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  • Mike Doran <mike@usinter.net>
    Nature 415, 512 - 514 (2002); doi:10.1038/415512a Quantifying the risk of extreme seasonal precipitation events in a changing climate T. N. PALMER* AND J.
    Message 1 of 1 , Jan 27, 2003
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      Nature 415, 512 - 514 (2002); doi:10.1038/415512a

      Quantifying the risk of extreme seasonal precipitation events in a
      changing climate


      * European Centre for Medium-Range Weather Forecasts, Shinfield Park,
      Reading, Berks RG2 9AX, UK † Rossby Centre, SMHI, S-60176 Norrköping,

      "Increasing concentrations of atmospheric carbon dioxide will almost
      certainly lead to changes in global mean climate. But because—by
      definition—extreme events are rare, it is significantly more
      difficult to quantify the risk of extremes. Ensemble-based
      probabilistic predictions, as used in short- and medium-term
      forecasts of weather and climate, are more useful than deterministic
      forecasts using a 'best guess' scenario to address this sort of
      problem. Here we present a probabilistic analysis of 19 global
      climate model simulations with a generic binary decision model. We
      estimate that the probability of total boreal winter precipitation
      exceeding two standard deviations above normal will increase by a
      factor of five over parts of the UK over the next 100 years. We find
      similar increases in probability for the Asian monsoon region in
      boreal summer, with implications for flooding in Bangladesh. Further
      practical applications of our techniques would be helped by the use
      of larger ensembles (for a more complete sampling of model
      uncertainty) and a wider range of scenarios at a resolution adequate
      to analyse average-size river basins. "

      From article:

      "Here we use 80-year integrations from the CMIP2 (second coupled
      model intercomparison project) multi-model ensemble of 19 global
      coupled ocean–atmosphere climate models7 as discussed in the recent
      IPCC assessment1. The benefit of the multi-model ensemble (over a
      single-model ensemble) accrues from its sampling some of the
      inevitable uncertainties in the computational representation of the
      equations of climate8. "



      Until there is an understanding of EMFs and the biospheric modulation
      of them via cirrus -- models will not be able to appreciate what is
      occurring. Perhaps, again, Wanderer can set the record straight on
      probability based studies that are experiance driven are meaningless
      in the wake of a new dynamic. Best example I can give is if you have
      a die that is perfectly balanced there is a one in six probability of
      a one being rolled. BUT if you wieght that die it may NEVER roll one.
      So if you change the characteristics of your object of study, you
      cannot know what the result of that change will be. That is how this
      study is GIGO.

      NOW, the fossil fuel candy store bought out false skeptics or cynics
      will jump all over this. But the problem is can be solved with
      differing methadologies which consider the real life causal impact of
      changes to pH in rainwater from CO2, how that changes conductivities,
      capacitance and so forth. And there is experiance basis once you know
      how to filter the error driving factors, such as direction of ocean
      current, solar activity, biological state, hydrological state and so

      To the authors of this study--I would say--see ya, wouldn't want to
      be ya.
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