Reduced Space Optimal Interpolation procedure has been applied to the global sea level pressure (SLP) record of COADS averaged on 4x4 grid. For more information about this data set please click here.

A climate's permanence
is caused by the prevalence
of nothingness in its texture
and atmospheric pressure.
J. Brodsky, "Anthem"

Least squares procedures of optimal estimation, when applied to gappy and erratic data, result in the solutions which predominantly project onto the most energetic patterns of a priori error covariance. This property of the solution allows to combine the classical least squares technique with the approach of a space reduction in order to develop a computationally effective procedure of objective analysis for observed historical climate data. (Such data are characterized by comparatively precise observations and good coverage in the last few decades, and poor observational coverage prior.) An important aspect of our approach is that it also produces verifiable error bars for analyzed values. For details of the technique see Kaplan et al. 1997, for a simple qualitative introduction see Kaplan et al. 2001.

Here we applied the reduced space optimal interpolation technique to the COADS sea level pressure (SLP) data set (Woodruff et al. 1987,1993) and produced an analyzed data set ( http://ingrid.ldgo.columbia.edu/SOURCES/.KAPLAN/.RSA_COADS_SLP1.cuf/.OI/.slpa/ ) which verifies surprisingly well by other data sources (particularly NCEP NCAR Reanalysis) and against independent data (selected land station records). The detailed description of this product and its verification is given in Kaplan et al. 2000, but there are a few points I feel compelled to make here:

1. Weak part of this product is that it is based on marine data only. Not only this means that we do not produce land coverage, but also that the coastal areas have higher error than they would have if the land station data were included into the analysis.
2. Strong part of this product is that it is produced in a conceptually integral and simple objective way from (allegedly quite poor) data. I think that the NCEP-NCAR Reanalysis SLP fields are better than this product. However, I beg to disagree with Kevin Trenberth when he dismisses all simple COADS-based products in comparison to the products based on historical weather maps (in his expert user guidance regarding the NCAR Sea Level Pressure data set). Weather maps in the pre-NWP times used less marine data than we have in historical data sets compiled nowadays, and despite that the dynamical principles of weather systems were used by the analysts, truly large-scale climatic connections which are solely responsible for every success of our analysis were probably missing from them.
3. A weakness of this product which it shares with all least squares based analyses of gappy and erratic data is that the analysis itself is spectrally redder and of smaller amplitude than the true signal, and this discrepancy in properties becomes stronger as the observed data becomes poorer (Kaplan et al. 2001). Since the data gets worse as we go back in time, the analysis might display some apparent shifts in spectral and energetic properties which in fact reflect data availability, not the true physical SLP changes.
4. My general advice to the user is two-fold:
   
   (a) If your study involves only reanalysis period, use the reanalysis data, by all means;
   
   (b) If you must use this product, then after you are done with your analysis, look at the large-scale time-dependent error in your domain of interest ( a href="http://ingrid.ldgo.columbia.edu/SOURCES/.KAPLAN/.RSA_COADS_SLP1.cuf/.OI/.err/), and also look at the raw data which went into the analysis ( http://ingrid.ldgo.columbia.edu/SOURCES/.KAPLAN/.RSA_COADS_SLP1.cuf/.obs/), and try to imagine how the uncertainty could affects your conclusions. Be particularly alerted if the key periods of changes in your conclusions coincide with periods of particulaly bad data, fast changes in the coverage, etc.

Alexey Kaplan
June 2001

Kalnay, E. et al., 1996: The NCEP/NCAR 40-year reanalysis project. Bull. Amer. Meteor. Soc., 77, 437-471.

Kaplan A., M.A. Cane, and Y. Kushnir, 2001: Reduced space approach to the optimal analysis interpolation of historical marine observations: Accomplishments, difficulties, and prospects, WMO Guide to the Applications of Marine Climatology, World Meteorological Organization, Geneva, Switzerland, in press; available at http://rainbow.ldeo.columbia.edu/~alexeyk/CLIMAR99/wmogr.ps

Kaplan A., Y. Kushnir, M.A. Cane, 2000: Reduced space optimal interpolation of historical marine sea level pressure, J. Climate. 13, 2987--3002.

Kaplan, A., Y. Kushnir, M. Cane, and M. Blumenthal, 1997: Reduced space optimal analysis for historical datasets: 136 years of Atlantic sea surface temperatures, J. Geophys. Res., 102, 27835--27860.

Woodruff, S.D., R.J. Slutz, R.L. Jenne, and P.M. Steurer, 1987: A comprehensive ocean-atmosphere data set. Bull. Amer. Meteor. Soc., 68, 1239-1250.

Woodruff, S.D., S.J. Lubker, K. Wolter, S.J. Worley, and J.D. Elms, 1993: Comprehensive Ocean-Atmosphere Data Set (COADS) Release 1a : 1980-92. Earth System Monitor, 4, No. 1, 1-8.

Click on the links below to view data coverage maps for a particular time period. Percentage of non-missing data per time period is plotted. Coverage is consistent throughout the period of record.

(4/1854-12/1992)

Updated: 1/10/03
Maintained by asphilli@ucar.edu