Climate variations during 20th Climate variations during 20th century in the Northwest Pacific century in the Northwest Pacific
Region.Region.Dmitry D. KaplunenkoDmitry D. Kaplunenko**,,****, Vladimir I. Ponomarev , Vladimir I. Ponomarev **, Young J. Ro , Young J. Ro ****, ,
Olga O. TrusenkovaOlga O. Trusenkova** and Serge T. Trusenkov and Serge T. Trusenkov**
** – V.I. Il’ichev Pacific Oceanological Institute, Vladivostok, Russia; – V.I. Il’ichev Pacific Oceanological Institute, Vladivostok, Russia;
**** – Chungnam National University, Daejeon, Republic of Korea. – Chungnam National University, Daejeon, Republic of Korea.
E-mail: [email protected]: [email protected]
Introduction
This work provides:- Study of climate variability by the datasets based
on different methods of data augmentation (instrumental and reanalysis based)
- Analysis on climate variability by the data on monthly air temperature for Northeast Asia and SST for North Pacific region for centennial (correlations, wavelets) and semicentennial period (correlations, wavelets, trends)
Used data sources
• Instrumental:
Air Temperature: Global History Climatic Network:http://lwf.ncdc.noaa.gov/oa/pub/data/ghcn/v2/ghcnftp.html SST: GLBSST (Japan Meteorological Agency) ftp://ddb.kishou.go.jp/pub/Climate/SeaSurfaceTemp
• Reanalysis based:
Air Temperature: NCEP/NCAR Reanalysis project data: http://www.cdc.noaa.gov/cdc/data.ncep.reanalysis.htmlSST: Hadley Centre for Climate Prediction and Research:http://badc.nerc.ac.uk/data/hadisst/
Data coverage (Tair)
Data coverage for meteorological data:
Contribution (GHCN stns.):
NCEP: 2.5°x2.5°
Russia 60
Korea 9
Japan 46
China 32
Mongolia 8
Total 155 80 100 120 140 160 180
20
40
60
Data coverage (SST)Sea surface temperature.
• GLBSST (Japan Meteorological Agency) (North Pacific, period 1946-2002, 2°x2° )
• Hadley Centre for Climate Prediction and Research (North Pacific,1946-2002,1870-2002,1°x1° )
Data coverage:
Assessing of climate changes by prepared Assessing of climate changes by prepared datasets using known statistical methodsdatasets using known statistical methods
Assessing methods:• Principal Component Analysis (EOF,CEOF)• Correlation and spectral analysis (wavelet)• Linear trend estimation
Object of interest:• Northeast Asia• North Pacific
Data for assessing:• Sea Surface Temperature, Air temperature mean values
for 1946-2002 (GHCN, NCEP/NCAR, JMA GLBSST, Hedley SST) and 1870-2002 (Hedley SST)
Wavelet derived oscillations for instr. data (Tair)Amlitude Phase Scalar EOF
Scale-averaged wavelet power over the 3–7-yr, 8-20-yr and 21-50-yr band for the GHCN dataset for winter
Wavelet derived oscillations for instr. data (SST)Amlitude Phase Scalar EOF
Scale-averaged wavelet power over the 3–7-yr, 8-20-yr and 21-50-yr band for the JMA dataset for winter
Wavelet derived oscillations for reanal. Data (Tair)
Scale-averaged wavelet power over the 3–7-yr, 8-20-yr and 21-50-yr band for the NCEP dataset for winter
Amlitude Phase Scalar EOF
Wavelet derived oscillations for reanal. data (SST57)Amlitude Phase Scalar EOF
Scale-averaged wavelet power over the 3–7-yr, 8-20-yr and 21-50-yr band for the Hedley (56 years) dataset for winter
Wavelet derived oscillations for reanal. Data (SST133)Amlitude Phase Scalar EOF
Scale-averaged wavelet power over the 3–7-yr, 8-20-yr and 21-50-yr band for the Hedley (132 years) dataset for winter
PDO-correlations with instr. dataAmplitude-winter Phase-winter Scalar EOF-winter
temporal modes and PDO for GHCN data on period 1946-2002. Amplitude-winter Phase-winter Scalar EOF-winter
modes and PDO for GLBSST (JMA) data on period 1946-2002
PDO-correlations with reanal. dataAmplitude-winter Phase-winter Scalar EOF-winter
Amplitude-winter Phase-winter Scalar EOF-winter
temporal modes and PDO for NCEP data on period 1948-2002
temporal modes and PDO for HEDLEY data on period 1946-2002
PDO-correlations with reanal. Data (Hedley SST 1900-2002)
Amplitude-winter Phase-winter Scalar EOF-winter
Amplitude-summer Phase-summer Scalar EOF-summer
Correlation analysis on temporal modes and PDO for Hedley SST data on period 1900-2002.
Conclusions• Both types of used dataset (instrumental and
reanalysis) is could be used for study climatic variability at the decadal and multidecadal scales and shows its relations to the climatic processes at ocean-atmosphere system observed by the other data.
• Scale averaged oscillations show the similar tendencies for the spectral analysis for correspondent data sets (air temperature/SST).
• The correlation analysis on the propagating signals influence for these dataset is rather complicated, but SST is highly correlated with the PDO in all cases
• Long-term tendencies analysis shows better agreement for instrumental data observations (more real) than for reanalysis data
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