Guest post by Clive Best
Every time that a new result shows a low climate sensitivity (ECS), climate scientists are queuing up to rubbish it. God forbid anyone implying that CMIP5 models are running too hot! Even Myles Allen received such a battering when referring to his recent paper on the BBC Today program:
“that a bunch of models developed in the mid 2000s indicate that we should have got to 1.3C by the time emissions reached what they are today (cumulative emissions), whereas we have only reached 1C”.
The predictable outcry from his fellow ‘scientists’ was enough to force him to write a ‘clarification’, see Guest post: Authors respond to misinterpretations of their 1.5C carbon budget paper
The one-third of ESMs that simulate the warmest temperatures for a given cumulative amount of inferred CO2 emissions determine the carbon budget estimated to keep temperatures “likely below” 1.5C. By the time estimated cumulative CO2 emissions in these models reach their observed 2015 level, this subset simulates temperatures 0.3C warmer than estimated human-induced warming in 2015 as defined by the observations used in AR5 (see figure below). It is this level of warming for a given level of cumulative emissions, not warming by a given date, that is relevant to the calculation of carbon budgets.
The remaining carbon budget before we reach 1.5C was found to be ~3 times greater than models predicted! In other words the climate sensitivity with cumulative emissions is lower than models ‘project’. That simply means that Earth System Models have got the carbon cycle wrong. That means they must also have carbon feedbacks wrong, which must reduce their values of ECS (doubling of CO2).
Now a new paper by Kate Marvel, Gavin Schmidt and co. continues this attack on data measurement derived values of ECS.
An emerging literature suggests that estimates of equilibrium climate sensitivity (ECS) derived from recent observations and energy balance models are biased low because models project more positive climate feedbacks in the far future. Here, we use simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5) to show that across models, ECS inferred from the recent historical period (1979-2005) is indeed almost uniformly lower than that inferred from simulations subject to abrupt increases in CO2 radiative forcing. However, ECS inferred from simulations in which sea surface temperatures are prescribed according to observations is lower still. ECS inferred from simulations with prescribed sea surface temperatures is strongly linked to changes to tropical marine low clouds. However, feedbacks from these clouds are a weak constraint on long-term model ECS. One interpretation is that observations of recent climate changes constitute a poor direct proxy for long term sensitivity.
Lewis & Curry and others all base their estimates of ECS on an energy balance model combined with observed temperatures. See also A new measurement of Equilibrium Climate Sensitivity (ECS)
Kate Marvel is now saying these are way too simplistic! She implies that long term feedback effects only emerge hundreds of years from now. Even if that were true it is still hard to justify reacting now to some future hypothetical problem which cannot be tested any time soon. The paper is really simply stating that the models are right, so therefore there is no point in looking at the temperature record to determine ECS.
This is like a ‘Tablets of Stone’ argument. Only an elite priesthood are qualified to interpret their meaning!
Contrast this to the Millar et al. Carbon Budget paper or to the recent Cox et al. sensitivity paper which are also based on CMIP5 models. Cox & co derived a low value of ECS = 2.8±0.5C by showing that the over-sensitive models predicted too much inter-annual variability. Only the few models with low sensitivity came close.
So this whole idea that climate models have some sort of status equal to a real physical theory like Relativity needs to be rebuffed. What is the probability that every Climate Model has bugs? Anyone who writes code knows that the answer is 100%. You cannot develop 1 million lines of code which are bug free. Then we have Earth Systems Models (ESMs) where hugely complex biological, geochemical, cloud nucleation, and even human processes are implemented simply as if they were linear parameterisations.
A real physical theory like Quantum Electrodynamics (QED) predicts the value of the anomalous electron magnetic dipole moment and experiments have confirmed QED theory to a precision of 1 part in a trillion. Climate Models on the other hand can only manage to ‘project’ ECS to be in the ‘likely’ range of 1.5 to 4.5C . Yet when a value is derived from observational data that finds a value even as low as 2.5C it is greeted with howls of protest from climate scientists.
They really are a hyper-sensitive bunch !