In the absence of widespread snowfall observations over the Arctic Ocean, reanalysis products provide a wide range of estimates of time‐evolving snowfall rates over Arctic sea ice, and it can be difficult to determine which product is most representative. In this work, Arctic snowfall rates retrieved from 2006 to 2016 CloudSat observations and snowfall products from three reanalyses are assessed. The products can be brought into encouraging agreement over the region on interannual time scales once differences in spatial representativeness and temporal sampling are accounted for. This motivates the use of CloudSat's snowfall product to calibrate reanalysis snowfall. The calibration is carried out for four Arctic quadrants and combined to produce regionally resolved and consistent estimates of interannually varying snowfall. Calibrated reanalysis snowfall inputs are then used to drive the NASA Eulerian Snow On Sea Ice Model, reducing the interproduct spread in the resulting simulated snow depths across the Arctic.

<p>ESA’s Aeolus mission, launched in August 2018, is designed to capture tropospheric wind profiles on a global scale in near-real time. The Aeolus lidar system, Atmospheric LAser Doppler INstrument (ALADIN), uses two modes of lidar-driven active scattering, Mie and Rayleigh scattering channels, to retrieve horizontal line-of-sight (HLOS) winds under both clear and cloudy conditions. ESA Aeolus aims to improve numerical weather and climate prediction, and to advance understanding of atmospheric circulation and weather systems.</p><p>This presentation will describe the Canadian validation activities for ESA Aeolus level-2B product, coordinated by the University of Toronto’s Department of Physics and Environment and Climate Change Canada (ECCC). The main focus is the evaluation of Aeolus overpasses using the fifth major global reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF ERA5), and in-situ measurements at Environment and Climate Change Canada’s (ECCC) Iqaluit and Whitehorse supersites where several wind sensing instruments are co-located. It will compare the Aeolus HLOS winds with the profiles of wind vector from regular radiosonde launches, line-of-sight winds from Doppler Lidar and Ka-Band Radar. The accuracy of the Aeolus measurements is analyzed based on the type of scattering and natural variability of the wind on different levels.</p><p>The radiosonde measures the profiles of temperature, relative humidity, pressure, and winds twice a day with a vertical resolution of 15 m up to 30 km. On the other hand, the Mie scattered 1.5 micron Doppler Lidar retrieves LOS winds at every 3 m as well as aerosol backscatter and depolarization ratio every 5 minutes up to 3 km. Lastly, for every 10 minutes, the dual-polarization Doppler Ka-Band Radar measures the LOS wind speed and direction, cloud and fog backscatter, and depolarization ratio up to a range of 25 km with a vertical resolution of 10 m.</p><p>The wind profiles were directly compared to the profiles derived from other instruments or reanalysis. The vertical structure of the Aeolus winds, for example the wind shear, will also be compared and discussed. The validation results showed that Aeolus is providing some promising initial products and that the ERA5 reanalysis is the most consistent dataset with the Aeolus wind measurements from level-2B product.</p>

<p><span>Snow on Arctic sea ice plays multiple—and sometimes contrasting—roles in several feedbacks between sea ice and the global climate </span><span>system.</span><span> For example, the presence of snow on sea ice may mitigate sea ice melt by</span><span> increasing the sea ice albedo </span><span>and enhancing the ice-albedo feedback. Conversely, snow can</span><span> in</span><span>hibit sea ice growth by insulating the ice from the atmosphere during the </span><span>sea ice </span><span>growth season. </span><span>In addition to its contribution to sea ice feedbacks, snow on sea ice also poses a challenge for sea ice observations. </span><span>In particular, </span><span>snow </span><span>contributes to uncertaint</span><span>ies</span><span> in retrievals of sea ice thickness from satellite altimetry </span><span>measurements, </span><span>such as those from ICESat-2</span><span>. </span><span>Snow-on-sea-ice models can</span><span> produce basin-wide snow depth estimates, but these models require snowfall input from reanalysis products. In-situ snowfall measurements are a</span><span>bsent</span><span> over most of the Arctic Ocean, so it can be difficult to determine which reanalysis </span><span>snowfall</span><span> product is b</span><span>est</span><span> suited to be used as</span><span> input for a snow-on-sea-ice model.</span></p><p><span>In the absence of in-situ snowfall rate measurements, </span><span>measurements from </span><span>satellite instruments can be used to quantify snowfall over the Arctic Ocean</span><span>. </span><span>The CloudSat satellite, which is equipped with a 94 GHz Cloud Profiling Radar instrument, measures vertical radar reflectivity profiles from which snowfall rate</span><span>s</span><span> can be retrieved. </span> <span>T</span><span>his instrument</span><span> provides the most extensive high-latitude snowfall rate observation dataset currently available. </span><span>CloudSat’s near-polar orbit enables it to make measurements at latitudes up to 82°N, with a 16-day repeat cycle, </span><span>over the time period from 2006-2016.</span></p><p><span>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</span><span>hen</span><span> different reanalysis inputs </span><span>are used</span><span>. </span><span>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. </span><span>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.</span></p>

<p>Planetary waves with zonal wavenumbers k ≤ 3 dominate poleward atmospheric energy transport and its associated Arctic warming and moistening impacts in reanalysis data. Previous work suggests planetary waves generated by tropical warm pool Sea-Surface Temperatures (SSTs) and midlatitude synoptic waves (k ≥ 4) can drive Arctic energy transport. Here, we investigate tropical and midlatitude drivers of Arctic planetary wave transport using an idealised aquaplanet model. First, we show that the zonally-symmetric model qualitatively captures the main characteristics of observed planetary wave transport, as well as its impacts in the Arctic. Next, we show that an idealised tropical warm pool, driven by regional SST forcing, amplifies but is not the dominant source of Arctic planetary wave transport. Finally, lag-regressions using reanalysis and model data suggest midlatitude synoptic waves compensate rather than drive Arctic planetary wave transport. The results do not support the simple geometric effect of midlatitude synoptic waves aliasing onto Arctic planetary waves on a sphere, but rather point towards more complex scale interactions and local drivers of Arctic planetary wave transport.</p>

<p><span>Snow on Arctic sea ice plays many, sometimes contrasting roles in Arctic climate feedbacks. During the sea ice growth season, the presence of snow on sea ice can enhance ice growth by increasing the sea ice albedo, or conversely, inhibit sea ice growth by insulating the ice from the cold atmosphere. Furthermore, estimates of snow depth on Arctic sea ice are also a key input for deriving sea ice thickness from altimetry measurements, such as satellite lidar altimetry measurements from ICESat-2. Due to the logistical challenges of making measurements in as remote a region as the Arctic, snow depth on Arctic sea ice is difficult to observationally constrain.<br><br>The NASA Eulerian Snow On Sea Ice Model (NESOSIM) can be used to provide snow depth and density estimates over Arctic sea ice with pan-Arctic coverage within a relatively simple framework. The latest version of NESOSIM, version 1.1, is a 2-layer model with simple representations of the processes of accumulation, wind packing, loss due to blowing snow, and redistribution due to sea ice motion. Relative to version 1.0, NESOSIM 1.1 features an extended model domain, and reanalysis snowfall input scaled to observed snowfall retrieved from CloudSat satellite radar reflectivity measurements.<br><br>In this work, we present a systematic calibration, and an accompanying estimate in the uncertainty of the free parameters in NESOSIM, targeting airborne snow radar measurements from Operation IceBridge. We further investigate uncertainties in snow depth and the resulting uncertainties in derived sea ice thickness from ICESat-2 altimetry measurements using NESOSIM snow depths.</span></p>