<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Myers, Madeline</style></author><author><style face="normal" font="default" size="100%">Peter T. Doran</style></author><author><style face="normal" font="default" size="100%">Myers, Krista F.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Valley-floor snowfall in Taylor Valley, Antarctica, from 1995 to 2017: Spring, summer and autumn</style></title><secondary-title><style face="normal" font="default" size="100%">Antarctic Science</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">automated weather station</style></keyword><keyword><style  face="normal" font="default" size="100%">camera</style></keyword><keyword><style  face="normal" font="default" size="100%">McMurdo Dry Valleys</style></keyword><keyword><style  face="normal" font="default" size="100%">snow cover</style></keyword><keyword><style  face="normal" font="default" size="100%">snow persistence</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2022</style></year><pub-dates><date><style  face="normal" font="default" size="100%">08/2022</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.cambridge.org/core/product/identifier/S0954102022000256/type/journal_article</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">34</style></volume><pages><style face="normal" font="default" size="100%">325-335</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We present an analysis of the 20 year snowfall dataset in Taylor Valley and the results of a new snow cover monitoring study. Snowfall has been measured at four sites in Taylor Valley from 1995 to 2017. We focus on valley-floor snowfall when wind does not exceed 5 m s&lt;sup&gt;-1&lt;/sup&gt;, and we exclude winter from our analysis due to poor data quality. Snowfall averaged 11 mm water equivalent (w.e.) from 1995 to 2017 across all stations and ranged from 1 to 58 mm w.e. Standard deviations ranged from 3 to 17 mm w.e., highlighting the strong interannual variability of snowfall in Taylor Valley. During spring and autumn there is a spatial gradient in snowfall such that the coast received twice as much snowfall as more central and inland stations. We identified a changepoint in 2007 from increasing snowfall (3 mm w.e. yr&lt;sup&gt;-1&lt;/sup&gt;) to decreasing snowfall (1 mm w.e. yr&lt;sup&gt;-1&lt;/sup&gt;), which coincides with a shift from decreasing temperature to no detectable temperature trend. Daily camera imagery from 2007 to 2017 augments the snowfall measurements. The camera imagery revealed a near tripling of the average number of days with snow cover from 37 days between 2006 and 2012 to 106 days with snow cover between 2012 and 2017.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Acosta, Dimitri R.</style></author><author><style face="normal" font="default" size="100%">Peter T. Doran</style></author><author><style face="normal" font="default" size="100%">Myers, Madeline</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">GIS tool to predict photosynthetically active radiation in a Dry Valley</style></title><secondary-title><style face="normal" font="default" size="100%">Antarctic Science</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">ArcMap</style></keyword><keyword><style  face="normal" font="default" size="100%">automated weather station</style></keyword><keyword><style  face="normal" font="default" size="100%">digital elevation model</style></keyword><keyword><style  face="normal" font="default" size="100%">ice-covered lakes</style></keyword><keyword><style  face="normal" font="default" size="100%">McMurdo Dry Valleys</style></keyword><keyword><style  face="normal" font="default" size="100%">R model</style></keyword><keyword><style  face="normal" font="default" size="100%">Taylor Valley</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">04/2020</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.cambridge.org/core/journals/antarctic-science/article/gis-tool-to-predict-photosynthetically-active-radiation-in-a-dry-valley/BD0BE4FF6A8F3DAAF32D698797287078</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Understanding primary productivity is a core research area of the National Science Foundation&amp;#39;s Long-Term Ecological Research Network. This study presents the development of the GIS-based Topographic Solar Photosynthetically Active Radiation (T-sPAR) toolbox for Taylor Valley. It maps surface photosynthetically active radiation using four meteorological stations with ~20 years of data. T-sPAR estimates were validated with ground-truth data collected at Taylor Valley&amp;#39;s major lakes during the 2014&amp;ndash;15 and 2015&amp;ndash;16 field seasons. The average daily error ranges from 0.13 mol photons m&lt;sup&gt;-2&lt;/sup&gt; day&lt;sup&gt;-1&lt;/sup&gt; (0.6%) at Lake Fryxell to 3.8 mol photons m&lt;sup&gt;-2&lt;/sup&gt; day&lt;sup&gt;-1&lt;/sup&gt; (5.8%) at Lake Hoare. We attribute error to variability in terrain and sun position. Finally, a user interface was developed in order to estimate total daily surface photosynthetically active radiation for any location and date within the basin. T-sPAR improves upon existing toolboxes and models by allowing for the inclusion of a statistical treatment of light attenuation due to cloud cover. The T-sPAR toolbox could be used to inform biological sampling sites based on radiation distribution, which could collectively improve estimates of net primary productivity, in some cases by up to 25%.&lt;/p&gt;</style></abstract></record></records></xml>