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Development of GCM Based Climate Scenarios Richard Palmer, Kathleen King, Courtney O’Neill, Austin Polebitski, and Lee Traynham Department of Civil and.

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Presentation on theme: "Development of GCM Based Climate Scenarios Richard Palmer, Kathleen King, Courtney O’Neill, Austin Polebitski, and Lee Traynham Department of Civil and."— Presentation transcript:

1 Development of GCM Based Climate Scenarios Richard Palmer, Kathleen King, Courtney O’Neill, Austin Polebitski, and Lee Traynham Department of Civil and Environmental Engineering University of Washington December 13, 2006

2 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

3 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

4 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

5 Downscaling in a Nutshell

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

7 Downscale from GCM to Regional Scale Downscaling takes us from 10 7 km 2  to a regional scale of 10 4 km 2

8 Overview of Stage 1 Downscaling Process Bias-correction Downscale CDF Transfer Function Regional Global 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,

9 Develop Transfer Functions and Bias-Correction Monthly temperature and precipitation CDF calculated for same historic period  Each grid cell in GCM  Each grid cell at regional scale  The 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”

10 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

11 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

12 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

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

14 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

15 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

16 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

17 Stage 3 Create Expanded Time Series

18 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

19 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

20 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

21 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

22 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

23 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

24 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

25 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 precipitation 6. 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

26 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

27 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

28 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 method  These rely only on changes in the means of climate variables to fully describe the range of potential impacts

29 New Method Improves Upon the Delta Method Allows for differential shifts in climate variables at different rates of change at the extremes of climate distribution  Monthly climate means simulated by GCMs  Probability of extreme events

30 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

31 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

32 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

33 Future Climate Variable Database

34 Goals of Website: Disseminate Climate Data:  Access to historic climate data  Access to projected data from GCMs  Ability to view trends from projected GCM data graphically through simple manipulations on website

35 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)

36 Regions 29 Stations 5 regions  Northwest  Southwest  Central  Southeast  Northeast

37 Home Page

38 Acquiring Data

39 Step 1: Select a Region

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

41 Regional Divisions

42 Step 2: Select a Station

43 Step 3: Select a Scenario/Year

44 Step 4: Select a Model

45 Step 5: Download Data

46 Access to Raw Data Downloadable as text or excel file by station Available data consists of daily Tmin, Tmax (°C), and Precipitation (mm) values

47 Graphical Display of Trends

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

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

50 Preliminary Results of the Downscaling Process



53 IPCC 2001: Scenarios Used in GCMs

54 USGS Palmer Station – Just Below Howard Hanson Dam












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