Diagnos(cs and new Ozone Climatology

Transcription

1 Diagnos(cs and new Ozone Climatology Simone Tilmes, Jean Francois, Louisa Emmons, Andrew Conley, Francis Vi9 Outline Model Chemistry Evalua3on Program New Ozone Climatology for Model Evalua3on Average profiles between ( ) Comparison to surface data Evalua3on of CAM3.15, CAM Chem fast chem. and WACCM Ref.1b Comparison of average profiles Comparison of Probability Distribu3on Func3ons Funding from NASA TCP and Na3onal Science Founda3on

2 Model Chemistry Evalua(on Program Purpose: quick look comparison of model results with established climatologies for chemical in the troposphere Input: monthly mean model output (h0 files) from CAM Chem, MOZART or WACCM Program will be further updated and is available at the CAM Chem web site, or Simone Tilmes, CAM Chem

3 Model Chemistry Evalua(on Program Purpose: quick look comparison of model results with established climatologies for chemical in the troposphere Input: monthly mean model output (h0 files) Data used for comparison: OH Climatology (Spivakovky et al. (2000) ) Surface carbon monoxide (CO ) measurements of the NOAA ESRL Global Monitoring Division between 1988 and 2001 (Novelli et al., 2003) Ozone Sonde climatology between 1995 and 2009 (Tilmes et al., in preparagon) Aircra[ observa3ons of various campaigns in different years and for various chemical species (Emmons et al., 2000) Sea salt and SO 4 observa3ons Program will be further updated and is available at the CAM Chem web site, or Simone Tilmes, CAM Chem Liaison:?lmes@ucar.edu

4 Model Chemistry Evalua(on Program Example: comparison of CAM Chem AR5 and fast chemistry run: Ozone Sondes Aircra[ Data Surface CO fast chem AR5 Program will be further updated and is available at the CAM Chem web site, or Simone Tilmes, CAM Chem

5 Selected Ozone Sondes Including: World Ozone and Ultraviolet Radia3on Data Centre (WOUDC) SHADOZ Global Monitoring Division (GMD) of NOAA

6 Ozone data for two sta(ons, 400 hpa Ozone (ppbv) Large variability at one pressure level > separa3on in seasons

7 Ozone data for two sta(ons, 400 hpa Hohenpeissenberg Payern Ozone (ppbv) Winter Spring Summer Fall Number/Season Year Year Year Year Number Profiles/Season: important to be considered

8 Comparison with Surface Observa(ons Uccle (sonde) Zingst Arkona (surface) Hohenpeissenberg (sonde) Hohenpeissenberg (surface) Payern (sonde) Zugspitze(surface) Surface ozone data: thanks to David Parrish

9 Ozone Sonde Sta(ons ( ) and number of observa(ons per season o N o N o N

10 Ozone Sonde Sta(ons ( ) and number of observa(ons per season o S 20 o N o S o S Climatology for and (similar to Logan et al., 1995)

11 Ozone Climatology (example: Hohenpeissenberg) Al3tude (hpa) Ozone (ppmv) Ozone (ppbv) Ozone (ppbv)

12 Model comparison CCMVal CAM3.5 (2x model run) CAM Chem Superfast Chemistry (1x model run) h0 output CCMVal WACCM3548 (2x model run)

13 Comparison CAM3.5 / O3Sondes Ozone (ppbv) Ozone (ppbv) Ozone (ppbv)

14 Comparison CAM3.5 / CAM superfast and WACCM CAM3.5 CAM3.5 CAM3.5 CAM fast CAM fast CAM fast WACCM WACCM WACCM

15 Comparison CAM3.5 / WACCM CAM3.5 CAM3.5 WACCM WACCM

16 Model Diagnos(cs: Comparing Destribtuions Observations The performance of a model might not be judged by comparing mean values of a distribu3on only. We propose an addi3onal diagnos3c for model evalua3on, using the Hellinger Distance. Mean Observa3ons: 360 ppb CAM 3.5.: 338 ppb CAM Superfast, h0: 279 ppb WACCM: 325 ppb

17 Model Diagnos(cs Mean Observa3ons: 360 ppb CAM 3.5: 338 ppb CAM Superfast, h0: 279 ppb WACCM: 325 ppb Cumula(ve Distribu(on Func(on (CDF) Using variable bin sizes to capture Changes in the slope of the distribu3on

18 Model Diagnos(cs Hellinger Distance is defined in the probability theory: used to quan3fy the similarity between two probability distribu3ons. 2 where P and Q are the Cumula3ve Distribu3on Func3ons of two distribu3ons. Here, we use the Hellinger Distance to define the similarity of model and observa3ons and therefore the performance of the model. From best to worst performance H goes from 0 to 1.

19 Model Diagnos(cs Mean Observa3ons: 360 ppb CAM 3.5: 338 ppb CAM Superfast, h0: 279 ppb WACCM: 325 ppb Similarity (H Value) between observa(ons model distribu(on: CAM 3.5: 0.22 CAM Superfast, h0: 0.40 WACCM: 0.23

20 Model Diagnos(cs Hohenpeissenberg, spring, 200hPa Super fast Winter Spring Summer Fall CAM3.5 /WACCM Mean difference between Observa3ons and Models

21 Model Diagnos(cs Winter Spring Summer Fall All sta3ons in NH mid la3tudes Mean in NH mid la3tudes

22 Winter Spring Summer Fall Comparison of model results Similarity (H Value) Mean % Difference Between Observa3ons and Models

23 Summary/ Conclusions Model Chemistry Evalua3on Program (idl): if you are interested please contact me! New Ozone Climatology for Model Evalua3on Sta3ons with a large number of data agree well with surface observa3ons Average profiles between ( ) in pressure coordinates and tropopause referenced al3tudes. Evalua3on of CAM3.15, CAM Chem fast chem. and WACCM Considering of the mean of a distribu3on for quick look comparisons Hellinger Distance is a useful measure to evaluate the similarity of a distribu3on. Monthly means reduce the performance of the model. Simone Tilmes (3lmes@ucar.edu)

24 Supplement

25 90 60 o N o N o N 20 o S 20 o N o S o S

26

27 Comparison CAM Chem Superfast / O3Sondes

28 Comparison CAM3.5h0 / O3Sondes

29 Comparison of model results cumula3ve distribu3on func3on Mean Value of the Distribu3on Observa3ons: 360 ppb CAM Superfast, h0: 279 ppb CAM 3.5., h0: 349 ppb CAM 3.5.: 338 ppb

30 Comparison of model results Winter Spring Summer Fall Between Observa3ons and Models

31 Ozone data for two sta(ons, 400 hpa Ozone (ppbv) Ozone (ppbv) Winter Spring Summer Fall Year Year

32 Time series of two sta(ons: 100hPa, 400hPa, and 800hPa; Number Profiles/Season: important to be considered

33 Comparison CAM3.5 / CAM superfast and WACCM CAM3.5 CAM3.5 CAM3.5 CAM fast CAM fast CAM fast WACCM WACCM WACCM