Guest Post by Willis Eschenbach
Whenever I find myself growing grim about the mouth; whenever it is a damp, drizzly November in my soul; whenever I find myself involuntarily pausing before coffin warehouses, and bringing up the rear of every funeral I meet; and especially whenever my hypos get such an upper hand of me, that it requires a strong moral principle to prevent me from deliberately stepping into the street, and methodically knocking people’s hats off—then, I account it high time to get to sea as soon as I can.
Ishmael, in Moby Dick.
Yeah, that pretty well describes it. I’d been spending too much time writing about the weather and the climate, and not enough time outdoors experiencing the weather and the climate. So following Ishmael’s excellent advice, I have been kayaking and walking the coast and generally spending time on and around the ocean. During this time I have been considering what I want to write about next. Being on the water again, after the last few years of being boatless, has been most invigorating.
I have chosen to write about my on-and-off investigation of the relationship between changes in surface temperature and corresponding changes in top-of-atmosphere (TOA) radiative balance. I wrote about this previously in a post entitled A Demonstration of Negative Climate Sensitivity. This is an interim report, no code, little analysis, just some thoughts and some graphics, as I am in the (infinitely) slow process of assembling code, data, and results for publication in a journal. Unlike my previous post which used 5°x5° data, in this post I am using 1°x1° data.
Let me start with an interesting question. Under the current paradigm, the assumption is made that surface temperature is a linear function of the TOA imbalance (forcing). But is it true? In particular, is it true all over the world? To answer this, I looked at the monthly TOA radiation imbalance (all downwelling radiation minus all upwelling radiation) versus the change in temperature.
Figure 1. Maximum of the R^2 value, temperature vs TOA imbalance. This is the maximum of the individual R^2 for each 1°x1°gridcell, calculated at lags of 0, 1, 2, and 3 months. An R^2 of 0 means there is no relation between the two datasets, and an R^2 of 1 means that they move in lockstep with each other. In the red areas, when the TOA radiation balance changes, the temperature changes in a similar fashion. In the blue areas, changes in temperature and TOA imbalance are not related to each other.
Figure 1 has some interesting aspects.
Figure 1 was created by displaying, for each gridcell, the largest of the four R^2’s, one from each of the four lag periods (0, 1, 2, and 3 months). One interesting result to me was that while the temperature of a large part of the earth slavishly follows the variations in the local TOA balance (red areas), this is not true at all, at any lag, for the area of the inter-tropical convergence zone (ITCZ, blue, green, and yellow areas). This is evidence in support of my tropical thunderstorm thermostat hypothesis, which I discuss in The Thermostat Hypothesis and It’s Not About Feedback. For that hypothesis to be correct, the surface temperature in the ITCZ must be decoupled from the TOA forcing … and it is obvious from Figure 1 that the ITCZ temperature has little to do with forcing.
Next, I wanted to look at the climate sensitivity. In a general sense, this is the amount of change in the surface temperature for a 1-unit change in the TOA radiation imbalance. There are a variety of sensitivities, from instantaneous to equilibrium. Because I have monthly data, I’m looking at an intermediate sensitivity.
Figure 2 shows the temperature change due to a 3.7 watt per metre squared (W/m2) at various time lags. When the TOA radiation changes, the surface (land or ocean) does not respond immediately. By examining the response at different time lags, we can see the characteristic lag times of the land and the ocean.
Figure 2. Climate sensitivity (temperature change from a 3.7 W/m2 TOA imbalance) for the earth. Sensitivity is determined as the slope of the linear regression line regarding TOA variations and surface temperature for each gridcell, over the period of record. Click on upper or lower image for larger version.
Consider first the land. For most of the land, the strongest response (orange and red) occurs after a 1-month lag. The maximum sensitivity is in the areas of Siberia and the Sahara Desert, at around 0.8° per doubling of CO2. Extratropical land areas are more sensitive to TOA variations than are tropical land areas. The highest sensitivity in the Southern Hemisphere is about 0.3°C per doubling of CO2
Curiously, tropical Africa shows a lagged negative sensitivity. This becomes evident at a 2-month lag, and increases with the 3-month lag.
The ocean, as we would expect, is nowhere near as sensitive to TOA variations as is the land, with a maximum sensitivity of about 0.4°C per doubling, The sensitivity over most of the ocean is on the order of 0.1°Ç per doubling.
Finally, Figure 3 shows the relationship between the climate sensitivity and the temperature. Because of the large difference between the land and the ocean, I have shown them separately.
Figure 3. The relationship between climate sensitivity and temperature. Each point represents one gridcell on the surface of the earth. For each gridcell, I have used the time lag which gives the greatest response. Colors show the latitude of the gridcells.
Here, let me point out that I have long maintained that climate sensitivity is inversely related to temperature. This is clearly true for the land.
As I said, not much analysis, just some thoughts and graphics.
Best to all,
w.
DATA
Sea Temps: NOAA ERSST
Surface Temps: CRU 3.1 1°x1° KNMI
TOA Radiation: CERES data
