This is part of a long term project to try to understand why the “two sides” in the climate debate look at pretty much the same information and come to very different conclusions. Having met both sides, and tried to understand their motivation and outlook, I am thoroughly convinced that both approach the subject in what they think is the right way and both are horrified at the “antics” of the other. If I have said anything that can be taken as derogatory, that was not the intention. I am sorry but I have done my best to describe what I see.

[From early responses it is clear I need to define more precisely what I mean by sceptic and non-sceptic. Broadly, those supporting the IPCC conclusions that we are heading toward catastrophic warming would be on one side and those who are sceptical of this on the other. For a more precise definition of sceptic I would consider sceptics to be those who generally agree with the statement outlined in the: “The Sceptic View“]

I WOULD VERY MUCH APPRECIATE COMMENTS FROM BOTH “SIDES”.

Sceptic Non-sceptic

(Academic/ “warmist”)

Employment sector Commercial & non-governmental Academia, public sector & campaign charities Employment Electronic engineering, chemical engineering, energy engineering, general engineering, weather forecasting. Environmental science, life sciences, climate science, civil service, journalism, campaign charities & general sciences. Main focus Prediction & hard facts. Understanding & empathy. Viewpoint Individualistic, libertarian & conservative (not politically)

Public sector, Guardian liberal. Viewpoint of Natural variation Natural variation is around us everywhere and dominates natural systems. Many things vary naturally and we capture these in our models. With enough data, measurement errors can be processed data so that we can ignore them. Model of natural variation. Measurement = Nat.Var.

after careful work …

Measurement =

f(t) + Nat.Var.(t) Theory = Natural system.

(After enough data measurement errors -> 0) Main Expertise Prediction, design & decision making

Theory, understanding and/or modelling through hindcasting. Communicating ideas.

Main Aim Best decision Best explanation Attitude if prediction/model doesn’t match new data. Poor quality like this cannot be tolerated by professionals. Good decisions require good models which include normal variation.

Those involved should sort the problem out or find another job. That is to be expected because this is how we improve our models. Attitude if they don’t understand what is happening Real life is like that and you learn to cope. That is a dreadful admission. How can you say you can’t explain what is happening. A careless attitude like this cannot be tolerated.

Those involved should sort out their problems or find another job. Attitude to long term forecasting. Forecasts get worse and natural variation increases the further away we try to predict from measured data. Errors become smaller with more data so over the long term measurement errors can be ignored. Extra discipline skill set. Holistic, multi-skilled, complex, time & resource limited.

Includes practical economics, understanding how people react in real situations and how they reach decisions in the real world.

Used to complex systems with non-linear, non-deterministic behaviour, real time decision making, safety critical. Able to cope where there is not enough time or resources. Single subject.

Focused on own area of expertise. Secure job with time to get to grips with subject. Reliant on peers to provide good data. Avoids messy, non-linear, non-deterministic systems operating in real time. Is almost never involved in commercial situations where there is too little time and resource (to involve academia). Problem solving approach Bottom up

Start with the brass tacks facts, assess the situation to a professional standard & if there is time make make sense of it. Top down.

Start with the overall picture & fills in the details as understanding improves. Ignore all extraneous detail which cannot be modelled. Experience in decision making Real time, high cost, critical to company’s survival and/or safety critical. Resource & information limited. Which journal/newspaper to send latest work to?

What to do next to get next grant? What quality means

Getting it right first time Work accepted by peers, newspaper, manager as “novel enough” & interesting enough for publication

Addendum

Approach

What is normal and is there any sign of anything abnormal happening which requires attention?

How do we model the system and what do our models suggest will happen?

Basis for validation /falsification of hypotheses

Empirical data derived from real-time physical observations or reproducible experimentation.

Model simulations based on theoretical considerations supported by interpretations of selected paleo-climate proxy data



Changes

1.0 after fair comment that the the text was patronising re the non-sceptic view of natural variation it has been changed as follows:

Columns: “Academic (warmist)”

changed to

‘Non-sceptic (Academic/ “warmist”)’

Model of natural variation under “non-sceptic”:

“(Ignoring measurement errors)”

changed to

“(After enough data measurement errors -> 0)”

Viewpoint of Natural variation

“Natural variation? You mean measurement error.”

changed to

“Many things vary naturally and we capture these in our models. With enough data, measurement errors can be processed data so that we can ignore them.”