A large increase in ozone in the stratosphere would result in a large change in stratospheric temperature. Since energy transfer in the stratosphere is essentially radiation only and the heat capacity is low, the temperature would change rapidly. While ozone does absorb and emit in the IR, it’s main effect is by increased absorption of incoming solar radiation. IIRC, Modtran doesn’t allow changes in temperature above about 15km and doesn’t automatically adjust temperatures for changes in atmospheric composition.

Because the temperature increases with altitude in the stratosphere, the greenhouse effect changes sign. IR absorbing/emitting gases which don’t significantly absorb solar radiation cause a decrease in temperature rather than an increase. But I don’t think Modtran, as implemented by Archer on the web, is really able to deal with that.

Overall, addition of water vapor cools the stratosphere while an increase in ozone heats it. The effect of stratosphere temperature change on the surface is more complicated.

]]>https://www.ipcc.ch/site/assets/uploads/2018/02/WG1AR5_SPM_FINAL.pdf

(For what it is worth, the online version of Modtran says that a 10X increase in stratospheric ozone causes no change in OLR at 70 km, suggesting that this calculation has stratospheric adjustment.)

Does anyone understand these counterintuitive results?

]]>My favorite paper has the observed (CERES) and modeled LWR feedback through clear skies during the seasonal cycle in GMST. Feedback through clear skies is the sum of Planck, WV and LR feedbacks. The paper covers both CMIP3 and CMIP5 models. All models produce the same Planck feedback, so any differences between models are due to differences in the sum of WV+LR. Unfortunately, the authors reported the gain associated with clear sky LWR feedback, not W/m2. The clear skies LWR gain times 3.21 W/m2/K is the WV+LR feedback in W/m2/K [Eq 6], (which is different than how feedback analysis uses the term gain).

]]>A dry bias in the boundary layer could create problems simulating boundary layer clouds and the feedback associated with those clouds, but the question is about WV+LR feedback.

By definition, change in forcing (dF) is a change in radiative flux across the TOA that is caused by GHGs not caused by something other than a temperature change: GHGs, aerosols, solar activity etc. At its simplest, it is the change in net flux calculated (by radiative transfer calculations) assuming an instantaneous change in today’s atmosphere. At its simplest, feedback (alpha) is the planet’s radiative response at the TOA to a change in surface temperature.

dT = dF/alpha

where dT is the warming in response to the forcing (dF).

alpha = dOLR/dTs + dOSR/dTs

= Planck + WV + LR + surface albedo + LWR&SWR cloud feedbacks

where OSR is reflected SWR.

The WV and LR feedback (FB) terms can be written:

WV FB = [d(OLR)/d(humidity)]*[d(humidity)/dTs]

LR FB = [d(OLR)/d(LR)]*[d(LR)/dTs]

So, as long as an AOGCM gets the derivatives in brackets right, it will calculate the correct WV+LR feedback despite the absolute biases. In the bottom of Figure 2 of your paper, I assume (but am not completely certain) the authors show that the models get approximately the same [d(humidity)/dTs] at three different altitudes even though the models have different temperature biases at those altitudes. (Those biases are shown on the x-axis.) In the top of Figure 2, I assume (but am not completely certain) the authors show that the models get approximately the same [d(LR)/dTs] at three different altitudes even though the models have different temperature biases at those altitudes. The d(OLR) terms come from radiative transfer calculations. I say “I assume” because the terminology and symbols are confusing and I haven’t studied the paper as thoroughly as I should.

]]>impact on climate feedbacks. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.597.8262&rep=rep1&type=pdf

“A comparison of AIRS and reanalysis temperature and humidity profiles to those simulated from climate models reveals large biases. The model simulated temperatures are systematically colder by 1 – 4 K throughout the troposphere.” I have not seen this adressed or discussed in a serious way. My ignorance?

” On average, current models also simulate a large moist bias

in the free troposphere (more than 100%) but a dry bias in the boundary layer (up to 25%). While the overall pattern of biases is fairly common from model to model, the magnitude of these biases is not. In particular, the free

tropospheric cold and moist bias varies significantly from one model to the next. In contrast, the response of water vapor and tropospheric temperature to a surface warming is shown to be remarkably consistent across models and

uncorrelated to the bias in the mean state.”

And I have some sceptisism toward their last sentence: “We further show that these biases, while significant, have little direct impacton the models’ simulation of water vapor and lapse-rate feedbacks.” ]]>

Above and below are the key figures from DeWitt’s interesting reference. The red line is the % increase in total column water vapor (absolute humidity) per degK change (local temperature, left; GMST, right). The change does vary locally. The global weighted average is 7.3%. Data are CMIP3 multi-model mean. Below is the change in relative humidity which is about zero overall, a slight increase over the ocean, and a decrease over arid land.

Is the 2%/K increase in relative humidity at 500 mb (bright red band near the equator) associated with the putative hot spot in the upper tropical troposphere?

However, these interesting details don’t appear to have a big influence on the WV+LR feedback produced by models. See the Gregory plots below, which are radiative imbalance balance vs temperature in abrupt 4X experiments, making feedback the slope. The CMIP5 multi-model mean LWR feedback through clear skies (Planck+WV+LR feedback) is linear with temperature for 5 degK of warming in abrupt 4X experiments. (Figure a, x’s). Planck feedback alone (= -4eoT^3) becomes slightly more negative with warming: -3.30 W/m2/K at 287 vs -3.47 W/m2/K at 292 K, but this difference would be hard to see on this graph. Cloud SWR feedback the only one that changes significantly with temperature in some, but not all, models. (Given the lack of consensus between models about this feedback, there is little reason to believe anything the models are saying).

https://journals.ametsoc.org/doi/full/10.1175/JCLI-D-14-00545.

As I see it, if climate sensitivity is low (2 K), future warming will remain within the domain where we can treat WV+LR feedback as a constant, and can hope the other rapid feedbacks are effectively constant. If climate sensitivity is high (say 4 K), then we probably can’t count of feedbacks remaining constant.

]]>“Making the simplifying assumption that water vapor amounts depend exponentially on temperature for small enough temperature changes, we can easily convert between the finite difference (r) and differential (r) rates of change using r=log(1+rT)T.(2) The assumption of an exponential dependence on temperature is not quite correct—for example, the Clausius–Clapeyron dependence on temperature is not exactly exponential—but it should be adequate to capture the leading-order correction for finite temperature changes.”

https://iopscience.iop.org/article/10.1088/1748-9326/5/2/025207/pdf

]]>The linear rule for water vapor is a 7% increase per degree K or 28% over 4 K. The exponential increase is (1.07^4) is 31%. A minor problem issue (10% error). The C-C calculator I used said there is actually a 27% increase in saturation vapor pressure between 14 and 18 degC.

DeWitt also wrote: “Saying that deltaT with deltaF is more important than absolute T is hand waving. You don’t know that and neither do the modelers.”

I’m confused by your objection. Let’s hypothesize that this 4 K range in GMST were all due model “mistakes” in cloud fraction and reflection of SWR. Does this mean that models must get SWR cloud feedback wrong?

My favorite paper (Tsushima and Manabe, PNAS 2013) shows that GCMs predict positive LWR cloud feedback in response to seasonal warming (3.5 K change in GMST) while CERES shows negligible LWR cloud feedback. I doubt the amount of error in this feedback varies with the model’s pre-industrial temperature.

The annual changes in reflected SWR are complicated and (unlike LWR) don’t vary linearly with GMST. Some SWR changes lag temperature change. However, I doubt you will find a systematic relationship between modeled PI GMST and the change in reflected SWR with temperature.

Well my doubts are meaningless and I’m not going to look up the data now. The figure below on the relationship between projected warming and current model temperature will have to do. It does say that none of the models with the higher current temperature show forecast higher future warming. The models with lower and “correct” current temperature forecast the full range of future warming. The warmer models forecast 1.4-2.2 K of warming while the cooler and “correct” models forecast 1.4-3.0 K of warming over 90 years. While not huge, this difference is also not trivial. Of course, models can and do get similar ECS while disagreeing substantially about individual feedbacks.

Figure 2: Mean global surface temperature from CMIP5 historical simulations over 1979-2008, is not significantly correlated with the change from 1979-2008 to 2071-2100. The grey squares show the ensemble mean for each of 42 CMIP5 models, with the error bars representing the min-max range from within each model’s own ensemble, where available. The coloured lines show the reanalysis estimates as in Fig. 1.

http://www.climate-lab-book.ac.uk/2015/connecting-projections-real-world/

https://journals.ametsoc.org/doi/full/10.1175/BAMS-D-14-00154.1

Thanks for challenging my assumptions and forcing me to find some data.

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