Dear all,
The Edinburgh local group of the Royal Statistical Society will be
hosting the following event next Tuesday (24th January):
"The role of uncertainty in adapting to the impacts of climate
change within Scotland"
The meeting will be held at ICMS, 15 South College Street, Edinburgh EH8
9AA. It is free to attend, open to all, and no registration is required.
The meeting starts at 6pm, with tea and coffee from 5.30pm.
The meeting will include three talks: Dr. Joseph Hagg will introduce the
meeting by giving a talk entitled "Probabilistic Climate Projections
(UKCP09) - more than two years on...". Dr. David Jenkins will then speak
on "The probability of building 'failure' due to future climate change"
and Prof. David Elston will speak on "Sources of variation and
uncertainty in models of the impacts of climate change". Full abstracts
and details are given below.
The meeting will be followed by the Annual General Meeting of the local
group at 7pm - all are welcome to stay for this. If you would be
interested in joining the local group committee for 2012, or would like
to find out more information about the work of the local group
committee, please reply to this email - we are keen to bring new
members onto the committee. Please also contact me if you have any items
that you wish to be discussed at the AGM.
ABSTRACTS & FULL DETAILS
Dr. Joseph Hagg (Adaptation Scotland / Scottish Environment Protection
Agency)
The UK Climate Projections (UKCP09) are the latest generation of climate
information for the United Kingdom, and for the first time provide a
measure of uncertainty in the range of possible climate outcomes.
However, more than two years after their release there remain
significant challenges in using these probabilistic projections to both
communicate climate change and inform decision-makers. I'll introduce
UKCP09 and provide a few examples of how they challenge us to think
about uncertainty in our future climate.
Dr. David Jenkins (Urban Energy Research Group, School of the Built
Environment, Heriot-Watt University)
The latest UK Climate Projections (UKCP09) provide an indication of
future climates in a probabilistic format; that is, they allow the
inherent uncertainty of climate models to be expressed in the output of
a weather generator. This weather generator provides the user with a
range of climate parameters (temperature, solar radiation, humidity etc)
for a range of probabilities across different future climate scenarios.
The data is therefore vast and requires appropriate management if
focussing this information on a specific application. One application of
climate information is for building simulation. Typically, someone
simulating the thermal performance of a building will use a single
reference climate to assess that building. UKCP09 requires a different
approach that is potentially time-consuming, namely multiple simulations
of hundreds (or even thousands) of climate files. To reduce the
computational time of such an exercise, the Low Carbon Futures project
(funded by the Adaptation and Resilience in a Changing Climate
Programme) has developed an algorithm that, once calibrated on a single
simulation, can emulate multiple simulations in an efficient way for a
given building. This raises the possibility that the quantified
uncertainty provided in UKCP09 can be translated into uncertainty in
building performance. Specifically, for the Low Carbon Futures project,
this uncertainty is applied to a future overheating risk analysis,
indicating the probability that a building might exceed certain
overheating criteria as a result of a warmer climate.</i>
David Elston, Adam Butler, Helen Kettle & Jackie Potts (Biomathematics
and Statistics Scotland)
Robin Matthews, Shibu Muhammad, Mike Rivington & Nikki Baggaley (James
Hutton Institute)
Kairsty Topp & Bob Rees (Scottish Agricultural College):
The UKCP09 web interface allows users to create sequences of simulated
weather data for a 5km by 5km square for a specified 30-year period.
Sequences can be obtained that correspond to one of 10,000 climate
samples for each of three emissions scenarios. We have investigated the
use of these weather simulations to drive process-based, dynamic
crop-environment models for silage production, spring barley and
short-rotation coppice. I will describe some of the statistical aspects
of this project, including the use of strata in the selection of climate
samples and the partitioning of variance in the simulated yields into
components associated with the strata, with the climate samples within
strata, and with year-to-year variations in weather within climate samples.
Best wishes,
Adam Butler, Secretary of the Edinburgh Local Group of the Royal
Statistical Society
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