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Marotzke & Forster Revisited >>PHYSICST CLIVE BEST In evidenza

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Marotzke & Forster Revisited

Marotzke & Forster(2015) found that 60 year trends in global surface temperatures are dominated by underlying climate physics. However, the  data show that climate models overestimate such 60 year decadel trends after 1940.

The recent paper in Nature by Jochem Marotzke & Piers Forster ‘Forcing, feedback and internal variability in global temperature trends’ has gained much attention because it makes the claim that climate models are just fine and do not overstimate warming despite the observed 17 year hiatus since 1998. They attempt to show this by demonstrating that 15y trends in the Hadcrut4 data can be expected in CMIP5 models through quasi random internal variability, whereas any 60y trends are deterministic (anthropogenic). They identify ‘deterministic’ and ‘internal variability’ in the models through a multi-regression analysis with their known forcings as input.

\Delta{T} = \frac{\Delta{F}}{(\alpha + \kappa)} + \epsilon

where \Delta{F} is the forcing, \alpha is a climate feedback and \kappa is fraction of ocean heat uptake and \epsilon is random variation.

This procedure was criticised by Nic Lewis and generated an endless discussion on Climate Audit and Climate-Lab  about whether this procedure made statistical sense. However for the most part I think this is irrelevant as it is an analysis of the difference between models and not observational data.

Firstly the assumption that all internal variability is quasi-random is likely wrong. In fact there is clear evidence of a 60y oscillation in the GMST data probably related to the AMO/PDO – see realclimate. In this sense all models are likely wrong because they fail to include this non-random variation. Secondly as I will show below the observed 15y trends in Hadcrut4 are themselves not quasi-random. Thirdly I demonstrate that the observed 60y trends after 1945 are poorly described by the models and that by 1954 essentially all of the models predict higher trends than those observed. This means that the ‘deterministic’ component of all CMIP5 models do indeed overestimate  the GMST response from increasing greenhouse gas concentrations.

Evidence of regular climate oscillations

Hadcrut4 anomaly data compared to a fit with a 60y oscillation and an underlying logarithmic anthropogenic term.

Hadcrut4 anomaly data compared to a fit with a 60y oscillation and an underlying logarithmic anthropogenic term.

Figure 1 shows that the surface data can be well described by a formula (described here) that includes both an net CO2 forcing term and a 60y oscillation as follows:

DT(t) = -0.3 + 2.5\ln{\frac{CO2(t)}{290.0}} + 0.14\sin(0.105(t-1860))-0.003 \sin(0.57(t-1867))-0.02\sin(0.68(t-1879))

The physical justification for such a 0.2C oscillation is the observed PDO/AMO which just like ENSO can effect global surface temperatures, but over a longer period. No models currently include any such  regular natural oscillations. Instead the albedo effect of aerosols and volcanoes have been tuned to agree with past GMST and follow its undulations. Many others have noted this oscillation in GMST, and even Michael Mann is now proposing that a downturn in the PDO/AMO is responsible for  the hiatus.

15y and 60y trends in observations and models

I have repeated the analysis described in M&F. I use linear regression fits over periods of 15y and 60y to the Hadcrut4 data and also to the fitted equation described above. In addition I have downloaded  42 CMIP5 model simulations of monthly surface temperature data from 1860 to 2014, calculated the monthly anomalies and then averaged them over each year. Then for each CMIP5 simulation  I calculated the 15y and 60y trends for increasing start year as described in M&F.

Figure 2 shows the calculated  15y trends in the H4 dataset compared to trends from the fit. For comparison we first show Fig 2a taken from  M&F below.

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Letto 2431 volte Ultima modifica il Martedì, 24 Marzo 2015 00:27


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