Archive for the ‘data’ Category

On UCLA’s main website there is a ‘space missions’ page. On it there is a section for the Diviner mission, which mapped the Moon’s surface temperature. We covered it in a series of posts a while back, as it is crucial to our understanding of Earth’s climate:

divops_lroflybyDiviner: The Diviner Lunar Radiometer is one of seven instruments aboard NASA’s Lunar Reconnaissance Orbiter spacecraft, which was launched on June 18, 2009. It is the first instrument to create detailed, global maps of surface temperature over the lunar day and year. Diviner’s measurements are also used to map compositional variations, derive subsurface temperatures, assess the stability of potential polar ice deposits, and infer landing hazards such as roughness and rock abundance. Read more here.

But the links to the diviner subdomain are broken, and although references to other pages about the mission such as press releases and news articles are found by searching the UCLA site, the science has gone. OK, so websites get changed, links get broken, servers crash and don’t get rebooted for a while. So what?  Why does this matter?


While giggling about the botched “Death blow” dealt by Anthony Watts and other members of team wassup to our solar-planetary theory yesterday, it occurred to me that the rather thin rolled-up paper they tried to bludgeon Nicola Scafetta with only considered the all too brief thermometer record. No wonder Sverre Holm found his windows too narrow to see the big picture through, as Nicola Scafetta pointed out in a comment deleted by Anthony Watts. When considering climate swings on the timescale of interest, in this case, around 60 years, we need to look at longer records.

A paper we discussed a few days ago used a paleoproxy to compare millennial scale changes in terrestrial climatic indicators with Steinhilber et al’s 2009 10Be proxy reconstruction of TSI (Total Solar Irradiance). Their work is sufficiently detailed to be able to discern sub-centennial swings in these climatic and solar indicators. Here’s panel ‘d’ of their figure 2, which I’ve annotated with vertical lines marking peaks in the curves.



Announcement on the SORCE Status page:

TSI-stpsat3Total Irradiance Monitor Status

(updated 24 Feb. 2014)

TIM daily solar measurements have resumed in a new operations mode.

The TIM, along with all other SORCE instruments, ceased collecting solar measurements after a battery cell failure on 30 July 2013. The LASP SORCE spacecraft operations team has implemented a new means of operating the instrument to acquire continued TSI measurements in the present limited-power mode. These measurements are expected to be more intermittent and of lower quality than those during the primary mission phase due to thermal and pointing issues, and this will be reflected in the time-dependent uncertainties given in the released data files.


A 0.6-Million Year Record of Millennial-Scale Climate Variability in the Tropics†

Kelly Ann Gibson2,*, Larry C. Peterson1DOI: 10.1002/2013GL058846©2014. American Geophysical Union


[1] A ~600-kyr long scanning X-ray fluorescence (XRF) record of redox variability from the Cariaco Basin, Venezuela, provides insight into rapid climate change in the tropics over the past five glacial-interglacial cycles. Variations in the sediment accumulation of the redox-sensitive element molybdenum (Mo) can be linked to changes in Intertropical Convergence Zone migration and reveal that millennial-scale variability is a persistent feature of tropical climate over the past 600 kyr, including during periods of interglacial warmth.


Guest Post By Doug Proctor.

What sparked my post is  Bob Tisdale’s graphs of global temperature anomalies AND a graph that split the anomalies into Northern and Southern Hemispheres. A clear example of a computational result that misleads: the Northern Hemisphere has been warming while the Southern Hemisphere has been cooling. Not global, regional.

Plot by Bob Tisdale

What does it tell us about, in this case, “global” warming when the temperatures of inland areas correlate well to cloud cover while the coast does not, with a mentioned “protection” from sea winds? It tells us the “global” (in this case the inland + coastal area) will have a temperature rise in its combined data while only the inland area did. And it also tells us that there is no “global” cause: it is the regional cloud cover and lack of cooling seawind that is responsible. Computational, yes, representational, no.



A conclusion and its implication in the summary paper was: because our scientific investigation leads us to the prediction that the Sun is headed into a protracted minimum, the warming forecast by the IPCC might not happen.

This has led to the journal being axed by the parent Publishing house Copernicus. The papers are still available at this link
Please download and disseminate them widely.

Heres the letter sent to Coordinating editor Nils Axel Mörner and chief editor Sid Ali Ouadfeul:


Guest post from Roger Andrews, who says: ” This is a review that extends Euan Mearns’ article on sunshine hours, cloud cover and SAT in the UK over mainland Europe and the North Atlantic. It reveals some interesting features that I make no attempt to explain – basically because I can’t – but someone else may have some ideas.” Apologies to Roger A for the delay in getting this article posted.

by Roger Andrews

The recent “UK temperatures since 1933” post discussed the relationships between sunshine hours, which were assumed to be an inverse cloud cover proxy, on surface air temperatures (hereafter SAT) at 23 UK stations. Here I summarize the relationships between sunshine hours, cloud cover and SAT over  Europe using observations from ~30 stations selected from the European Climate Assessment (ECA) data set (acknowledgement as requested to Klein Tank, A.M.G. and Coauthors, 2002. Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment. Int. J. of Climatol., 22, 1441-1453.) Station locations are shown in Figure 1:



This is a guest post by Jerry Lundry

Two plots are presented for annual average temperature in the United States Historical Climate Network (USHCN). This data set is highly regarded by some in climate science and is sometimes used as a surrogate for world-wide temperatures. Among its attributes are its coverage of a large land mass (the forty-eight contiguous United States), dense coverage of that land mass (1218 stations), and records that are complete to 1912 and missing only about eighty stations back to 1895. Temperatures for all stations are also provided for 1908.

In 2012, the author downloaded and produced annual average temperatures for this data set. The first figure below provides average annual temperatures for 1908 and 1912-2011. The curve faired through the data is a standard Excel sixth-order polynomial. This curve shows minima in years 1914 and 1970, and maxima in years 1940 and 2004, give or take a year or two.


Figure 1


IPCC neglected to account for coastal waters absorbing far more co2 than they emit since the industrial revolution, according to research published in Nature today. This from Science Daily:

Coastal portions of the world’s oceans, once believed to be a source of carbon dioxide (CO2) to the atmosphere, are now thought to absorb as much as two-thirds more carbon than they emitted in the preindustrial age, researchers estimate.

“The evidence suggests that human activities in coastal zones will continue to have an important impact on global carbon cycling,” Bauer said. “It’s a tricky area of study, but omitting the coastal ocean from the overall carbon budget leaves a gap in projections for future atmospheric CO2 levels.”

Prior to the industrial age, decomposing plant materials in coastal waters and sediments likely led to the release of carbon dioxide. The Nature paper suggests that microscopic plant growth in coastal areas, fueled by fertilizer runoff, is now leading to greater uptake of CO2. It also suggests that the atmospheric buildup of carbon dioxide caused by the burning of fossil fuels is further contributing to this uptake of CO2 by coastal waters.


Having been down a similar route with my own simple model which replicates HADsstV3 to a Pearson R^2 of 0.9 for monthly data, I’m happy to put up this model which achieves an accuracy of R^2=0.95 for smoothed data, using a slightly different technique. Sunspot numbers are a major component in this model by Andrew McRae, although so far as I can tell there is no integration to simulate ocean heat retention as there is with my own effort. Hopefully, he’ll make the spreadsheet available for sharing to interested parties:

Andrew writes:

I was inspired by the work of Dan Pangburn and decided to try to create a simple climate model using the external solar magnetic forcing and internal 60yr ocean cycle as the main factors, with a bit of CO2 thrown in just so it doesn’t feel left out.
The results were quite… interesting.

Here is a screen shot of the model output compared to measurements, plus a few background details in the caption of which I’m sure all of you are already aware but I wanted to write the caption for a potentially wider audience.