data: root mean sq anom $partialdiff sub T$ [ NOAA NCDC ERSST version3b err ]

root mean sq anom ∂_T [ NOAA NCDC ERSST version3b err ] : ∂_T Estimated error standard deviation of analyzed_ersst data

err partial_T partial_T ∂_T Estimated error standard deviation of analyzed_ersst from NOAA NCDC ERSST version3b: ERSST.v3b: Extended Reconstructed Sea Surface Temperature Dataset, Verson 3b.

Independent Variables (Grids)

Latitude (latitude): grid: /Y (degree_north) ordered (88S) to (88N) by 2.0 N= 89 pts :grid
zlev: grid: /zlev (meters) ordered [ (0.0)] :grid

Other Info

bufferwordsize

CE

null

CS

null

datatype

doublearraytype

missing_value

NaN

pointwidth

1.0

units

12 degree_Kelvin year^-1

history

root mean sq anom $partialdiff sub T$ [ NOAA NCDC ERSST version3b err ]

Version 3b
Averaged over X[1E, 1W] T[16 Jan 1854 - 15 Feb 1854, 16 Apr 2015 - 15 May 2015] minimum 0.0% data present

References

Smith, T.M., R.W. Reynolds, Thomas C. Peterson, and Jay Lawrimore, 2008: Improvements to NOAA's Historical Merged Land-Ocean Surface Temperature Analysis (1880-2006). Journal of Climate, 21, 2283-2296.

Xue, Y., T. M. Smith, and R. W. Reynolds, 2003: Interdecadal changes of 30-yr SST normals during 1871-2000. J. Climate, 16, 1601-1612.

Last updated: Thu, 02 Nov 2023 13:29:44 GMT
Expires: Sat, 02 Dec 2023 00:00:00 GMT

Data Views

	Y	zlev
root mean sq anom $partialdiff sub T$ [ NOAA NCDC ERSST version3b err ] 0.0 meters[ Y zlev \|]	M	M

Filters

Here are some filters that are useful for manipulating data. There are actually many more available, but they have to be entered manually. See Ingrid Function Documentation for more information.

Monthly Climatology calculates a monthly climatology by averaging over all years.
anomalies calculates the difference between the (above) monthly climatology and the original data.
Integrate along Y
Differentiate along Y
Take differences along Y