SPARC Data Assimilation Working Group Report

Transcription

1 SPARC Data Assimilation Working Group Report Saroja Polavarapu Meteorological Research Division Environment Canada, and, University of Toronto SPARC SSG meeting, Kyoto, Japan, October 2009

2 Review of previous workshops Baltimore June 2002 Florence June 2003 ECMWF/SPARC seminar June 2003 Banff Sept DA+strat winds Noordwijk Sept DA Toronto Sept DA+IPY No workshop 2008 (GA Bologna)

3 SPARC DA 2009 meeting Subsessions of MOCA-09 (IAMAS) in Montreal, July 2009 M01 Middle Atmosphere session (1 afternoon, Tues. July 21) J21 Adv in Data Assimilation (1 afternoon, Fri. July 24) DAWG Side meeting (morning, Friday July 21) Motivation: attract new people to SPARC-DAWG Side benefit: low cost Reality: Too much competition with parallel sessions M01 DA session in parallel with seamless prediction (J07) DAWG meeting in parallel with two strat-trop sessions and DA session (ice, ocean DA) Discussion had about 10 people. Can t compete with parallel sessions Organization requires 2-years advance preparation to avoid competition with parallel sessions Conclusion: Stick to dedicated workshops

4 SPARC-DA workshop participants Participants MOCA-09 Toronto Noordwijk Banff July Sept Oct Sep 2005 Canada USA Europe Asia TOTAL 19 46/ D.A

5 SPARC DA 2009 meeting M01 (middle atm session): 4 talks + 2 posters = 6 J21 (adv in DA): 8 talks + 4 posters = 12 DAWG side meeting: ranged from 30 to 10 people Invited speakers on seamless prediction: Mark Rodwell, ECMWF Using DA to improve climate models troposphere Manuel Pulido, Argentina Using DA to improve climate models stratosphere Craig Bishop, NRL Seamless prediction Scheduled discussion

6 Seamless prediction and model error Seamless Prediction Is a broad term, generally referring to a unified framework for research on prediction in weather and climate is a major theme of the WCRP Coordinated Observation and Prediction of the Earth System (COPES) strategic framework. Climate models need to describe processes on time scales from years to seasons to weeks to one day (Palmer et al. 2008) implies validating models on a variety of time scales Data assimilation is a tool for learning about models indirect information about model error is contained in analysis increments methodology can be used to estimate uncertain parameters in climate models (instead of initial conditions).

7 Shorter forecasts connected to local error sources Figure courtesy of Mark Rodwell 500 hpa temperature forecast errors over winter day forecast Deep colours show statistical significance Tropics have significant systematic model errors in 1- day forecasts 10-day forecast In 10-day forecasts, error patterns more complex, significance is reduce

8 Estimating GW source parameters Figure courtesy of Manuel Pulido 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. 2009, in preparation).

9 Comparison of CMAM-DAS chemistry to Eureka FTIR Figure courtesy of Rebecca Batchelor ozone Compare to polar point measurements (80 N) of partial column ozone CMAM-DAS has no ozone assimilation Good agreement over 250 days (longer too) But CMAM has 50 or so species. What about the others?

10 Comparison of CMAM-DAS chemistry to Eureka FTIR Figure courtesy of Rebecca Batchelor CMAM HCl is too high HCl ozone

11 Comparison of CMAM-DAS chemistry to Eureka FTIR Figure courtesy of Rebecca Batchelor HCl is too high ClONO 2 too low Total Cl is fine HCl ClONO2 partitioning of Cl is local only. HCl not advected separately PSC module had no NAT chem mesoscale dynamics important? T forecast are poor?

12 ECMWF is focusing on model error (not initial error) estimation for the future Craig Bishop reviewed ECMWF issues mentioned at WCRP-THORPEX meeting in Nov (Buenos Aires) by Yannick Tremolet (ECMWF): Weak constraint 4D-Var experiments produce model errors with very large wind components in stratosphere Errors were traced to incorrect specification of masswind balance terms in stratosphere in background error covariances Removing balance constraints above 20 hpa improved analyses in weak and strong contraint 4D-Var

13

14 Figure from Kuroda s talk

15 Figure from Kuroda s talk

16 SPARC DAWG 2010 Workshop June 21-23, 2010 Exeter, UK (Met Office) David Jackson local organizer Coordination with other meetings: WMO/UNEP Scientific Assessment of Ozone Depletion, Switzerland, June 28-July 2. Regular flights from Bristol to Geneva make transfer from SPARC DAWG to WMO/UNEP easy. Possible themes: meso-strat-trop coupling Seamless prediction (model error, seasonal prediction) Air quality and aerosol assimilation Invited speakers not yet identified

17 SPARC Data Assimilation Working Group: 5 year review Created in 2002 to coordinate and promote data assimilation work relevant to SPARC Collect information on stratospheric data sets on meteorology and chemistry (quality, availability, software ). Process-focused quality assessments. Collect and document information in data assimilation systems. Liaise with space and other agencies (e.g. GCOS) on SPARC data needs.

18 How can we achieve these goals? Suggestions from Alan O Neill in 2002: Workshops with a theme Invite experts in related areas who normally don t attend SPARC-DA workshops. Connect with user community. YES Dedicated workshops, special sessions of EGU, etc. YES Preparation of report or review article For SPARC newsletter YES For general audience, i.e. BAMS NO Intercomparison, or collaborative projects YES/NO Ozone, e.g. ASSET Transport (age of air, trajectory calc ns) Water vapour GW body force SPARC-IPY GEOSS and GMES projects (PROMOTE II, GEMS, )

19 WMO EC PORS meeting WMO Executive Council on Polar Observations, Research and Services (EC-PORS) meeting was held in Ottawa, Canada on October 2009 SP presented lunch time talk (30 min) on The polar stratosphere The stratosphere is a global circulation. There can be impact on the troposphere during polar winter on weekly to seasonal timescales. Ozone recovery in the southern hemisphere has an impact on tropospheric climate, even outside of high latitudes Climate models will need to simulate the stratosphere for future climate prediction studies Observations of polar regions are needed to advance understanding of the stratosphere and how it is coupled to the troposphere on weather and climate timescales Workshop focused on how to facilitate a Polar Decade building upon IPY activities

20 SPARC DAWG beyond 2013 Is SPARC DAWG currently serving SPARC needs? Should it morph into an activity focused on seamless prediction?