1. Climate Downscaling Techniques Marina Timofeyeva UCAR/NWS/NOAA

2. TALK OUTLINE  What is Downscaling  NWS NCEP CPC climate outlooks and regional climate  Downscaling methods  Application of CPC methods in Developing Local Climate Products  Messages to take home

3. DOWNSCALING DOWNSCALING is the transformation from a LARGE SCALE feature to a SMALL SCALE one (not necessarily of the same kind). DOWNSCALING implies increases resolution of output.

4. NWS NCEP CPC weather & climate outlooks

5. NWS NCEP CPC climate outlooks This is a map of 344 climate divisions currently in use over the U.S. Note the changing size as one goes from east to west, as well as from one state to another. CPC uses 102 mega or forecast divisions in their forecasts. The divisions in the West closely correspond to NCDC climate divisions.

6. NWS NCEP CPC climate outlooks

7. National precipitation map based on high-resolution PRISM data (left) and on Climate Divisions (right). Note the large gradients and fine-scale variability in the Western U.S. that is not reproduced in the right map. NWS NCEP CPC climate outlooks Precipitation Climatology 1971 - 2000 Slide courtesy of Klaus Wolter, CDC

8. Official CDs for Colorado (left) and Experimental CDs (right) based on multivariate statistical analysis of climate data that also include SNOTEL data. Such new CDs are being derived for the entire U.S.A. NWS NCEP CPC climate outlooks and Regional Climate Slide courtesy of Klaus Wolter, CDC

9. NWS NCEP CPC climate outlooks and Regional Climate # of stations that did not reject the test H 0 If climate at station is the same as at climate division, then mean and variance at station and climate divisions should be the same. THEY ARE NOT!

10. NUMBER OF STATIONS (OUT OF 9) THAT HAVE SQUARED CORRELATION WITH CD >=0.8 NWS NCEP CPC climate outlooks and Regional Climate

11. DYNAMICAL DOWNSCALING

13.  ETA and AVN are examples on meteorological scale  Climate applications :  Regional Spectral Model (RSM) and Seasonal Forecast Model (SFM)  Nested in T62 and T40 NCEP coupled AOGCM using 50, 30 and 20 km resolution grids  tested for 1997 winter El Nino  results: RSM shows improvement in temperature forecast in comparison with AOGCM RSM shows improvement in temperature forecast in comparison with AOGCM “50-km RSM is unable to forecast anomalies over high mountains” “50-km RSM is unable to forecast anomalies over high mountains” 20-km RSM provides “realistic distribution of precipitation”, but overestimate its maxima 20-km RSM provides “realistic distribution of precipitation”, but overestimate its maxima DYNAMICAL DOWNSCALING

14. STATISTICAL DOWNSCALING WEATHER GENERATORS Climate observations Statistics Climate predictions Global Circulation Pattern predicted Statistics Class Calibration Prediction Frequency analysis Global Circulation Pattern observed

15. STATISTICAL DOWNSCALING CORRELATION MODELS Climate Observations GCM fields Statistics Statistical relationship Modeled climate Statistics Prediction Calibration

16. EXAMPLE: PARTNERSHIP PROJECT  Western Region HQ (Andrea Bair)  NWS OCWWS CSD (Marina Timofeyeva)  NWS NCEP CPC (David Unger) APPLICATION OF CPC METHODS IN DEVELOPING LOCAL CLIMATE PRODUCTS

17. Utilized CPC Methods of Downscaling Use of composites in the forecast of local climate using climate variability modes Translation of probability of exeedance (POE) outlooks from climate divisions to local station temperature forecasts

18. Temperature Outlook Precipitation Outlook Degree Day Outlook Temperature Outlook Precipitation Outlook Degree Day Outlook Utilized CPC Methods of Downscaling

19. Utilized CPC Methods of Downscaling - POE POF (%) Forecasted Temperature (°F) Translation of forecast division POE to station forecast was developed in CPC by Barnston, Unger, and He. The POE outlooks became available in December 1994, and the translations to station temperature in 2000. Observed T

20. http://www.cpc.ncep.noaa.gov/pacdir/NFORdir/citydir/cpcctytd.dat Utilized CPC Methods of Downscaling - POE

21. Step 1: Defining stations where the station outlooks are used

22. Utilized CPC Methods of Downscaling - POE In most cases there is a strong relationship between temperature at station (y axis) and climate division (x axis). Step 2: Developing regression equations for those stations

23. Utilized CPC Methods of Downscaling - POE Step 2: Developing regression equations for those stations Regression coefficients are adjusted for the trend years Difference Tstation – Tcd (ºF)

24. Utilized CPC Methods of Downscaling - POE Step 3: Test of regression equations stability - Cross validation Calibration data setVerification Year 1971 1972 1973 1974 1975 …… 1997 1998 1999 2000 Cross validation allows expansion of the test sample and protects against over fitting

25. Calibration data setVerification Year 1971 1972 1973 1974 1975 …… 1997 1998 1999 2000 Utilized CPC Methods of Downscaling - POE Step 3: Test of regression equations stability - Cross validation

26. Methods for Climate Forecast Verification Ranked Probability ScoreContinuous Ranked Probability Score Ranked Probability Score and Continuous Ranked Probability Score The Rank Probability Score (RPS), graphically represents the performance of the probability forecast (Wilks, 1995). Tobs RPS is not sensitive for forecast spread. Tobs Continuous Rank Probability Score (CRPS) takes into account spread of the forecasted distribution. CRPS Skill Score (CRPSS) is the final measure of the forecast performance.

27. Progress Report - CRPSS

28. Reliability Diagrams Reliability Diagrams exhibit the correspondence between the observed and forecasted percentiles. Reliability Diagrams allow verification of each POE for each station. The analysis is done for a forecast and compared with climatology. Under-forecasting Over-forecasting Methods for Climate Forecast Verification

29. Progress Report – Reliability Diagrams

30. Bias Analysis The bias is computed for CPC forecasts and for climatology using the equation shown on the right for climate divisions and stations for each forecast month and each lead season. The bias shows range and sign of deviations between forecasts and observations. Mean Square Error (MSE) is other common accuracy measure of climate forecast leading to skill score (SS) estimates. Methods for Climate Forecast Verification Expected PDF of difference between forecasts and observation is normal distribution with mean that is not significantly different from 0.

31. Utilized CPC Methods of Downscaling - POE When this method fails…

32. Utilized CPC Methods of Downscaling - POE When this method fails… R – Measure of Confidence in Downscaling ρ Station Forecast Spread

33. Utilized CPC Methods of Downscaling - POE When this method fails… Mean = 0.30 St. Dev.= 0.38 Median = 0.19 Mode = 0.01 Skewness = 3.11 Kurtosis = 14.67 Mean = 60.7 St.Dev.= 13.6 Median = 59.5 Mode = 52.0 Skewness = 0.225 Kurt = -0.526 Temperature is a normally distributed variable, therefore the downscaling method based on regression can provide good estimates Precipitation (right chart) is too skewed for normal distribution. The regression would require a transformation of this variable. Compositing can be used for Precipitation forecasts because it does not employ regression analysis.

34. Levels of sophistication in use of composites: 1.Composite means 2.Raw Composite distribution 3.Smooth resampled Composite Distribution - boot strapping techniques 4.All of the above with trend and some other mode of climate variability taken into account using new approach developed by Higgins, Unger and Kim Utilized CPC Methods of Downscaling – COMPOSITES

35. Nino 3.4 SST (°C) North Dakota, DJF Temp ( °C) 1983 1992 1987 1973 1958 1995 1966 1988 1998 Utilized CPC Methods of Downscaling – COMPOSITES

36. COMPOSITES – level 1

37. Utilized CPC Methods of Downscaling – COMPOSITES – level 2

38. Utilized CPC Methods of Downscaling – COMPOSITES – level 2 BNA Jan135 Feb144 Mar531 JFM072 FORECAST: FORECAST: Given El Nino, Denver Tmean has a shift in Tmean toward above normal for Jan, below normal for Mar, and near normal for JFM

39. Nino3.4 Term WarmNeutralCold Above2/9=0.229/32=0.282/9=0.22 Near7/9=0.7812/32=0.384/9=0.45 Below0/9=011/32=0.343/9=0.33 JFM composites For each Nino 3.4. event we compute probability of the climate variable to be in Below/Near/Above normal category. Utilized CPC Methods of Downscaling – COMPOSITES – level 2

40. FORECAST USING CURRENT CPC Nino 3.4: CPC CURRENT ENSO FORECAST: http://www.cpc.ncep.noaa.gov/products/precip/CWlink/ENSO/total.html NINO 3.4 INITIAL TIME 3 2003 PROJECTION FRACTION BELOWNORMALABOVE AMJ10.039 0.372 0.589 MJJ2 0.066 0.446 0.488 JJA 3 0.126 0.494 0.380 … JFM 90.3430.4340.243 FMA10 0.3180.4380.244 Nino3.4 Term WarmNeutralCold Above2/9=0.229/32=0.282/9=0.22 Near7/9=0.7812/32=0.384/9=0.45 Below0/9=011/32=0.343/9=0.33 Example – ElNino with 8.5 month lead (forecast for JFM 2004): Utilized CPC Methods of Downscaling – COMPOSITES – level 2

41. Utilized CPC Methods of Downscaling – COMPOSITES – level 3 El Nino Years JanuaryFebruaryMarch 1958 1966 1969 1973 1983 1987 1992 1995 1998 X X X X X X X X X X X X To obtain a smooth distribution we can use resampling, which allows 9 3 = 729 different combinations of the months in each season. To better represent extremes in the distribution we sample with replacement using boot strapping technique.

42. Utilized CPC Methods of Downscaling – COMPOSITES – level 4 DJF North Dakota Temp ( °C) The trend should not be removed arbitrarily. Hinge, for example, explains climate changes during the last decades. 11-year Moving Average (MA) explains decadal variations in climate.

43. Research Products Versus Operational Products MethodResearch OutputOperational Products POE Regression equation coefficients Method performance evaluation based on hind cast applications (1995- 2003) Seasonal forecasts issued monthly for 13 seasonal leads using equations and CPC CD forecasts Verification issued monthly for forecast made in preceding month Composites All composite statistics: mean, variance, each category probabilities with and without trend, and POE. Method performance evaluation based on hind cast applications (1982 – 2003) Seasonal forecasts issued monthly for 16 seasonal leads using station statistics and CPC Nino 3.4. forecasts Verification issued monthly for forecast made in preceding month

44. WR NEW LOCAL CLIMATE PRODUCTS Where do we go… EXPECTED OPERATIONAL FUNCTIONS: REGIONAL CSPM 1. POE downscaling 1.1. Developing the translation equations for 87 sites (completed) 1.1. Developing the translation equations for 87 sites (completed) 1.2. Tests of the equations (2003-2004) 1.2. Tests of the equations (2003-2004) 1.3. Monthly coordination of local product release (starting 2005) 1.3. Monthly coordination of local product release (starting 2005) 1.4. Annual update of the equations (starting 2005) 1.4. Annual update of the equations (starting 2005) 2. Composites 2.1. Coordination of Composites products existing or released in the local offices 2.1. Coordination of Composites products existing or released in the local offices 2.2. Developing Composites for 87 sites (2003-2004) 2.2. Developing Composites for 87 sites (2003-2004) 2.3. Hind cast test of the composites (2003-2004) 2.3. Hind cast test of the composites (2003-2004) 2.4. Monthly coordination of local product release (starting 2005) 2.4. Monthly coordination of local product release (starting 2005) 2.5. Annual update of the composites (starting 2005) 2.5. Annual update of the composites (starting 2005)

45. WR NEW LOCAL CLIMATE PRODUCTS Where do we go… EXPECTED OPERATIONAL FUNCTIONS: WFO CSFP 1. POE downscaling: 1.1. Selection of sites within WFO CWA for downscaling (completed) 1.1. Selection of sites within WFO CWA for downscaling (completed) 1.2. Delivering on monthly basis seasonal outlooks for selected 1.2. Delivering on monthly basis seasonal outlooks for selected sites within WFO CWA (starting in FY05) 1.3. Verification of the previous month forecasts (starting in FY05) 1.3. Verification of the previous month forecasts (starting in FY05) 2. Composites: 2.1. Keeping self updated on climate variability mode status 2.1. Keeping self updated on climate variability mode status (starting in October 2003) 2.2. Developing composites local studies (optional) 2.2. Developing composites local studies (optional) 2.3. Release of monthly and seasonal outlooks for selected 2.3. Release of monthly and seasonal outlooks for selected sites within each WFO CWA (starting in 2005) 2.4. Verification of the Composites forecasts (starting in 2005) 2.4. Verification of the Composites forecasts (starting in 2005) 3. Public outreach on these new products

46. MESSAGES TO TAKE HOME  THERE ARE NO NWS CONSISTENT LOCAL CLIMATE PRODUCTS AVAILABLE NOW  DOWSCALING CAN BE USED AS A TOOL FOR LOCAL CLIMATE PRODUCTS  THE LOCAL CLIMATE PRODUCT SHOULD BE CONSISTENT WITH THE NATIONAL WEATHER SERVICE PRODUCTS (CPC)  CPC METHODS COULD BE USED IN DEVELOPING SUCH LOCAL CLIMATE PRODUCTS  IN DEVELOPING OF DOWNSCALING AT LEAST THREE NWS ENTITIES SHOULD BE INVOLVED: REGIONAL OFFICE, CSD, AND CPC