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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 & Chen (2006); Simmonds & Keay (2000); Sinclair (1994); and Wernli & 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); and 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 & 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) and the vertical motion of clouds (e.g., Booth et al., 2013). 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.


Use of the MCMS method was described in Bauer et al. (2016). Previous presentations about the code include a 4th PAN-GCSS meeting poster (2008) and an ICR4 meeting poster (2012).

There are also several papers based on research using 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); and Romanski et al. (2012).


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 version of the software for reading existing MCMS datasets, as well as that for applying the MCMS methods to new SLP data sources, is available from a Github repository maintained by Dr. James Booth of CCNY. This version 4 is an improvement on the MCMS described in Bauer & Del Genio (2006). The version described by Bauer et al. (2016) is not available here.


Bauer, M., and A.D. Del Genio, 2006: Composite analysis of winter cyclones in a GCM: Influence on climatological humidity. J. Climate, 19, 1652-1672, doi:10.1175/JCLI3690.1.

Bauer, M.P., G. Tselioudis, and W.B. Rossow, 2016: A new climatology for investigating storm influences in and on the extratropics. J. Appl. Meteorol. Climatol., 55, no. 5, 1287-1303, doi:10.1175/JAMC-D-15-0245.1.

Benestad, R.E., R. Senan, M. Balmaseda, L. Ferranti, Y. Orsolini, and A. Melsom, 2011: Sensitivity of summer 2-m temperature to sea ice conditions. Tellus A, 63, no. 2, 324-337, doi:10.1111/j.1600-0870.2006.00191.x.

Booth, J. F., C. Naud, A. D. Del Genio, 2013: Diagnosing warm frontal cloud formation in a GCM: A novel approach using conditional subsetting. J. Climate, 26, no. 16 5827-5845, doi:10.1175/JCLI-D-12-00637.1.

Govekar, P.D., C. Jakob, and J. Catto, 2014: The relationship between clouds and dynamics in Southern Hemisphere extratropical cyclones in the real world and a climate model. J. Geophys. Res. Atmos., 119, no. 11, 6609-6628, doi:10.1002/2013JD020699.

Field, P.R., A. Bodas-Salcedo, and M. E. Brooks, 2011: Using model analysis and satellite data to assess cloud and precipitation in midlatitude cyclones. Q. J. Roy. Meteorol. Soc., 137, no. 659, 1501-1515, doi:10.1002/qj.858.

Field, P.R., and R. Wood, 2007: Precipitation and cloud structure in midlatitude cyclones. J. Climate, 20, no. 2, 233–254, doi:10.1175/JCLI3998.1.

Lau, N.-C., and M.W. Crane, 1997. Comparing satellite and surface observations of cloud patterns in synoptic-scale circulation systems. M. Weath. Rev., 125, no. 2, 3172–3189, doi:10.1175/1520-0493(1997)125<3172:CSASOO>2.0.CO;2.

Naud, C.M., A.D. Del Genio, M. Bauer, and W. Kovari, 2010: Cloud vertical distribution across warm and cold fronts in CloudSat-CALIPSO data and a general circulation model. J. Climate, 23, 3397-3415, doi:10.1175/2010JCLI3282.1.

Naud, C.M., D.J. Posselt, and S.C. van den Heever, 2012: Observational analysis of cloud and precipitation in midlatitude cyclones: Northern versus southern hemisphere warm fronts. J. Climate, 25, 5135-5151, doi:10.1175/JCLI-D-11-00569.1.

Romanski, J., A. Romanou, M. Bauer, and G. Tselioudis, 2012: Atmospheric forcing of the Eastern Mediterranean Transient by midlatitude cyclones. Geophys. Res. Lett., 39, L03703, doi:10.1029/2011GL050298.

Simmonds, I., and K. Keay, 2000: Mean Southern Hemisphere extratropical cyclone behavior in the 40-Year NCEP–NCAR reanalysis. J. Clim., 13, no. 5, 873-885, doi:10.1175/1520-0442(2000)013<0873:MSHECB>2.0.CO;2.

Sinclair, M.R., 1994 An objective cyclone climatology for the Southern Hemisphere. M. Weath. Rev., 133, no. 10, 2239-2256, doi:10.1175/1520-0493(1994)122<2239:AOCCFT>2.0.CO;2.

Sinclair, M.R., and M.J. Revell, 2000: Classification and composite diagnosis of extratropical cyclogenesis events in the Southwest Pacific. M. Weath. Rev., 128, no. 4, 1089–1105, doi:10.1175/1520-0493(2000)128<1089:CACDOE>2.0.CO;2.

Turner, A.J., A.M. Fiore, L.W. Horowitz, and M. Bauer, 2013: Summertime cyclones over the Great Lakes Storm Track from 1860-2100: variability, trends, and association with ozone pollution. Atmos. Chem. Phys., 13, 565-578, doi:10.5194/acp-13-565-2013.

Wernli, H., and C. Schierz, 2006: Surface cyclones in the ERA-40 Dataset (1958–2001). Part I: Novel identification method and global climatology. J. Atmos. Sci., 63, no. 10, 2486–2507, doi:10.1175/JAS3766.1.


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.