Dmitry D. Kaplunenko*,**, Vladimir I. Ponomarev *, Young J. Ro **, Olga O. Trusenkova* and Serge T. Trusenkov* * – V.I. Il’ichev Pacific Oceanological Institute, Vladivostok, Russia; ** – Chungnam National University, Daejeon, Republic of Korea.

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: SST: GLBSST (Japan Meteorological Agency) ftp://ddb.kishou.go.jp/pub/Climate/SeaSurfaceTemp ftp://ddb.kishou.go.jp/pub/Climate/SeaSurfaceTemp Reanalysis based: Air Temperature: NCEP/NCAR Reanalysis project data: SST: Hadley Centre for Climate Prediction and Research:

Data coverage (Tair) Data coverage for meteorological data: Contribution (GHCN stns.): NCEP: 2.5°x2.5° Russia60 Korea9 Japan46 China32 Mongolia8 Total155

Data coverage (SST) Sea surface temperature. GLBSST (Japan Meteorological Agency) (North Pacific, period , 2°x2° ) Hadley Centre for Climate Prediction and Research (North Pacific, , ,1°x1° ) Data coverage:

Assessing of climate changes by prepared datasets 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 (GHCN, NCEP/NCAR, JMA GLBSST, Hedley SST) and (Hedley SST)

EOF-decomposition instrumental data Air temperatureSST

CEOF-decomposition instrumental data Air temperatureSST

EOF-decomposition reanalysis data Air temperatureSST NCEP

CEOF-decomposition reanalysis data Air temperatureSST

Wavelet derived oscillations for instr. data (Tair) Scale-averaged wavelet power over the 3–7-yr, 8-20-yr and yr band for the GHCN dataset for winter

Wavelet derived oscillations for instr. data (SST) Scale-averaged wavelet power over the 3–7-yr, 8-20-yr and 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 yr band for the NCEP dataset for winter

Wavelet derived oscillations for reanal. data (SST57) Scale-averaged wavelet power over the 3–7-yr, 8-20-yr and yr band for the Hedley (56 years) dataset for winter

Wavelet derived oscillations for reanal. Data (SST133) Scale-averaged wavelet power over the 3–7-yr, 8-20-yr and yr band for the Hedley (132 years) dataset for winter

PDO-correlations with instr. data temporal modes and PDO for GHCN data on period modes and PDO for GLBSST (JMA) data on period

PDO-correlations with reanal. data temporal modes and PDO for NCEP data on period temporal modes and PDO for HEDLEY data on period

PDO-correlations with reanal. Data (Hedley SST ) Correlation analysis on temporal modes and PDO for Hedley SST data on period

Linear trends estimation results Instrumental Data: Reanalysis Data

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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