1. Feasibility of data assimilation using documented weather record for reconstruction of historical climate Kei Yoshimura and Kinya Toride AORI, Univ Tokyo Yoshimura, Miyoshi, Kanamitsu, 2013 Yoshimura, Miyoshi, Kanamitsu, 2014 Toride and Yoshimura, in prep. H 18 O H H D 16 O

2. Weather Reconstruction Tree Ring Sediment in Lake Floods, Droughts Weather Famine Records Ice Core 3

3. Weather Records & Numerical Modeling Cloud amount (Rain rate) Global Spectral Model 4 Numerical simulation Objective: To make clear what kinds of information could be derived by assimilating only cloud amount

4. Local Ensemble Transformed Kalman Filter (Miyoshi and Yamane, 2008) Not only the assimilated variables, but also other variables will be corrected to be a consistent field.

5. Experiments by using Reanalysis data In order to validate this system, experiments were done by using NCEP-DOE reanalysis data Ideal observations of cloud amount were made by adding error to reanalysis data Earth System Research Laboratory =nature + error Standard error of cloud observation is 30% Observations were taken once a day 7

6. Experiments settings Global Spectral Model(GSM) was simulated from Jan.1,2005 to Jan.1, 2006 beforehand Use the output on Jan.1, 2006 as an initial condition Assimilate observations of cloud amount No assimilation run is also done to examine the effects of assimilation Jan.1, 2005 Jan.1, 2006 GSM No relationship!! We don’t know!We know! 8 Feb.1, 2006

7. Record Points Official meteorological network started Assume 18 Observation stations in Japan Historical Weather Data Base http://hwdb.yamanashi.ac.jp/ [Yoshimura,2007] The historical records are available on this website 9 17401870

8. Cloud Cover RMSE(noobs) – RMSE(assim) Red: Improved Blue: Worsened Cloud Assim No obs Reanalysis Cloud AssimNo obs >>>> Cloud is reproduced well!! Correlation Coefficient 11

9. Time series @ observation station Blue: cloud assim Red: No obs Black: Truth Results improved by assimilating cloud!! Specific humidity 12

10. Precipitation Wind (Surface) Wind (500hPa) Distribution Precipitation in Kyushu improved 13 Most of other variables clearly improved! Surface Pressure Precipitable water

11. Global impact: RMSE(noobs) – RMSE(assim) Amount of cloud Blue: Improved Red: Worsened Surface Pressure Japan 14 Jan1,2006 to Feb1,2006

12. Precipitable water Specific humidity(1000hPa) Precipitation Blue: Improved Red: Worsened 15

13. Data assimilation using Observed Cloud Data from Japan Meteorological Agency Based on visual observation or equipment on ground Correspondences with NECP data vary by observation point 17

14. Fig.8 Comparison with daily TCC obs. Toride and Yoshimura, in prep Blue: Worsened Red: Improved

15. Fig.9 Comparison with 6-hourly SAT observation Toride and Yoshimura, in prep Blue: Worsened Red: Improved

16. Summary and Conclusion Developed cloud coverage data assimilation system. Reconstruction from reanalysis data shows good results from various aspects. Reconstruction from observed data shows also good results. However bias treatment would be the key. Overall we apply this system for short term, long term effects should also be analyzed. This system has high potential to derive some information just from analog data in old diaries! Thank you!!