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\begin{center}
  \large \textbf Breakout Group I \\

  Identification of specific weather phenomena which couple strongly
  to climate and for which a subgrid model is necessary
\end{center}

\section*{What is climate?}

One of the main questions that came up is what exactly we mean by
``climate'' in this context.  Some possible approaches to
classification:
\begin{itemize}
  \item{Global vs. regional: some processes are important for the
    global climate, some processes only for particular regions.}
  \item{Time scales: there was some discussion about what timescale
    cutoff marks the boundary between ``weather'' and ``climate'' for
    the purposes of approaching this problem.  Is everything beyond
    seasonal timescales climate?  The conclusion seemed to be, as has
    been said by a couple of people already, that there isn't a sharp
    cutoff and we should be fairly flexible about the definitions
    here.}
  \item{Spatial scales: there is a clear link between the temporal and
    spatial scales of phenomena in many cases, particularly those
    involving wave propagation.}
  \item{Statistics: there was some discussion about whether we should
    be interested in more than just mean and variability in climate
    variables; this ties in to Prashant's comments about
    parameterisations as being representations of the relationship
    between first and second moments and higher moments of climate
    fields.}
  \item{Application-specific requirements: from the point of view of
    applications of climate model output (e.g. for predictions of
    drought for agricultural forecasting), there is a need for more
    than just mean climatology: in particular, precipitation extremes
    are very important.}
\end{itemize}

\section*{Quantification of importance}

We spent quite a bit of time discussing methodological approaches to
the quantification of which weather phenomena have an impact on
climate.  Some ideas we came up with:
\begin{itemize}
  \item{One of the most promising ideas seems to be the use of nesting
    of higher resolution models (either regional climate models or
    cloud resolving models) withing coarser-scale GCMs.  By examining
    the difference between simulated climate for nested models with
    one-way coupling and those with two-way coupling, we may be able
    to learn something about the feedbacks between different elements
    of the climate system.  One question that arose here is what this
    really tells us about the climate impact of the weather phenomenon
    we are considering.  It isn't completely clear that this simple
    approach will work.}
  \item{There are some more complex approaches to feedback analysis in
    models that may be applicable, for example \citet{colman-2003}.}
  \item{Another obvious simple approach is to compare climate model
    simulations with and without particular parameterisations, or to
    perform parameter sensitivity studies.  This approach may be
    limited by the ability to perform sufficient model simulations to
    produce effective sensitivity results.}
  \item{Downscaling of high-resolution climate simulations may help to
    quantify the importance of processes that are resolved in the high
    resolution simulations that are not resolved in lower resolution
    simulations.}
  \item{There was some question about how we might think about
    different treatments of processes that are close to being resolved
    compared to processes that we have no chance of resolving.}
  \item{Following on from a question of Adam's earlier, it may be of
    use to go back to older models, or to develop a hierarchy of
    simplified models from existing GCMs.  This would allow us to
    explore the use of more sophisticated analysis techniques, such as
    numerical continuation and bifurcation analysis, as exemplified by
    the work of Henk Dijkstra's group on circulation regimes in ocean
    models \citep{dijkstra-2003}.}
\end{itemize}


\section*{Processes}

Here, ``G'' means that a process is important globally, ``R''
important in some regions, ``?'' means there is question about the
importance of a particular process, and ``-'' means a process is, as
far as we can tell, not important.

For each process, we show the importance for the global mean,
variability and the extremes (``tails'') of the climate distribution.

\begin{center}
  \begin{tabular}{|l|l|l|p{0.85\textwidth}|}
    \hline
    \rotatebox{90}{\bf Mean} &
    \rotatebox{90}{\bf Var.} &
    \rotatebox{90}{\bf Tails} &
              {\bf Process} \\
              \hline
              G & G & G & Boundary layer state (for tropical convection;
              upscale processes) \\
              \hline
              G & G & G & Convection (moisture transport; momentum
              transport; mesoscale convective systems; convectively
              coupled waves; MJO) \\
              \hline
              G & G & R & Baroclinic eddies \\
              \hline
              ? & - & G & Hurricanes/cyclones (ocean mixing; evaporation;
              transport of water to stratosphere) \\
              \hline
              R & G & R & Diurnal cycle (propagation of convection; boundary
              layer) \\
              \hline
              G & - & G & Surface fluxes (ocean--air) \\
              \hline
              G & G & G & Cloud microphysics \\
              \hline
              G & - & G & Gravity waves \\
              \hline
  \end{tabular}
\end{center}

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