RaspberryPi is an effort to bring a bare-bones computer to the work benches of schools and hobbiest for as little as $25 or, with ethernet and more memory, $35. It is essentially a micro-motherboard with an 700 MHZ ARM1176JZFS CPU, 128-256Mb memory, 10/100 Mbs ethernet. It includes several different connectors to support devices such as usb keyboards, video, and audio. You can learn more at http://www.raspberrypi.org/faqs. They are very close to going to market.
I’m not sure how I stumbled over the RaspberryPi project, but like any good Linux geek, my first thought when seeing the microboard was: I want a Beowulf cluster of those things.
Two years ago, Steven Goddard presented the WUWT crowd with multiple analysis of Northern Hemisphere Winter snow cover. In one entitled “North American snow models miss the mark – observed trend opposite of the predictions”, Goddard used data from Rutger’s Global Snow Lab to claim that the 22-year trend beginning in 1989 for Winter (Dec, Jan, Feb) in the Northern Hemisphere invalidates the CMIP3 modeling of snow extent as presented by Frei and Gong in 2005 in their paper “Decadal to Century Scale Trends in North American Snow Extent in Coupled Atmosphere-Ocean General Circulation Models”. This paper is summarized at a Columbia University web page Will Climate Change Affect Snow Cover Over North America?. How has Goddard’s analysis held up? Did the decreasing snow trend he describe hold over time? My original response was here: Steve Goddard’s Snowjob. It was one of my first posts and I though it would be fun to revisit it.
STUDENT: Which climate models should the student seek out?
Which papers are good at outlining the points of failure?
Beside ‘institutional knowledge’, where are the strengths and weaknesses of various models catalogued?
PROFESSOR: All those answers await, and many more, once you deposit $25000 in an educational institution near you.
A first small step can be found in the IPCC AR4 WG1 in its table of Earth Models of Intermediate Complexity (table 8.3). EMICs are the models of my current interest. The UVIC_ESCM is an EMIC.
There are eight models listed with some high level summaries of their atmosphere, ocean, land, ice, and flux characteristics.
“Begin at the beginning,”, the King said, very gravely, “and go on till you come to the end: then stop”
Last week’s dump of the entire calling structure was an interesting exercise, but didn’t go very far in revealing the structure of UVic_ESCM. This week I focus on just the major components of the “main” routine. The graphic to the left is my first pass at a high-level summary.
A year ago, I was working through some curve fitting exercises. Girma was advancing his line+sine model as superior to climate modeling partially based on its hight correlation. He had allowed that an equally simple model with a higher correlation than his would be a superior model. I demonstrated 3 variations of an exp+sine model, one for each surface record, each had higher correlation than his. Neither one of us dealt with autocorrelation.
I thought I would make a quick update on that original post. Nothing fancy, just adding the data points for the 2011 annual surface temperature record indexes.
Way back in the day, as a new hire, in one of my first ‘think outside of the box’ exercises, I color formatted and hyper-linked a bunch of FORTRAN code. A large and aging defense project, there was a lot of it and my pet project quickly became a preferred method within the team for browsing the code. Ah … recall the days when Slackware came on several dozen floppy disks that you downloaded yourself.
Confronted with the UVIC_ESCM code, I wasn’t sure where to start to digest it. Eventually I decided to parse out its calling structure. It’s not a pretty chart. It might not even be complete or accurate. But its a start. Don’t bother clicking ‘more’. I promise it won’t be of any interest to any of you! (Except maybe Kate or Steve😀 )
Abstract: Seven different tree-ring parameters (tree-ring width, earlywood width, latewood width, maximum density, minimum density, mean earlywood density, and mean latewood density) were obtained from Qinghai spruce (Picea crassifolia) at one chronology site in the Hexi Corridor, China. The chronologies were analyzed individually and then compared with each other. Growth–climate response analyses showed that the tree-ring width and maximum latewood density (MXD) are mainly influenced by warm season temperature variability. Based on the relationships derived from the climate response analysis, the MXD chronology was used to reconstruct the May–August maximum temperature for the period 1775–2008 A.D., and it explained the 38.1% of the total temperature variance. It shows cooling in the late 1700s to early 1800s and warming in the twentieth century. Spatial climate correlation analyses with gridded land surface data revealed that our warm season temperature reconstruction contains a strong large-scale temperature signal for north China. Comparison with regional and Northern Hemisphere reconstructions revealed similar low-frequency change to longer-term variability. Several cold years coincide with major volcanic eruptions.
Temperature reconstruction from tree-ring maximum latewood density of Qinghai spruce in middle Hexi Corridor, China
Feng Chen, Yu-jiang Yuan, Wen-shou Wei, Shu-long Yu and Zi-ang Fan, et al.
THEORETICAL AND APPLIED CLIMATOLOGY
Volume 107, Numbers 3-4, 633-643, DOI: 10.1007/s00704-011-0512-y