Bifurcated Temperature Trend
Posted by Jeff Condon on January 23, 2009
I have been studying the satellite vs ground temperature measurements lately trying to understand why they are so different. There is an unresolved dichotomy in the data. Thirty year longer term trend in the RSS and UAH data is less than GISS data while the short term 2 – 5 year temperature variation in RSS and UAH is between 10 and 25 percent greater than GISS. What makes this worse is that the models actually predict that RSS and UAH should have a 20 percent greater variation than ground measurement on both the short term and the longer term scales. This model predicted difference occurs becasue the measurements cover different sections of the troposphere. Satellite data measures a thick layer of lower atmosphere whereas ground measurments reflect temperatures immediately next to the surface.
The three datasets are plotted below. The GISS anomaly graph has been offset to lay on top of the others for easier comparison.
The slopes of the data over the last 30 years are:
GISS 30yr = 0.183 C/Decade
RSS 30yr = 0.157 C/Decade
UAH 30yr = 0.127 C/Decade
After I did a homogenization analysis using ground data I contacted Dr. John Christy and had a short email conversation about my results. I am now convinced the best long term trend for sat data is the UAH trend. Not that it is perfect, it is just that after we remove a step in the data at 1992 related to satellite changeover the two series match almost perfectly. This places sat trend about 0.127 C/Decade.
Well from the graph above it is difficult to make out the difference in either short or long term trends. To improve the visibility of the difference I removed the trend from the three graphs and overlaid them below.
This plot shows GISS overlaid on UAH and RSS with a two year filter. If you look close you can see the GISS black line is slightly closer to zero at each peak of the dataset. The standard deviation confirms the short term variance effect.
For disclosure purposes, the higher frequency of noise level in GISS data creates a more matched SD value. Unfiltered data is a poor comparison when the frequency of the datasets doesn’t match. The gaussian filter removes the highest frequency element so we can see 1.5 year and longer variance more clearly.
GISS SD = 0.107
RSS SD = 0.128
UAH SD = 0.132
SD RSS/GISS = 1.20
SD UAH/GISS = 1.23
RSS and UAH both have higher variation around the trend than GISS alone by a factor of 1.2. This is caused because satellite profiles are controlled by the microwave emissions from the atmosphere resulting in complex pressure/altitude sensitivity of the temperature measurement. A plot of the various profiles and their sensitivity is below, T’LT is what this post deals with.
If the SD isn’t convincing enough for you, I did a covariance plot of the two year filtered data.
The slopes of the covariance have a lesser ratio than the standard deviation above. For GISS it is 1.14 and for RSS it was 1.08. From my post at the link above, the primary difference between RSS and UAH is the single step at 1992 so I trust the UAH variance more. Either way with a covariance slope greater than 1 and a 30 year slope ratio substantially less than 1. Ratio (.127 C/Decade UAH) / (0.183 C/decade GISS) = 0.694, Something is amiss. Yes there is an expected high degree of serial correlation in the filtered data and I didn’t present an r value simply because I don’t see any use for them here.
From a discussion on climate audit, Dr. Christy recommended that when comparing GISS to satellite data the models demonstrate a 1.2 times multiplier for surface temps to satellite lower troposphere temps . After several discussions with Dr. Christy looking for an explanation, he gave me permission to use this paragraph.
The global-mean short term tropospheric amplification factor of 1.2 (it’s 1.3 in the tropics) indicates (a) that the ocean’s thermal inertia (sfc datasets use SSTs) works against large shorter-term changes while the atmosphere is much less massive and can respond to a greater extent and (b) there is a lapse-rate feedback process where the lapse rate tends to move toward the moist adiabat when thermally forced from below. Why we don’t see this amplification factor in the trend metric (which models show also occurs for the trend) likely deals with the feedbacks of the climate system – there appear to be negative feedbacks on longer time scales that models don’t capture. This is a hypothesis we want to test.
Ok, so if your like me you need to read this a few times. It has interesting implications for sure. Models agree with the 1.2 times more variance for the satellite data as shown in the SD ratios. Where the models don’t agree is that they also predict this 1.2 times trend in the long term data for the TLT channel. The conclusions about the potential missing feedback in the models relate to a more serious issue.
Six months ago when I started the Air Vent, it was an entertainment project where I was going to honestly explore climate science, read a bunch of papers, make a conclusion and get on with life. Well you can’t always get what you want, now I have an even bigger problem. There are three possibilities as I see it and all, two or only one of them may be correct. I beleive the former is the case but here’s what we have.
1. GISS trend is exaggerated from reality. Corrections don’t remove enough UHI effect, especially in foreign countries so the slope is too high.
2. Models are missing a negative feedback or several which would mitigate the long term trend but not the shorter term.
3. Satellite data long term trend is reduced from actual and may actually be 1.2 times GISS or 1.83*1.2 = .22 Deg C/Decade.
I always say, I am a skeptic not a denier but one thing I am a ‘denier’ on is that we can project temperature out a hundred years with any degree of accuracy. Santer’s latest work inadvertently demonstrated that we can’t even do it on a 30 year timescale. We’re still trying to figure out how to rectify the differences in our temperature measurements, the concept that we could project temps for a hundred years is almost an insanity. Still the attempt to model the climate isn’t in vain, I believe it will eventually work. The task isn’t insurmountable yet it may be far away. When it does the temp curves, model curves and projections will all be in agreement.
Why we don’t wait until then to change our way of life?