Study: Climate Models Need More Historical Fudge Factors

Fromm the movie “young Frankenstein” by Mel Brooks. Igor peruses the brain of “Abby Normal”

Guest essay by Eric Worrall

The researchers claim adding historical data derived fudge factors to correct the discrepancy between climate models and historical observations, producing a Frankenmodel mix of fudge factors and defective physics, will make climate predictions more reliable.

New approach to global-warming projections could make regional estimates more precise

Computer models found to overestimate warming rate in some regions, underestimate it in others

Date: May 15, 2018
Source: McGill University

A new method for projecting how the temperature will respond to human impacts supports the outlook for substantial global warming throughout this century – but also indicates that, in many regions, warming patterns are likely to vary significantly from those estimated by widely used computer models.

“By establishing a historical relationship, the new method effectively models the collective atmospheric response to the huge numbers of interacting forces and structures, ranging from clouds to weather systems to ocean currents,” says Shaun Lovejoy, a McGill physics professor and senior author of the study.

Our approach vindicates the conclusion of the Intergovernmental Panel on Climate Change (IPCC) that drastic reductions in greenhouse gas emissions are needed in order to avoid catastrophic warming,” he adds. “But it also brings some important nuances, and underscores a need to develop historical methods for regional climate projections in order to evaluate climate-change impacts and inform policy.

Read more:

The abstract of the study;

Regional Climate Sensitivity‐ and Historical‐Based Projections to 2100

Raphaël Hébert, Shaun Lovejoy
First published: 13 March 2018

Reliable climate projections at the regional scale are needed in order to evaluate climate change impacts and inform policy. We develop an alternative method for projections based on the transient climate sensitivity (TCS), which relies on a linear relationship between the forced temperature response and the strongly increasing anthropogenic forcing. The TCS is evaluated at the regional scale (5° by 5°), and projections are made accordingly to 2100 using the high and low Representative Concentration Pathways emission scenarios. We find that there are large spatial discrepancies between the regional TCS from 5 historical data sets and 32 global climate model (GCM) historical runs and furthermore that the global mean GCM TCS is about 15% too high. Given that the GCM Representative Concentration Pathway scenario runs are mostly linear with respect to their (inadequate) TCS, we conclude that historical methods of regional projection are better suited given that they are directly calibrated on the real world (historical) climate.

Plain Language Summary?
In this paper, we estimate the transient climate sensitivity, that is, the expected short‐term increase in temperature for a doubling of carbon dioxide concentration in the atmosphere, for historical regional series of temperature. We compare our results with historical simulations made using global climate models and find that there are significant regional discrepancies between the two. We argue that historical methods can be more reliable, especially for the more policy‐relevant short‐term projections, given that the discrepancies of the global climate models directly bias their projections.

Read more (paywalled):

The researchers hope this mixture of historical fudge factors and defective physics will be more acceptable as the basis of climate policy decisions than just using the defective physics.

via Watts Up With That?

May 16, 2018 at 04:39PM

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