Adaptive parameter estimation of climate model closure parameters

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. Questions arise, however:

“Do the parameters represent observable quantities?”, or “Do the

parameters depend on the model discretization, such as grid interval?”

In fact, closure parameters span a low-dimensional space, and the

parameter probability densities can be objectively estimated

simultaneously for all relevant parameters.


Several estimation methods can be applied. Here, adaptive parameter

estimation, based on the Monte Carlo Markov Chain (MCMC) method, is

introduced. Initial experimentation with the ECHAM5 climate model is

presented. The results obtained so far show that:

(a) Adaptive MCMC is technically relatively easy to implement,

(b) It is computationally quite expensive, and

(c) The key question for successful estimation in case of ECHAM5, or any

climate model for that matter, is the definition of a so-called

objective function.


The adaptive MCMC sampling tends to converge towards an equilibrium

probability density corresponding to a chosen Bayesian objective

function: this is a “criterion” against which the short climate

simulation is assessed. A simple objective function can be, for

instance, the global net radiation which should be close to zero in a

“good” climate simulation. This alone does not necessarily constrain the

parameters sufficiently: near zero net radiation can be obtained by

using an unrealistic parameter combination. Additional terms must be

added therefore to the objective function, constraining the simulation

more tightly to the observed regime. [This research is funded by the

Academy of Finland for 2010-2013.]




13:30 Uhr


Bundesstr. 53, room 022/023
Seminar Room 022/023, Ground Floor, Bundesstrasse 53, 20146 Hamburg, Hamburg


Heikki Järvinen, Finish Meteorological Instititute


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