I spent my post graduate year (1964-5!) and my early career on control engineering (analog computer control of steel rolling mills, digital three term control of motors mixing of stone for road surfaces etc etc )
I would hate to see the subject of controlling the global temperature made excessively complicated. We are not trying to control the weather. That would be complicated! We are trying to control the overall global temperature. Just make the adjustments of control inputs very slow to avoid over reaction. (oscilations/ instabilities) I am sure David Keith at Harvard has got good information on this for his proposals. John Gorman Ps -and the same subject, control theory, is called homeostasis in medicine where I find my previous knowledge very useful in my second career. From: Andrew Lockley Sent: 25 August 2018 22:48 To: geoengineering Cc: [email protected] Subject: [geo] Can We Use Linear Response Theory to Assess Geoengineering Strategies? Poster's note: a primer on linear response theory is available at https://en.m.wikipedia.org/wiki/Linear_response_function - I hope that the corresponding author will be available to join the group and post a plain English summary. The application of control theory to geoengineering is IMO an important advance, and apparently a relatively recent one. http://scholar.google.com/scholar_url?url=https://www.earth-syst-dynam-discuss.net/esd-2018-30/esd-2018-30-AC1-supplement.pdf&hl=en&sa=X&d=8709275723963611654&scisig=AAGBfm1hG13oxCRJ0QzTgJdX4Jg-35qpxg&nossl=1&oi=scholaralrt&hist=tDjNe6QAAAAJ:15126857386591841230:AAGBfm3hgOHpwz-gQp1d_Wc583FiI4qafA Can We Use Linear Response Theory to Assess Geoengineering Strategies? Tamás Bódai1,2, Valerio Lucarini1,2,3, and Frank Lunkeit3 1Centre for the Mathematics of Planet Earth, University of Reading, UK 2Department of Mathematics and Statistics, University of Reading, UK 3CEN, Meteorological Institute, University of Hamburg, Germany Correspondence: T. Bódai ([email protected]) Abstract. Geoengineering can control only some variables but not others, resulting in side-effects. We investigate in an intermediate-complexity climate model the applicability of linear response theory to assessing a geoengineering method. The application of response theory for the assessment methodology that we are proposing is two-fold. First, as a new ap- proach, (I) we wish to assess only the best possible geoengineering scenario for any given circumstances. This requires 5 solving the following inverse problem. A given rise in carbon dioxide concentration [CO2] would result in a global climate change with respect to an appropriate ensemble average of the surface air temperature ∆h[Ts]i. We are looking for a suit- able modulation of solar forcing which can cancel out the said global change – the only case that we will analyse here – or modulate it in some other desired fashion. It is rather straightforward to predict this solar forcing, considering an infinite time period, by linear response theory in frequency-domain as: fs(ω) = (∆h[Ts]i(ω)−χg(ω)fg(ω))/χs(ω), where the χ’s are 10 linear susceptibilities; and we will spell out an iterative procedure suitable for numerical implementation that applies to finite time periods too. Second, (II) to quantify side-effects using response theory, the response with respect to uncontreolled observables, such as regional averages hTsi, must of course be approximately linear. We find that under geoengineering in the sense of (I), i.e. the combined greenhouse and required solar forcing, the response ∆h[Ts]i asymptotically is actually not zero. This turns out to be not due to nonlinearity of the response under geoengi- 15 neering, but that the linear susceptibilities χ are not determined correctly. The error is in fact due to a significant quadratic nonlinearity of the response under system identification achieved by a forced experiment. This nonlinear contribution can be easily removed, which results in much better estimates of the linear susceptibility, and, in turn, in a five-fold reduction in ∆h[Ts]i under geoengineering. This correction improves dramatically the agreement of the spatial patterns of the pre- dicted linear and true model responses (that are actually consistent with the findings of previous studies). However, (II) 20 due to the nonlinearity of the response with respect to local quantities, e.g. hTsi, even under goengineering, the linear prediction is still erroneous. We find that in the examined model nonlinearities are stronger for precipitation compared to surface air temperature. -- You received this message because you are subscribed to the Google Groups "geoengineering" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To post to this group, send email to [email protected]. Visit this group at https://groups.google.com/group/geoengineering. For more options, visit https://groups.google.com/d/optout. -- You received this message because you are subscribed to the Google Groups "geoengineering" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To post to this group, send email to [email protected]. Visit this group at https://groups.google.com/group/geoengineering. For more options, visit https://groups.google.com/d/optout.
