Atmospheric Reanalysis for Multi Centuries using Historical Weather Archives and Isotopic Proxies Kei Yoshimura (UT/SDSU), A. Okazaki, K. Toride, P. Neluwala, S. Shoji, T. Miyoshi, et al. Contents based on Toride et al., 2017, MWR Okazaki and Yoshimura, 2017, CP Yoshimura, 2015, JMSJ H 18 O H H D 16 O
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Atmospheric Reanalysis for Multi Centuries using Historical Weather Archives and Isotopic Proxies
Kei Yoshimura (UT/SDSU), A. Okazaki, K. Toride, P. Neluwala, S. Shoji, T. Miyoshi, et al.
Contents based onToride et al., 2017, MWR
Okazaki and Yoshimura, 2017, CPYoshimura, 2015, JMSJ
H18OH
HD16O
(Atmospheric) Reanalysis"Reanalysis" means producing historical global objective analysis dataset for over last decades in a consistent manner with a fixed state-of-the-art numerical weather prediction data assimilation system. (from JRA25 HP)
Our Research Q: How can we make such high-temporal resolution global dataset much longer?
Analyses of long-term historical weather-climate variations
Reconstruction from climate proxies
Analyses from direct measurement
Long-term variations (>years)
Remote signals aredumped
No data before 19thC
Isotopic Proxy Data Assimilation
Old Weather Data Assimilation
Short-term variations (hr~day)
Limit Limit
Breakthrough!
Multi Century Atmospheric ReanalysisApplication: Validation of CGCMs, Causality of social changes
Data AssimilationIsotopic Proxies
Old weather diaries
Info-Measure Fusion Info-Measure Fusion
Our Strategy for “Multi-Cent. Atmos. Reanalysis”
Breakthrough!
Toride et al., 2017Okazaki and Yoshimura, 2017
dividesfading?
Integration
Long term (>1yr) variation:(Isotopic) Proxy Data Assimilation• It’s a recent Hot Topic!!
• Last Millennium Reanalysis (Hakim et al.) now V3• Franke data (Franke et al.) (Non-isotopic so far)• Our activity (Yoshimura et al.)
• Online or Offline?• Online DA: Back to DATUN (von Storch, 2000), Forcing
Singular Vector (van der Schrier and Barkmeijer 2005; 2007), Particle Filters (Crespin et al. 2009), Mathiot et al. 2013)
• Offline DA: Initiated by Goosse et al. (2006), Bhend et al. (2012), 1-member ensemble DA (Steiger et al. 2014)
• Reconstructed or Measured?• Very few examples of DA with measured values so far.
• Variances by time and ensemble space are equivalent each other: Ergodic theory “1-member ensemble”
• No prediction cycle is done. Only analysis step. “Offline” data assimilation
k=1
k=2k=3k=4
k=5
k=6
Period 1 Period 2 …
Time resolution of proxy data
☆:y--- : xb× : xa
● : Ensemble spread of xb
( )( ) 1−
+=
−+=
RHHPHPK
xHyKxxT
bT
b
bba
xa Analysis (n x 1)
xb First Guess (n x 1)
y Observation (m x 1)
K Kalman Gain(n x m)
H Observation Operator (m x n)
Pb Model Error Covariance (n x n)
R Observation Error Covariance (m x m)
n: (model grid number) x (vertical levels) x (number of variables)m: number of observation points
Y=1850
Y=1851Y=1852Y=1853
Y=1854
Y=1855
Result of Idealized Experiment
Correlation between the truth and the analysis The assimilation skill is high for temperature
and precipitation as well as for the assimilated variable
Initial Condition:1 year spin-up (single GSM) (1829-1830) starting with 1995/01/01.
Ensemble selection:20 ensembles using different dates from a GSM only run from 1830-1331 (Nature run)
Sola
r rad
iatio
n (w
/m2 )
TC C
loud
(%)
Wea
ther
Cat
egor
y W
eath
er C
ateg
ory
Application to 1830’s (Preliminary results)
Neluwala and Yoshimura,
in prep.
Summary
• Two key technologies for multi-century atmospheric reanalysis have been developed and tested.
• Isotopic Proxy Data Assimilation used offline & measured data assimilation. Good quality for interannual SAT/P variations for 19c-20c. Lack of proxy data further back to past is crucial.
• Old Weather Data Assimilation used online & reconstructed (TCC) data assimilation. Positive skill for TCC/SAT/q2m etc. Footprint may be extended remotely. Data availability is also crucial.
21Solar radiation (W/m2) vs Categories Cloud content vs Categories
Relationship between Jan-March JMA Downward shortwave Solar radiation, Total Cloud content vs weather categories derived from weather descriptions (1995 , 18 stations)
Relationship between cloud and weather categoriesJanuary to March (two months-1995)
Locations of diaries used
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Application to 1830’s (Preliminary results)
-- No assimilation-- Assimilated
-- No assimilation-- Assimilated-- Category (1-3)
Japan Domain (18 grids average) 1830 Jan –March (daily mean)
Point (Okayama 35.07, 134.01 ) 1830 Jan -March
Cloud % Solar radiation (w/m2)
Solar radiation (w/m2)%
Neluwala and Yoshimura, in prep.
Data assimilation using Observed Cloud Data
from Japan Meteorological Agency
0
20
40
60
80
100
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
Chosi
ncep jma
0
20
40
60
80
100
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
Nemuro
ncep jma
0
50
100
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
Hamada
ncep jma
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
R (JMA data & NCEP data)
Based on visual observation or equipment on ground
Correspondences with NECP data vary by observation point 17
25
Location of available personally diaries in 1830-1840-- Model grids (T62 grids)
Locations of diaries
Latit
ude
-->
Longitude -->
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Experimental setup • Here we assimilate weather diary information.• Weather diary data
• The analog weather diaries has been converted to three categories (1,2,3) considering the clearness in the sky (i.e sunny, moderately cloudy, cloudy) (Mika Ichino, 1997)
• In this experiments 8 diary data are assimilated daily.• Diary data are converted to solar radiation using the emphatical
relation ship considering weather description and solar radiation data from 1979-199 (MikaIchino, 1997)
• Boundary condition • SST and sea-ice reconstruction data of Jörg Franke et.al (2017)
• Initial condition• Two year(1829-1831) GSM run was carried out with the initial
condition from a recent year(1995). • 1830 model run 20 ensembles were initiated from the above model
run 1830 January 1 to January 20 output.
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Relationship between January clearness (KT) index vs weather categories(K) derived from weather descriptions) Japan metrological agency data from 1979-1999.
Ichino, M. (1995)
( )( )s
dT QInsolationTotal
QradiationsolardaccumulatedailyK −=
Total insolation can be calculated considering earth revolution and point location according to Kondo et. El (1995). Thus when weather category is known solar radiation can be calculated from about relationship
Relationship between Downward shortwave Solar radiation and weather
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Next slides 1830 results
• I have plot the ensemble mean and ensemble spread (1 standard deviation ).
• Top two graphs shows the domain average Tc Cloud and solar radiation including the the ensemble spread .
• I would try to plot the bottom graphs as well
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Other slides
• Next slides shows the analysis of the diurnal cycle which can be used for our next discussion
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These are experiments using JMA data in 1995.JJMS data is converted to 5 categories and then solar radiation is assimilated using those 5 categories.
-- No assimilation – Assimilated -- Observation (JMA) -- Category (1-5)After solar radiation assimilation we can observe some noise in temperature and precipitation, as these are important variables next task is to investigate this and improve these variables.
This investigation will be done using JMA data
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ReferencesFranke, J., Brönnimann, S., Bhend, J., Brugnara, Y., & Wanner, H. (2017). A
monthly global paleo-reanalysis of the atmosphere from 1600 to 2005 for studying past climatic variations. Scientific Data, 4, 170076. http://doi.org/10.1038/sdata.2017.76
Ichino, M and Takehiko Mikami (2003), Spatial and Temporal Differences of Global Solar Radiation: Applicability of Mean Daily clearness index, Geographical reports of Tokyo Metropolitan University, Vol.38, p.15-21.
Ichino, M. (2007), Climatological study on distribution of the frequencies of global solar radiation for each weather categories at Tokyo. Ochanomizu Geotechnical Societyお茶の水地理, Vol.47, p.15-26. Retrieved from http://hdl.handle.net/10083/12722