Wednesday, October 29, 2014

Calculating the environmental lapse rate

I have posted over the years on the mechanisms of the lapse rate - the vertical temperature gradient in the atmosphere. It started with one of my first posts on what if there were no GHE. My basic contention has been that lapse rates are a consequence of vertical air motions in a gravity field. Wind tends to drive the gradient toward the DALR - dry adiabatic lapse rate = -9.8 °K/km. Maintaining the gradient takes kinetic energy from the wind to operate a heat pump. The pump forces heat down, to make up for the flux transported up by the gradient. The pump effect is proportional to the difference between the actual lapse rate La and the DALR L. L is the stability limit, and a steeper gradient will convert the pump into an engine, with convective instability. This also pushes La (down) toward L.

I developed these ideas in posts here, here and here. But I have wondered about the role of infrared radiation (with GHGs), and why the actual gradient is usually below the DALR. The latter is often attributed to latent heat of water, and called the moist ALR. But that is only effective if there is actual phase change (usually condensation).

I now see how it works. The heat pump reduces entropy, proportionally to the energy it takes from the wind. The entropy can indeed be related to the gradient and the effective thermal conductivity; the largest component of that is a radiative mechanism. So the lapse rate rises to the maximum level that the wind energy can sustain, given the conductive leakage.

Thursday, October 23, 2014

Checking ENSO forecasts

I few days ago I commented here on the latest NOAA ENSO advisory:
""ENSO-neutral conditions continue.*
Positive equatorial sea surface temperature (SST) anomalies continue across most of the Pacific Ocean.
El Niño is favored to begin in the next 1-2 months and last into the Northern Hemisphere spring 2015.*""


I repeated this at WUWT, and someone said, but they have been saying that all year. So I ran a check on ENSO predictions.

The NOAA Climate Prediction Center posts a monthly series of CDBs (Diagnostic Bulletins) here. They are full of graphs and useful information. They include compilations of ENSO predictions (Nino3.4), nicely graphed by IRI. I downloaded the plots for each month of 2014, and overlaid with the observed value from this file.

It's an active plot, so you can click through the months. The year started out with a dip, mostly unforeseen. This coincided with the global cool in February. There was then a underpredicted recovery, and since then there has been a tendency for the index to be below predictions, esp June and July.

CPC warns that only modest predictive skill is to be expected, and that is fortified by the spread in forecasts. The index does indeed seem to move beyond the predicted range rather easily. It's not always overpredicted, though.

Monday, October 20, 2014

More "pause" trend datasets.

In two recent posts (here and here), I have shown with some major indices how trends, measures from some variable time over the last two decades and now, have been rising. This is partly due to recent warmth, and partly to the shifting effect (on trend) of past events, as time passes.

This has significance for talk of a pause in warming. People like to catalogue past periods of zero or negative trend. A senior British politician recently referred to "18 years without warming". That echoes Lord Monckton's persistent posts about MSU-RSS, which does have, of all indices, by far the lowest trends over the period.

Here I want to show results about other indices. Cowtan and Way showed that over this period, the trend in Hadcrut was biased low because of non-coverage of Arctic warming. I believe that TempLS with mesh weighting would also account properly for Arctic trend, and this would be a good way to compare the two, and see the effect of full interpolation. I expected GISS to behave similarly; it does to a limited extent.

So a new active plot is below the jump. You can rotate between datasets and months separately. There is also a swap facility so you can compare the images. And I have individual discussion of interpolation data vs grid data groups.

Sunday, October 19, 2014

Tails of the Pause.

I've been writing lately about matters which, I'm sorry to say, lack scientific gravity. One is the possible record warm 2014, and the other is the tailing of the Pause, as measured by periods of negative trend. My excuse is, people do talk about them, and there is interesting arithmetic which I can illustrate.

In my "pause" posts, I showed plots of trend of global temperature to present, plotted for periods shown on the x-axis, with trend shown at the starting point. A "pause" starts when, for some index, the axis is first crossed from pos to neg. The plots were active, and you could see the curves rising steadily over recent months. This meant the start of the pause moves forward, with eventual jumps where a previous excursion below the line no longer makes it.

Here is the recent active (buttons) plot to show that effect:

 

In this post, I'll quantify the rate of motion, and describe how much cooling would be required to reverse the trend. The effect of a new month's reading depends on its status as a residual relative to the regression line for the period - ie is it above or below the line, and by how much. But one reading is a different residual for each such period. I plot the present month as a residual, again referred to the start year, and also plot the rate of change of trend produced by the current (August) temperatures.

Friday, October 17, 2014

Record warmth in 2014?

Not according to the satellite measures; they are showing quite a cool year so far. But surface measures, apparently propelled by SST, have been consistently high since March, and a record for calendar 2014 looks possible.

In August 2010, I showed a plot of the progress of the cumulative monthly anomaly sums, which will reach the final sum that determines the year average. 2010 did turn out to be the hottest year in many indices. It was different in that the El Nino was late 2009/10, so late 2010 was cooling. At this stage 2014 seems to be warming.

So I started to repeat that 2010-style plot, which is below the jump. It didn't work as well; the variation doesn't much show. But it puts the thing calculated in context - a cumulative sum that, if it exceeds 2010 at year end, will set a record. I've shown the progress of 2005 (a previous record), 2010 and 2014, with a line showing the 2010 average rate. The plots are spaced with an arbitrary offset.

But, more effectively, there is then an active plot with the average 2010 trend subtracted. The variation is clearer. The key thing is not so much whether the current total is above the line, but how it is trending, which is a measure of current warmth.

Thursday, October 16, 2014

QC for TempLS


I plan to do more with TempLS (see last post) so I want a stable quality control (data) scheme. GHCN unadjusted is a document of record, and there is weird stuff in there which it seems they don't like to touch. I've noted current examples earlier in the year. So I did a survey of the data since 1850. Here is R's summary of the monthly averages:


Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-87.0 6.6 15.9 14.5 24.1 154.41166444

That's out of 10349232 months (of years with some data). Yes, the max is 154.4°C. There were 28 months with a min/max (not max) average >50°C.

To be fair, they use flags, and these oddities seem to be mostly where a decimal point slipped in the originating data. But they are big enough to have effects, so I have been using my own QC. On first look, I found the GHCN flags numerous and unhelpful, so I used a scheme where I checked with the adjusted file. This seemed to weed out the problem points without replacement. However, it excluded a lot of other points, so I allowed those if within 3°C of the appropriate mean.

Monday, October 13, 2014

A catch-up on TempLS

I've been writing a lot about TempLS (my global temperature index) recently, and realizing that I don't have a unique reference that explains exactly what it is and what has recently been happening to it.

TempLS dates back to a period in early 2010 when there was a flurry of amateur efforts to replicate the monthly global surface temperature indices from the major producers (which some thought suspect). This post by Zeke (with links to earlier) gives an overview. Jeff Id and Romanm started it with a reconstruction that used a least squares method for aggregating a single cell, yielding offsets rather than requiring a fixed anomaly period. I thought that could be applied to the whole recon.

So I developed TempLS, which was basically a big OLS regression, based on GHCN unadjusted station monthly averages. It was quick to run, and I incorporated choice mechanisms which made it easy to calculate regional or special (eg rural, airport) averages. A rather complete summary of this stage of development is here. An important feature was the incorporation of SST data. This comes gridded, often 5°x5°, and so I simply entered these as stations.

I made a point of using unadjusted GHCN, because there were many claims that warming was an artefact of adjustment. I have myself no objections to adjustment, though I did show that it makes relatively little difference to the index.

TempLS combines weighted regression with spatial integration, much as BEST did later. It weighted initially by the inverse of grid density, estimated by stations/cell in a 5°x5° grid. I posted at one stage a very simple version for incorporation in Steven Mosher's RGHCNv3. You can regard this weighting as that which a spatial integration formula woud provide, with each grid estimated by its station average anomaly, or equivalently, each function value (observed average) assigned an area equal to its share of the cell.