final_program_abstracts[1]

11 IMSC Session Program
Novel advanced mathematical and statistical methods for
understanding climate (NOVAC)
Wednesday - Poster Session 5
Heikki Haario 1 , Erkki Oja 2 , Alexander Ilin 2 , Heikki Järvinen 3 and Johanna
Tamminen 3
1
Lappeenranta University of Technology, Finland
2 Aalto University, School of Science and Technology, Finland
3 Finnish Meteorological Institute, Finland
Climate models contain closure parameters which can act as effective “tuning
handles” of the simulated climate. These appear in physical parameterization schemes
where unresolved variables are expressed by predefined parameters rather than being
explicitly modeled. In the current climate model tuning process, best expert
knowledge is used to define the optimal closure parameter values, based on
observations, process studies, large eddy simulations, etc. In fact, closure parameters
span a low-dimensional space, and the parameter probability densities should be
objectively estimated simultaneously for all relevant parameters.
The authors have just started a project called “Novel advanced mathematical and
statistical methods for understanding climate” (NOVAC, 2010-2013) which is funded
by the Academy of Finland. Several research problems addressed during the project
are discussed here.
The uncertainties of climate model closure parameters are estimated to improve
understanding of reliability of climate predictions. We focus on the ECHAM5 model
closure parameter distribution, and study the impacts on the reliability of future
climate predictions. The methodology is, however, generic and applicable in any
multi-scale problem with similar closure parameters.
Efficient Markov chain Monte Carlo (MCMC) sampling techniques are developed to
tackle computationally challenging problems. MCMC simulations is an attractive tool
for solving complex inverse problems. However, they are computationally very
expensive and only efficient and maximally optimized MCMC techniques make the
approach realistic in practice. We develop new tools based on adaptive algorithms,
multiple computational grids, parallel chains as well as methods based on early
rejection. Also, methodologies for collecting and archiving information for future runs
is developed.
Novel statistical methods are developed to analyze very large observed and modeled
data sets involved in climate research. For compression and for finding underlying
spatio-temporal factors, methods such as empirical orthogonal functions (principal
components) and related techniques are commonly used. The objective here is to
develop advanced statistical data mining methods that surpass these well-known
techniques, to more effectively detect the most significant multidimensional signals
from the data. This enables us to find efficient cost function criteria for the application
of the sampling methods for closure parameter estimation. Also, it enables us to
visualize climate and to detect known climate phenomena, as well as to discover
unknown ones in the observed climate and to detect consistent climate modes and
anomalous features in simulated climates. We will focus on the formulation of
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