Tracking down climate feedbacks

Update  26/4:  The feedback should really be calculated by the ratio DT1/DT2 = 1-g0F. Applying this for the full period of CRUTEM4 data 1900-2005  the value derived for F= -1.5+/- 0.8 Wm-2K-1.

I have been studying differences in climate data between those  areas  of the world with very low atmospheric water vapour (Deserts and Polar regions) and those areas with very high water vapour content (Tropical Wet regions).  The data set consists of all 5500 station data corresponding to CRUTEM4 kindly provided by the UK Met office. Each station has then been classified by climatology using it’s geographic location and a lat,lon grid based on the Köppen-Geiger climate classification [1].

We define ARID stations as all those situated in Deserts or Polar regions i.e. those in areas with precipitation ‘W’ or climate ‘E’ in [1]. These areas have the lowest atmospheric water content on Earth. The WET stations instead are defined as those within Tropical fully humid areas – ‘Af” in [1]. These include tropical rain-forests and  have the highest atmospheric water vapour content on Earth.  In a previous post I already showed that the temperature anomalies in the Sahara rose faster than a similar area in S. Asia, so I wanted to extend this study globally using the latest CRUTEM4 data.   I therefore calculated the global anomalies for both sets of stations ARID and WET using the same algorithm as used for CRUTEM4. The results are shown in Figure 1.

Fig 1: Temperature anomalies for ARID stations in red and WET stations in blue. The smooth curves are FFT smoothed curves. The black dashed curve is an FFT smooth to the full CRUTEM4 temperature anomalies.

There is a clear trend in the data that ARID stations cool faster and warm faster than WET stations. They seemingly react stronger to external forcing. The WET humid stations respond less than  both the ARID stations and the global average.  The location of the stations are shown in Figure 2 which is taken from reference [1]. The ARID stations are the yellow desert areas and the light blue polar areas. The WET stations are located in the  red zones – Amazon, central Africa and SE. Asia.

Fig 2:Climatic zones defined by KÖPPEN-GEIGER classification. DRY in yellow+polar regions. WET in red. see:

We will assume that there are external forcings on the climate both anthropogenic and natural which are reflected in temperature anomalies. If we label the forcing DS and the consequent  change in temperature anomaly as DT.

DT1 = g1*DS   for  ARID   and   DT2 = g2*DS    for  WET. We then divide the last 110 years of data into 3 main time  periods to measure DT1 and DT2, and assume that DS is a universal global external forcing ( for example due to CO2 )

Period DT1(DRY) DT2 (DT2-DT1)
1900 – 1940: 0.4+- 0.05  0.18+-0.05 -0.22+-0.07
1940 – 1970: -0.11+-0.05 -0.03+- 0.05  0.08+-0.07
1970 – 2005: 0.83+-0.05 0.60+- 0.05 -0.23+-0.07

Critics may argue that heat inertia effects due to nearby oceans are causing tropical climates react slower than desert regions. However, the IPCC argues that  feedbacks from increased water evaporation will lead to enhanced warming. This is not observed in those regions most affected by water vapour. In fact the opposite seems to be the case implying negative feedback. If we make the assumption that there have been 3 separate forcings for the 3 time periods above and that there is no other difference other than humidity, then we can estimate the water feedback F.  Taking F=0 for ARID stations:

(DT2-DT1) = F*DS  ;    where DT1 = g0*DS   and DT2 = (g0+F)*DS

F/g0 for the 3 periods :  -0.5+-0.1   ,  -0.7+-0.1  ,  -0.3+-0.1

Average Feedback parameter  =   -0.5 (+- 0.1)*g0  which taking G0 =  the Stefan-Boltzman value 4*sigma*T^3 = 3.75W/m2K-1.

Water Feedback =  – 1.8 +- 0.2 W/m2K-1  

Remarkably this is the same value as that derived from  a simple argument regarding the Faint Sun paradox see here. It has been pointed out by Richard Lindzen [2]  that much of the Earth’s heat is transported bodily through evaporation and convection to the upper atmosphere where IR opacity is low and  can then escape to space. Therefore water feedback effects depend on the water vapor content of the upper atmosphere more than that at the surface.  Increased evaporation, convection and rain out could even dry out the upper atmosphere. This could be a possible mechanism for negative feedbacks.  Such effects would be much smaller in ARID areas with little or no evaporation.

1. Rubel, F., and M. Kottek, 2010: Observed and projected climate shifts 1901-2100 depicted by world maps of the Köppen-Geiger climate classification. Meteorol. Z., 19, 135-141. DOI: 10.1127/0941-2948/2010/0430.

2. Richard Lindzen, Some uncertainties with respect to water vapor’s role in climate sensitivity. Proceedings NASA workshop on the role of Water Vapor in Climate Processes.

About Clive Best

PhD High Energy Physics Worked at CERN, Rutherford Lab, JET, JRC, OSVision
This entry was posted in AGW, Climate Change, climate science, Science and tagged , , . Bookmark the permalink.

6 Responses to Tracking down climate feedbacks

  1. Pingback: Hockey Schtick: New analysis finds water vapor is a negative feedback |

  2. Interesting study… This corresponds well with why the models fail and grossly over predict warming by using positive feedback.

    I should mention that there is a grammatical error.

    This is not observed in those regions most effected by water vapour Should be “most AFFECTED.”

  3. Charles Higley says:

    It would also seem that the negative feedback produced by the water cycle would respond to increases in temperature by ramping up in speed until the temperature had dropped, just as any good heat engine would behave.

  4. pochas says:

    Arid areas will certainly cool faster due to reduced greenhouse effect, and heat faster due to reduced cloudiness. The outgoing radiation can be seen by looking at satellite images in the water vapor bands, at the Unisys site for example. The dark areas in arid regions are basically escaping heat. You can compare rates of outgoing radiation in different climate zones with the modtran program. I wouldn’t draw any conclusions about sensitivity from this, as the main actors are water vapor and clouds, of which, sadly, there seems to be little ‘official’ knowledge. My own opinion is that water vapor/clouds act as a powerful stabilizing mechanism, acting to counter any external forcing and the fact that you see this arid/moist difference in your analysis supports this. Neverless, the Svensmark mechanism may change the equilibrium by drying out or moistening the atmosphere, thus reducing or enhancing the greenhouse effect. I think Lindzen has the right idea about convection as an important part of the heat removal mechanism, and about water vapor/clouds as an important feedback.
    I don’t really like to see feedback expressed as a w/(m^2 degK) ‘forcing’ type value. I think we are really looking at an all-atmosphere ‘tau’ value that tends to remain constant, as per Miskolczi. Thus the ‘forcing’ can be either positive or negative depending on departure from the value of tau at equilibrium. Lindzen’s feedback factor f fixes this as in this mode factors greater than zero are positive forcings and less than zero are negative. ?T = ?T0/(1-f)
    I hope you will continue your very interesting work.

  5. Pingback: Climate feedback | F1services

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