01961nas a2200217   4500008004100000245010100041210006900142260001200211300001200223490000700235520124400242653003001486653001101516653002401527653001501551653002101566100002001587700002101607700002201628856009301650       2022                        eng d00aValley-floor snowfall in Taylor Valley, Antarctica, from 1995 to 2017: Spring, summer and autumn0 aValleyfloor snowfall in Taylor Valley Antarctica from 1995 to 20  c08/2022  a325-3350 v343 a<p>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<sup>-1</sup>, 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<sup>-1</sup>) to decreasing snowfall (1 mm w.e. yr<sup>-1</sup>), 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.</p>10aautomated weather station10acamera10aMcMurdo Dry Valleys10asnow cover10asnow persistence1 aMyers, Madeline1 aDoran, Peter, T.1 aMyers, Krista, F.  uhttps://www.cambridge.org/core/product/identifier/S0954102022000256/type/journal_article02119nas a2200217   4500008004100000245007600041210006900117260001200186520131800198653001101516653003001527653002801557653002201585653002401607653001201631653001801643100002401661700002101685700002001706856017501726       2020                        eng d00aGIS tool to predict photosynthetically active radiation in a Dry Valley0 aGIS tool to predict photosynthetically active radiation in a Dry  c04/20203 a<p>Understanding primary productivity is a core research area of the National Science Foundation&#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&#39;s major lakes during the 2014&ndash;15 and 2015&ndash;16 field seasons. The average daily error ranges from 0.13 mol photons m<sup>-2</sup> day<sup>-1</sup> (0.6%) at Lake Fryxell to 3.8 mol photons m<sup>-2</sup> day<sup>-1</sup> (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%.</p>10aArcMap10aautomated weather station10adigital elevation model10aice-covered lakes10aMcMurdo Dry Valleys10aR model10aTaylor Valley1 aAcosta, Dimitri, R.1 aDoran, Peter, T.1 aMyers, Madeline  uhttps://www.cambridge.org/core/journals/antarctic-science/article/gis-tool-to-predict-photosynthetically-active-radiation-in-a-dry-valley/BD0BE4FF6A8F3DAAF32D698797287078