how many times can that argument be used before the conclusion becomes either: 1. the models are wrong or 2. we are terrible at measuring this stuff? (and i note the former is much more plausible)

“On multidecadal time scales, tropospheric amplification of surface warming is a robust feature of model simulations, but it occurs in only one observational data set. Other observations show weak, or even negative, amplification. These results suggest either that different physical mechanisms control amplification processes on monthly and decadal time scales, and models fail to capture such behavior; or (more plausibly) that residual errors in several observational data sets used here affect their representation of long-term trends.”

so it’s “robust” in models but absent from most of the data. this must be a new definition of “robust” that i am not familiar with.

and how fortunate that radiosondes do not share your alleged issues with tropospheric monitoring and that they also show a lack of AGW fingerprint through direct measurement. or is there a problem with their thermometers as well?

regarding the second, i have a question about how they make a jump of logic:

“A model-predicted fingerprint of tropopause height changes is statistically detectable in two different observational (“reanalysis”) data sets. This positive detection result allows us to attribute overall tropopause height changes to a combination of anthropogenic and natural external forcings, with the anthropogenic component predominating.”

ok, so they predicted a change, it seems to have happened, but how to they get from there to the attribution of the anthropogenic component dominating? this seems like the same bad assumption the IPCC uses that change is caused by humans until otherwise explained.

where is the evidence that humans did this? i would be very interested to see that. how is this “predominant” attribution arrived at?

and go back and read the comments, no one on this thread is abusive to you until you start it. (see your post 3/27 9.42am)

still waiting on that CO2 evidence…

Lest it need reiterating, we can tell almost nothing from just five years of data, but sensible treatments of the data show upward trends consistent with the previous trends. If you only choose the noisiest data, and cherry-pick the data so that it starts with a particularly warm month and ends with a particularly cold month, then you can derive a linear fit which is virtually flat, but such an exercise is basically meaningless.

Dean Clark – plenty of people here are highly abusive towards anyone who points out basic flaws in their arguments. Anyone who is not abusive, I’ll have a perfectly civil conversation with. Anyone who is abusive, I respond in kind. Fair enough, no?

Vermont weatherman – if data after 1996 is analysed, the conclusions derived from pre-1996 data only are not supported. Incidentally, model predictions of a troposphere warming faster than the surface are not specific to greenhouse gas forcing scenarios. Increased solar activity would have the same effect. The difference is that increasing greenhouse gases should cause the stratosphere to cool, while increasing solar insolation would cause the stratosphere to warm. The stratosphere is definitely cooling, and actually that makes it quite difficult to determine the actual temperature of the troposphere from satellites, because the channel which measures the temperature of the troposphere has some overlap with the stratosphere, introducing a cool bias.

A couple of interesting papers: Amplification of Surface Temperature Trends and Variability in the Tropical Atmosphere, Contributions of Anthropogenic and Natural Forcing to Recent Tropopause Height Changes; and a discussion at realclimate.

If you go to http://data.giss.nasa.gov/gistemp/tabledata/GLB.Ts+dSST.txt, plot the monthly anomaly data from Jan. 2001 through Feb. 2008, and add a linear regression fit, you get a slope of .0024 deg C per year or .024 deg C per decade. The satellite data from RSS or IAH show an equally insignificant amount of cooling over the same time span.

“We believe the Wilkins has been in place for at least a few hundred years, but warm air and exposure to ocean waves are causing it to break up,” Scambos said:

Doesn’t that kinda imply that within historical times the Wilkins was *not* “in place,” presumably because of “…warm air and exposure to ocean waves…” at least comparable to those of today? And, if so, then isn’t it plausible that current conditions are not drastically unusual, or indisputably determined by human actions?

the climate system has response inertia. it takes a period of time to overcome that. but once you do, it keeps moving until a new equilibrium is reached reflecting the new inputs. water can warm for a very long time and hold a lot of heat if you give it slow, steady energy. i really can’t believe you can not understand this basic fact. did you get your degree at al gore university?

a straight line has a constant derivative moron. therefore it tells us nothing. besides, i said “straight line interpolation”, again your reading comp fails you and your ludicrous straw man proves meritless.

Linear interpolation on a set of data points (x_0, y_0),\, (x_1, y_1),\,\dots,\,(x_n, y_n) is defined as the concatenation of linear interpolants between each pair of data points. This results in a continuous curve, with a discontinuous derivative, thus of differentiability class C0.

If a C0 function is insufficient, for example if the process that has produced the data points is known be smoother than C0, it is common to replace linear interpolation with spline interpolation, or even polynomial interpolation in some cases.

you really do believe that a ball thrown up in the air that reaches an apex and comes halfway down is still in an uptrend because the straight line from origin to current position is angled up, don’t you?

i give up with you. i had thought you were just ignorant. but it’s obviously a much deeper issue.

unless, of course, you finally want to show me the CO2 evidence i keep asking for.

thought not…

And yes, the units of forcing are W/m². Lots of other quantities also have the same units. The numbers you quoted are not forcing. They are values of the solar constant. Look it up.

And you can’t differentiate a straight line? You really don’t know any maths!

You really do epitomise the standard of debate I’ve come to expect here. You make incredibly basic errors that you just don’t have the capability to understand, you don’t do even the most basic checking of the things you are quoting, and you will not deviate from something you’ve chosen to think no matter how ridiculous it is.

solar heat is flat, to temps stop?

you must be being willfully ignorant here. no one is that dumb.

put a pot of water on the stove. turn up the heat. if you can show that the water stops heating once you stop turning up the heat, go get your nobel prize. it will keep warming until it reaches a max potential. higher heat makes it warm faster. this is so elementary i can’t believe you don’t get it. you know less science than al gore or most school kids.

and as i said, these are FORCING numbers. the w/m2 unit measure is a dead giveaway there. albedo is taken into effect. you are ridiculous.

and for the last time: straight line interpolation is useless for determining a trend change. they are not differentiatable. if a trend is changing, a stright line cannot tell us anything about that. and as we are discussing a change in trend (as shown by the prior linear trend breaking with .05 or so avg p value of break by chow test) there is no basis for using linear analysis except to OBSCURE what is going on.

which seems to be your intent…