Category Archives: Modeling

Two Blogs on Climate Sensitivity

Two interesting blog posts on climate sensitivity. Troy CA here and Paul_K at Lucia’s here. I haven’t parsed either post, but both are by thoughtful commenters and deserve a read.

NASA: “Hide this after Jim checks it”

The word “hide” has obviously attracted a lot of attention lately – “hide the decline” even occasioning its own song. Today I’d like to discuss the following remarkable instructions by a NASA employee in the recently disclosed NASA emails (available at Judicial Watch): Robert, please move to the CU site and hide this after Jim […]

FOI Myth #1: USA

Climate scientists have recently been promoting the myth that providing data in response to FOI requests was interfering with their work. Nature uncritically accepted this myth in a recent editorial calling for action to protect climate-change researchers from “endless time-consuming demands for information under the US and UK Freedom of Information Acts.”: If there are […]

Boundary Layer Clouds: IPCC Bowdlerizes Bony

As we’ve discussed before (and is well known), clouds are the greatest source of uncertainty in climate sensitivity. Low-level (“boundary layer”) tropical clouds have been shown to be the largest source of inter-model difference among GCMs. Clouds have been known to be problematic for GCMs since at least the Charney Report in 1979. Given the […]

March 2106

According to KNMI’s version of UKMO CM3, in March 2106, the tropics (20S-20N) will temporarily have an inhospitable temperature of 0.1E21. In a statement, the Hadley Center said that these results showed that the situation was “worse than we thought”. In an interview, Stefan Rahmstorf said that not only was it worse than we thought, […]

Rahmsmoothing and the Canadian GCM

Quite aside from the realclimatescientistsmoothingalgorithmparameterselectioncontroversy, another interesting aspect of Figure 3 of the Copnhagen Synthesis Report is the cone of model projections. Today I’ll show you how to do a similar comparison for an AR4 model of your choice. Unlike Rahmstorf, I’ll show how this is done, complete with turnkey code. I realize that this […]

Opportunism and the Models

Many CA readers have probably been checking out some interesting post at Lucia’s about Stefan Rahmstorf’s opportunistic smoothing of temperature observations in Copenhagen. See here here and here at Lucia’s. Also see David Stockwell’s recent post here and his recent E&E paper on Rahmstorf et al (Science 2007) (Rahmstorf here). Also see the recent Copenhagen […]

Today's GISS Conundrum

Jean S has written to me with another installment in our ongoing series about GISS conundrums. The puzzle starts with plotting the annual (Dec-Nov) GISS 1200 km anomaly map for the period 1991-2008 (here using 1961-1990 reference.) As you see, there is a Gavinesque red spot offshore Ecuador. Radio buttons generate plots at GISS here. […]

TAS vs TOS

My new script for scraping KNMI model makes it very convenient to look at model data without a lot of setup overhead. Up till now, I’d only downloaded air temperature data (tas) and I tested downloading SST data (tos). KNMI’s collection of tos data is unfortunately quite spotty and this information is not consistently available. […]

More on Retrieving KNMI Data

I’ve done a considerable upgrade to my function for retrieving model data from KNMI within R. This builds on the KNMI webpage but IMO is a considerable enhancement of it. I’ve made the script available here . The function read.knmi.models is built as an emulator of the radio buttons. Geert’s radio buttons (if I’m understanding […]

Follow

Get every new post delivered to your Inbox.

Join 3,196 other followers