New “simplified” Russian climate model promises faster results

New “simplified” Russian climate model promises faster results

From KAZAN FEDERAL UNIVERSITY and the “Russian Collusion Department”

First results were published in Geoscientific Model Development.

Kinetic energy of winds at 5 km altitude in December – February (Aeolus calculations)

Professor Aleksey Eliseev, Chief Research Associate at Kazan University’s Near Space Research Lab, comments,

“To find solutions for some tasks in climate research, we need calculations for hundreds, thousands, or even millions of years. Such tasks are, for example, ice age periodization. Another group of tasks that requires huge longitudinal calculations is climate forecasting, a type of research where we don’t have definitive information about coefficients of used models.

“If we use models of the general circulation of atmosphere, then required calculations can take up months or years with the use of the most advanced modern computers. To accelerate research, scientists use simplified models – the so-called climate models of intermediate complexity. In Russia, the only such model has been created by the Institute of Atmospheric Physics.

“Our team, comprising employees of Potsdam Institute for Climate Impact Research, Moscow State University, Kazan Federal University, and the Institute of Atmospheric Physics, is working on one such model. We called it the Potsdam Earth System Model.”

Currently, one of the components of POEM, called Aeolus, is ready for use. Two parts of the model, for large-scale zonal-mean winds and planetary waves, have been designed by Dr. Eliseev. He has also partaken in the creation of automatic tuning process for model parameters.


The dynamical core of the Aeolus 1.0 statistical–dynamical atmosphere model: validation and parameter optimization


We present and validate a set of equations for representing the atmosphere’s large-scale general circulation in an Earth system model of intermediate complexity (EMIC). These dynamical equations have been implemented in Aeolus 1.0, which is a statistical–dynamical atmosphere model (SDAM) and includes radiative transfer and cloud modules (Coumou et al., 2011; Eliseev et al., 2013). The statistical dynamical approach is computationally efficient and thus enables us to perform climate simulations at multimillennia timescales, which is a prime aim of our model development. Further, this computational efficiency enables us to scan large and high-dimensional parameter space to tune the model parameters, e.g., for sensitivity studies.

Here, we present novel equations for the large-scale zonal-mean wind as well as those for planetary waves. Together with synoptic parameterization (as presented by Coumou et al., 2011), these form the mathematical description of the dynamical core of Aeolus 1.0.

We optimize the dynamical core parameter values by tuning all relevant dynamical fields to ERA-Interim reanalysis data (1983–2009) forcing the dynamical core with prescribed surface temperature, surface humidity and cumulus cloud fraction. We test the model’s performance in reproducing the seasonal cycle and the influence of the El Niño–Southern Oscillation (ENSO). We use a simulated annealing optimization algorithm, which approximates the global minimum of a high-dimensional function.

With non-tuned parameter values, the model performs reasonably in terms of its representation of zonal-mean circulation, planetary waves and storm tracks. The simulated annealing optimization improves in particular the model’s representation of the Northern Hemisphere jet stream and storm tracks as well as the Hadley circulation.

The regions of high azonal wind velocities (planetary waves) are accurately captured for all validation experiments. The zonal-mean zonal wind and the integrated lower troposphere mass flux show good results in particular in the Northern Hemisphere. In the Southern Hemisphere, the model tends to produce too-weak zonal-mean zonal winds and a too-narrow Hadley circulation. We discuss possible reasons for these model biases as well as planned future model improvements and applications.

Superforest,Climate Change

via Watts Up With That?

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