Statistical downscaling using Localized Constructed Analogs (LOCA) David Pierce and Dan Cayan...

Statistical downscaling using Localized

Constructed Analogs (LOCA)

David Pierce and Dan CayanScripps Institution of Oceanography

Bridget Thrasher, Edwin Maurer, John Abatzoglou, Katherine Hegewisch

Development sponsored by

The California Energy Commission

Department of Interior/US Geological Survey  via the Southwest Climate Science Center

NOAA RISA Program through the California Nevada Applications Program

Production runs sponsored by

U.S. Army Core of Engineers/USBR

NASA via computing resources

Downscaling system

Quantile Mapping (QM)

Constructed Analogs (CA; Hidalgo et al. 2008)

Bias Correction and Constructed Analogs (BCCA; Maurer et al. 2010)

Multivariate Adapted Constructed analogs (MACA; Abatzoglou & Brown 2012)

Bias Correction with Spatial Disaggregation (BCSD; Wood et al. 2004)

Global Regridding Bias SpatialModels Correction Downscaling

Issues with bias correction

• Tmax

• Difference between original model-predicted change and change after bias correction

• 2070-2100 minus 1976-2005

• Ensemble averaged across 21 GCMs

deg-C

1. QM does not preserve model-predicted changes(Maurer and Pierce, HESS, 2014)

EDCDFm reference:Li, H., J. Sheffield, and E. F. Wood, 2010: Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistant quantile matching. J. Geophys. Res. Atmos., 115 (D10101), doi:10.1029/2009JD012882.

What about precipitation?

• Evaluate temperature changes as a difference (degrees C)• Evaluate precipitation changes as a ratio (percent)

– Positive definite– Wide dynamic range– Rain shadow regions

• Precipitation

• Difference between original model-predicted change and change after bias correction in percentage points

• 2070-2100 minus 1976-2005

• Ensemble averaged across 21 GCMs

“PresRat” scheme

• Like EDCDFm (Li et al. 2010) except:

1. Preserves the ratio of model-predicted changes (not the difference)

2. Zero-precipitation threshold (preserve observed number of dry days in historical period)

3. Final correction factor to preserve mean change

“PresRat” scheme

• Like EDCDFm (Li et al. 2010) except:

1. Preserves the ratio of model-predicted changes (not the difference)

2. Zero-precipitation threshold (preserve observed number of dry days in historical period)

3. Final correction factor to preserve mean change

• Correction factors necessary to preserve model-predicted changes (2070-2099 vs. 1976-2005) in mean precipitation

• Averaged across 21 GCMs

Correction factor

• Precipitation

• Difference between original model-predicted change and change after bias correction in percentage points

• 2070-2100 minus 1976-2005

• Ensemble averaged across 21 GCMs

If log-RMSE is f, then models are off by factor of (1 + f), on average

2. Model Errors can be a Function of Frequency

Log-RMSE metrics

How much does frequency-dependent bias correction change values?

3. Standard QM not multivariate

• Temperature on precipitating days affects snow cover (Abatzoglou et al.)• Bias correct temperature conditional on precipitation > 0 or not

Issues with spatial downscaling

Hidalgo, H.G., Dettinger, M.D., and Cayan, D.R., 2008

Slide from Mike Dettinger, USGS (tenaya.ucsd.edu)

Spatial Downscaling with constructed analogs

Slide from Mike Dettinger, USGS (tenaya.ucsd.edu)

Spatial Downscaling with constructed analogs

Hidalgo, H.G., Dettinger, M.D., and Cayan, D.R., 2008

Slide from Mike Dettinger, USGS (tenaya.ucsd.edu)

Spatial Downscaling with constructed analogs

Hidalgo, H.G., Dettinger, M.D., and Cayan, D.R., 2008

Slide from Mike Dettinger, USGS (tenaya.ucsd.edu)

Spatial Downscaling with constructed analogs

Hidalgo, H.G., Dettinger, M.D., and Cayan, D.R., 2008

Issues with current downscaling (BCCA)

• “BCCA” = “Bias correction with Constructed Analogs”• Averaging step reduces temporal variance (i.e., mute extremes)

2. Frequency of occurrence -> percent of amount

• Take an extreme example for illustration:

Slide 22

60% of the time

40% of the time

Contributes to reduction in extremes

3. Drizzle problem from downscaling

Slide 23

New downscaling (LOCA) (Step 1 of 2)• BCCA uses 30 best matching analog

days over entire domain

• LOCA starts with 30 best matching analog days over the region around the point

• Region: everywhere correlation with point being downscaled is > 0 (in obs)

• Regions are calculated by season (DJF, MAM, JJA, SON) and variable (pr, tasmax, tasmin, etc.)

• Gives a natural domain independence to LOCA (extending domain past region does not affect results at the point)

Example shown for precipitation

New downscaling (LOCA) (Step 2 of 2)• Once 30 regional analog days are

selected:

• Find best one (of the 30) matching days in a small localized region (~1 degree) around each point

• This two step process means each point:– Is consistent with what’s

happening regionally– Is the best match locally

• Points whose selected analog day is different from a neighbor’s (“edge points”) use a weighted average of the relevant analog days

• ~30% of points are edge points• Greatly reduced averaging means:

– Better extremes– Better spatial coherence– Far less “drizzle” problem

Example for precip, 1 Jan 1940

(P=0)

4. Run out of analogs for extreme days?

1950-99……………………………………………………………………………….2070-99

Existing methods:

1950-99……2000-2009….…2010-2039…..2040-2069……….2070-99

Anomaly w.r.t. 30-year climatology

Use LOCA to downscale changes in climatology

LOCA:

Anomaly w.r.t. historical period (Tmin, Tmax)

Project in association with Keith Dixon, GFDL

5. Averaging increases spatial coherence precip (red = more coherent)

Evaluation:

Seasonal mean of daily precipitation (mm/day) in CCSM4

Error in %

Evaluation:

Seasonal mean of daily Tmax (degC) in CCSM4

Error in degC

Evaluation:

Standard deviation of daily precip (mm/day), averaged by seasonCCSM4

Error in %

Evaluation:

Standard deviation of daily Tmax (degC), averaged by seasonCCSM4

Error in degC

Goal: Realistic daily extremes (Precip)W

inter, m

m/day

Summ

er, m

m/day

Goal: Realistic daily extremes (Temp)W

inter, CSum

mer, C

Goal: Preserve model-predicted changes(Precipitation, CCSM4, rcp 8.5, 2070-2100 minus 1950-1999)

Winter, %

Summ

er, %

Goal: Preserve model-predicted changes(Tmax, CCSM4, rcp 8.5, 2070-2100 minus 1950-1999)

Winter, C

Summ

er, C

Example VIC output

(water yr avgs)

Slide 36

Available variables:

EvapotranspirationSnowpackHumidityTotal runoffSoil moisture

Black = with obs forcingGreen = 10 models

Red = model average

Summary of Production Runs

• 32 CMIP5 models• Historical: 1950-2005. RCP 4.5 and

RCP 8.5: 2006-2100 (2099 some models)

• Climatological period: 1950-99• Interpolated model calendars to

standard calendar w/leap days• North America 24.5 N to 52.8 N at

1/16th degree resolution• Daily Tmin, Tmax, Precip (specific

humidity? 23 models).

ACCESS1-0ACCESS1-3CCSM4CESM1-BGCCESM1-CAM5CMCC-CMCMCC-CMSCNRM-CM5CSIRO-Mk3-6-0CanESM2EC-EARTHFGOALS-g2GFDL-CM3GFDL-ESM2GGFDL-ESM2MGISS-E2-HGISS-E2-R

HadGEM2-AOHadGEM2-CCHadGEM2-ESIPSL-CM5A-LRIPSL-CM5A-MRMIROC-ESMMIROC-ESM-CHEMMIROC5MPI-ESM-LRMPI-ESM-MRMRI-CGCM3NorESM1-Mbcc-csm1-1bcc-csm1-1-minmcm4

Summary

• Many bias correction & downscaling schemes…• Quantile mapping, BCCA:

– Muted extremes– Different biases at different frequencies– Too much spatial coherence– Drizzle problems– Wrong temperature of precipitation

• New bias correction and LOCA downscaling– Extremes preserved pretty well, along with seasonal means and std deviations– Reasonable preservation of original model-predicted changes– Frequency dependent bias correction– Spatial coherence not degraded as much– Greatly reduces drizzle problem– Bias correct temperature conditional on precipitation

Pierce, D. W., D. R. Cayan, and B. L. Thrasher, 2014: Statistical downscaling using Localized Constructed Analogs (LOCA). Journal of Hydrometeorology, v. 15, page 2558-2585

Analysis plots: loca.ucsd.edu

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