Why El Niño and not the AMO?

We can take it out because it is an oscillation that has Zero trend over time and well-defined impacts on temperature that are reasonably predictable from event to event. The total impact approached about +/-0.25C or, more accurately, Temp Impact = 0.08 * Nino 3.4 (of three months prior). This a LARGE enough variation that it should be accounted for if we want to understand the climate.
The ENSO also affects global water vapour levels in exactly the same manner (with the same lag), cloud patterns, OLR patterns, wind patterns, even the rotation of the Earth, etc. Or in other words, just about everything that is defined as the climate. It is the biggest weather phenomenon on the planet.
The AMO is also a big impactor. The AMO is the driver of the 60 year cycle in temperatures or it is a very, very good proxy for whatever is driving the 60 year cycle in temperatures. It can affect global temperatures by +/-0.3C and like the ENSO’s impact, that is a BIG enough impact that it should be accounted for. There is also a southern ocean version of the AMO which I quite can’t nail down yet.

Anyone know the derivation of the word “jigger?” As in: “Someone has jiggered the numbers to make it work.”
I believe NASA refers to the surface temperature of the earth as “combined Land-Surface Air and Sea-Surface.” There is a lot in that little phrase. Notice that they use the word “combined” rather than “average” or “weighted average.” NOAA (Tom Karl?) on the other hand uses the phrase “merged land air and sea surface temperature.” Here the word becomes “merged.” To me both “combined” and “merged” imply that an extraneous method was employed backed by some sort of ad hoc justification – the jigger factor.

Doesn’t Newton’s law of cooling (ie that the hotter something is the faster it cools) mean that if the earth “stores up” high temperature water that the water will necessarily cool at a different (and higher) rate than a well mixed pool? I suppose what I’m asking is whether two jugs of water one at 50 degrees and the other at 100 degrees and averaged, will cool faster than one big jug at 75. My guess is that since temperatures follow an exponential curve during cooling the two averaged would cool faster than the one. If cooling were straight line then you would expect the two to cool in the same way as the one.

My only comment on this description of the heat pump is this. The planet cools more efficiently when the heat absorbed in the tropics is not transported towards the poles. Because the heat is ultimately lost via radiation, anything that reduces temperature variation on the surface area by transporting hot spot heat elsewhere has a net warming influence.
This effect is substantial. The transportation of heat from the equatorial regions into the temperate zones is one of the only things that keeps the planet out of glaciation. In the Atlantic, the key player is the Gulf Stream (and its subsidiary currents) in the thermohaline circulation. A glance at the SSTs visible e.g. here:
http://classic.wunderground.com/tropical/?index_region=at
clearly shows the plume of warm water carrying tropical heat up to Europe and the Atlantic side of the Arctic. Interrupt that flow, or divert it East so that it runs into Africa instead of Europe, and Europe goes into the icebox and the tropical Atlantic warms until they both are in equilibrium without heat transport. However, the temperate zone temperature will drop more than the tropical temperature will rise to maintain stasis because of that .
The issue is further complicated, of course, by vertical transport. The Enthalpy content “in the ocean” or at ground level is subject to the fully atmospheric greenhouse effect. Heat that rises, however, penetrates the greenhouse layer and is lost more efficiently wherever it might be found. Wet heat (water vapor) that rises is particularly efficient as it carries the latent heat of vaporization in addition to “just” the enthalpy content in the water molecules themselves at constant temperature, and have to give up this heat in order to form clouds. Clouds then are highly nonlinear temperature regulators — both increasing daytime albedo and increasing nighttime greenhouse trapping. Clouds are negative feedback driven nucleation points — when daytime clouds start to form the albedo causes further cooling beneath them and heat-engine thunderstorms form from the updrafts of warm wet air lofted up to the stratosphere to efficiently cool and spread, creating a local convective cell that pulls heat out of the ocean (or the moist land or air) and moves it to a cold reservoir. Sometimes enormous chunks of the ocean self-organize into the heat engines we call hurricanes, sucking latent heat out of the warm ocean underneath and moving it up into the stratosphere to be radiatively lost with great force and efficiency.
These are the “natural signal” eggs that must be juggled, for all of the oceans and decadal oscillations and the THC that turns over waters heated or cooled a few centuries ago and feeds it back into the system determining surface temperatures. The general circulation models attempt to do the juggling but it is obviously an enormously difficult problem. I, like you, am very skeptical of any attempt to remove some particular “natural signal” from a coupled nonlinear chaotic system with gazillions of feedbacks known and unknown and (mostly) transient, fluctuations that happened to nucleate at some particular point and grow.
Watching the evolution of hurricanes in the Atlantic is particularly instructive. A tiny wave in circulation/air pressure forms off of the coast of Africa somewhere. Most times — depending on things like the shear in the upper atmosphere, which is in turn determined by things that went on thousands of miles away and weeks in the past in ways that depend on e.g. the state of the ENSO and more — the waves peter out, forming nothing but a few thunderstorms and then dissipating. Every now and then, however, the shear is low and a cluster of thunderstorms joins up and creates a persistent updraft as warm wet air is pulled into the low pressure underneath, is lifted up (cooling), and falls back as much cooler rain. The winds build up and start to bend from the coriolis force as they approach the low pressure center, and the whole thing migrates to the west and north, moving like a vacuum cleaner over warm water and growing in size and strength as long as the shear remains low.
We can barely predict in the roughest of terms how many of the waves are likely to nucleate into storms (not all of which start off of Africa — sometimes they nucleate in the Gulf of Mexico, sometimes in the Caribbean, sometimes in the middle of the Atlantic), and the distribution of energy among those storms. We do somewhat better with predicting the future trajectory and evolution of storms once they form, at least when there are distinct steering patterns of weather present (not always true). Who would have predicted that we would be continuing the longest single stretch without a major (cat 3 or better) Atlantic hurricane at this point in 2013? Who can predict at this point how much longer this stretch will continue? This seems as though it would be a feature of the climate, given the importance of hurricanes as transporters of energy, factors in the warming or cooling of the tropical ocean and yet we are completely clueless about this, helpless to do more than observe what happens and try to “explain” it away after the fact. Who would have predicted the erratic pattern of the ENSO, or anticipated its obvious role in the bulk of the observed “global warming” in the modern satellite era, where a single strong ENSO event is associated with almost the entire warming observed over the entire 33 year period? And ENSO in turn regulates the rainfall in West Africa and hence the hurricane pattern in the Atlantic, which influences the end-of-summer temperatures of the tropics and residual warmth that is transported to Europe, which affects (no doubt) the monsoon, which probably couples back to the ENSO with a lag of anywhere from years to decades, with additional contributions from what happens in the Antarctic, in the Indian Ocean, in the waters off of Australia, from the severity of the winter in Siberia and Mongolia, from what the weather/climate was like a century ago when a band of upwelling water in the thermohaline circulation was first pulled under to circulate as a deep current.
We haven’t a prayer of simulating, or understanding this, especially without data. Data we are still just barely beginning to obtain from projects like ARGO and ever-improving satellite observations. Most of the world’s oceans is a mystery as far as temperature, motion, salinity and contribution to the climate are concerned. Our sampling of surface temperatures with actual thermometers is horribly non-uniform, biased and often just plain corrupt. Our knowledge of what is going on in the various layers of the atmosphere is improving, but even less sampled than the oceans (except by satellite). And our models of the sun and its varied coupling to the earth’s geomagnetic system and atmospheric chemistry is primitive but improving.
In a century, we probably will be able to make quantitative climate predictions with some skill. In the current decade, we cannot. AGW is by no means disproven by the last 15 to 18 years of arguably flat temperatures, just as it was by no means proven by the temperature rise that occurred during the ENSO event or since the end of the LIA or the Dalton minimum. Temperature change cannot either prove or disprove the (C)AGW hypothesis, not without a full understanding of the climate system sufficient to predict what the temperature would be in the absence of extra CO_2, which we utterly lack. All the more so since we have to understand it in the presence or absence of CO_2, soot, various aerosols of anthropogenic or natural origin, with a variable sun, varying phases of decadal oscillations, and an unknown ocean sucking heat down or delivering heat up in a global circulation process with timescales ranging from years to centuries, with land use changes and pollutants in the waters that have visible global effects that we do not yet understand, all in a highly nonlinear chaotic system with numerous feedbacks and spontaneous self-organizing stabilizing macroscopic phenomena with global impact, on a planet that is inexorably pursing an orbital cycle that completely changes the underlying “equilibrium” over time in ways we do not fully understand and cannot predict or compute.
In the meantime, prudence suggests that we concentrate on the ongoing disaster of global energy poverty first as it is a certain disaster that is happening now and forces 1/3 of the world’s population to live in near prehistoric levels of poverty and misery. Even if CO_2 were precisely as disastrous as the worst-case CAGW scenarios suggest — which few people believe any more, including climate scientists — the impact of a 2.5-3.5 C rise in global temperature by the end of the century will be smaller than the impact of a century more of global energy poverty, even if the ocean does rise a full meter or more, even if storms do actually get discernibly worse eventually, even if there is increased desertification, none of which are currently observible.
Somewhere in the world, as I type this, not one but hundreds of millions of people are cooking a sparse day’s meal on animal dung or a small charcoal fire. Their children are breathing in particulates and smoke and suffering from malnutrition and diseases. Their clothes must be hand washed, if they are washed at all. They have neither fresh, clean water nor anything but the great outdoors as a sewer system. Some two billion people will light their homes — if one can call a tin shanty or mud or grass hut a home — with an oil lamp or nothing at all tonight. The children of those two billion people will not go to school tomorrow, cannot read or do simple arithmetic, and will go to bed hungry (indeed, live always hungry, as they do not take in enough food to support their growth). They will grow up stunted in stature and damaged in their brains, all because they lack access to cheap electricity, running clean water and sewer facilities and clothes washing and refrigeration and schools and houses and adequate supplies of fertilizer-grown food that electricity enables. Many will die young, or live to become “criminals” as they do what they must to stay alive, or will become cannon fodder for anyone who promises to give them a better life if they will fight and die for them.
They, not the threat of a supposed apocalypse that might or might not happen in a century, are the moral imperative of the twenty-first century. There is no need for 1/3 of the world’s population to live in squalid misery — not any more. We have the technology, we have the wealth, to utterly eliminate global poverty within a few decades. What we lack is the will and the vision to do so.
And we will never succeed in doing so at the same time we make energy more expensive and discourage its use. The poverty in question is energy poverty. Fundamentally. With enough, cheap enough, energy, we can make the deserts bloom, create jobs in the heart of Africa or India or South America, bring medicine and electric lights and running water to the world. Cheap, clean energy solves all problems; it is the fundamental scarcity.
rgb

Just what is the history of treating Nino 3-4 as “noise”.
And is it being subtracted because it is “noise” or because it is a strong non-CO2 forcing?
Hypothetically, if you want to bracket T sensitivity to CO2 doubling, you ought to do your best to remove the Temp impact of other known or suspected forcings such as the Nino 3-4. This bring up two flaws… Once you admit there is a forcing other than GHG, such as Nino 3-4, you then need to forthright in estimates for all other identifiable forcings. A bigger flaw is that it must increase uncertainty rather than reduce it.
Willis, what you describe seems to be a process where people want to estimate the climate sensitivity,
Mean(GHG_double) = Mean(Global Temp Recorded(Tortured)) – Mean(Nino3-4).
Missing in this exercise is proper treatment of the uncertainty. Variance(GHG_Double). It cannot be a subtraction of variances.
If you have two uncertain components A, B, uncorrelated, independent, making a total T,
where A and B are measured components, then we can statistically add them for an estimated T
Mean(T_estimated) = Mean(A_measured) + Mean(B_measured)
Variance(T_estimated) = Variance(A_measured) + Variance(B_measured)
But what we have in the real world is not T_estimated but T_measured and we are trying to estimate its component A_estimate from a guess of B_guess.
If our physical model is:
T_measured = A_estimated + B_guess, with A (GHG) and B (Nino3-4) uncorrelated, then by trying to solve for A we face a big problem.
A_estimated = T_measured – B_guess, T and B necessarily must be positively correlated by an unconstrained degree between 0 and 1.
At it’s simplest, and wrong, level you could assume T_measured and B_guess are independent, but then
Mean(A_estimated) = Mean(T_measured) – Mean(B_guess)
Variance(A_estimated) = Variance(T_measured) + Variance(B_guess) ‘Note the PLUS sign.
The bigger the variance on B_guess, the bigger will be the variance on the sought after A_estimate.
Or in this case, Variance(GHG_double) can be no smaller than Variance(Nino3-4) unless we have VERY good knowledge of their covariance.
= = = = = = =
I’ll close by refreshing a link to the topic of tracking uncertainty through Tmax, Tmin data.
I fear we are throwing away a lot of variance by starting with monthly Tavg or Tmean and ignoring the variance that comes from the (Tmax-Tmin)/2 mean std error.
Every adjustment you make to a temperature record, be it adding or subtracting a value, you must ALWAYS ADD the variance of uncertainty of the result.
How many adjustments has GISS made to the temperature record?
How much variance needs to be been added to the temperature record from all these uncertain adjustments?
How much bigger must the error bars really be because of adjustment uncertainty and proper accounting of uncertainty in the averaging?