\documentclass[11pt]{article}
\setlength{\oddsidemargin}{0.0truein}
\setlength{\evensidemargin}{0.0truein}
\setlength{\textwidth}{6.5truein}
\setlength{\topmargin}{0.0truein}
\setlength{\textheight}{9.0truein} \setlength{\headsep}{0.0truein}
\setlength{\headheight}{0.0truein} \setlength{\topskip}{10.0pt}
\setlength{\parskip}{5mm}
\usepackage{url}
\usepackage{amsmath}
\usepackage{amssymb}
\pagestyle{empty}
\begin{document}

\begin{center}
  \textbf{\Large{\textsc{STANFORD UNIVERSITY}}}\\[5pt]
  \textbf{\Large{\textsc{DEPARTMENT OF STATISTICS}}}\\[5pt]
  \Large{\textsc{DEPARTMENTAL SEMINAR}}
\end{center}

% In the following statements, replace "Time of talk",
% "Weekday", and "Date of talk". An example is provided.
% If you are not sure about this, just skip this part.


\begin{center}
  4:15 p.m., Tuesday, April 10, 2007\\
  %% Example: 4:15 p.m., Tuesday, February 13, 2007\\
  Sequoia Hall Room 200\\
  (Cookies at 3:45 in 1st Floor Lounge)
\end{center}

% In the following statements, replace "Name of the speaker" with your
% name, "Department Affiliation" with your department affiliation, and
%"University Affiliation" with your university affiliation.

\begin{center}
   \textsl{Claudia Tebaldi} \\
National Center for Atmospheric Research\\
Boulder, CO\\
currently visiting the  Center for Environmental Science and Policy\\
   Stanford University
\end{center}


% In the following statements, replace "Title of the talk"
% with your title of the talk.

\begin{center}
  \subsection*{Making sense of ensembles of climate models: a Bayesian approach to estimating future climate change and its
uncertainty }
\end{center}

% In the following statements, replace "Abstract of the talk"
% with your abstract.

\noindent

Future projections of climate change rely for the most part on the
results of simulations by complex computer models, able to
simulate the main climate processes at work in the Earth's
atmosphere and oceans. Different climate models, however, produce
different climate projections, even under the same future forcing
scenario, and even on average over large-scale regions. How do we
best estimate what future climate will be like, and the
uncertainty associated with it, on the basis of an ensemble of
these experiments? I will describe the main issues that underlie
the analysis of ensembles of climate models, and briefly offer an
overview of alternative lines of attack. I will then propose a
Bayesian approach that treats projections from different models
over different regions in an ANOVA-like setting. As a result,
posterior distributions can be derived for the climate change
signal and numerous parameters representing region-specific and
model-specific precisions and biases. A cross-validation step
addresses the need of verifying the statistical model predictive
distributions, in a setting where actual validation will have to
wait some!

This work is in collaboration with Richard L. Smith (UNC-Chapel
Hill), and Doug Nychka and Linda O. Mearns (NCAR)

\end{document}