Your example above using rainfall has some advantages over temperature, which is a more prevalent test bed. Rainfall is bounded at the lower end and observationally, if a rain gauge is not read for a few days the signal has accumulated, not disappeared, and can be spread with little error. Is there a way in which these 2 properties can be used to further refine the graphs (which you might label a bit more explicitly for us) or has that implicitly been done already? Measurement uncertainty at a site should be less for rainfall than for temperature and the signal should show better through the noise.

Straight answers to three questions:
1. How confident are we about the projected regional climate changes? [Not at all].
2. Is “model democracy” a valid scientific method? [No]
3. Does dynamical downscaling for regional climate change provide a robust scientific basis for action? [No]

Climate modellers have failed those who need climate forecasts most by chasing after a small human signal, that from the emails, they are not sure rises above natural variation.

1202939193.txt
From: J Shukla
To: IPCC-Sec
Subject: Future of the IPCC:
Date: Wed, 13 Feb 2008 16:46:33 -0500
Dear All,

I would like to respond to some of the items in the attached text on
issues etc. in particular to the statement in the section 3.1.1
(sections 3: Drivers of required change in the future).

“There is now greater demand for a higher level of policy relevance in
the work of IPCC, which could provide policymakers a robust scientific
basis for action”.

1. While it is true that a vast majority of the public and the
policymakers have accepted the reality of human influence on climate
change (in fact many of us were arguing for stronger language with a
higher level of confidence at the last meetings of the LAs), how
confident are we about the projected regional climate changes?

I would like to submit that the current climate models have such large
errors in simulating the statistics of regional (climate) that we are
not ready to provide policymakers a robust scientific basis for “action”
at regional scale. I am not referring to mitigation, I am strictly
referring to science based adaptation.

For example, we can not advise the policymakers about re-building the
city of New Orleans – or more generally about the habitability of the
Gulf-Coast – using climate models which have serious deficiencies in
simulating the strength, frequency and tracks of hurricanes.

We will serve society better by enhancing our efforts on improving our
models so that they can simulate the statistics of regional climate
fluctuations; for example: tropical (monsoon depressions, easterly
waves, hurricanes, typhoons, Madden-Julian oscillations) and
extratropical (storms, blocking) systems in the atmosphere; tropical
instability waves, energetic eddies, upwelling zones in the oceans;
floods and droughts on the land; and various manifestations (ENSO,
monsoons, decadal variations, etc.) of the coupled ocean-land-atmosphere
processes.

It is inconceivable that policymakers will be willing to make
billion-and trillion-dollar decisions for adaptation to the projected
regional climate change based on models that do not even describe and
simulate the processes that are the building blocks of climate
variability. Of course, even a hypothetical, perfect model does not
guarantee accurate prediction of the future regional climate, but at the
very least, our suggestion for action will be based on the best possible
science.

It is urgently required that the climate modeling community arrive at a
consensus on the required accuracy of the climate models to meet the
“greater demand for a higher level of policy relevance”.

2. Is “model democracy” a valid scientific method? The “I” in the IPCC
desires that all models submitted by all governments be considered
equally probable. This should be thoroughly discussed, because it may
have serious implications for regional adaptation strategies. AR4 has
shown that model fidelity and model sensitivity are related. The models
used for IPCC assessments should be evaluated using a consensus metric.

3. Does dynamical downscaling for regional climate change provide a
robust scientific basis for action?

Is there a consensus in the climate modeling community on the validity
of regional climate prediction by dynamical downscaling? A large number
of dynamical downscaling efforts are underway worldwide. This is not
necessarily because it is meaningful to do it, but simply because it is
possible to do it. It is not without precedent that quite deficient
climate models are used by large communities simply because it is
convenient to use them. It is self-evident that if a coarse resolution
IPCC model does not correctly capture the large-scale mean and transient
response, a high-resolution regional model, forced by the lateral
boundary conditions from the coarse model, can not improve the response.
Considering the important role of multi-scale interactions and feedbacks
in the climate system, it is essential that the IPCC-class global models
themselves be run at sufficiently high resolution.

Regards,
Shukla