Predicting September sea ice: Ensemble skill of the SEARCH Sea Ice Outlook 2008-2013

Abstract Since 2008, the Study of Environmental Arctic Change Sea Ice Outlook has solicited predictions of September sea-ice extent from the Arctic research community. Individuals and teams employ a variety of modeling, statistical, and heuristic approaches to make these predictions. Viewed as monthly ensembles each with one or two dozen individual predictions, they display a bimodal pattern of success. In years when observed ice extent is near its trend, the median predictions tend to be accurate. In years when the observed extent is anomalous, the median and most individual predictions are less accurate. Statistical analysis suggests that year-to-year variability, rather than methods, dominate the variation in ensemble prediction success. Furthermore, ensemble predictions do not improve as the season evolves. We consider the role of initial ice, atmosphere and ocean conditions, and summer storms and weather in contributing to the challenge of sea-ice prediction. Key Points Analysis of Sea Ice Outlook contributions 2008-2013 shows bimodal success Years when observations depart from trend are hard to predict despite preconditioning Yearly conditions dominate variations in ensemble prediction success

Predicting September sea ice: Ensemble skill of the SEARCH Sea Ice Outlook 2008–2013

Since 2008, the Study of Environmental Arctic Change Sea Ice Outlook has solicited predictions of September sea-ice extent from the Arctic research community. Individuals and teams employ a variety of modeling, statistical, and heuristic approaches to make these predictions. Viewed as monthly ensembles each with one or two dozen individual predictions, they display a bimodal pattern of success. In years when observed ice extent is near its trend, the median predictions tend to be accurate. In years when the observed extent is anomalous, the median and most individual predictions are less accurate. Statistical analysis suggests that year-to-year variability, rather than methods, dominate the variation in ensemble prediction success. Furthermore, ensemble predictions do not improve as the season evolves. We consider the role of initial ice, atmosphere and ocean conditions, and summer storms and weather in contributing to the challenge of sea-ice prediction

Sea ice roughness overlooked as a key source of uncertainty in CryoSat-2 ice freeboard retrievals

ESA's CryoSat‐2 has transformed the way we monitor Arctic sea ice, providing routine measurements of the ice thickness with near basin‐wide coverage. Past studies have shown that uncertainties in the sea ice thickness retrievals can be introduced at several steps of the processing chain, for instance in the estimation of snow depth, and snow and sea ice densities. Here, we apply a new physical model to CryoSat‐2 which further reveals sea ice surface roughness as a key overlooked feature of the conventional retrieval process. High‐resolution airborne observations demonstrate that snow and sea ice surface topography can be better characterized by a Lognormal distribution, which varies based on the ice age and surface roughness within a CryoSat‐2 footprint, than a Gaussian distribution. Based on these observations, we perform a set of simulations for the CryoSat‐2 echo waveform over ‘virtual’ sea ice surfaces with a range of roughness and radar backscattering configurations. By accounting for the variable roughness, our new Lognormal retracker produces sea ice freeboards which compare well with those derived from NASA's Operation IceBridge airborne data and extends the capability of CryoSat‐2 to profile the thinnest/smoothest sea ice and thickest/roughest ice. Our results indicate that the variable ice surface roughness contributes a systematic uncertainty in sea ice thickness of up to 20% over first‐year ice and 30% over multi‐year ice, representing one of the principal sources of pan‐Arctic sea ice thickness uncertainty

Extreme Precipitation in the Eastern Canadian Arctic and Greenland: An Evaluation of Atmospheric Reanalyses

Extreme precipitation events are becoming more common in the Arctic as the climate warms, but characterizing these events is notoriously challenging. Atmospheric reanalyses have become popular tools for climate studies in data-sparse regions such as the Arctic. While modern reanalyses have been shown to perform reasonably well at reproducing Arctic climate, their ability to represent extreme precipitation events has not been investigated in depth. In this study, three of the most recent reanalyses, ERA-5, MERRA-2, and CFSR, are compared to surface precipitation observations in the Eastern Canadian Arctic and Greenland from 1980 to 2016 to assess how well they represent the most intense observed events. Overall, the reanalyses struggled to match observed accumulations from individual events (−0.11 ≤ r ≤ 0.47) but matched the observed seasonality of precipitation extremes. The region with the strongest match between observations and reanalyses was Southwest Greenland. Performance varies by event, and the best match between reanalyses and station observations may have a spatial/temporal offset (up to one grid cell or 1 day). The three products saw similar performance in general; however, ERA-5 tends to see slightly higher correlations and lower biases than MERRA-2 or CFSR. Considering the limitations of in situ observations, these results suggest that the reanalyses are capable of representing aggregate extreme precipitation (e.g., seasonal or annual time scales), but struggle to consistently match the timing and location of specific observed events

Generic Supersonic and Hypersonic Configurations

Abstract: A geometry generator for preliminary aerodynamic design, parametric optimization and the preprocessing of CFD boundary conditions is presented. With emphasis on supersonic aircraft components, ranging from waverider caret wings to generic lifting bodies derived from recent aerospace research projects, the simple mathematical basis and its consequent use throughout various applications is illustrated

Rain on snow (ROS) understudied in sea ice remote sensing: a multi-sensor analysis of ROS during MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate)

Arctic rain on snow (ROS) deposits liquid water onto existing snowpacks. Upon refreezing, this can form icy crusts at the surface or within the snowpack. By altering radar backscatter and microwave emissivity, ROS over sea ice can influence the accuracy of sea ice variables retrieved from satellite radar altimetry, scatterometers, and passive microwave radiometers. During the Arctic Ocean MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) expedition, there was an unprecedented opportunity to observe a ROS event using in situ active and passive microwave instruments similar to those deployed on satellite platforms. During liquid water accumulation in the snowpack from rain and increased melt, there was a 4-fold decrease in radar energy returned at Ku- and Ka-bands. After the snowpack refroze and ice layers formed, this decrease was followed by a 6-fold increase in returned energy. Besides altering the radar backscatter, analysis of the returned waveforms shows the waveform shape changed in response to rain and refreezing. Microwave emissivity at 19 and 89&thinsp;GHz increased with increasing liquid water content and decreased as the snowpack refroze, yet subsequent ice layers altered the polarization difference. Corresponding analysis of the CryoSat-2 waveform shape and backscatter as well as AMSR2 brightness temperatures further shows that the rain and refreeze were significant enough to impact satellite returns. Our analysis provides the first detailed in situ analysis of the impacts of ROS and subsequent refreezing on both active and passive microwave observations, providing important baseline knowledge for detecting ROS over sea ice and assessing their impacts on satellite-derived sea ice variables. &nbsp;</p

Brief communication:Conventional assumptions involving the speed of radar waves in snow introduce systematic underestimates to sea ice thickness and seasonal growth rate estimates

Pan-Arctic sea ice thickness has been monitored over recent decades by satellite radar altimeters such as CryoSat-2, which emits Ku-band radar waves that are assumed in publicly available sea ice thickness products to penetrate overlying snow and scatter from the ice–snow interface. Here we examine two expressions for the time delay caused by slower radar wave propagation through the snow layer and related assumptions concerning the time evolution of overlying snow density. Two conventional treatments introduce systematic underestimates of up to 15 cm into ice thickness estimates and up to 10 cm into thermodynamic growth rate estimates over multi-year ice in winter. Correcting these biases would impact a wide variety of model projections, calibrations, validations and reanalyses

Snowfall and snow accumulation during the MOSAiC winter and spring seasons

Data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition allowed us to investigate the temporal dynamics of snowfall, snow accumulation and erosion in great detail for almost the whole accumulation season (November&nbsp;2019 to May&nbsp;2020). We computed cumulative snow water equivalent (SWE) over the sea ice based on snow depth and density retrievals from a SnowMicroPen and approximately weekly measured snow depths along fixed transect paths. We used the derived SWE from the snow cover to compare with precipitation sensors installed during MOSAiC. The data were also compared with ERA5 reanalysis snowfall rates for the drift track. We found an accumulated snow mass of 38&thinsp;mm SWE between the end of October&nbsp;2019 and end of April&nbsp;2020. The initial SWE over first-year ice relative to second-year ice increased from 50&thinsp;% to 90&thinsp;% by end of the investigation period. Further, we found that the Vaisala Present Weather Detector&nbsp;22, an optical precipitation sensor, and installed on a railing on the top deck of research vessel&nbsp;Polarstern, was least affected by blowing snow and showed good agreements with SWE retrievals along the transect. On the contrary, the OTT Pluvio2 pluviometer and the OTT Parsivel2 laser disdrometer were largely affected by wind and blowing snow, leading to too high measured precipitation rates. These are largely reduced when eliminating drifting snow periods in the comparison. ERA5 reveals good timing of the snowfall events and good agreement with ground measurements with an overestimation tendency. Retrieved snowfall from the ship-based Ka-band ARM zenith radar shows good agreements with SWE of the snow cover and differences comparable to those of ERA5. Based on the results, we suggest the Ka-band radar-derived snowfall as an upper limit and the present weather detector on RV&nbsp;Polarstern as a lower limit of a cumulative snowfall range. Based on these findings, we suggest a cumulative snowfall of 72 to 107&thinsp;mm and a precipitation mass loss of the snow cover due to erosion and sublimation as between 47&thinsp;% and 68&thinsp;%, for the time period between 31 October 2019 and 26 April 2020. Extending this period beyond available snow cover measurements, we suggest a cumulative snowfall of 98&ndash;114&thinsp;mm. &nbsp;</p

Sensitivity of Northern Hemisphere Cyclone Detection and Tracking Results to Fine Spatial and Temporal Resolution Using ERA5

Lagrangian detection and tracking algorithms are frequently used to study the development, distribution, and trends of extratropical cyclones. Past research shows that results from these algorithms are sensitive to both spatial and temporal resolution of the gridded input fields, with coarser resolutions typically underestimating cyclone frequency by failing to capture weak, small, and short-lived systems. The fifth-generation atmospheric reanalysis from the European Centre for Medium-Range Weather Forecasts (ERA5) offers finer resolution, and therefore more precise information regarding storm locations and development than previous global reanalyses. However, our sensitivity tests show that using ERA5 sea-level pressure fields at their finest possible resolution does not necessarily lead to better cyclone detection and tracking. If a common number of nearest neighbors is used when detecting minima in sea-level pressure (like past studies), finer spatial resolution leads to noisier fields that unrealistically break up multi-center cyclones. Using a common search distance instead (with more neighbors at finer resolution) resolves the issue without smoothing inputs. Doing this also makes cyclone frequency, lifespan, and average depth insensitive to refining spatial resolution beyond 100 km. Results using 6-h and 3-h temporal resolutions have only minor differences, but using 1-h temporal resolution with a maximum allowed propagation speed of 150 km h-1 leads to unrealistic track splitting. This can be counteracted by increasing the maximum propagation speed, but modest sensitivity to temporal resolution persists for several cyclone characteristics. Therefore, we recommend caution if applying existing algorithms to temporal resolutions finer than 3-h and careful evaluation of algorithm settings