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MAP Climatology of Mid-latitude Storminess (MCMS)


Baroclinic cyclones are the primary weather-makers in extra-tropical regimes. The specific goal of the MAP Climatology of Mid-latitude Storminess (MCMS) project is to provide a detailed 40 year climatology of the areas that come under cyclone influence at a given point in time. This task requires that we solve specific problems such as how to find cyclones in space and time, and then once this is done, how to demarcate the area of influence around them. These are unsettled issues, the second, more so than the first. Indeed, there are many proposed methods for locating cyclones (e.g., Benstad, R. & Chen, D. (2006); Simmonds & Keay (2000); Sinclair (1994); Wernli and Schwierz (2006)). We use the most popular method; locating cyclones as depressions in the sea level pressure (SLP) field. Delineating cyclone influence is a lesser explored topic and we currently lack well established criteria for doing it. A commonly used, but blunt, solution to this problem is to simply place a fixed-size box around a found cyclone and extract a composite (e.g., Bauer & Del Genio (2006); Field & Wood (2007); Lau & Crane (1997); Sinclair & Revell (2000)). This one-size-fits-all method has a major limitation for our purposes given that cyclones vary widely in terms of size and shape. In principle we'd like to treat each cyclone individually and adaptively. Following the ideas in Wernli and Schwierz (2006) we derived such a method based on the idea that a cyclone's area of influence or "storminess" is bound by the unique set of concentric SLP contours surrounding that cyclone. Essentially, we treat storminess as being confined to the bowl of the cyclone's SLP depression. There are some caveats with this method too of course. For example, cyclone features sometimes extend beyond the confines of the closed SLP contours surrounding the cyclone. In other cases, cyclones simply lack closed SLP contours altogether or they depart from the ideal of simple isolated features and exist in complex cyclone families. That said, our method works well most times and we're working continuously to make it better.

So who might find MCMS useful? Anyone who wants to contextualize their data (observed or modeled) by the presence or absence of a nearby cyclone is the general answer. An obvious research avenue is to use MCMS to examine cyclones themselves. For example, model makers can use MCMS to improve and validate the dynamic and parameterized model response to baroclinic waves (e.g. Bauer & Del Genio (2006)). Observations can likewise be sifted and organized with MCMS to extend our basic understanding of extratropical climate and weather (e.g., Field & Wood (2007)). Less obviously, MCMS can be used as a weather sensitive filter for any sort of data or model component. Ecological and oceanographic studies come to mind as do aerosol and pollution studies.


A publication detailing the MCMS method is in the works. In the mean time we have this poster and this poster, plus the software itself (see below). There are also a number of papers that have used MCMS data (e.g., Naud et al. 2010; Govekar et al. 2011; Field et al. 2011; Naud et al. 2012; Turner et al. 2012 (In review) and Romanski et al. 2012). Preliminary documentation can be found here.


Sample image of MCMS attribution

The Perfect Storm at peak intensity 12:00 UTC on 30 October 1991 (the triad of cyclones off the North American eastern seaboard) from the NCEP/NCAR Reanalysis (6 hourly). Shown are the found centers (white Xs) and the underlying SLP field (grey shaded with darker being lower values). Many centers have so-called "attributed" grids (red dots) surrounding them. These grids represent the largest set of closed SLP contours around that center that contain just that center. Some situations require an additional set of grids, the so-called "stormy" grids (yellow dots), which represent closed SLP contours that enclose multiple centers. A simple gallery of MCMS generated images is available here.


The software for reading existing MCMS datasets, as well as that for applying the MCMS methods to new SLP data sources, are available as mercurial repositories (see instructions for acquiring them at reading and making). The following links detail the software dependences and requirements for reading and making MCMS datasets.


Public releases of MCMS datasets are planned for a number of reanalysis products (see instructions for reading these files):

Public releases of MCMS datasets are planned for a number of reanalysis products:

Contact: Dr. Michael Bauer.


MCMS development was supported by NASA's Earth Science Program for Modeling, Analysis, and Prediction (MAP). Additional support for MCMS testing and its display was provided by the EU Seventh Framework Programme (FP7) as part of the InfraStructure for the European Network for Earth System Modelling (IS-ENES) project.