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Validation of the FSU/COAPS Climate Model

Mary Beth Engelman [T. LaRow, D.W. Shin, S.Cocke, and M. Griffin] CDPW Meeting – Tallahassee, FL October 22, 2007. Validation of the FSU/COAPS Climate Model. Outline:. Introduction to work Background (Previous Studies) Data Methodology Results Summary/Conclusions Future Work.

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Validation of the FSU/COAPS Climate Model

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  1. Mary Beth Engelman [T. LaRow, D.W. Shin, S.Cocke, and M. Griffin] CDPW Meeting – Tallahassee, FL October 22, 2007 Validation of the FSU/COAPS Climate Model

  2. Outline: • Introduction to work • Background (Previous Studies) • Data • Methodology • Results • Summary/Conclusions • Future Work

  3. Introduction: The study • What I’m Doing… • Scientific Objective: • Validating the FSU/COAPS Climate Model • Variables: • Surface Temperature (Maximum and Minimum) • Precipitation • Comparing: • Model forecast vs. Observations • Real-time runs (persisted SSTA) vs. Hindcast runs (observed weekly SSTA) • Scientific Questions: • 1. How “well” does the FSU/COAPS climate model forecast surface temperature and precipitation over the southeast United States? • 2. How do forecasts made from persisted SSTA compare to hindcasts made from weekly updated prescribed SSTA?

  4. Introduction: Reasons for study • And Why… • Benefit to society: • Crop models can assist decision makers in minimizing loses and maximizing profits in the agricultural community (Hansen et al., 1997) • Benefit to the science: • Evaluation of the need for a coupled ocean-atmosphere climate model • Validation potentially leads to improved climate models/forecasts

  5. Previous Studies: Persisted vs. prescribed SSTA • Why use persisted anomalies for climate forecasts? • Currently, coupled models do not out perform predictions given by persisted SSTA at lead times less than 3 months (exception: ENSO events) • Why compare real-time runs to hindcasts runs? • Overestimates skill, but evaluates model’s possible potential • Determines how much skill is lost from using persisted SSTA • Performance of real-time vs. hindcasts runs? • Similar values of skill, unless large SSTA errors (Goddard and Mason, 2002)

  6. Previous Studies: SST and the southeast climate • SOI and Southeast United States El Nino Signals (Ropelewski and Halpert, 1986) • Consistent signals: • Above normal precipitation (October-March) • Below normal temperature • Physical mechanisms causing precipitation and temperature signals (teleconnections vs. direct link) • Tropical Pacific SSTA and ENSO Signals (Montroy, 1997) • Strongest signals: Precipitation in the Southeast US • Positive: November - March • Negative: July-August • Teleconnections (Gershunov and Barnett, 1997) • Strong teleconnection between tropical Pacific SSTs and wintertime precipitation over the coastal southeast US (negatively correlated in the interior states)

  7. Previous Studies: The model • The FSU/COAPS Climate Model: • Large Scale Features: • Capable of reproducing large scale features associated with ENSO (Cocke and LaRow, 2000) • Capable of reproducing teleconnections response (Cocke et al., 2007) • Temperature and Precipitation: • Regional and global model are both in “reasonable” agreement with observations (Cocke and LaRow, 2000) • Alterations to the physics of the model resulted in a strong wet bias and cold surface temperature bias, upgrading the model (inclusion of the NCAR CLM2 as land parameterization) reduced biases (Shin et al., 2004) • Regional model rainfall events: • Over predicts small rainfall events • “Accurately” produces higher magnitude rainfall events (Cocke et al., 2007) • “Seasonal variations, anomalies, and changes in extreme daily events for maximum temperature are successfully forecasted” (Lim et al., 2007)

  8. Previous Studies: Summary • What to expect in my study (Hypothesis): • Model Forecast vs. Observations: • Model forecasts will show a wet bias in precipitation and cold bias in maximum surface temperature (unless bias corrected) • Forecasts should show “reasonable” skill (anomalies, seasonal variations, various skill scores) • Forecasts will tend to over predict the number of small rainfall events • Real-Time Runs vs. Hindcast Runs (Persisted vs. Prescribed SSTA ) • In general, hindcast runs will out perform real-time runs • Real-time runs should be comparable to hindcasts runs when equatorial Pacific SSTA remain fairly constant throughout the duration of the forecast • Loss of skill in the real-time runs will occur • Whenequatorial Pacific SSTA take a sharp change • Where areas are greatly influenced by SSTs (high teleconnection signal) • Skill of real-time runs will decrease after 3 months

  9. Data: Model FSU/COAPS Climate Model • Details of model (Spectral Model): • Resolution: • GSM: T63 (1.875˚ x 1.875˚) • NRSM: 20 x 20 km • Bias: • Precipitation: wet • Max Temperature: cold • Real time run: • Persisted SSTA • Model Runs (5 ensembles): • January 2005 and 2006 (3 month forecasts) • Hindcast runs: • Prescribed SSTA (updated weekly) • Model Runs (5 ensembles): • October 2004 and 2005 (6 month forecasts) • Model Climatology (20 ensembles): • 1987-2005 Model Details

  10. Data: Observations • COOP Observation Network: • Daily/Monthly Temperature and Precipitation (January 1986-June 2007) • Station to grid: • 172 long term stations (1948-present) • For each grid point… • Finds the closest stations (using the great circle distance) • Assigns the station’s temperature and precipitation to that grid

  11. Methodology • In General: • Looking for the ability to forecast: • Correct anomaly • Extreme events • Heavy Precipitation/Drought • Freezing Temperatures • Today’s Talk (Temperature): ability to forecast: • 1. Average monthly temperature anomaly • 2. Max temperature distributions (bias corrected)* • 3. Freeze events (bias corrected)* *bias corrected: where:

  12. Comparing JFM 05 vs JFM 06 SSTA 2005 2006 2005: SSTA switch from warm to cool 2006: SSTA remain fairly constant (figures provided by: http://www.pmel.noaa.gov/tao/jsdisplay/ )

  13. Results: Ability to forecast anomalies Error in Monthly Temperature Anomalies (˚C): 2006 2005 • Error in Temperature • (ε= f ` - o`) • 2006 real-time runs resulted in less error than the 2005 real-time runs • 2005 and 2006 hindcasts runs showed approximately the same magnitude of error • In 2006 the real-time runs and hindcast runs resulted in “similar” forecast error Real-Time Hindcast Real-Time Hindcast Jan. |ε| = 4.28 |ε| = 3.14 |ε| = 0.77 |ε| = 0.95 Feb. |ε| = 2.72 |ε| = 1.96 |ε| = 2.50 |ε| = 2.79 Mar. |ε| = 4.86 |ε| = 0.87 |ε| = 2.65 |ε| = 1.40 Initial time: Jan. 05 Oct. 05 Jan. 06 Oct. 06

  14. Results: Ability to forecast distributions • Daily Maximum Temperatures • (January-March) • Observed distributions more accurately captured by the models in 2006 • Hindcasts vs. real-time runs differ more in 2005 than in 2006 • In both years, model over estimates tails of the distribution

  15. Results: Ability to forecast extreme events Tmin < -2˚C Tmin < -4˚C 2005 2006 2005 2006 Observed • Deep Freeze Events • (January – March) • Model tends to over predicts number of freeze events • Real-time and hindcasts runs are more similar in 2006 Real-Time Hindcast

  16. Summary/Conclusions: • Model forecasts show a cold bias in surface temperature • The model tends to over predict “extreme” events • Real-time runs resembled hindcasts runs when equatorial Pacific SSTA remain fairly constant throughout the duration of the forecast (as in 2006) • When SSTA changed during the time period of the forecast (as in 2005), hindcasts runs outperform real-time runs even at lead times of 3 months

  17. Future Work: • Evaluate Real-Time October runs: • Currently comparing Januray real-time runs (0 month lead time) to October hindcast runs (3 month lead time) • Examine other verification methods/skill scores: • Anomaly correlations • Taylor diagrams • Relative Operating Characteristics (ROC) • Heidke Skill Score (HSS) • Further investigate SSTA in the tropical Pacific and their effects on model forecast errors

  18. Questions?

  19. References: • Cocke, S., and LaRow, T.E., 2000: Seasonal predictions using a regional spectral model embedded within a coupled ocean-atmosphere model. Mon. Wea. Rev., 128, 689-708. • Goddard, L, S.J. Mason, S.E. Zebiak, C.F. Ropelewski, R. Basher, and M.A. Cane. 2001. Current approaches to seasonal-to-interannual climate predictions. International Journal of Climatology. 21: 1111-1152. DOI: 10.1002/joc.636 • Goddard L, Mason SJ, 2002. Sensitivity of seasonal climate forecasts to persisted SST anomalies. Climate Dynamics 19: 619–631 • Gershunov, A., 1998: ENSO influence on intraseasonal extreme rainfall and temperature frequencies in the contiguous United States:Implications for long-range predictability. J. Climate, 11, 3192–3203.. • Jolliffe, I.T., and D.B. Stephenson, 2003. Forecast Verification. Wiley, 240 pp. • Lim, Y.K., Shin, D.W., Cocke, S., LaRow, T.E., Schoof, J.T., O’Brien, J.J, and Chassignet, E., 2007: Dynamically and statistically downscaled seasonal forecasts of maximum surface air temperature over the southeast United States. • Montroy, D., 1997: Linear relation of central and eastern North American precipitation to tropical Pacific sea surface temperature anomalies. J. Climate, 10, 541–558.. • Murphy, A.H., 1993. What is a good forecast? An essay on the nature of goodness in weather forecasting. Weather and Forecasting, 8, 281-293. • Ropelewski, C. F., and M. S. Halpert, 1986: North American precipitation and temperature patterns associated with the El Niño/Southern Oscillation (ENSO). Mon. Wea. Rev., 114, 2352–2362. • Shin, D.W., S. Cocke, T. E. LaRow, and J. J. O’Brien (2005), Seasonal surface air temperature and precipitation in the FSU climate model coupled to the CLM2, J. Clim., 18, 3217-3228. • Wilks, D.S., 1995. Statistical Methods in the Atmospheric Sciences. An Introduction. Sans Diego: Academic Press. • Wilks, D.S. and R.L. Wilby. 1999. The weather generation game: A review of stochastic weather models. Progress in Physical Geography 23:329-357.

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