anne stoner, katharine hayhoe texas tech university keith dixon, john lanzante, aparna...
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![Page 1: Anne Stoner, Katharine Hayhoe Texas Tech University Keith Dixon, John Lanzante, Aparna RadhakrishnanGFDL COMPARING STATISTICAL DOWNSCALING METHODS: FROM](https://reader035.vdocuments.us/reader035/viewer/2022070306/5519d0cf550346443e8b4a5d/html5/thumbnails/1.jpg)
Anne Stoner, Katharine Hayhoe Texas Tech UniversityKeith Dixon, John Lanzante, Aparna Radhakrishnan GFDL
COMPARING STATISTICAL DOWNSCALING METHODS: FROM SIMPLE TO COMPLEX
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Goal: Evaluate and compare multiple statistical downscaling methods using the same framework Monthly and daily versions of Delta, Quantile Mapping, and
Asynchronous Regional Regression ModelVariables –
Minimum, maximum daily 2m temperature Daily accumulative precipitation
Input: GFDL-HiRES experimental model as both model and observations OBS: 25km GFDL-HiRES (1979-2008) Model: 200km coarsened GFDL-HiRES (1979-2008, 2086-
2095)Output: Daily 25km downscaled Tmin, Tmax, Prcp
(2086-2095)
APPROACH
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Calculates average diff erence between present and future GCM simulations, then adds that diff erence to the observed time series for the point of interest Here: individually for each high-resolution grid cell
METHOD 1: DELTA CHANGE
Assumptions – GCMs are more successful
at simulating changes in climate rather than actual local values
Stationarity in local climate variability
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Projects PDFs for monthly or daily simulated GCM variables onto historical observations
METHOD 2: QUANTILE MAPPING (e.g. BCSD)
Changes the shape of the simulated PDF to appear more like the observed PDF, but allowing the mean and variance of the GCM to change in accordance with GCM future simulations
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Asynchronous Regional Regression Model
METHOD 3: QUANTILE REGRESSION (e.g. ARRM)
Daily quantile regression using piecewise linear segments to improve fit for the training period
Individual monthly models allows for different distributions throughout the year
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COMPARISON
The shape of the resulting downscaled distribution depends highly on the downscaling method used
Delta
Quantile Mapping
ARRM
Colorado National Monument, CO
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MAXIMUM TEMPERATURE
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MINIMUM TEMPERATURE
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PRECIPITATION
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PRECIPITATION
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MAXIMUM TEMPERATURE
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MINIMUM TEMPERATURE
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PRECIPITATION
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PRECIPITATION
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DAILY DOWNSCALED TMAX
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MONTHLY DOWNSCALED TMAX
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Comparing multiple downscaling methods in a standardized framework gives us useful information
If someone has already used a certain downscaling method they can correctly interpret the biases
If someone is trying to decide which method to use, this can help their decision, because there’s no perfect method
Simple methods can be fine for studying monthly/annual means, daily output for low latitudes
More complex methods are required when studying climate extremes and high latitudes
CONCLUSIONS
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Downscale relative humidityFigure out physical causes of the biases we’re
seeingExplore the influence of different predictorsIncorporate more downscaling techniques
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