Using CloudSat snowfall rate observations to constrain and characterize the uncertainties of Arctic snow-on-sea-ice
Authors: Cabaj Alex , Kushner Paul , Petty Alek , Howell Stephen , Fletcher Christopher 
Published: March 2020

&lt;p&gt;&lt;span&gt;Snow on Arctic sea ice plays multiple&amp;#8212;and sometimes contrasting&amp;#8212;roles in several feedbacks between sea ice and the global climate &lt;/span&gt;&lt;span&gt;system.&lt;/span&gt;&lt;span&gt; For example, the presence of snow on sea ice may mitigate sea ice melt by&lt;/span&gt;&lt;span&gt; increasing the sea ice albedo &lt;/span&gt;&lt;span&gt;and enhancing the ice-albedo feedback. Conversely, snow can&lt;/span&gt;&lt;span&gt; in&lt;/span&gt;&lt;span&gt;hibit sea ice growth by insulating the ice from the atmosphere during the &lt;/span&gt;&lt;span&gt;sea ice &lt;/span&gt;&lt;span&gt;growth season. &lt;/span&gt;&lt;span&gt;In addition to its contribution to sea ice feedbacks, snow on sea ice also poses a challenge for sea ice observations. &lt;/span&gt;&lt;span&gt;In particular, &lt;/span&gt;&lt;span&gt;snow &lt;/span&gt;&lt;span&gt;contributes to uncertaint&lt;/span&gt;&lt;span&gt;ies&lt;/span&gt;&lt;span&gt; in retrievals of sea ice thickness from satellite altimetry &lt;/span&gt;&lt;span&gt;measurements, &lt;/span&gt;&lt;span&gt;such as those from ICESat-2&lt;/span&gt;&lt;span&gt;. &lt;/span&gt;&lt;span&gt;Snow-on-sea-ice models can&lt;/span&gt;&lt;span&gt; produce basin-wide snow depth estimates, but these models require snowfall input from reanalysis products. In-situ snowfall measurements are a&lt;/span&gt;&lt;span&gt;bsent&lt;/span&gt;&lt;span&gt; over most of the Arctic Ocean, so it can be difficult to determine which reanalysis &lt;/span&gt;&lt;span&gt;snowfall&lt;/span&gt;&lt;span&gt; product is b&lt;/span&gt;&lt;span&gt;est&lt;/span&gt;&lt;span&gt; suited to be used as&lt;/span&gt;&lt;span&gt; input for a snow-on-sea-ice model.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;In the absence of in-situ snowfall rate measurements, &lt;/span&gt;&lt;span&gt;measurements from &lt;/span&gt;&lt;span&gt;satellite instruments can be used to quantify snowfall over the Arctic Ocean&lt;/span&gt;&lt;span&gt;. &lt;/span&gt;&lt;span&gt;The CloudSat satellite, which is equipped with a 94 GHz Cloud Profiling Radar instrument, measures vertical radar reflectivity profiles from which snowfall rate&lt;/span&gt;&lt;span&gt;s&lt;/span&gt;&lt;span&gt; can be retrieved. &lt;/span&gt; &lt;span&gt;T&lt;/span&gt;&lt;span&gt;his instrument&lt;/span&gt;&lt;span&gt; provides the most extensive high-latitude snowfall rate observation dataset currently available. &lt;/span&gt;&lt;span&gt;CloudSat&amp;#8217;s near-polar orbit enables it to make measurements at latitudes up to 82&amp;#176;N, with a 16-day repeat cycle, &lt;/span&gt;&lt;span&gt;over the time period from 2006-2016.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;We present a calibration of reanalysis snowfall to CloudSat observations over the Arctic Ocean, which we then apply to reanalysis snowfall input for the NASA Eulerian Snow On Sea Ice Model (NESOSIM). This calibration reduces the spread in snow depths produced by NESOSIM w&lt;/span&gt;&lt;span&gt;hen&lt;/span&gt;&lt;span&gt; different reanalysis inputs &lt;/span&gt;&lt;span&gt;are used&lt;/span&gt;&lt;span&gt;. &lt;/span&gt;&lt;span&gt;In light of this calibration, we revise the NESOSIM parametrizations of wind-driven snow processes, and we characterize the uncertainties in NESOSIM-generated snow depths resulting from uncertainties in snowfall input. &lt;/span&gt;&lt;span&gt;We then extend this analysis further to estimate the resulting uncertainties in sea ice thickness retrieved from ICESat-2 when snow depth estimates from NESOSIM are used as input for the retrieval.&lt;/span&gt;&lt;/p&gt;