An Example Of The Reasons That Skillful Multi-Decadal Predictions Of Climate Change Has Not Been Achieved – “Long Tails In Regional Surface Temperature Probability Distributions With Implications For Extremes Under Global Warming” By Ruff and Neelin 2012

The recognition that the models need to skillfully predict changes in the statistics of climate variables (and they have not in hindcasts), if properly recognized by the IPCC impacts assessment group, would have major implications. So far they have mostly ignored this issue when seeking to convince people as to why the multi-decadal regional and global modal predictions should be considered robust.

Jos de Laat has alerted us to a new paper which addresses part of this issue. While the article contains the usual acceptance of the multi-decadal model predictions as robust, their paper actually illustrates why the models have so far not passed this test. The models have not passed this test. The paper is

Ruff, T. W., and J. D. Neelin (2012), Long tails in regional surface temperature probability distributions with implications for extremes under global warming, Geophys. Res. Lett., 39, L04704, doi:10.1029/2011GL050610.

The abstract reads [highlight added]

“Prior work has shown that probability distributions of column water vapor and several passive tropospheric chemical tracers exhibit longer-than-Gaussian (approximately exponential) tails. The tracer-advection prototypes explaining the formation of these long-tailed distributions motivate
exploration of observed surface temperature distributions for non-Gaussian tails. Stations with long records in various climate regimes in the National Climatic Data Center Global Surface Summary of Day observations are used to examine tail characteristics for daily average, maximum and minimum surface temperature probability distributions. Each is examined for departures from a Gaussian fit to the core (here approximated as the portion of the distribution exceeding 30% of the maximum). While the core conforms to Gaussian for most distributions, roughly half the cases exhibit non-Gaussian tails in both winter and summer seasons. Most of these are asymmetric, with a long, roughly exponential, tail on only one side. The shape of the tail has substantial implications for potential changes in extreme event occurrences under global warming. Here the change in the probability of exceeding a given threshold temperature is quantified in the simplest case of a shift in the present-day observed distribution. Surface temperature distributions with long tails have a much smaller change in threshold exceedances (smaller increases for high-side and smaller decreases for low-side exceedances relative to exceedances in current climate) under a given warming than do near-Gaussian distributions. This implies that models used to estimate changes in extreme event occurrences due to global warming should be verified regionally for accuracy of simulations of probability distribution tails.”

The conclusion of the paper has the text

“The sensitive dependence of tail characteristics on regional effects noted here suggests that it will be (i) useful to understand the physical mechanisms that produce them (including the observed asymmetry, and the sources of regional dependence); and (ii) essential to verify whether high-resolution models accurately reproduce observed tail characteristics for any region for which an assessment of extreme events is being conducted. A model that has an error in the nature of the tail, e.g., erroneously produces a Gaussian rather than a long tail under current climate for a particular region, will likely have serious errors in quantitatively predicting the increase in exceedances under future climate.”

As we wrote in our article

Pielke Sr., R.A., and R.L. Wilby, 2012: Regional climate downscaling – what’s the point? Eos Forum,  93, No. 5, 52-53, doi:10.1029/2012EO050008.

“….for regional downscaling (and global) models to add value (beyond what is available to the impacts community via the historical, recent paleorecord and a worst case sequence of days), they must be able to skillfully predict changes in regional weather statistics in response to human climate forcings. This is a greater challenge than even skillfully simulating current weather statistics.”

The new Ruff and Neelin 2012 provide support for this conclusion.

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