Tropospheric Temperature Trend Amplification
Posted by Jeff Id on January 30, 2009
After some thought, I’ve decided to break this post into several parts. It’s just too big to do all at once and in blog world, the worst thing for your blog is to wait a week between posts. First I will do the surface vs sat data, then I will do the climate model data. It should provide some interesting comparisons between actual measurements and climate models.
I’ve been working on Lower troposphere temperature trend amplification as compared to ground temperatures. Models predict an even amplification factor at various timescales of about 1.3 times in the tropics and 1.2 times globally. Here’s a quote from Dr John Christy which makes this the third time I’ve used it but I think it sets the meaning behind some of the graphs.
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.
So you can see how climatology is thinking. Well I got interested in this because there is a visibly higher signal in the lower troposphere RSS and UAH on the short term than ground data, yet the signal has a lower amplitude for long term data. The difference extends to the the mid troposphere measurements as well. Willis Eschenbach did an interesting article which was carried on Climate Audit here.
He developed a unique method for determination of the amplification factor which as I understand it, doesn’t separate the long term trend covariance from the short term trend covariance. The result is a unique looking plot, which I present here because this post uses the same data and there is a lot to be learned on that thread.
Since, I was looking for feedback mechanisms as described by Dr. Christy, it was important for me to separate the signal at each year as much as possible. I used a unique method to sort the amplification factor by signal length. The steps are as follows.
– Filter the satellite time series using a low pass filter at time N+ offset time.
– Subtract low pass data from original series to create a high pass series.
– Filter the high pass data at N
– save point SN
– Perform the same for ground data with the same values
– save point GN
– When the series are built do a scatter plot with ground data GN values on the X axis and satellite SN data on the Y.
– Take the slope of the scatter plot – This is the amplification at time N.
– Repeat the process for all monthly times in the 30 year satellite record.
I ran this process using an offset time of 1 year and the Climate Audit gaussian filter function which allows enough signal to create a reasonable graph. In using this filter method there is some rounding of the final amplification result due to the bleed through of frequencies below the cutoff. Still the graph is reasonably accurate for visualization of the amplification at each time value. Read it like this, at 2.5 years there is a 2.4 times amplification of the satellite 2.5 year trend in comparison to ground 2.5 year trends.
Wow, a tropical magnification of 2.5 times at 3 years – does this make any sense?
You can see the three year wide variances are much higher in the UAH data than Hadcrut surface temperatures. After looking at this plot, I don’t think 2.5 times is unreasonable. Below is the same graph as the second one with only the global data included so the scale is more visible.
The sharp cutoff in tropical trends after about 6 years seems like pretty strong evidence of a feedback mechanism. If the models don’t predict this cutoff in my later post, Dr. Christy is right that there is a serious missing feedback mechanism which this plot shows has about 7 years length.