A Data-driven Journey across the Mid-latitude Atmosphere: from Blockings to Teleconnections

Weather patterns and teleconnections are key elements of the atmospheric variability. Modern ideas of statistical physics supported by data-driven techniques provide solid foundations for coarse-graining the dynamics of the mid-latitudes and for detecting regional modes of variability.
A Data-driven Journey across the Mid-latitude Atmosphere: from Blockings to Teleconnections
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Background

Detecting recurrent weather patterns and understanding the transitions between such regimes are key to advancing our knowledge of the low-frequency variability of the atmosphere . Large-scale weather configurations in the mid-latitudes hold significant importance due to their impact on weather conditions over vast regions. 

One of the critical aspects of the mid-latitude low-frequency variability is the presence of transitions between two distinct flow regimes: blockings and zonal flow. Blockings are synoptic features that are characterized by persistent high-pressure systems, leading to a disruption of the typical west-to-east flow of the jet stream. Blocking events typically manifest in either the Atlantic or Pacific sectors and, more rarely, in both concurrently

The low-frequency variability of the northern hemisphere mid-latitudes features also atmospheric configurations having spatial and temporal scales larger than blockings. These are called teleconnection patterns, and are responsible for driving the dynamics of the atmosphere at planetary scale and for facilitating the coupling between atmospheric and oceanic processes. Particularly relevant are the so-called Pacific-North American teleconnection pattern (PNA) and the North Atlantic Oscillation (NAO). 

In a coarse-grained sense, the weather evolution in the mid-latitudes can then be seen as an alternation between competing weather regimes such as those described above. Defining such regimes and the pathways of transitions between them has long been a great challenge of theoretical and applied research in dynamical meteorology and climate science, because of the key implications for weather forecasting and climate change studies.

Main Results

Here we approach the problem of defining weather regimes and detecting the transitions between them by a two-step procedure that allows us to distillate the main feature of the dynamics of the system, going beyond a diagnostic analysis. Following a statistical mechanical methodology originally devised for studying molecular dynamical processes, we detect in an unsupervised manner the dominant mid-latitude Northern Hemisphere weather patterns.

We first classify the observed state of the atmosphere in a relatively number of coherent states. Such states are then used as a basis for defining the actual modes of variability of the system  By targeting our analysis in different geographical regions - namely, the Atlantic and Pacific sectors - we are able to discover modes corresponding to the alternation between zonal and blocked states and to the onset of the simultaneous Atlantic and Pacific blocking.  When considering larger geographical regions, we identify key teleconnections like the North Atlantic Oscillation, the Pacific North America pattern, and the North Pacific Oscillation/West Pacific pattern.

Other Take-Home Messages

The method used here has a much stronger dynamical foundation than classical approaches based e.g. on EOF analysis, because it embraces the full nonlinear dynamics of the system. As a side results we find a convincing mathematical explanation for the reason why it is hard to  represent the atmospheric dynamics in terms of just few, well-defined modes of variability, as a result of the multi scale nature of the system.

The approach we have proposed can be seamlessly applied across different regions of the globe for detecting regional modes of variability, and has a great potential for intercomparing climate models and for assessing the impact of climate change on the low-frequency variability of the atmosphere.

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Statistical Physics
Physical Sciences > Physics and Astronomy > Theoretical, Mathematical and Computational Physics > Statistical Physics
Climate Change
Physical Sciences > Earth and Environmental Sciences > Earth Sciences > Climate Sciences > Climate Change
Meteorology
Physical Sciences > Earth and Environmental Sciences > Earth Sciences > Atmospheric Science > Meteorology
Mathematics of Planet Earth
Mathematics and Computing > Mathematics > Applications of Mathematics > Mathematics of Planet Earth
Climate and Earth System Modelling
Mathematics and Computing > Mathematics > Applications of Mathematics > Mathematics of Planet Earth > Climate and Earth System Modelling
Machine Learning
Mathematics and Computing > Statistics > Statistics and Computing > Machine Learning

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