Mike Dettinger USGS, La Jolla, CA DOWNSCALING to local climate

The “downscaling” problem Downscaled Original GCM values Global Climate Model (actually, general circulation model)

(GFDL A2)‏ One day in the 21st Century Downscaled Original GCM values

Downscaling options:  T and %P rescaling Synthetic statistical Deterministic statistical Dynamical simulation

dT and %P re-scalings ADD projected mean temperature changes to a historical record Multiply historical record by projected mean precipitation (as fraction of historical) Easy, maintains realistic variability, know exactly what changed No new variability or extremes, not realistic, minimal use of GCM Historical Future (+3C) Historical Future (80%)

Synthetic statistical scenarios e.g., Wilby et al., 2001

Synthetic statistical scenarios e.g., Wilby et al., 2001 Explained Precipitation Temperature

Deterministic statistical Local example (Dettinger et al, 2004 Clim Chg) Map GCM variables into historical distribution of variables, maintaining ranks from GCM but absolute values from historical records

Deterministic statistical: A continental scale example Hidalgo, H.G., Dettinger, M.D., and Cayan, D.R., in review, Downscaling using constructed analogues daily US precipitation and temperatures: J. Climate, 24 p. The constructed-analogs method Given a daily GCM map to downscale,

Hidalgo, H.G., Dettinger, M.D., and Cayan, D.R., in review, Downscaling using constructed analogues daily US precipitation and temperatures: J. Climate, 24 p.

The Bias-Correction Spatial Disaggregation Method (e.g., Maurer & Hidalgo, 2008 HESS) Hybrid statistical: BCSD

(e.g., Maurer & Hidalgo, 2008 HESS) BCSD

(e.g., Maurer & Hidalgo, 2008 HESS) BCSD

Global Model Regional Climate Model Dynamical simulation for downscaling

Winds Temperatures Vapor Pressure levels Winds Temperatures Vapor Pressure levels Winds Temperatures Vapor Pressure levels Winds Temperatures Vapor Pressure levels Approximately the same physics, dynamics & drivers as in global model

What are you going to use the downscaled scenarios for? Which climate variables/statistics matter most? Do you need daily resolution, daily congruence among climate variable? Monthly? Long-term mean? Is interannual variability important? Is long-term evolution of impacts significant? What spatial resolution do you really need? What resolution do you get by with now? Are key changes really as small scale as all that?

Downscaling “other” variables Reanalysis: Jan surface humidity Dynamical: CARD10 by Kanamitsu & Kei Statistical: Constructed analogs by Hidalgo et al Downscaling the usual variables Temperatures & precipitation Probable skills: Temperature Humidity Longwave rad Solar rad Precipitation Surface winds

Temporal resolution & characteristics:  T and %P rescaling Time scales match historical record used Variability duplicates historical unless shuffled Interannual etc characteristics can be lost if shuffled Higher-order statistics duplicate historical Synthetic statistical Daily harder to get right than monthly than annual (especially for precipitation ) Deterministic statistical Daily is possible if daily GCM output available Temporal characteristics drawn from GCM Dynamical simulation High temporal resolutions Long-term sequencing from GCM Higher-order statistics can change in consistent ways Long scenarios expensive Model biases still need correction

Downscaled Projected Trends in December Precipitation by Two Approaches (GFDL CM2.1, A2 emissions, 21st Century) Constructed analogs BCSD Bias correction & spatial downscaling, from Ed Maurer, SCU

My downscaling wish list for CASCaDE: -- Resolution: Daily time resolution, ~ 10-km spatial resolution, multiple climate variables -- Accuracy: Reproduction of high-resolution historical records -- Feasibility: Not too computationally burdensome (?) -- Synchronicity: Downscaled weather synchronized with climate model weather (just a Delta SLR-floods thing?) -- Theoretical: Doesn't constrain future higher-order stats to be same as historical -- Aesthetic: Climate (& trends) arise from entire weather field rather than being imposed GCM-grid cell by grid cell -- Practical: Ability to downscale to grids + stations at same time, maintaining internal consistency

Skill of downscaling as indicated by application of method to historical OBSERVATIONS Skill at monthly average scale

Application of method to historical OBSERVATIONS shows that even extremes are captured accurately Hidalgo, H.G., Dettinger, M.D., and Cayan, D.R., in review, Downscaling using constructed analogues daily US precipitation and temperatures: J. Climate, 24 p. Distributions of daily precip at selected sites

Trends, annual precipitation (GFDL-A2 scenario)

Some PROs: dT %P: Easy, know exactly what you changed, realistic variability & geography, no biases Statistical synthesis: Can be easy (not always), draws on best parts of GCMs, computationally quick, multiple realizations, biases handled Deterministic statistical: Computational middle ground, uses variability from GCM (thus can reflect changing climate modes), whole distributions can be preserved (high-order debiasing), realistic geography Dynamical (regional) simulation: Draws large scale variability from GCM, first-principles physics, all variables downscaled

Some CONs: dT %P: No new variability, only minimal info from GCM being used, changes not realistic Statistical synthesis: Difficult to retain spatial & variable inter-relations, climate variability is not really simple noise, some variables  purely random Deterministic statistical: Computational middle ground, assumes local climate responses to large-scale climate are stationary, single climate realization Dynamical (regional) simulation: Computationally burdensome, bias correction still needed, relatively short simulations provided

What is in a typical set of downscaled scenarios these days? 1. Projections by multiple climate models 2. Projections under various greenhouse-gas emission scenarios 3. Historical-period simulations for use in calibrations/testing e.g., along with the 21 st Century climate projections 4. Daily to monthly time steps (over, say, 150 years) to 12-km spatial resolution (statistical) or km (dynamical), regional to continental scales 6. Precipitation & temperatures (just beginning other variables)

Some available downscaled projections: BCSD—dozens of scenarios at 12 km, monthly ConstrAnalogs—several scenarios at 12 km, daily cascade.wr.usgs.gov/data/Task1-climate/ Numerical regional model projections—multiple models, not so much off the shelf An upcoming (statistical) downscaling improvement: BCCA—best of BCSD & CA methods!