Eurasian October snow water equivalent: using self‐organizing maps to characterize variability and identify relationships to the MJO

Abstract

Variability in October daily snow water equivalent (SWE) change using self‐organizing maps (SOMs) was explored in this study. In addition, connections between October Eurasian daily snow water equivalent change (ΔSWE) and the leading mode of atmospheric intra‐seasonal variability, the Madden–Julian Oscillation (MJO), were considered. Through this analysis, dipole and tripole patterns of daily ΔSWE over Eurasia were identified and were moderately negatively correlated to mid‐tropospheric geopotential height anomalies. Additionally, SOM nodes capturing over 91% of October days were found moderately correlated with at least one MJO phase. The majority of correlation coefficients with magnitude above 0.30 were found for MJO phases 4–7, indicating that tropical convective anomalies over the Indian Ocean and Maritime Continent have the most impact on October circulation and snow variability. Furthermore, MJO phases with above‐normal frequency were most often found in SOM nodes with positive correlations between their respective 500‐hPa height anomalies, while MJO phases with below‐normal frequency were most often found in SOM nodes with negative correlations between their respective 500‐hPa height anomalies. These correlation patterns provide additional evidence linking the MJO to Eurasian snow variability. These results highlight a new application of SOMs in identifying snow variability throughout Eurasia during the month of October, in addition to providing evidence for tropical modulation of the extratropics on the intra‐seasonal timescale.

Local and Remote Atmospheric Circulation Drivers of Arctic Change: A Review

“…Daily changes in Northern Hemisphere (NH) spring snow depth by phase of MJO was explored by Barrett et al (2015), with statistically significant depth anomalies found in March, april and May for both North America and Eurasia. In October, correlations between patterns of snow water equivalent (SWE) variability over Eurasia and mid-tropospheric geopotential heights were largest during MJO phases 4-7, indicating that tropical convection anomalies over the Indian Ocean and Maritime continent had the most impact on October circulation and snow variability (Henderson et al, 2017). These studies therefore provide additional evidence for connections between the tropics and the extratropics on subseasonal timescales.…”

“…With the nature of these Arctic moisture intrusions being associated with short-lived, intense events linked to cyclones (Sorteberg and Walsh, 2008;Dufour et al, 2016;Rinke et al, 2017;Villamil-Otero et al, 2018;Fearon et al, 2020) and Rossby wave breaking (Liu and Barnes, 2015), we have also reviewed the role of tropical subseasonal atmospheric variability in forcing extratropical and polar atmospheric circulation (Tropical-High Latitude Subseasonal Teleconnections). The MJO has been found to be an effective source of Rossby wave generation to the extratropics (Hoskins and Karoly, 1981;Sardeshmukh and Hoskins, 1988;Bladé and Hartmann 1995;Jin and Hoskins, 1995;Hendon and Salby, 1996), with poleward-propagating Rossby waves excited by MJO-related tropical convection being linked to polar amplification of surface air temperature Lee et al, 2011;Yao et al, 2011;Yoo et al, 2011;Yoo et al, 2012;Zhou et al, 2012;Rodney et al, 2013;Johnson et al, 2014;Yoo et al, 2014;Lin, 2015;Oliver, 2015), precipitation (Bond and Vecchi, 2003;Jeong et al, 2008;Lin et al, 2010;Becker et al, 2011;He et al, 2011;Baxter et al, 2014;Jones and Carvalho 2014), sea-level pressure, snow depth (Barrett et al, 2015;Henderson et al, 2017), and sea ice (Henderson et al, 2014).…”

“…Modulation of the Arctic atmosphere specifically, by phase of MJO has also been documented (L'Heureux and Higgins 2008;Yoo et al, 2011), while Yoo et al (2012) further confirmed the MJOdriven, poleward propagating wave train drove changes in the Arctic overturning circulation, heat flux, and downward infrared radiation. Barrett et al (2015), Klotzbach et al (2016), Henderson et al (2017), and Barrett (2019) noted that the MJO's influence on circulation and temperature extends to snowfall, and that for the eastern parts of North America, snowfall, snow depth change, and snow water equivalent change all tended to be above normal (or snowier) after MJO convection in phases 8 and 1. This relationship between the MJO and NAO appears to be strongest when the El Niño-Southern Oscillation (ENSO) is in negative (La Niña) phase (Roundy et al, 2010), and as discussed above, when the stratospheric QBO is in its easterly phase (Yoo and Son, 2016).…”

A SOM‐based analysis of the drivers of the 2015–2017 Western Cape drought in South Africa

“…A SOM analysis organizes its input vectors into characteristic nodes or patterns by grouping similar patterns closer together and non‐similar patterns apart away, typically within a 2‐dimensional array of states (Sheridan and Lee, 2011). SOMs have been extensively used in atmospheric studies as a synoptic classification approach to characterize atmospheric circulation states and how they relate to the local climate (Hewitson and Crane, 2002; MacKellar et al ., 2009; Lennard and Hegerl, 2015; Henderson et al ., 2016).…”

Weather regimes associated with summer rainfall variability over southern Mexico

“…; Henderson et al . ). Note that in Figure the patterns in which high‐pressure anomalies are dominant are aligned on the left side of the matrix, while patterns with dominant low‐pressure anomalies appear on the right side of the matrix.…”