Author Archives: niclewis

Are energy budget TCR estimates biased low, as Richardson et al (2016) claim?

A guest post by Nic Lewis   Introduction and Summary In a recently published paper (REA16),[1] Mark Richardson et al. claim that recent observation-based energy budget estimates of the Earth’s transient climate response (TCR) are biased substantially low, with the true value some 24% higher. This claim is based purely on simulations by CMIP5 climate […]

Objective Bayesian parameter estimation: Incorporating prior information

A guest article by Nic Lewis Introduction In a recent article I discussed Bayesian parameter inference in the context of radiocarbon dating. I compared Subjective Bayesian methodology based on a known probability distribution, from which one or more values were drawn at random, with an Objective Bayesian approach using a noninformative prior that produced results […]

Marvel et al.: GISS did omit land use forcing

A guest article by Nic Lewis I reported in a previous post, here, a number of serious problems that I had identified in Marvel et al. (2015): Implications for climate sensitivity from the response to individual forcings. This Nature Climate Change paper concluded, based purely on simulations by the GISS-E2-R climate model, that estimates of […]

Bayesian parameter estimation: Radiocarbon dating re-examined

A guest article by Nic Lewis Introduction In April 2014 I published a guest article about statistical methods applicable to radiocarbon dating, which criticised existing Bayesian approaches to the problem. A standard – subjective Bayesian – method of inference about the true calendar age of a single artefact from a radiocarbon date determination (measurement) involved […]

Marvel et al. – Gavin Schmidt admits key error but disputes everything else

A guest article by Nicholas Lewis Introduction Gavin Schmidt has finally provided, at the GISS website, the iRF and ERF forcing values for a doubling of CO2 (F2xCO2) in GISS-E2-R, and related to this has made wholesale corrections to the results of Marvel et al. 2015 (MEA15). He has coupled this with a criticism at […]

Marvel et al.: Implications of forcing efficacies for climate sensitivity estimates – update

A guest article by Nicholas Lewis Introduction In a recent article I discussed the December 2015 Marvel et al.[1] paper, which contends that estimates of the transient climate response (TCR) and equilibrium climate sensitivity (ECS) derived from recent observations of changes in global mean surface temperature (GMST) are biased low. Marvel et al. reached this […]

Appraising Marvel et al.: Implications of forcing efficacies for climate sensitivity estimates

A guest article by Nicholas Lewis Note: This is a long article: a summary is available here. Introduction In a recent paper[1], NASA scientists led by Kate Marvel and Gavin Schmidt derive the global mean surface temperature (GMST) response of the GISS-E2-R climate model to different types of forcing. They do this by simulations over […]

Implications of recent multimodel attribution studies for climate sensitivity

Last year, a paper of mine (Lewis 2014) showing that the approach used in Frame et al (2005), which argued for using a uniform prior for estimating equilibrium (strictly, effective) climate sensitivity (ECS), in fact led to a unique, objective Bayesian estimate for ECS upon undertaking a simple transformation (change) of variables. The estimate was […]

Scientific American article: “How to Misinterpret Climate Change Research”

A Scientific American article concerning Bjorn Stevens’ recent paper “Rethinking the lower bound on aerosol radiative forcing” has led to some confusion. The article states, referring to a blog post of mine at Climate Audit, “The misinterpretation of Stevens’ paper began with Nic Lewis, an independent climate scientist.”. My blog post showed how climate sensitivity […]

Pitfalls in climate sensitivity estimation: Part 3

A guest post by Nicholas Lewis In Part 1 I introduced the talk I gave at Ringberg 2015, explained why it focussed on estimation based on warming over the instrumental period, and covered problems relating to aerosol forcing and bias caused by the influence of the AMO. In Part 2 I dealt with poor Bayesian […]

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