Incorporating Climate Change Information Decisions, merits and limitations

Incorporating Climate Change Information Decisions, merits and limitations John Abatzoglou University of Idaho Assistant Professor Department of Geography

Uncertainty Cascade of Achieving Local Climate Change Information Emission Scenarios Socioeconomic Political GCMs Downscaling Carbon cycle Parameterization Resolution End-user Needs Statistical or Dynamical 3

Climate Modeling GCM: primary tool physically model gamut of processes contributing to climate Modeling has advanced Earth System Models Higher-resolution Global and regional response Models uncertainties linger At local-regional scales Cloud/precipitation/aerosol based processes Land-atmosphere interactions Coupled biogeochemistry 4

What Model(s) to Use? Climatology Jackson, Wyoming, OBS = black, colored = 15 RAW GCM output (20C3M) 1. (In)Ability to capture seasonal cycle of variables + meteorology biases in time, space and spatio-temporal autocorrelation 2. (In)Ability to simulate 20 th century trends 3. (In)Ability to capture magnitude of extremes (e.g., heavy precip) 4. (In)Ability to represent magnitude/spectra of climate modes (e.g., ENSO) 5

Evaluating how well computer models simulate seasonal changes in climate Climate variable Latent heat flux at surface Sensible heat flux at surface Surface temperature Reflected SW radiation (clear sky) Reflected SW radiation Outgoing LW radiation (clear sky) Outgoing LW radiation Total cloud cover Precipitation Total column water vapor Sea-level pressure Meridional wind stress Zonal wind stress Meridional wind at surface Zonal wind at surface Specific humidity at 400 mb Specific humidity at 850 mb Meridional wind at 200 mb Zonal wind at 200 mb Temperature at 200 mb Geopotential height at 500 mb Meridional wind at 850 mb Zonal wind at 850 mb Temperature at 850 mb Mean Median A B C D E F G H I J K L M N O P Q R S T U V Worst Best 6 CMIP3 (IPCC) model Model used in IPCC Fourth Assessment Gleckler, Taylor, and Doutriaux, Journal of Geophysical Research (2008)

Optimal Use of GCM Data What models to use: A subset of models Democratic system: every model weighted equally Weight models according to the above criteria in determining most likely solution (REA method: Giorgi and Mearns 2002) Suggested Appropriate Use Ensemble, probabilistic approach provides for full spectrum of information (Average response, worst-case scenario) http://probcast.washington.edu Cayan et al, 2006 7

Multi-model perspective provides basis for conclusions (IPCC AR4) Change in PPT 2080-2099 vs 1980-1999 for SRES-A1B 8

Sources of Uncertainty for Projections Sources of uncertainty: model, scenario and initial/boundary conditions Sutton and Hawkins, 2009 9

New Experiments and GCMs CMIP5 for the IPCC s Fifth Assessment Report (2013) New experiments Near term decadal prediction (2010-2035) ** Better understanding of intermodel differences Improved Representation of Processes Biogeochemical cycles Clouds/Aerosols Higher resolution (0.5 to 1 ) Perturbed Physics climateprediction.net Model output available in mid-2010 PCMDI, Taylor, 201010

Climate Models Regional models Global models in 5 yrs? Wish 30 m 11

Desirable Qualities of Downscaling Resolution: Daily time resolution, high spatial resolution (<10km) Products: Multiple variables (not just T & P), physically based predictors Accuracy: Reproduction of high-resolution historical records Feasibility: Not too computationally burdensome (ability for ensembles) Synchronicity: Downscaled weather fields adhere to physical laws across variables and across spatial domains Theoretical: Retains core information from driving GCM; e.g., future synoptic patterns aren t the same as historical Practical: Tractable to observations (gridded/station) for immediate use in applied studies. 12

Downscaling Methods Simple (Simple Change Factors) Region-wide changes in mean temperature Intermediate (Synthetic Statistical) Measures of Variability Advanced (Deterministic Statistical) Daily time-scales Multiple variables Ability to capture regional climate/wx patterns Dynamical Complexity 13

Pros and Cons of Statistical Downscaling Advantages: Computationally cheap Ability to create mass ensembles Ability to downscale to point locations Ability to easily compare results to observed climate Disadvantages Assumes stationarity in coarse-local relationships Neglects local feedback processes such as snowalbedo, soil moisture and cloud cover 14

Change Factor Method + False Realism?? 16

Change Factor Method Pros: Simple Retains observed spatial-temporal relationships Easy to compare to historical data Cons: Simple Unable to resolve regional-local scale processes and relationships (point of downscaling) Assumes fixed step change, higher statistical moments unchanged Unable to examine changes in variability and/or extremes, therein missing low-risk high-impact events 17

Bias Correction Spatial Downscaling (BCSD) (Wood et al, 2004; Maurer, 2007; Salathe et al, 2007) http://gdo-dcp.ucllnl.org/downscaled_cmip3_projections/dcpinterface.html UI: Brandon Moore and Von Walden completed 84 scenarios 19

BCSD Step I: Bias Correction Quantile-mapping Match statistical Moment to obs. Quantile-mapping of model to obs. (e.g., Wood et al. 2002) Mapping carried through for future run Monthly time-scales in most products shown here for daily timescales 20

BCSD Step II: Spatial Downscaling (a) Interpolate bias-corrected GCM anomaly field to 1/8 th degree resolution (b) Scale anomaly field with OBS fields to create downscaled output 21

Caveats of Interpolation in the West 22

BCSD: Pros and Cons Pros Cons Shifts in variability and extremes at monthly and longer timescales are carried into downscaled fields Spatial aggregation and interpolation assumes homogeneous climate anomalies Independent bias-correction of T and P may be problematic for examining hydrologic processes Development of daily data from monthly data 23

Constructed Analogues (CA) (Hidalgo et al, 2008; Zorita and von Storch, 1999 ) GCM Target Obs Library Concept Weather-typing approach based on spatial pattern analogs Value-added over interpolation based approaches Identical : 10^30 years (Van den Dool, 1994) Single: best pattern match Constructed : many sampling opportunities (Hidalgo et al. 2008) Basis: model coarse scale fields from analogues and fine-scale will follow http://cascade.wr.usgs.gov/data/task1-climate/ 25

Constructed Analogues Given Target (GCM) e.g., May 14, 2048 Library of Potential Analogs 30 Best Analogs From ±45-days of target date Constructed Analog Coarse Res. Constructed Analog Downscaled Res. 26

CA: Pros and Cons Pros Uses daily output from GCMs Develops fine-scale fields from synoptic-scale meteorological fields created by GCMs adhering closer to physical processes than interpolation approaches Daily extremes, autocorrelation better represented Cons Does not account for GCM biases (means, variance) Limited analogs in a future climate Domain sensitivity for pattern matching (Fowler et al, 2007) 27

Multivariate Adapted Constructed Analogues (MACA) Combines attributes of CA and BCSD (Maurer and Hidalgo, 2008, 2010) Input Data - Observations (fine + coarse) - GCM - Similar time period of obs (20C3M) - Future runs (SRES-A1B) Steps 1. Bias-correct GCM fields 2. Epoch adjustment: remove GCM differences 3. Identify a subset of the 30 best patterns through spatial RMSE Constructed analog of patterns* 4. Epoch adjustment: Introduce difference fields back 5. Adjusts end variables using quantile-mapping at fine-scale 28

Disappearing Analogues What about the day without an analog? e.g., July 24 th, 2050? (temperature extremes), Dec 30, 2060 (precip extremes) TMAX (K) TMAX (K) Solution: Epoch adjustment Removal of difference field prior to CA (even shift across CDF) Reintroduction of difference fields after final step 29

Multivariate Adapted Constructed Analogues (MACA) Input Data - Observations (fine + coarse) - GCM - Similar time period of obs (20C3M) - Future runs (SRES-A1B) Steps 1. Bias-correct GCM fields 2. Epoch adjustment: remove GCM difference 3. Identify a subset of the 30 best patterns through spatial RMSE Constructed analog of patterns 4. Epoch adjustment: Introduce difference fields back 5. Adjusts end variables using quantile-mapping at fine-scale 30

Multivariate Analogs Joint analog search performed for grouped/coupled variables by physical descriptors to achieve coherence Predictor Variables 2-m TMAX 2-m TMIN 2-m Specific Humidity 10-m U and V PPT Downscaled Fields TMAX TMIN RHMAX RHMIN PPT U V Applied by having predictor variables converted to standardized anomalies relative to target window 31

Multivariate Adapted Constructed Analogues (MACA) Input Data - Observations (fine + coarse) - GCM - Similar time period of obs (20C3M) - Future runs (SRES-A1B) Steps 1. Bias-correct GCM fields 2. Epoch adjustment: remove GCM differences 3. Identify a subset of the 30 best patterns through spatial RMSE Constructed analog of patterns 4. Epoch adjustment: Introduce difference fields back 5. Adjusts end variables using quantile-mapping at fine-scale 32

Validation of MACA Strategy: Use ECMWF Interim reanalysis as surrogate GCM Cross Validation 1989-2008, Daily Data Compare skill to interpolation-only based approach MACA Interpolation r-value 33

Validation: Multivariate Data Monthly Correlation of Accumulated Snowfall Daily PPT w/ TAVG<2C is snow, else rain 34

MACA Resolution: Daily, 8-km Products: TMAX, TMIN, RHMAX, RHMIN, 10-m U & V and PPT Accuracy: See validation figs Feasibility: Yes Synchronicity: Simulates integrated response across variables Theoretical: Driven by daily synoptic wx from GCMs Practical: Direct applicability to raster or station data Data Availability Time Slices: 1971-2000; 2046-2065; 2080-2099 Scenarios: SRES-A1B Models: 13 CMIP AR4 models 35

Dynamical Downscaling Regional Climate Modeling Mesoscale models are currently used in numerical weather prediction Forced by a global model that provides: Initial conditions soil moisture, SST, sea ice Updates meteorological conditions (temperature, pressure ) Regional model provides finer scale (10s km) response 37

Dynamical Downscaling North American Regional Climate Change Assessment Program NARCCAP 1. Multiple high-res (50-km) scenarions for impact assessment 2. Designed to evaluate performance over N. America and explore compatibility issues with GCMs and parameterizations http://www.narccap.ucar.edu/ 38

Dynamical Downscaling Global TEMP DJF Regional Global PPT DJF Regional 39

Lessons from Regional Climate Models MM5 (15-km) nested from ECHAM 5 output (10-yr slices) Wintertime Change from 2045-54 from 1990-1999 Temperature Change Snow Cover Change Change in Winter Temperature Change in fraction of days with snow cover Salathé et al 2008 Amplification of warming due to snow-albedo feedback RCM parameterization schemes are important 40

When Good Models Go Bad or Why NWS still employs Meteorologists AREA FORECAST DISCUSSION NATIONAL WEATHER SERVICE SPOKANE WA 1023 PM PDT WED MAR 31 2010 THE FORECAST WOULD SEEM RATHER STRAIGHTFORWARD THE 00Z NAM REALLY SEEMS TO GO ABSOLUTELY CRAZY WITH ALMOST 2 INCHES OF CONVECTIVE QPF OVER SPOKANE THROUGH MORNING...WHICH WOULD MEAN AT LEAST 20 INCHES OF SNOW. BECAUSE OF THE PROBLEMS WITH SOME THERMODYNAMIC AND DYNAMIC PARAMETERIZATIONS ON THE NAM MODEL...THE FORECAST FOLLOWS MORE CLOSELY TO THE GFS WHICH INDICATES NOTHING MORE THAN 0.02 INCHES BY MORNING Observed PPT on 4-1-2010: Trace 41

RCMs: Pros and Cons Pros Uses 6-hrly output from GCMs, adheres to meteorology Does not assume stationarity in response between local and GCM fields (e.g., snow-albedo feedback, cloud changes) Argued to better represent extremes than statistical methods Cons Computation expense (inability for ensembles) Does not account for GCM biases (means, variance) Bias multiplier Domain sensitivity issues Inability to relate to observations directly (application issues) Future steps Dynamical-Statistical hybrid downscaling OCCRI and UI collaborative effort 42

The Additional Thanks to: Von Walden1 Brandon Moore1 Crystal A. Kolden1,2 Timothy J. Brown2 1University of Idaho, Moscow, Idaho 2Desert Research Institute, Reno, Nevada John Abatzoglou jabatzoglou@uidaho.edu 43