Tag Archives: Numbers are Tricky

GISS Polar Interpolation

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“GISS Polar Interpolation“. Like the Wandering Albatross, Steven Goddard returns once more to complaining that because the NASA Goddard Institute for Space Studies (GISS) doesn’t have 5000 weather stations on the Arctic sea ice their global temperature analysis is a lie composed of “incorrect, fabricated data”. James Hansen even admits that !

Steven cherry-picks June 2010 from the Danish Meteorological Institute (DMI) model for comparison because it’s the only month he can use to “prove” that the Arctic is colder than GISS reports. Steven loathes any kind of “modeling” because they let scientists ‘manipulate the truth’, but the DMI model suits his purpose today so its OK I guess. The DMI model uses a different set of records and different assumptions, in particular with a cold bias due to inclusion of Arctic buoy readings, so naturally it gives a slightly different result. This is useful to Steven.

Daily mean temperature north of 80th northern parallel. Steven likes June 2010 here. It's the only month he can play games with. Source: DMI.

It’s always fun to work yourself up into a nice lather, but if data isn’t available scientists will try to find ways to compensate. It’s called research and it doesn’t involve playing games with Photoshop. Just because it suits Steven’s purpose doesn’t mean that, for example, rejecting the interpolation of temperature beyond 250 km is legitimate. GISS explains their choice clearly:

The correlation of temperature anomaly time series for neighboring stations was illustrated by Hansen and Lebedeff [1987] as a function of station separation for different latitude bands. The average correlation coefficient was shown to remain above 50 percent to distances of about 1200 km at most latitudes, but in the tropics the correlation falls to about 35 percent at station separation of 1200 km. The GISS analysis specifies the temperature anomaly at a given location as the weighted average of the anomalies for all stations located within 1200 km of that point, with the weight decreasing linearly from unity for a station located at that point to zero for stations located 1200 km or further from the point in question.

So what if there was a fatal flaw in the GISS temperature analysis? Well there are several different estimates of global temperature trends, based on different sets of temperature records and different assumptions. They all show a similar pattern of warming, so howling about the specific flaws of one or the other of these analyses is really just meaningless noise.

I can’t let Goddard’s final statement that “GISS Arctic anomalies are high by as much as 4 degrees, and yet he claims a global record measured in hundredths of a degree” go unchallenged. This is plain scientific ignorance (or the pretense of it). The significant digits of a result can be much higher than the accuracy of the individual measured values if the sample size is large. Guess what? In this case, it is.

Sea Ice News #15

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“Sea Ice News #15“. Steven Goddard returns to his weekly “how many angels can dance on the head of a pin?” exercise in trying to explain away the Arctic Sea Ice trends. In this “report” he decides to talk about data coverage and not the data itself and about Arctic air temperature and not sea ice. Oh, he has some webcam photos as evidence too.

In passing, he mentions that “ice loss accelerated during the past week over the East Siberian Sea due to above normal temperatures.” But pay no heed to that!

GRACE’s warts – new peer reviewed paper suggests errors and adjustments may be large

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That's a spicy meatball! Credit: U of Texas Center for Space Research

“GRACE’s warts – new peer reviewed paper suggests errors and adjustments may be large“. Anthony Watts copies-and-pastes a post from CO2 Science (the website for those tired of “alarmist global warming propaganda”). They report that denialists can safely ignore any troubling conclusions based on the Gravity Recovery and Climate Experiment (GRACE) satellite, because there are “errors and biases” and “the GRACE data time series is still very short”. And of course any adjustments to correct these things are simply ‘tricks’.

Actually, that’s what the GRACE scientists themselves are saying in their 2010 Geophysical Journal International article, Uncertainty in ocean mass trends from GRACE. CO2 Science is taking routine scientific discussion about how to improve data analysis out of context and trying to use it to discredit that very effort. Here’s Quinn & Ponte’s abstract:

Ocean mass, together with steric sea level, are the key components of total observed sea level change. Monthly observations from the Gravity Recovery and Climate Experiment (GRACE) can provide estimates of the ocean mass component of the sea level budget, but full use of the data requires a detailed understanding of its errors and biases. We have examined trends in ocean mass calculated from 6 yr of GRACE data and found differences of up to 1 mm yr−1 between estimates derived from different GRACE processing centre solutions. In addition, variations in post-processing masking and filtering procedures required to convert the GRACE data into ocean mass lead to trend differences of up to 0.5 mm yr−1. Necessary external model adjustments add to these uncertainties, with reported post-glacial rebound corrections differing by as much as 1 mm yr−1. Disagreement in the regional trends between the GRACE processing centres is most noticeably in areas south of Greenland, and in the southeast and northwest Pacific Ocean. Non-ocean signals, such as in the Indian Ocean due to the 2004 Sumatran-Andean earthquake, and near Greenland and West Antarctica due to land signal leakage, can also corrupt the ocean trend estimates. Based on our analyses, formal errors may not capture the true uncertainty in either regional or global ocean mass trends derived from GRACE.

So the controversy is… what exactly? That is a cool warty globe though.

Arctic Forecast Verification Update

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“Arctic Forecast Verification Update“. Arctic Sea Ice Extent has ticked upwards a bit faster than expected, so Steven Goddard has returned to arm-waving about how everything’s back to normal, just like he predicted by drawing a dashed line on some real data. Or should I say re-predicted. Oh great sage, why do you yap so much about short-term weather variables?

Steven's analysis-by-graphics-editor Arctic Sea Ice Extent prediction, complete with fuzzy pixel deletions.

GISS land and sea ratios revisited

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“GISS land and sea ratios revisited“. When Anthony Watts cross-posts a teammate’s refutation of an earlier post you know that it must have been the source of a lot of embarrassment.

Fellow-denialist Bob Tisdale explains, gently, how Frank Lansner’s ignorant beef about the way GISS produces global temperature estimates from land station records is baseless. Zeke did it better though.

Spot the differences? The "Trick" is revealed! From Climate Observations.

Tipping point at GISS? Land and sea weight out of balance

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“Tipping point at GISS? Land and sea weight out of balance“. Anthony Watts gives us Frank Lanser’s ill-informed assumptions about how GISS integrates land and sea temperature readings and hopes we’ll bite.

Frank maintains that GISS uses a land weighting of 67%, which is the reverse of the land/ocean ratio. They’re lying! Aussie dunce Joanne Nova is in enthusiastic agreement with Frank’s stunning discovery.

Except Frank, Joanne and Anthony have no clue what they’re talking about. Zeke explains it to them in mostly small words.

NOAA’s Jan-Jun 2010 Warmest Ever: Missing Data, False Impressions

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“NOAA’s Jan-Jun 2010 Warmest Ever: Missing Data, False Impressions“. Anthony Watts finds more denialist whining (by “Alan”) about the NOAA’s recent summary of 2010 global temperatures. Apparently “NOAA performs manipulations to create false impressions”. Also, how dare those scientists not space their temperature recording stations evenly across the planet!

I love how Anthony’s been sucked into Steven Goddard’s losing game of magnifying summary illustrations and arguing over the colour of each pixel. It’s a dunce’s game.

CO2 Optical Illusion

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“CO2 Optical Illusion“. Steven Goddard is nothing if not stubborn. He still thinks that graphics editors can be used to prove that Global Warming is a lie. NASA’s Earth Observatory image of the day has him all riled up.

Here he once again mangles legitimate scientific images and then counts pixels to prove… something. Although he admits that “This is not a perfect equal area projection – so the pixel count method is not 100% accurate” it doesn’t stop him from speaking from the mountaintop. He declares that “5% more pixels were below normal than were above normal” but ignores the unreported areas (most of India and China) that almost all lie within hotter than normal regions.

Pixels, eh Steven? I think you’re actually looking at pixies. I suppose it makes a change from arguing about how many angels can dance on the head of a pin.

Website News

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Yes, I’ve been a bad boy lately, focusing on my own activities and generally letting the WUWT howlers fade into the background. There have certainly been a few doosies though!

I’ve made use of the break to do some jigging of the intertubes and now wotsupwiththat.com and wottsupwiththat.com both redirect to this WordPress site. That’s something, isn’t it?

Here are a few recent entertaining links.

What is PIPS?

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“What is PIPS?” Steven Goddard defends his continued use of the US Navy’s deprecated Polar Ice Prediction System (PIPS) Arctic Sea Ice model. The US Navy uses it! Case closed. This is the same obstinate mindset that lies at the root of Anthony Watts’ obsession with surface weather station records. PIPS is not intended for climate usage. It is a repurposed navigational tool.

Steven likes PIPS because, as the Navy states, “PIPS 2.0 often over-predicts the amount of ice in the Barents Sea and therefore often places the ice edge too far south.” This is very useful for a desperate denialist.

Steven concludes by stating that any critics “ignore the facts, and post instead what suits their agenda.”  Unsurprisingly, this is actually Steven’s motivation for using PIPS. It’s the easiest to manipulate toward a desired conclusion. Just restrict your analysis to the areas where PIPS over-predicts ice and pass it off as impartial.