Producing Climate Models that are Consistent with Observations
Supervisors
Professor Simon Tett (School of GeoSciences, University of Edinburgh and Dr
Coralia Cartis (Mathematical Institute and Balliol College, University of Oxford)
Simon.tett@ed.ac.uk
http://en.wikipedia.org/wiki/File:Global_Climate_Model.png
Project Background: Climate models are
complex models of the Earth’s atmosphere,
ocean and land surface. They explicitly
represent the resolved dynamics through the
numerical solution of the appropriate
equations of motion. However, many
processes are unresolved and so need
“parameterisation” in terms of the large scale
and resolved flow. Parameterisation
schemes simulate processes like radiation,
cloud formation and interactions between the
land-surface and the atmosphere.
Parameterisation schemes are controlled by
numerical parameters which control the strength of the processes. For example the relative
humidity at which clouds form in a large-scale climate model is less than 100% due to subgrid variability. Changing the representation of these processes has an impact on the
response of the climate system to CO2 and other drivers. The value of the various
parameters in models, though bounded, is largely arbitrary and the final stage in the
generation of a new climate model is “tuning” in which parameter values are adjusted to
produce a reasonable climate model. Optimisation methods offer one automatic way of
doing this tuning (Tett et al., 2013a). However, there is uncertainty in observations so
efficiently generating several climate models consistent with observations is the aim of this
project.
Key research questions
1. What optimisation methods most efficiently generate plausible climate model
configurations?
2. What current observations most constrain future climate change as simulated by
a specific climate model
Methodology
The aim of the project is to build on earlier work of Tett and Cartis to objectively tune climate
models to a range of different observations in order to generate several model configurations
that are consistent with observations. The work would focus on an atmospheric model that is
relatively cheap to run and so can be easily used to explore techniques and implications.
The difficult part of the tuning algorithm is that there are no explicit representations of the
derivative of the difference between simulation and observations. The student would explore
the impact of different derivative-free optimisation methods using different modelling
strategies with the aim of tackling problems with 10-40 parameters to generate a set of
“plausible” models. Such problems are also applicable to other complex modelling problems
where bringing models and observations together in an objective manner would allow
stronger tests of model realism and increase the confidence in model predictions.
Training
A comprehensive training programme will be provided comprising both specialist scientific
training and generic transferable and professional skills. In addition the student would
receiving training in the use of climate models and climate observations as well as the use of
advanced numerical methods for optimising complex models. Training will be provided in
climate modelling and optimisation techniques through existing courses in the Schools of
GeoSciences and (Oxford) Mathematics. The student could also attend lectures from the
Edinburgh Mathematics MSc in Optimisation (http://msc.maths.ed.ac.uk/or/index).
Requirements
The project would suit a student with strong mathematical and computational skills, and an
interest in climate modelling and observations.
Further reading or any references referred to in the proposal
Simon F. B. Tett, Michael J. Mineter, Coralia Cartis, Daniel J. Rowlands, and
Ping Liu. Can top of atmosphere radiation measurements constrain climate predictions? part
1: Tuning. J. Climate, 26:9348–9366, 2013. doi: 10.1175/JCLID12-00595.1.
Simon F. B. Tett, Daniel J. Rowlands, Michael J. Mineter, and Coralia Cartis. Can
top of atmosphere radiation measurements constrain climate predictions? part 2:
Climate sensitivity. J. Climate, 26:9367–9383, 2013. doi: 10.1175/JCLI-D-1200596.1.