A right click on the graph shows the source as :
[COLOR="Red"]http://nsidc.org/data/seaice_index/images/daily_images/N_stddev_timeseries.png[/COLOR]
So the original source for this graph image was nsidc.org National Snow and Ice Data Center (NSIDC)
The secret to creativity is knowing how to hide your sources.
Albert Einstein"
and @ NSIDC they freely discuss the problems they had (amongst themselves) , these problems are quite common, but not very often disclosed
http://nsidc.org/pubs/special/5/index.html[/COLOR]]NSIDC Special Report 5: Intercomparison of DMSP F11- and F13-derived Sea Ice Products
The SSM/I instrument as flown on the DMSP spacecraft is a seven channel, four frequency, linearly polarized, passive microwave radiometric system that records emitted energy at 19.3, 22.2, 37.0 and 85.5 GHz. The SSM/I data used here were processed at the National Snow and Ice Data Center in Boulder, Colorado. The final product is a series of daily maps of brightness temperatures for the Northern and Southern hemispheres, gridded into 25 km by 25 km pixels for the 19, 22, and 37 GHz channels, and 12.5 km by 12.5 km pixels at 85 GHz (NSIDC 1996). Since in high latitudes multiple orbit crossings occur over some locations, each pixel consists of an average of all orbit swaths during a 24-hour period.
For the months of May through September 1995, a common data set for the two different SSM/I instruments is available. As indicated in Table 1, the orbit configurations and equator crossing times between F11 and F13 are quite similar, so a reasonable assumption is that any differences between the F11 and F13 brightness temperatures are due primarily to sensor calibration. However, initial examination of the overlapping data showed considerable scatter on specific days. Closer examination of the data for these days indicates that this scatter is due to differences over open ocean areas, resulting from missing orbits in the F11 data. Over such areas, the varying effects of weather over the 24-hour averaging period are substantial enough to introduce considerable differences in the daily averages for F11 and F13. This problem does not affect the systematic sensor-related differences we are considering here, so a visual inspection of brightness temperature scatter plots was used to exclude days with particularly large scatter due to orbit averaging. The end result used for subsequent analysis is a data set of 139 days of overlapping F11 and F13 data, with data excluded for 5 May, 14 July, 1-2 September and 21-22 September.
To assess the impacts of changing from the F11 to the F13 spacecraft on the derivation of ice fractions, DMSP-F11 and F13 SSM/I daily averaged sea ice concentration grids for the Northern and Southern hemispheres were generated using the NASA Team algorithm (Gloersen and Cavalieri 1986; NSIDC 1996). The existing F11 ice concentration data set archived by NSIDC is based on brightness temperatures adjusted using F8 versus F11 coefficients of Abdalati et al. (1995) applied to the 19, 22 and 37 GHz channels used by the sea ice algorithm. We first adjusted our F11 brightness temperatures in an identical manner. The impact of these adjustments on the F11-F13 comparisons and on F8-F11 differences is discussed in later sections. A weather filter, which uses the 22V and 37V channels, is applied to the data to eliminate most of the spurious ice concentrations resulting from wind-roughening of the ocean surface, cloud liquid water and rainfall (Cavalieri et al. 1995). Cavalieri (1992) discusses the general performance of, and sources of error in, the NASA Team algorithm.
In the following two sections, comparisons are made between total and multiyear ice fractions for the Northern Hemisphere, and between total ice fractions in the Southern Hemisphere. In addition, differences in total ice area and ice extent are evaluated.
Comparisons of Sea Ice Fraction
Northern Hemisphere
Table 2 summarizes the comparison of mean monthly total ice concentrations derived from F11 and F13 in terms of mean difference, standard deviation, root-mean-squared (r.m.s.) error and correlation, respectively.
The equations of linear least squares best fit are also given in Table 2. The statistics are computed for the Northern Hemisphere sea ice with and without an expanded land mask. As noted later, the largest differences occur along coastlines as a result of geolocation uncertainty and false retrievals of ice cover in open-ocean coastal pixels due to mixed-pixel effects from adjoining land (land contamination). A land mask expanded to 3 pixels out from the original land mask was used to minimize these effects. Table 2 lists the comparison results using the regular and expanded land mask. An additional calculation was performed for the central Arctic region, where any contaminated land pixels were filtered out using the expanded land mask.
Don`t read me wrong please, I am a fan of satellite data gathering, but You simply can`t beat close up inspection...applies to military intelligence gathering as well as science.
Satellites are great though for spotting subtle changes that You`l never notice close up, however when it comes to detail they are the wrong tool.
Sometimes a microscope is better suited than a telescope, to put it into an analogy what I am saying here.
Looking at the grey shaded Standard deviation on graph You posted here we are not really far off the 79-2000 average, and in that data set You have to wonder about the data quality even more than that of DMSP F11, which was launched November 1991.
So there are 12 years worth of un-explained data in that set 79 -2000 data set.
But the most important feature in the graph You posted is that 79-2000, 2007 and 2011 data from January till May...although they don`t fall exactly on the same Y points all lines proceed almost EXACTLY parallel to each other.
Had there been a "warming climate" these lines would certainly not track along in a perfect unison formation like that, but each line below would have began diverging downwards from the lines above it, as we proceed from left to right on the X-axis....to put it simply, a "warmer climate" also means a sooner melt
But I`m glad You did post this graph, I was always waiting for an opportunity to show how a lot of this kind of data has been washed through various algorithms.......making it not much better than computer model generated "data" of a virtual reality.
Again I am in no way saying You can`t gather valuable data with passive infrared satellite imagery but there are limitations!
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