On the use of self-organizing maps for studying climate extremes

Journal of Geophysical Research

Volumn 122, Issue 7, 2017, Pages 3891-3903

  Gibson, Peter B ^a   Perkins Kirkpatrick, Sarah E ^a   Uotila, Petteri ^b   Pepler, Acacia S ^a   Alexander, Lisa V ^a  

^a UNIVERSITY OF NEW SOUTH WALES   (Australia)

^b FINNISH METEOROLOGICAL INSTITUTE   (Finland)

Author keywords

[No Author keywords available]

Indexed keywords

CIRCULATION; CLIMATE CHANGE; COMPOSITE; EXTREME EVENT; HEAT WAVE; WEATHER;

EID: 85017530493     PISSN: 01480227     EISSN: 21562202     Source Type: Journal    
DOI: 10.1002/2016JD026256     Document Type: Article

References (56)

1. Alexander, L. V., P. Uotila, N. Nicholls, and A. Lynch (2010), A new daily pressure dataset for Australia and its application to the assessment of changes in synoptic patterns during the last century, J. Clim., 23(5), 1111-1126, doi:10.1175/2009JCLI2972.1.
2. Bação, F., V. Lobo, and M. Painho (2005), Self-organizing maps as substitutes for K-means clustering, in Computational Science-ICCS 2005, Lect. Notes Comput. Sci., vol. 3516, edited by V. S. Sunderam et al., pp. 476-483, Springer, Berlin.
3. Boschat, G., I. Simmonds, A. Purich, T. Cowan, and A. B. Pezza (2016), On the use of composite analyses to form physical hypotheses: An example from heat wave-SST associations, Sci. Rep., 6, 29599, doi:10.1038/srep29599.
4. Bosilovich, M., et al. (2015), MERRA-2: Initial evaluation of the climate, NASA Technical Report Series on Global Modeling and Data Assimilation, NASA/TM-2015-104606, vol. 43, 139 pp., Natl. Aeronaut. and Space Admin., Goddard Space Flight Cent., Greenbelt, Md.
5. Brown, J. R., C. Jakob, and J. M. Haynes (2010), An evaluation of rainfall frequency and intensity over the Australian region in a global climate model, J. Clim., 23(24), 6504-6525, doi:10.1175/2010JCLI3571.1.
6. Cassano, E. N., J. M. Glisan, J. J. Cassano, W. J. Gutowski Jr., and M. W. Seefeldt (2015), Self-organizing map analysis of widespread temperature extremes in Alaska and Canada, Clim. Res., 62(3), 199-218.
7. Cassano, J. J., P. Uotila, and A. Lynch (2006), Changes in synoptic weather patterns in the polar regions in the twentieth and twenty-first centuries. Part 1: Arctic, Int. J. Climatol., 26(8), 1027-1049, doi:10.1002/joc.1306.
8. Cassano, J. J., E. N. Cassano, M. W. Seefeldt, W. J. Gutowski, and J. M. Glisan (2016), Synoptic conditions during wintertime temperature extremes in Alaska, J. Geophys. Res. Atmos., 121, 3241-3262, doi:10.1002/2015JD024404.
9. Catto, J. L., and S. Pfahl (2013), The importance of fronts for extreme precipitation, J. Geophys. Res. Atmos., 118, 10,791-710,801, doi:10.1002/ jgrd.50852.
10. Cowan, T., A. Purich, S. Perkins, A. Pezza, G. Boschat, and K. Sadler (2014), More frequent, longer, and hotter heat waves for Australia in the twenty-first century, J. Clim., 27(15), 5851-5871, doi:10.1175/JCLI-D-14-00092.1.
11. Dee, D. P., et al. (2011), The ERA-interim reanalysis: Configuration and performance of the data assimilation system, Q. J. R. Meteorol. Soc., 137(656), 553-597, doi:10.1002/qj.828.
12. Engmann, S., and D. Cousineau (2011), Comparing distributions: The two-sample Anderson-Darling test as an alternative to the Kolmogorov-Smirnoff test, J. Appl. Quant. Methods, 6(3), 1-17.
13. Feldstein, S. B., and S. Lee (2014), Intraseasonal and interdecadal jet shifts in the Northern Hemisphere: The role of warm pool tropical convection and sea ice, J. Clim., 27(17), 6497-6518, doi:10.1175/JCLI-D-14-00057.1.
14. Finnis, J., J. Cassano, M. Holland, M. Serreze, and P. Uotila (2009), Synoptically forced hydroclimatology of major Arctic watersheds in general circulation models. Part 1: The Mackenzie River basin, Int. J. Climatol., 29(9), 1226-1243, doi:10.1002/joc.1753.
15. Gibson, P. B., S. E. Perkins-Kirkpatrick, and J. A. Renwick (2016a), Projected changes in synoptic weather patterns over New Zealand examined through self-organizing maps, Int. J. Climatol., 36, 3934-3948, doi:10.1002/joc.4604.
16. Gibson, P. B., P. Uotila, S. E. Perkins-Kirkpatrick, L. V. Alexander, and A. J. Pitman (2016b), Evaluating synoptic systems in the CMIP5 climate models over the Australian region, Clim. Dyn., 47, 2235-2251, doi:10.1007/s00382-015-2961-y.
17. Grotjahn, R., et al. (2016), North American extreme temperature events and related large scale meteorological patterns: A review of statistical methods, dynamics, modeling, and trends, Clim. Dyn., 46(3), 1151-1184, doi:10.1007/s00382-015-2638-6.
18. Hartigan, J. A., and M. A. Wong (1979), Algorithm AS 136: A k-means clustering algorithm, J. R. Stat. Soc. Ser. C. Appl. Stat., 28(1), 100-108.
19. Herold, N., J. Kala, and L. V. Alexander (2016), The influence of soil moisture deficits on Australian heatwaves, Environ. Res. Lett., 11(6), 064,003.
20. Hewitson, B., and R. Crane (2002), Self-organizing maps: Applications to synoptic climatology, Clim. Res., 22(1), 13-26.
21. Hirsch, A. L., A. J. Pitman, J. Kala, R. Lorenz, and M. G. Donat (2015), Modulation of land-use change impacts on temperature extremes via land-atmosphere coupling over Australia, Earth Interact., 19(12), 1-24, doi:10.1175/EI-D-15-0011.1.
22. Hope, P., K. Keay, M. Pook, J. Catto, I. Simmonds, G. Mills, P. McIntosh, J. Risbey, and G. Berry (2014), A comparison of automated methods of front recognition for climate studies: A case study in Southwest Western Australia, Mon. Weather Rev., 142(1), 343-363, doi:10.1175/MWRD-12-00252.1.
23. Horton, D. E., N. C. Johnson, D. Singh, D. L. Swain, B. Rajaratnam, and N. S. Diffenbaugh (2015), Contribution of changes in atmospheric circulation patterns to extreme temperature trends, Nature, 522(7557), 465-469, doi:10.1038/nature14550.
24. Huth, R., C. Beck, A. Philipp, M. Demuzere, Z. Ustrnul, M. Cahynová, J. Kyselý, and O. E. Tveito (2008), Classifications of atmospheric circulation patterns, Ann. N. Y. Acad. Sci., 1146(1), 105-152, doi:10.1196/annals.1446.019.
25. Huva, R., R. Dargaville, and P. Rayner (2015), The impact of filtering self-organizing maps: A case study with Australian pressure and rainfall, Int. J. Climatol., 35(4), 624-633, doi:10.1002/joc.4008.
26. Jiang, N., K. Luo, P. J. Beggs, K. Cheung, and Y. Scorgie (2015), Insights into the implementation of synoptic weather-type classification using self-organizing maps: An Australian case study, Int. J. Climatol., 35(12), 3471-3485, doi:10.1002/joc.4221.
27. Johnson, N. C. (2013), How many ENSO flavors can we distinguish?, J. Clim., 26(13), 4816-4827, doi:10.1175/JCLI-D-12-00649.1.
28. Johnson, N. C., S. B. Feldstein, and B. Tremblay (2008), The continuum of Northern Hemisphere teleconnection patterns and a description of the NAO shift with the use of self-organizing maps, J. Clim., 21(23), 6354-6371, doi:10.1175/2008JCLI2380.1.
29. Jones, D. A., W. Wang, and R. Fawcett (2009), High-quality spatial climate data-sets for Australia, Aust. Meteorol. Oceanogr. J., 58(4), 233.
30. Kohonen, T. (2013), Essentials of the self-organizing map, Neural Netw., 37, 52-65, doi:10.1016/j.neunet.2012.09.018.
31. Lennard, C., and G. Hegerl (2015), Relating changes in synoptic circulation to the surface rainfall response using self-organising maps, Clim. Dyn., 44(3), 861-879, doi:10.1007/s00382-014-2169-6.
32. Lim, E.-P., and I. Simmonds (2007), Southern Hemisphere winter extratropical cyclone characteristics and vertical organization observed with the ERA-40 data in 1979-2001, J. Clim., 20(11), 2675-2690, doi:10.1175/JCLI4135.1.
33. Liu, Y., R. H. Weisberg, and C. N. K. Mooers (2006), Performance evaluation of the self-organizing map for feature extraction, J. Geophys. Res., 111, C05018, doi:10.1029/2005JC003117.
34. Loikith, P. C., and A. J. Broccoli (2015), Comparison between observed and model-simulated atmospheric circulation patterns associated with extreme temperature days over North America using CMIP5 historical simulations, J. Clim., 28(5), 2063-2079, doi:10.1175/JCLI-D-13-00544.1.
35. Loikith, P. C., B. R. Lintner, and A. Sweeney (2017), Characterizing large-scale meteorological patterns and associated temperature and precipitation extremes over the northwestern United States using self organizing maps. J. Clim., doi:10.1175/jcli-d-16-0670.1, in press.
36. Lynch, A. H., M. C. Serreze, E. N. Cassano, A. D. Crawford, and J. Stroeve (2016), Linkages between Arctic summer circulation regimes and regional sea ice anomalies, J. Geophys. Res. Atmos., 121, 7868-7880, doi:10.1002/2016JD025164.
37. Murray, R. J., and I. Simmonds (1991), A numerical scheme for tracking cyclone centres from digital data. Part I: Development and operation of the scheme, Aust. Meteorol. Mag., 39, 155-166.
38. Nairn, J. R., and R. J. Fawcett (2015), The excess heat factor: A metric for heatwave intensity and its use in classifying heatwave severity, Int. J. Environ. Res. Public Health, 12(1), 227-253, doi:10.3390/ijerph120100227.
39. Nicholls, N., P. Uotila, and L. Alexander (2010), Synoptic influences on seasonal, interannual and decadal temperature variations in Melbourne, Australia, Int. J. Climatol., 30(9), 1372-1381, doi:10.1002/joc.1965.
40. Ohba, M., S. Kadokura, Y. Yoshida, D. Nohara, and Y. Toyoda (2015), Anomalous weather patterns in relation to heavy precipitation events in Japan during the Baiu season, J. Hydrometeorol., 16(2), 688-701, doi:10.1175/jhm-d-14-0124.1.
41. Pepler, A., A. Coutts-Smith, and B. Timbal (2014), The role of east coast lows on rainfall patterns and inter-annual variability across the East Coast of Australia, Int. J. Climatol., 34(4), 1011-1021, doi:10.1002/joc.3741.
42. Pepler, A. S., A. D. Luca, F. Ji, L. V. Alexander, J. P. Evans, and S. C. Sherwood (2015), Impact of identification method on the inferred characteristics and variability of Australian east coast lows, Mon. Weather Rev., 143(3), 864-877, doi:10.1175/MWR-D-14-00188.1.
43. Perkins, S. E., and L. V. Alexander (2013), On the measurement of heat waves, J. Clim., 26(13), 4500-4517, doi:10.1175/JCLI-D-12-00383.1.
44. Perkins, S. E., D. Argüeso, and C. J. White (2015), Relationships between climate variability, soil moisture, and Australian heatwaves, J. Geophys. Res. Atmos., 120, 8144-8164, doi:10.1002/2015JD023592.
45. Pezza, A. B., P. van Rensch, and W. Cai (2012), Severe heat waves in Southern Australia: Synoptic climatology and large scale connections, Clim. Dyn., 38(1), 209-224, doi:10.1007/s00382-011-1016-2.
46. Pfahl, S., and H. Wernli (2012), Quantifying the relevance of atmospheric blocking for co-located temperature extremes in the Northern Hemisphere on (sub-)daily time scales, Geophys. Res. Lett., 39, L12807, doi:10.1029/2012GL052261.
47. Pope, M., C. Jakob, and M. J. Reeder (2009), Regimes of the North Australian wet season, J. Clim., 22(24), 6699-6715, doi:10.1175/ 2009JCLI3057.1.
48. Purich, A., T. Cowan, W. Cai, P. van Rensch, P. Uotila, A. Pezza, G. Boschat, and S. Perkins (2014), Atmospheric and oceanic conditions associated with southern Australian heat waves: A CMIP5 analysis, J. Clim., 27(20), 7807-7829, doi:10.1175/JCLI-D-14-00098.1.
49. Rienecker, M. M., et al. (2011), MERRA: NASA’s modern-era retrospective analysis for research and applications, J. Clim., 24(14), 3624-3648, doi:10.1175/JCLI-D-11-00015.1.
50. Sammon, J. W. (1969), A nonlinear mapping for data structure analysis, IEEE Trans. Comput., 100(5), 401-409.
51. Scholz, F. W., and M. A. Stephens (1987), K-sample Anderson-Darling tests, J. Am. Stat. Assoc., 82(399), 918-924, doi:10.1080/ 01621459.1987.10478517.
52. Sheridan, S. C., and C. C. Lee (2011), The self-organizing map in synoptic climatological research, Prog. Phys. Geogr., 35(1), 109-119, doi:10.1177/0309133310397582.
53. Simmonds, I., R. J. Murray, and R. Leighton (1999), A refinement of cyclone tracking methods with data from FROST, Aust. Meteorol. Mag., 35-49.
54. Swales, D., M. Alexander, and M. Hughes (2016), Examining moisture pathways and extreme precipitation in the U.S. intermountain west using self-organizing maps, Geophys. Res. Lett., 43, 1727-1735, doi:10.1002/2015GL067478.
55. Uotila, P., A. H. Lynch, J. J. Cassano, and R. I. Cullather (2007), Changes in Antarctic net precipitation in the 21st century based on Intergovernmental Panel on Climate Change (IPCC) model scenarios, J. Geophys. Res., 112, D10107, doi:10.1029/2006JD007482.
56. Wilks, D. S. (2006), On “field significance” and the false discovery rate, J. Appl. Meteorol. Climatol., 45(9), 1181-1189, doi:10.1175/JAM2404.1.