These results are based on a comparison of observed and multi-model
simulated changes in extreme precipitation over the latter half of the twentieth century analyzed with an optimal fingerprinting technique.
Not exact matches
By understanding this complex relationship, we may be able to better
simulate and detect
changes in the prevalence of
extreme weather events
in the midlatitudes, particularly across the northeastern United States.
There is a major industry that involves taking GCM output and using that to evaluate local impacts on crops, endangered species, and ecosystems, and often what gives the biggest impact is
changes in the
extremes, but even the mean climate at a local scale has not been demonstrated to be accurately
simulated.
Cannon, A.J., Sobie, S.R., Murdock, T.Q., (2015) Bias correction of
simulated precipitation by quantile mapping: how well do methods preserve relative
changes in quantiles and
extremes?
Such an improvement is essential not only for correctly
simulating climate sensitivity, but also for characterizing
changes in climate
extremes and related impacts.
* As was recently stated
in Nature, «Climate: The real holes
in climate science» 463 (7279): 284 (2010): «Such holes do not undermine the fundamental conclusion that humans are warming the climate, which is based on the
extreme rate of the twentieth - century temperature
changes and the inability of climate models to
simulate such warming without including the role of greenhouse - gas pollution.»