Rather than impose upon you further, I’ll await your paper to see how this makes it necessary for Murphy and Forster to “change the averaging time of the model output to be consistent with the relatively short satellite datasets we have to compare to.” (Since the datasets have monthly values, and each time step in the model is one month, I still don’t see why smoothing not shown in the spreadsheet would be necessary.)

There is much disagreement about how to quantify ocean mixing depth over time….there is no best answer to your question. I asked oceanographers a couple years ago and got all kinds of answers.

1. Thanks for getting back to me after I got cranky.

2. Thanks for pointing me to the data.

3. I don’t agree with you that the output is what is averaged in the simple model your spreadsheet illustrates. The output is “dSST” column L. For any given row (time step), its value is simply the sum of the previous row’s (previous time step’s, i.e., previous month’s): (1) CO2-forcing value (column D), (2) non-feedback component (column H) of the radiative-forcing value, (3) the non-radiative-forcing value (column K), and (4) the radiative feedback (column N). That is, the output results exclusively from the previous-time-step values, not from an average of their values over multiple previous time steps. So there’s no output smoothing. True, you do some smoothing in columns G and J, but that’s smoothing the *inputs*: it’s tantamount to providing inputs with desired auto-correlation characteristics.

4. I already (pretty much) understood about the mixing, i.e. that you assume the effective mixing depth varies with “time scale” (which I take it to mean spectral component of the stimulus (“forcing”). My question was how you came up with the *quantitative* values relating mixing depth to “time scale.”

you will find the satellite data if you search on CERES data or ERBE data, available from NASA Langley Research Center

yes, of course heat mixes through turbulence in the ocean….but that takes time. So, rapid and large week-to-week SST changes are obviously for relatively shallow layers…low frequency changes go deeper.

On the numerous previous occasions when you ignored my questions, I made allowances for the value of your time.

I would similarly have made allowances when you ignored my questions above. Those are good questions. They are directed to enabling your readers to replicate some of your work and to clarifying ambiguities in and eliminating lacunae from your defense of a controversial (but to me at least until now quite compelling) theory. Still, there are only so many hours in a day.

But then I noticed that in another, contemporaneous thread you take time to answer a ton of (pardon me, questioners, but) really lame questions about “back radiation.” That issue is well settled. It is controversial only among some whose grasp of basic physics is tenuous. And there are plenty of sites where back radiation is covered perfectly well by others, whose time would not, like yours, be better spent explaining their own theories. (And I might add that in that thread my helpful comment, which merely gave links to other sites explaining the phenomenon, was moderated out.)

So I’m wondering. Is it something I said? Or should I pay attention to my experience, which is that reluctance to answer questions is often indicative of weakness in the theory?

Lets just say that your understanding and approach to the science is welcome and refreshing in the field.

So quote Arnold H. Glasow – “The fewer the facts, the stronger the opinion.”.

But happily not in your case. For which we can all be grateful.

Doesn’t the estimate of uncertainty in the slope parameter (and the corresponding 95% range for the slope) from the OLS regression (your first figure above) also say clearly that no meaningful estimate of sensitivity can be extracted from 10 years of data via OLS, regardless of any bias that may exists? My goodness, this data just looks like noise!