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SPARC and WGNE

SPARC and WGNE. Saroja Polavarapu Chemistry Climate Measurements and Research Climate Research Division. WGNE, Tokyo, Japan. 18-22 October 2010. OUTLINE. Why is SPARC interested in data assimilation? Impact of stratosphere on medium range weather forecasts Seamless prediction

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SPARC and WGNE

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  1. SPARC and WGNE Saroja Polavarapu Chemistry Climate Measurements and Research Climate Research Division WGNE, Tokyo, Japan. 18-22 October 2010

  2. OUTLINE • Why is SPARC interested in data assimilation? • Impact of stratosphere on medium range weather forecasts • Seamless prediction • Using data assimilation to learn about model errors • Using data assimilation to tune parameterization schemes and reduce model error

  3. Why is SPARC interested in data assimilation?

  4. Why is SPARC interested in data assimilation? SPARC Needs • Climate trends (long term global datasets free from trends) • Variability of climate (QBO, SAO, etc.) • Improved understanding of processes (Polar, TTL, etc.) • 3-D velocity fields with reduced data assimilation “noise” (which leads to spurious transport) • Improved parameterizations in models (e.g. GWD) Resources • Measurements– potentially good control over calibration, but sparse and incomplete • Analyses – complete data set but need data gaps filled by models (can introduce errors)

  5. Activities that respond to SPARC’s interest in data assimilation • SPARC Data Assimilation Working Group (DAWG) • Annual workshops which have themes (connect DA with science, measurement, user communities) • Reports in SPARC Newsletters • Collaborations: SPARC International Polar Year project • SPARC assessments • SPARC Report on middle atmosphere climatologies (2002) • SPARC CCMVal Report (2010) • Available at http://www.atmosp.physics.utoronto.ca/SPARC

  6. Quasi-Biennial Oscillation SPARC (2002) Vertical structure of QBO amplitude at the equator. • Dunkerton and Delisi (1985) • Singapore radiosondes

  7. Equatorial wave activity SPARC CCMVal (2010) Report on the Evaluation of Chemistry-Climate Models SPARC Report No. 5, WCRP-132, WMO/TD-No. 1526 Kelvin wave amplitudes differ by factor of 3 in reanalyses

  8. SPARC-WGNE links • SPARC reports provide feedback on reanalyses, often using process-oriented diagnostics • SPARC reports continue to find disagreements among reanalyses in the tropics • SPARC’s data assimilation working group: annual workshops, newsletter reports, collaborations • 21-23 June 2010 Exeter • 20-22 June 2011 Brussels

  9. Impact of the stratosphere on medium range weather forecasts

  10. Good stratospheric representation may help improve troposphere forecasts • Lower stratospheric NAM is a better predictor of surface AO pattern 10 days later than the AO itself (Baldwin 2003, Science) • Stratospheric initial conditions affect 15-day tropospheric forecasts (Charlton et al. 2004, QJRMS) Time delay Long timescale Baldwin and Dunkerton (2001)

  11. 4D-Var High Top High Top 4D-var 3D-var Low Top A good stratosphere impacts troposphere forecasts as much as 4D-Var dam dam Improving the stratosphere improves 5-day forecasts in the troposphere On June 22, 2009 Canadian Meteorological Centre implemented operationally a global stratospheric model (0.1 hPa) for medium range weather forecasts O-F(5 day) against NH sondes for GZ Winter Dec. 20 – Jan. 26, 2006 (75 cases)

  12. NH summer NH winter -1.2 -1.8 SH summer -4.1 -2.8 -9.5 -4.9 Improvement in forecast error stddev Winter NH Dec. 26 – Feb. 2, 2007 (77 cases) SH winter -8.0 -3.7 Winter SH June 22 – Aug. 21, 2006 (122 cases)

  13. Results • Forecast error standard deviations are improved at all forecast ranges in winter • Improvement is much greater in winter than summer (improvement depends on season, not hemisphere) • Improvement in skill spreads downward with forecast range in winter • Improvement in troposphere is comparable to that seen when upgrading from 3D to 4D-Var in winter

  14. Attribution of the improvement Geopotential height standard deviation (%) NH winter (High Top – Low Top)/Low Top Impact of model/(High Top – Low Top) -75

  15. -0.3 Summer SH stddevImpact of obs 4D-Var No significant impact of obs changes in summer -0.3 3D-Var

  16. Time delay Long timescale Modelling stratosphere is important • Winter polar stratospheric variability is due to forcing from tropospheric waves • Sufficient to observe tropospheric waves correctly since they will propagate up • Role of stratosphere is to modulate response to waves so it must be modelled accurately Baldwin and Dunkerton (2001)

  17. Stratopause is above 0.01 hPa! 70ºN zonal mean temperatures during 2006 SSW Gloria Manney ECMWF too low too cold GEOS-5 too low too warm

  18. Impact of GWD scheme on forecasts Orr et al. (2010) Mean 5-day forecast error for Aug 2009 (ECMWF,T511L91) Scinocca (2003) nonoro GWD scheme Bias at winter pole stratopause Rayleigh friction

  19. Implications for WGNE • Results show model lids should be raised above stratopause in order to model entire stratosphere • Improved modelling of stratosphere can be obtained by raising lid (CMC, GMAO) or replacing Rayleigh friction with a GWD scheme (ECMWF) • Intercomparisons may not be appropriate since they may be hard to interpret. If modelling of stratosphere is critical to modulation of tropospheric forcing, then results from one system refer to only its model errors. • Individual groups could document sensitivities of own system to understand causes

  20. Seamless prediction

  21. Learning about models with data assimilation • For climate applications, the data assimilation process can be a diagnostic tool: • Comparing model states with measurements on a given day can lead to insight into model deficiencies • Example: Comparing constituent forecasts to measurements • Use assimilation methodology to determine model parameters instead of initial conditions • Example: Estimating parameters in physical parameterizations of subgrid scale processes

  22. Insight into model deficiencies

  23. Learning about model errors Figure courtesy of Andreas Jonsson CMAM climatology is too warm in upper stratosphere Global mean temperature errors point to problems with radiation schemes CMAM=Canadian Middle Atmosphere Model

  24. Compare to partial column ozone obs at Eureka (80 N) CMAM-DAS has no ozone assimilation Good agreement over 250 days (longer too) But CMAM has 50 or so species. What about the others? Can compare model states to obs on a given day Figure courtesy of Rebecca Batchelor ozone DAS=Data Assimilation System

  25. Can compare model states to obs on a given day Figure courtesy of Rebecca Batchelor HCl CMAM HCl is too high ozone

  26. Can compare model states to obs on a given day Figure courtesy of Rebecca Batchelor • HCl is too high • ClONO2 too low • Total Cl is fine HCl ClONO2 Points to problem of chlorine partitioning in CMAM

  27. Estimating model parameters

  28. Non-orographic Gravity wave drag • Parameterizes forcing on zonal flow due to subgrid scale gravity waves • Small changes in momentum flux at launch level in GWD schemes lead to changes in downwelling over winter pole, and temperature changesdetermine whether PSC threshold is surpassed • How are parameters in GWD set? • Prescribed ozone: Tune parameters to zonal mean flow • Interactive ozone: need to get seasonal cycle of ozone also

  29. Using 4D-Var to estimate forcing due to gravity wave drag Pulido and Thuburn (2005,2006,2008) • Instead of using mismatch between observations and forecast to determine initial conditions (ICs), assume ICs correct and determine drag on u and v • Can estimate 3D daily drag field. Resulting drag field consistent with previous estimates • Strength and location of winter deceleration centres • Descent of drag with QBO, SAO in tropics

  30. Estimating parameters in GWD scheme Figure courtesy of Manuel Pulido m/s/day Missing zonal force for July 2002 due to unresolved waves. Estimated with a 4DVar assimilation system (Pulido and Thuburn 2008, JC). Forcing from Scinocca (2003, JAS) GWD scheme using the optimum parameters (Pulido et al. 2010, in preparation).

  31. Implications for WGNE • “Seamless prediction” is at heart of strategic framework of the World Climate Research Programme (WCRP) • Data assimilation can play a role in characterizing model errors or tuning model parameters to reduce errors. Examples of both given here • Longer time scales: CMIP5 will contain some high top models (8) • Improving seasonal forecasting: Working Group on Seasonal to Interannual Prediction (WGSIP) • CLIVAR-WGSIP CHFP. Parallel expts with High Top models • Transpose Atmospheric Model Intercomparison Project (AMIP) asks climate models to run 5-day forecasts.

  32. Transpose Atmospheric Model Intercomparison Project (AMIP) asks climate models to run 5-day forecasts. • High top model participation? • Can stratospheric modeling issues be addressed with this experimental setup? • Can raise issue at SPARC SSG, Pune, India Feb. 2011 • Can gauge interest from SPARC DAWG at workshop in Brussels in June 2011 • WCRP, SPARC, SPARC-DAWG are evolving. What role will SPARC-DAWG play? New applications dealing with long timescales e.g. carbon flux estimation (Miyazaki), Earth rotation parameters assimilation (Neef)

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