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ACRE Downscaling: 20C Reanalysis Application for Paleoclimate tracer simulations

ACRE Downscaling: 20C Reanalysis Application for Paleoclimate tracer simulations. Dyn . Downscaling with 20C-Rean 20C Isotope Reanalysis Kei Yoshimura. 20 th century Reanalysis (Compo et al., 2011). 1 December 1918 . Using only surface pressure data historically recorded since 1870’s

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ACRE Downscaling: 20C Reanalysis Application for Paleoclimate tracer simulations

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  1. ACRE Downscaling: 20C Reanalysis Application for Paleoclimate tracer simulations Dyn. Downscaling with 20C-Rean 20C Isotope Reanalysis Kei Yoshimura

  2. 20th century Reanalysis (Compo et al., 2011) 1 December 1918 • Using only surface pressure data historically recorded since 1870’s • Ensemble Kalman Filter for data assimilation (56 member) • T62L28 GFS with NOAH LSM SLP 500 hPa GPH Whitaker et al. (2009) • Reanalysis skill is comparable to current Day-3 forecast skill (Whitaker et al., 2009) • Ensemble Mean (EM) fields are publically available.

  3. Question for Dynamical Downscaling Are the ensemble mean fields appropriate as lateral boundary conditions for dynamical downscaling?

  4. Global Dynamical Downscaling NO! ORIG EM 19C 20C Diff

  5. Transient component of moisture divergence is smoothed out in Ensemble mean 19C 20C Total divergence Mean Transient No di/convergence

  6. Straight forward remedy: Downscale a single member many times and making an average of them. Is there any other way to reduce computation?

  7. Modification of single member by ensemble mean increment • where F is full field of physical variable, n is an ensemble member, bar indicates ensemble mean, and <> indicates running mean (e.g. one-month). • The downscaling will be performed using Fnnew as a lateral boundary forcing. Other members Single member forecast Fn Monthly running mean of a single member <Fn> Monthly running mean of all members <Fbar> increment Time Corrected member

  8. Specification of experiments • Atmospheric Forcing: 20thC Reanalysis (Compo et al., 2011) • Also regarded as “truth”. • Experiments: Different by the atmospheric forcings. • EM: Ensemble mean is used. • S1: Arbitrary chosen single member (run01) is used. • S3: Mean of the runs in which arbitrary chosen three single members (run01, run11, & run21) are used. • S6: Similar to S3, but 6 single members (S3 + run31, run41, & run51) are used. • MS: Modified single member is used. • Periods: • 1871-2008 for EM and MS. • 1871-1873 and 1981-1983 for S1-S6. • Model: IsoGSMwith global spectral nudging (Yoshimura et al., 2008)

  9. Seasonal mean precipitation with MS field ORIG MS 19C 20C Diff

  10. DJF mean of moisture divergence in MS 19C 20C Total divergence Mean Transient Appropriate di/convergence

  11. RMSD in 500Z against “truth” 19C 20C Globe NH SH

  12. RMSD in Precipitation against “truth” 19C 20C Globe NH SH

  13. RMSD in Wind against “truth” 19C 20C Globe NH SH

  14. Long term global precipitation MS EM Figure 10: Global mean precipitation by each experiments from 1871 to 2008. Original 20th century Reanalysis (green), EM (black), and MS (blue) are shown for all period. Those runs with the direct use of single members (which consists S6) are shown only for 1871-73 and 1981-83.

  15. Summary of Part 1 • Use of ensemble mean field as atmospheric forcings for downscaling study makes big shortcomings, particularly too small precipitation, when the spread of ensemble members is large. • Downscaling of each single ensemble member is straightforward, but requires lots of resources and time. • To avoid these problems, we propose a new method which modifies a single member field to have the same monthly skills as ensemble mean field (MS method) . • Use of the MS method clearly improves skill than direct usage of a single member. About the same skill as when 3 members are directly used.

  16. 2. 20C Isotope Reanalysis

  17. Yoshimura et al., 2008 Oceanic sediment d18O(millions yBP) Icesheet cores d18O・dD (~800 kyBP) Icecap cores d18O・dD (~20 kyBP) Speleothemd18O (~2000 yBP) Treeringd18O (~1000 yBP) Coral d18O (~400 yBP) nudged free Bristlecone pine tree at SE CA

  18. Why 20C Isotope Reanalysis? Impact Analysis(a.k.a., forward proxy modeling) Real World Model World Uncertainty of the impacts is quantifiable by reproducibility. Circulation Circulation Temperature Temperature Ice Core δ18O Ice Core δ18O Snow/Glacier Snow/Glacier Precipitation Precipitation Source Source Etc.Etc. Etc.Etc. The impact from each component is not quantifiable. The impact from each component is explicitly quantifiable.

  19. Isotopes in GCM/RCM Typical convective precipitation process • Incorporate water isotopes as passive tracers in GCMs/RCMs. Whenever water phase change takes place, isotopic water (HDO, H218O) behave differently to ordinary water (H2O). Courtesy of JMA Risi et al. 2008

  20. Comparison with GNIP data Bad Good Bad Good

  21. Treering δ18O in Cambodia Larger amplitude? @Kiriromnational park Wrong phase? Courtesy of M. Zhu and L. Stott

  22. Treering δ18O in West US Courtesy of L. Stott

  23. Seawater δ18Ofrom Coral and Model near Philippines δ18Osw (Red: ModelBlue: Coral) R= 0.54 SSS(Red: Model Blue:SODA) R= 0.55 Kojima et al., submitted

  24. Seawater δ18O from Coral and Model Bunaken (2N 125E) Modeledδ18Osw(‰) Coralδ18Osw(‰) Philippine (13N 124E) Modeledδ18Osw(‰) Coralδ18Osw(‰) Courtesy of K. Kojima

  25. Reproducibility of Interannual δ18Osw and Precip Amount Correlation StdDev of Annual Precip (mm/year) Large Precip Variability 　　　→ Local precipitation is recorded Small Precip Variability 　　　→Other factors (current, river flow, etc) may play big roles. Kojima et al., in prep.

  26. Icecoreδ18O at Eclipse Icefieldlon=-139.47 ; lat=60.51 Yalcinet al., 2003; 2006

  27. Icecap δ18O at Mt Huascaranm, Peru lon=-77.6 ; lat=-9.1 Thompson et al., 1995

  28. Summary of Part 2 • First 20th century Quasi Reanalysis for Isotope is now available. • First comparisons with paleoclimate proxy data are now underway. • This effort helps to develop the “forward proxy modeling” approach to more comprehensively understand the proxy data.

  29. Thank you! kei@aori.u-tokyo.ac.jp

  30. Pseudo Global Warming (Kawase et al., 2009)との違い Kawase et al., 2009 本手法 低周波：モデル 高周波：観測（再解析） 低周波：観測（アンサンブル平均） 高周波：モデル

  31. Method (c.f. Roden et al., 2000) • Data • NPP weighted annual temperature for esat • NPP weighted annual RH for eair • NPP weighted annual d18O in vapor for Rair • NPP weighted annual surface pressure for eleaf • NPP&amount weighted annual rain water d18O for Rsw • Rleaf= a*[1.032*Rleaf*(esat-eleaf)/esat + 1.021*Rleaf*(eleaf-eair)/esat + Rair*eair/esat]

  32. サンゴ同位体比から得られる海水同位体比の再現性サンゴ同位体比から得られる海水同位体比の再現性 Fiji (18S 179E) Modeledδ18Osw(‰) Coralδ18Osw(‰) Nauru (0.5S 166E) Modeledδ18Osw(‰) Coralδ18Osw(‰) Courtesy of K. Kojima

  33. Previous works Spectral Nudging + Isotope GSM– Poor man’s data assimilation for isotopes – http://meteora.ucsd.edu/~kyoshimura/IsoGSM1 Use large scale (>1000km) winds from R2 to constrain dynamical field, so that the isotopic field is also constrained and reproduced in daily to inter-annual time scales. Yoshimura and Kanamitsu, 2008; Yoshimura et al., 2008

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