)

Regions

29 Stations 5 regions

Northwest Southwest Central Southeast Northeast

Home Page

Acquiring Data

Step 1: Select a Region

What is This?

A ‘What is This?’ icon helps users navigate to data of interest and explain data available and format

Regional Divisions

Step 2: Select a Station

Step 3: Select a Scenario/Year

Step 4: Select a Model

Step 5: Download Data

Access to Raw Data

Downloadable as text or excel file by station

Available data consists of daily Tmin, Tmax (°C), and Precipitation (mm) values

Graphical Display of Trends

Look at Projected Trends

Through selection of region, station, climate variable, and GCM, user will be able to create graphical displays of projected trends

Generating Graphs

Monthly statistics for climate variables are easily visualized and navigated by users

Preliminary Results of the Downscaling Process

IPCC 2001: http://www.grida.no/climate/ipcc_tar/wg1/figts-17.htm

Scenarios Used in GCMs

Departure from Historic Temperature - ECHAM5

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Departure from Total Monthly Precipitation - ECHAM5

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