Download Development of GCM Based Climate Scenarios Presentation

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
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)
2)
3)
Downscale climate variables from the
GCM scale grid to a regional scale grid
Bias-correct a single regional grid cell to
an individual station location
Expand the station scale transient
scenario into multiple, quasi-steady-state
time scenarios with the full historic
variability
10
January Average Temperature (C)
January Average Temperature (C)
Downscaling in a Nutshell
HadCM3 Cell (47.5, -120.0)
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Regional Cell (47.5625,-121.8125)
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Non-Exceedance Probablility
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Regional Cell (47.5625,-121.8125)
Snoqualmie Falls
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Non-Exceedance Probablility
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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
CDF – cumulative
distribution function,
CDF
Quantile Mapping
Global
Transfer Function
Regional
Develop Transfer Functions from Historic Climate
Simulation from GCM and Historic Observed Data
Downscale
Downscale Bias-Corrected
GCM output to finer scale
Bias-correction
Use Transfer Functions developed to
Bias-Correct Future Climate Output
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”
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
January TEMP
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Cedar
GFDL_R30
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Non-Exceedence Probability
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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


These averages do change




Assumes that the climate state being defined is stationary (Long
term average does not change over time)
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

2.
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
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
Create CDFs of extracted climate data and aggregated
historic observed data. Develop Quantile Maps
between historic observed and GCM CDFs.
Output from mapping is historic time series shifted by
GCM based climate. This is compared to historic
monthly CDFs.
3.
4.


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.


6.
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
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
method
 These
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 distribution
 Monthly
climate means simulated by GCMs
 Probability 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 longterm means
Future Climate
Variable Database
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
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
Scenarios Used in GCMs
IPCC 2001: http://www.grida.no/climate/ipcc_tar/wg1/figts-17.htm
Departure from Historic Temperature - ECHAM5
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Departure from Monthly Average Temperature (Degrees C)
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Departure from Historic Temperature- GISS
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Departure from Historic Average Monthly Temperature (degrees C)
Departure from Historic Temperature - IPSL
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Departure from Average Monthly Historic Temperature (degrees C)
Departure from Historic Temperature - Average of GCMs
Month
USGS Palmer Station – Just Below Howard Hanson Dam
Departure from Total Monthly Precipitation - ECHAM5
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Departure from Total Average Monthly Precipitation (inches)
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USGS Palmer Station – Just Below Howard Hanson Dam
Departure from Total Monthly Precipitation - GISS
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Departure from Total Average Monthly Precipitation (inches)
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Departure from Total Monthly Precipitation - IPSL
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Departure from Total Average Monthly Precipitation (inches)
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Departure from Total Monthly Precipitation - Average of GCMs
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Departure from Total Average Monthly Precipitation (inches)
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USGS Palmer Station – Just Below Howard Hanson Dam
Monthly Streamflows Forecasted w/ ECHAM5
Howard Hanson Inflow
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DHSVM Historic
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ECHAM5 2025
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ECHAM5 2050
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Monthly Streamflows Forecasted w/ GISS
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Monthly Streamflows Forecasted w/ IPSL
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IPSL 2025
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IPSL 2075
Monthly Streamflows Forecasted w/ All GCM's
Howard Hanson Inflow
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Average 2025
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