Global autocorrelation scales of the partial pressure of oceanic CO 2
Author(s) -
Li Zhen,
Adamec David,
Takahashi Taro,
Sutherland Stewart C.
Publication year - 2005
Publication title -
journal of geophysical research: oceans
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.67
H-Index - 298
eISSN - 2156-2202
pISSN - 0148-0227
DOI - 10.1029/2004jc002723
Subject(s) - autocorrelation , climatology , environmental science , baroclinity , equator , atmospheric sciences , empirical orthogonal functions , correlogram , series (stratigraphy) , geology , latitude , geodesy , mathematics , statistics , paleontology
A global database of approximately 1.7 million observations of the partial pressure of carbon dioxide in surface ocean waters ( p CO 2 ) collected between 1970 and 2003 is used to estimate its spatial autocorrelation structure. The patterns of the lag distance where the autocorrelation exceeds 0.8 is similar to patterns in the spatial distribution of the first baroclinic Rossby radius of deformation indicating that ocean circulation processes play a significant role in determining the spatial variability of p CO 2 . Separate calculations for times when the Sun is north and south of the equator revealed no obvious seasonal dependence of the spatial autocorrelation scales. The p CO 2 measurements at Ocean Weather Station (OWS) “P” in the eastern subarctic Pacific (50°N, 145°W) is the only fixed location where an uninterrupted time series of sufficient length exists to calculate a meaningful temporal autocorrelation function for lags greater than a few days. The estimated temporal autocorrelation function at OWS “P” is highly variable. A spectral analysis of the longest four p CO 2 time series indicates a high level of variability occurring over periods from the atmospheric synoptic to the maximum length of the time series, in this case 42 days. It is likely that a relative peak in variability with a period of 3–6 days is related to atmospheric synoptic period variability and ocean mixing events due to wind stirring. However, the short length of available time series makes identifying temporal relationships between p CO 2 and atmospheric or ocean processes problematic.
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