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Climate Sensitivity Estimates and Corrections

You need to be careful in inferring climate sensitivity from observations.

Two climate sensitivity stories this week – both related to how careful you need to be before you can infer constraints from observational data. (You can brush up on the background and definitions here). Both cases – a “Brief Comment Arising” in Nature (that I led) and a new paper from Proistosescu and Huybers (2017) – examine basic assumptions underlying previously published estimates of climate sensitivity and find them wanting.

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  1. C. Proistosescu, and P.J. Huybers, "Slow climate mode reconciles historical and model-based estimates of climate sensitivity", Science Advances, vol. 3, pp. e1602821, 2017.

Why global emissions must peak by 2020

Filed under: — stefan @ 2 June 2017

(by Stefan Rahmstorf and Anders Levermann)

In the landmark Paris Climate Agreement, the world’s nations have committed to “holding the increase in the global average temperature to well below 2 °C above pre-industrial levels and to pursue efforts to limit the temperature increase to 1.5 °C above pre-industrial levels”. This goal is deemed necessary to avoid incalculable risks to humanity, and it is feasible – but realistically only if global emissions peak by the year 2020 at the latest.

Let us first address the importance of remaining well below 2°C of global warming, and as close to 1.5°C as possible. The World Meteorological Organization climate report[i] for the past year has highlighted that global temperature and sea levels keep rising, reaching record highs once again in 2016. Global sea ice cover reached a record low, and mountain glaciers and the huge ice sheets in Greenland and Antarctica are on a trajectory of accelerating mass loss. More and more people are suffering from increasing and often unprecedented extreme weather events[ii], both in terms of casualties and financial losses. This is the situation after about 1°C global warming since the late 19th Century. More »

Judy Curry’s attribution non-argument

Filed under: — gavin @ 18 April 2017

Following on from the ‘interesting’ House Science Committee hearing two weeks ago, there was an excellent rebuttal curated by ClimateFeedback of the unsupported and often-times misleading claims from the majority witnesses. In response, Judy Curry has (yet again) declared herself unconvinced by the evidence for a dominant role for human forcing of recent climate changes. And as before she fails to give any quantitative argument to support her contention that human drivers are not the dominant cause of recent trends.

Her reasoning consists of a small number of plausible sounding, but ultimately unconvincing issues that are nonetheless worth diving into. She summarizes her claims in the following comment:

… They use models that are tuned to the period of interest, which should disqualify them from be used in attribution study for the same period (circular reasoning, and all that). The attribution studies fail to account for the large multi-decadal (and longer) oscillations in the ocean, which have been estimated to account for 20% to 40% to 50% to 100% of the recent warming. The models fail to account for solar indirect effects that have been hypothesized to be important. And finally, the CMIP5 climate models used values of aerosol forcing that are now thought to be far too large.

These claims are either wrong or simply don’t have the implications she claims. Let’s go through them one more time.

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Why correlations of CO2 and Temperature over ice age cycles don’t define climate sensitivity

Filed under: — gavin @ 24 September 2016

We’ve all seen how well temperature proxies and CO2 concentrations are correlated in the Antarctic ice cores – this has been known since the early 1990’s and has featured in many high-profile discussions of climate change.

EPICA Dome C ice core greenhouse gas and isotope records.

The temperature proxies are water isotope ratios that can be used to estimate Antarctic temperatures and, via a scaling, the global values. The CO2 and CH4 concentration changes can be converted to radiative forcing in W/m2 based on standard formulas. These two timeseries can be correlated and the regression (in ºC/(W/m2)) has the units of climate sensitivity – but what does it represent?

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Comparing models to the satellite datasets

How should one make graphics that appropriately compare models and observations? There are basically two key points (explored in more depth here) – comparisons should be ‘like with like’, and different sources of uncertainty should be clear, whether uncertainties are related to ‘weather’ and/or structural uncertainty in either the observations or the models. There are unfortunately many graphics going around that fail to do this properly, and some prominent ones are associated with satellite temperatures made by John Christy. This post explains exactly why these graphs are misleading and how more honest presentations of the comparison allow for more informed discussions of why and how these records are changing and differ from models.
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