Archive for June, 2013

The SALBY Hamburg conference: is CO2 the integral of temperature?

June 25, 2013

In this post I will reflect on the relationship between observed CO2 increase in the atmosphere and its relation to global temperature increase. Here I really can not quite see eye into eye with Prof. Salby.
First a note on terminology, which is regrettably imprecise: when Salby speaks of temperature, he means temperature anomaly dT; net CO2 emission is taken as the atmospheric mixing ratio in ppmV and the rate r as ppmV/year

1. Time series CO2 and global temperature anomaly.

In a first graph, Salby shows (and says so) that CO2 annual variations are proportional to the annual variations of dT:

dr_dt_and_delta_T

In this graph, dT (Anomalous Temp) effectively seems to follow the pattern of the CO2 emission rate ( = yearly mixing ratio variation) EXCEPT during the last period from 2001 where clearly the relation-ship has changed. It is regrettable that Salby has not insisted on this departure from the usual pattern.
Let us check this graph using as in the previous post the MLO CO2 data from 1979 to 2012 and the NCDC land+ocean global temperature anomalies for the same period. A moving average of 13 months is applied before plotting.

deltaCO2_Temp_1months

There is quite a difference between the two figures; it seems that the CO2 data have been heavily smoothed in Salby’s plot; probably he also used yearly data only, and not monthly ones.

2. The proportionality of CO2 rate to the integral of temperature anomaly.

Salby says that the CO2 molecules emitted into the atmosphere (by both natural and human sources) will stay there for a very long time. In that case the net emission rate per year is equal to the delta(CO2) derived from atmospheric measurements.  As he says that global temp. anomalies are (linearly) correlated to delta(CO2), this assumption gives the following relations (where the second is simply the mathematical conclusion from the first):

dr_integTThe oral explanations that Salby gives of the last (rather trivial) relation are a bit confusing; I listed carefully several times, but do not quite get the point when he explains the integral with the notion of “sum”.

To check this last relation, I will assume that the initial rate r is zero. Here are the steps of my calculations:

–  the available monthly data series  are divided into chunks of 12 months.

– from the CO2 data, compute the annual mean, and then the yearly delta (which reduces the 34 years period to 32 years, 1980-2011; this reduction comes from the use of the DADiSP delay function).

– from the temperature anomaly chunks, compute the sum for each year, which is the integral over that year

– now divide the delta(CO2) data by the sums, which should give the “constant” temperature sensitivity gamma.

Here is the figure with the results:

dr_by_integT_12months

If we look at the full period, clearly the first 10 years (1980-1989) fall out. But restricting the computation to the last part (years 1990 to 2011) gives a “reasonable” constant sensitivity varying between 0.2 and 0.4  (unit is (ppmV/y)/(K).


3. Conclusion

The assumption about the linear  correlation between deltaCO2 and temp. anomaly should be taken with a grain of salt (better with quite a lot of grains!). As a consequence temperature sensitivity does not seem a constant over longer periods, and this parameter should be handled like a hot potato. As many authors have speculated, the atmosphere is too complicated to be content with proportionalities i.e.  linear relationships!

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26 June 2013: some minor housekeeping in the text.

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The SALBY Hamburg conference: phase lag between CO2 and temperature

June 16, 2013

Prof. Murray SALBY presented his conference “Relationship between Greenhouse Gases and Global Temperature” the 18 April 2013 at the University of Hamburg (see Youtube version here and MP4 version here). His presentation was similar, but not identical to that I discussed in a previous post. It was quite technical in several parts (the video shows a very silent public, but this could simply show that German academics are well-mannered), but not overwhelming for someone who is familiar with the usual tools used in signal or time series processing. Nevertheless, it is good idea to go several times through this great presentation (the Quicktime player is handy for making precise stops at a certain slide), and to make some musings on several aspects.

1. The lag between observed CO2 and temperature changes.

In this first comment, I will compare some findings concerning the time lag between the observed measurements between CO2 and some of the global temperatures. I made several calculations myself, using the exceptional DADiSP software (which remains my favorite tool since many, many years).
Here is what Prof. Salby shows concerning the cross-correlation between CO2 and global temperature (colored elements added by me): CO2 levels lag temperature by about 8.5 months (temperature rises first, CO2 follows).

xcorr_co2_T

I made the same calculations using various monthly CO2 and temperature data for the 1979 to 2012 period: the seasonal detrended Mauna Loa CO2 data , the NCDC series of various monthly global temperature anomalies (ocean, land and ocean, land) and the RSS satellite data of lower troposphere temperature anomalies.

The next figure shows the cross-correlation between CO2 and the NCDC ocean temperature (SST anomaly):

xcorr_MLO_NCDC_ocean_large

One clearly sees a small uptick on the right side of the vertical blue line defining the zero lag. Zooming into the graph shows this:
xcorr_MLO_NCDC_ocean

The lag between SST and CO2 is 13 months: temperature first rises, and 13 month after its (statistical) maximum, CO2 reaches its next peak value.
Prof. Ole Humlum (from the climate4you blog) and co-authors  published in “Global and Planetary Change 100 (2013) 51–69” a paper “The phase relation between atmospheric carbon dioxide and global temperature” (pay-walled, see abstract here). Using a different calculation method, they too find CO2 levels lagging temperature.

The following table summaries the different findings:

table01

The RSS cross-correlation in the last row has a first miniscule peak at 12 months lag, and a next one at 15 months.

Normalizing the correlations (using the XCORR function of DADiSP) gives the following cross-correlation maxima for the NDC and RSS series: NCDC land: 0.64, NCDC ocean: 0.77,  NCDC land + ocean: 0.77 , RSS lower troposphere: 0.59
These numbers (to be compared to the Salby 0.5 maximum) show that one should use either SST alone, or the global land plus ocean series. The satellite derived lower troposphere anomalies seem to be less influential in documenting the CO2 changes.

All these lags are of the same sign, i.e. all point to an observed temperature rise preceding the CO2 rise. This would invalidate the essential IPCC “consensus” that atmospheric CO2 levels are the primary drivers of global temperature change. The lags found above are a hint that temperature change is the (or at least one of others) cause, and CO2 change the effect, and not the other way around.

2. First conclusion

The Salby, Humlum and my own calculations all show that global temperature change is not driven by atmospheric CO2 mixing ratio, but that statistically speaking, it is the inverse: if temperature rises, CO2 follows. This lag has been found for instance in the Vostok ice core series, albeit with much longer delays (about 800 years). Our short term observations simply document the well known physical effect that a warmer ocean will absorb less CO2 than a colder one. Hardly surprising!

What can not be deduced from these correlations is that the CO2 increase in the atmosphere has  a predominant natural origin. More on this in a next comment.

PS: The Humlum paper has not been well received by different researchers. M. Richardson has a comment in print (pay-walled, see abstract here) that seems to show that Humlum’s method violates conservation of mass. A second critique is that it can not be shown that the natural contribution to atmospheric CO2 levels is distinguishable from zero. More on this in a next comment.

Quing-Bin LU: Global warming since 1970 is caused by CFC’s

June 2, 2013

By a bit of luck I found a short discussion of a really “inconvenient” paper by associate-professor Q-B Lu of the University of Waterloo (see personal website) in Lubos Motl’s blog “The Reference Frame“. There are quite a lot of versions of this paper flying around the internet, some pay-walled, some free. The title is: Cosmic-ray driven reaction and greenhouse effect of halogenated molecules: culprits for atmospheric ozone depletion and global climate change.

It is well worth the time to spend an hour or two reading this 24 pages paper. It is well written, and relatively easy to understand. The main conclusion of Lu is that the global warming observed from 1970 to 2002 and the following slight cooling can be explained by the action of cosmic-rays on (anthropogenic) atmospheric halogens; the absorption bands of the usual GHG’s are more or less saturated, so that increasing CO2 for instance does not play a visible role from 1970 to present, and will not in the future.

Variations of total ozone column

The chlorine needed for ozone destruction is created by cosmic rays through a process called DET (dissociative electron transfer, a process running on the tiny ice crystals in Polar Stratospheric Clouds (PSC’s). As the intensity of cosmic rays is modulated by the solar wind (and as such by solar activity), one should observe an 11 year cycle in the Antarctic ozone hole. This is indeed the case:

LU_image1LU_image6ATwo figures showing the 11-year cycle in the Antarctic ozone hole and the cosmic ray induced reaction (CRE).

LU gives a very simple equation that shows that the relative change in total ozone column is proportional to the concentration of halogens (or more precisely the “equivalent effective chlorine” Ci delayed by about 2 years for Antarctica and 10 years for mid-latitudes) and the cosmic ray intensities in the preceding and current year:

LU_eq2A good portion of the paper tries to validate this equation through the observational data.

LU_image4AThis figure shows the equivalent effective chlorine (due to halogen dissociation) peaking around 1995, and the excellent parabolic fit of the relative total ozone column change to time (note spelling error in legend to red curve!). As anthropogenic halogen emissions are supposed to fall during the coming years (in accordance to the Montreal protocol), a total recovery back to the 1980 level is expected for 2050-2060.
LU accepts without discussion that all CFC’s in the atmosphere have a human origin. This might not be absolutely true, as some researchers show that CFC’s also may have a natural origin, like volcanoes (see here). So the conjecture of continuously lower concentrations in the future might not be rock-solid. Nevertheless, if LU is correct up to this point, reducing CFC’s emission through the Montreal  protocol was the correct thing to do, and the photo-chemical reactions discovered by Molina and Crutzen are at least broadly correct.

Link to global warming

This second part of the papers has attracted the most vigorous critics (see here and here), often with the argument that a good correlation is not necessarily a sign of a strong causation. As according to LU all absorption bands of GHG’s are saturated (an argument also given by numerous greenhouse “sceptics”), one need only compute the radiative forcing from the halogens, and use this forcing to compute a global temperature change. Doing this he finds a really high correlation between global warming and total halogen concentration, as shown in this figure:

LU_image10Note that the CFC concentration nicely follows the plateau in global temperature anomaly (see C and D). Extending his conclusions into the near future would suggest a gradual cooling extending at least until 2050, as resumed in this figure:

LU_image12

The wiggles in the green temperature curve represent the influence of the 11 year solar cycle.

Some remarks

According to LU, the only variations in solar activity that have a climatic effect are those that change the intensity of cosmic rays, i.e. the solar wind. Neither changes in total solar  nor UV irradiances would do much to the global temperature, nor the (rising) concentrations of greenhouse cases. With these two assumptions, LU squeezes the toes of the alarmists as well of those who think that climate change is a total natural phenomenon. Hypothesizing that cosmic rays mediated changes in halogens is the climate driver does in fact unite both those who see human activity as the evil, and those who think that the sun is the BIG driver of climate change. No wonder that overall reactions are mostly unsympathetic!
Nevertheless this is a quite interesting paper, and as said above, very readable. It does not try to bury the arguments in obscure mathematics or baroque statistics.  Hopefully another team of researchers will try its hands on this, so that the scientific debate over the climatic role of halogens would become more intense.