ORCID Profile
0000-0002-0973-9982
Current Organisation
University of Colorado at Boulder
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Publisher: American Geophysical Union (AGU)
Date: 27-08-2006
DOI: 10.1029/2005JD006641
Publisher: American Meteorological Society
Date: 23-11-2016
Abstract: Understanding phase transitions in mixed-phase clouds is of great importance because the hydrometeor phase controls the lifetime and radiative effects of clouds. In high latitudes, these cloud radiative effects have a crucial impact on the surface energy budget and thus on the evolution of the ice cover. For a springtime low-level mixed-phase stratiform cloud case from Barrow, Alaska, a unique combination of instruments and retrieval methods is combined with multiple modeling perspectives to determine key processes that control cloud phase partitioning. The interplay of local cloud-scale versus large-scale processes is considered. Rapid changes in phase partitioning were found to be caused by several main factors. Major influences were the large-scale advection of different air masses with different aerosol concentrations and humidity content, cloud-scale processes such as a change in the thermodynamical coupling state, and local-scale dynamics influencing the residence time of ice particles. Other factors such as radiative shielding by a cirrus and the influence of the solar cycle were found to only play a minor role for the specific case study (11–12 March 2013). For an even better understanding of cloud phase transitions, observations of key aerosol parameters such as profiles of cloud condensation nucleus and ice nucleus concentration are desirable.
Publisher: American Geophysical Union (AGU)
Date: 06-11-2007
DOI: 10.1029/2007JD008737
Publisher: Copernicus GmbH
Date: 04-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-12692
Abstract: & & Due to logistical challenges, snowfall in the high Arctic has rarely been measured, which is particularly valid for longer time-spans and the polar night. When estimating snowfall with precipitation gauges, a snowfall reference and detailed knowledge of how the precipitated snow accumulated or eroded is required.& & & & To overcome snowfall uncertainties and to improve accumulated and eroded snow estimates, we used data from precipitation gauges, snow particle counters (SPCs) and a K& sub& a& /sub& -Band ARM Zenith Radar (KAZR) installed on and around research vessel (RV) Polarstern during the snow accumulation season of MOSAiC (October 2019 - May 2020). In addition to this, direct snow water equivalent (SWE) measurements were conducted and SWE estimates were retrieved from SnowMicroPen (SMP) measurements distributed all over the floe. The evolution of accumulated snow mass was finally computed by applying a simple fitted z-SWE function to snow depths that were measured approximately weekly along a fixed transect path with a Magnaprobe. The transects paths were along two distinct ice types: predominantly level remnant ice that at the start of the winter had large refrozen melt ponds, and predominantly deformed thick second year ice (SYI).& & & & We could show that at least 34 mm of snow has accumulated and approximately 9 kg m& sup& -2& /sup& of snow mass was eroded between 31 October 2019 and 26 April 2020. In the beginning of the winter, the total estimated SWE on level remnant ice was only 42 % of SWE on deformed SYI. By end of April 2020 the values almost equalized as the snow mass on remnant ice reached almost 90 % of the snow mass over deformed SYI.& & & & Based on the SWE evolution of the snowpack, we validated precipitation sensors and the reanalysis ERA5 for their capability to estimate snowfall.& Eroded snow mass, among other processes, led to a discrepancy of precipitation- sensor estimated snowfall and computed SWE of the snow cover from 20 February 2020 on. However, for the time period before the first net erosion could be observed we found best agreements of cumulated snowfall and SWE for the Vaisala Present Weather Detector (PWD22) installed on the vessel (RMSE = 2 mm) and for snowfall retrievals from the KAZR (RMSE = 4 mm). Other sensors largely overestimated snowfall (corrected OTT Pluvio& sup& & /sup& : 14 mm Vaisala PWD22 on the ice: 26 mm, OTT Parsivel& sup& & /sup& on RV Polarstern: 51 mm). ERA5 overestimates snowfall too, with 13 mm and an increasing positive bias from March 2020 on. With horizontal snow mass fluxes derived from SPCs we could show that the Vaisala PWD22 on RV Polarstern was effectively protected against blowing snow. This, however, greatly affected snowfall measurements of instruments collocated on the ice. Further, we investigated a high-wind event in February 2020 resulting in high blowing snow mass fluxes and an average eroded snow mass of 5.5 kg m& sup& -2& /sup& . The lifted blowing snow particles from the surface led to strong overestimation of snowfall from instruments installed on the ice which cannot be corrected with conventional correction methods.& &
Publisher: University of California Press
Date: 2022
DOI: 10.1525/ELEMENTA.2021.000046
Abstract: Year-round observations of the physical snow and ice properties and processes that govern the ice pack evolution and its interaction with the atmosphere and the ocean were conducted during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition of the research vessel Polarstern in the Arctic Ocean from October 2019 to September 2020. This work was embedded into the interdisciplinary design of the 5 MOSAiC teams, studying the atmosphere, the sea ice, the ocean, the ecosystem, and biogeochemical processes. The overall aim of the snow and sea ice observations during MOSAiC was to characterize the physical properties of the snow and ice cover comprehensively in the central Arctic over an entire annual cycle. This objective was achieved by detailed observations of physical properties and of energy and mass balance of snow and ice. By studying snow and sea ice dynamics over nested spatial scales from centimeters to tens of kilometers, the variability across scales can be considered. On-ice observations of in situ and remote sensing properties of the different surface types over all seasons will help to improve numerical process and climate models and to establish and validate novel satellite remote sensing methods the linkages to accompanying airborne measurements, satellite observations, and results of numerical models are discussed. We found large spatial variabilities of snow metamorphism and thermal regimes impacting sea ice growth. We conclude that the highly variable snow cover needs to be considered in more detail (in observations, remote sensing, and models) to better understand snow-related feedback processes. The ice pack revealed rapid transformations and motions along the drift in all seasons. The number of coupled ice–ocean interface processes observed in detail are expected to guide upcoming research with respect to the changing Arctic sea ice.
Publisher: Copernicus GmbH
Date: 23-05-2013
DOI: 10.5194/ACPD-13-13541-2013
Abstract: Abstract. The climate in the Arctic is changing faster than anywhere else on Earth. Poorly understood feedback processes relating to Arctic clouds and aerosol-cloud interactions contribute to a poor understanding of the present changes in the Arctic climate system, and also to a large spread in projections of future climate in the Arctic. The problem is exacerbated by the paucity of research-quality observations in the central Arctic. Improved formulations in climate models require such observations, which can only come from measurements in-situ in this difficult to reach region with logistically demanding environmental conditions. The Arctic Summer Cloud-Ocean Study (ASCOS) was the most extensive central Arctic Ocean expedition with an atmospheric focus during the International Polar Year (IPY) 2007–2008. ASCOS focused on the study of the formation and life cycle of low-level Arctic clouds. ASCOS departed from Longyearbyen on Svalbard on 2 August and returned on 9 September 2008. In transit into and out of the pack ice, four short research stations were undertaken in the Fram Strait two in open water and two in the marginal ice zone. After traversing the pack-ice northward an ice c was set up on 12 August at 87°21' N 01°29' W and remained in operation through 1 September, drifting with the ice. During this time extensive measurements were taken of atmospheric gas and particle chemistry and physics, mesoscale and boundary-layer meteorology, marine biology and chemistry, and upper ocean physics. ASCOS provides a unique interdisciplinary data set for development and testing of new hypotheses on cloud processes, their interactions with the sea ice and ocean and associated physical, chemical, and biological processes and interactions. For ex le, the first ever quantitative observation of bubbles in Arctic leads, combined with the unique discovery of marine organic material, polymer gels with an origin in the ocean, inside cloud droplets suggest the possibility of primary marine organically derived cloud condensation nuclei in Arctic stratocumulus clouds. Direct observations of surface fluxes of aerosols could, however, not explain observed variability in aerosol concentrations and the balance between local and remote aerosols sources remains open. Lack of CCN was at times a controlling factor in low-level cloud formation, and hence for the impact of clouds on the surface energy budget. ASCOS provided detailed measurements of the surface energy balance from late summer melt into the initial autumn freeze-up, and documented the effects of clouds and storms on the surface energy balance during this transition. In addition to such process-level studies, the unique, independent ASCOS data set can and is being used for validation of satellite retrievals, operational models, and reanalysis data sets.
Publisher: Copernicus GmbH
Date: 19-03-2014
Abstract: Abstract. The climate in the Arctic is changing faster than anywhere else on earth. Poorly understood feedback processes relating to Arctic clouds and aerosol–cloud interactions contribute to a poor understanding of the present changes in the Arctic climate system, and also to a large spread in projections of future climate in the Arctic. The problem is exacerbated by the paucity of research-quality observations in the central Arctic. Improved formulations in climate models require such observations, which can only come from measurements in situ in this difficult-to-reach region with logistically demanding environmental conditions. The Arctic Summer Cloud Ocean Study (ASCOS) was the most extensive central Arctic Ocean expedition with an atmospheric focus during the International Polar Year (IPY) 2007–2008. ASCOS focused on the study of the formation and life cycle of low-level Arctic clouds. ASCOS departed from Longyearbyen on Svalbard on 2 August and returned on 9 September 2008. In transit into and out of the pack ice, four short research stations were undertaken in the Fram Strait: two in open water and two in the marginal ice zone. After traversing the pack ice northward, an ice c was set up on 12 August at 87°21' N, 01°29' W and remained in operation through 1 September, drifting with the ice. During this time, extensive measurements were taken of atmospheric gas and particle chemistry and physics, mesoscale and boundary-layer meteorology, marine biology and chemistry, and upper ocean physics. ASCOS provides a unique interdisciplinary data set for development and testing of new hypotheses on cloud processes, their interactions with the sea ice and ocean and associated physical, chemical, and biological processes and interactions. For ex le, the first-ever quantitative observation of bubbles in Arctic leads, combined with the unique discovery of marine organic material, polymer gels with an origin in the ocean, inside cloud droplets suggests the possibility of primary marine organically derived cloud condensation nuclei in Arctic stratocumulus clouds. Direct observations of surface fluxes of aerosols could, however, not explain observed variability in aerosol concentrations, and the balance between local and remote aerosols sources remains open. Lack of cloud condensation nuclei (CCN) was at times a controlling factor in low-level cloud formation, and hence for the impact of clouds on the surface energy budget. ASCOS provided detailed measurements of the surface energy balance from late summer melt into the initial autumn freeze-up, and documented the effects of clouds and storms on the surface energy balance during this transition. In addition to such process-level studies, the unique, independent ASCOS data set can and is being used for validation of satellite retrievals, operational models, and reanalysis data sets.
Publisher: American Geophysical Union (AGU)
Date: 27-05-2012
DOI: 10.1029/2011JD016792
Publisher: Copernicus GmbH
Date: 17-06-2022
Abstract: Abstract. 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 2019 to May 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 mm SWE between the end of October 2019 and end of April 2020. The initial SWE over first-year ice relative to second-year ice increased from 50 % to 90 % by end of the investigation period. Further, we found that the Vaisala Present Weather Detector 22, an optical precipitation sensor, and installed on a railing on the top deck of research vessel 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 Polarstern as a lower limit of a cumulative snowfall range. Based on these findings, we suggest a cumulative snowfall of 72 to 107 mm and a precipitation mass loss of the snow cover due to erosion and sublimation as between 47 % and 68 %, 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 98–114 mm.
Publisher: University of California Press
Date: 2022
DOI: 10.1525/ELEMENTA.2021.00062
Abstract: Arctic Ocean properties and processes are highly relevant to the regional and global coupled climate system, yet still scarcely observed, especially in winter. Team OCEAN conducted a full year of physical oceanography observations as part of the Multidisciplinary drifting Observatory for the Study of the Arctic Climate (MOSAiC), a drift with the Arctic sea ice from October 2019 to September 2020. An international team designed and implemented the program to characterize the Arctic Ocean system in unprecedented detail, from the seafloor to the air-sea ice-ocean interface, from sub-mesoscales to pan-Arctic. The oceanographic measurements were coordinated with the other teams to explore the ocean physics and linkages to the climate and ecosystem. This paper introduces the major components of the physical oceanography program and complements the other team overviews of the MOSAiC observational program. Team OCEAN’s s ling strategy was designed around hydrographic ship-, ice- and autonomous platform-based measurements to improve the understanding of regional circulation and mixing processes. Measurements were carried out both routinely, with a regular schedule, and in response to storms or opening leads. Here we present along-drift time series of hydrographic properties, allowing insights into the seasonal and regional evolution of the water column from winter in the Laptev Sea to early summer in Fram Strait: freshening of the surface, deepening of the mixed layer, increase in temperature and salinity of the Atlantic Water. We also highlight the presence of Canada Basin deep water intrusions and a surface meltwater layer in leads. MOSAiC most likely was the most comprehensive program ever conducted over the ice-covered Arctic Ocean. While data analysis and interpretation are ongoing, the acquired datasets will support a wide range of physical oceanography and multi-disciplinary research. They will provide a significant foundation for assessing and advancing modeling capabilities in the Arctic Ocean.
Publisher: Copernicus GmbH
Date: 26-04-2021
DOI: 10.5194/TC-2021-126
Abstract: Abstract. 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 2019 to May 2020). We computed cumulative snow water equivalent (SWE) over the sea ice based on snow depth (HS) and density retrievals from a SnowMicroPen (SMP) and approximately weekly-measured snow depths along fixed transect paths. Hence, the computed SWE considers surface heterogeneities over an average path length of 1469 m. We used the SWE from the snow cover to compare with precipitation sensors installed during MOSAiC. The data were compared with ERA5 reanalysis snowfall rates for the drift track. Our study shows that the simple fitted HS-SWE function can well be used to compute SWE along a transect path based on SMP SWE retrievals and snow-depth measurements. We found an accumulated snow mass of 34 mm SWE until 26 April 2020. Further, we found that the Vaisala Present Weather Detector 22 (PWD22), installed on a railing on the top deck of research vessel Polarstern was least affected by blowing snow and showed good agreements with SWE retrievals along the transect, however, it also systematically underestimated snowfall. The OTT Pluvio2 and the OTT Parsivel2 were largely affected by wind and blowing snow, leading to higher measured precipitation rates, but when eliminating drifting snow periods, especially the OTT Pluvio2 shows good agreements with ground measurements. A comparison with ERA5 snowfall data reveals a good timing of the snowfall events and good agreement with ground measurements but also a tendency towards overestimation. Retrieved snowfall from the ship-based Ka-band ARM Zenith Radar (KAZR) shows good agreements with SWE of the snow cover and comparable differences as ERA5. Assuming the KAZR derived snowfall as an upper limit and PWD22 as a lower limit of a cumulative snowfall range, we estimate 72 to 107 mm measured between 31 October 2019 and 26 April 2020. For the same period, we estimate the precipitation mass loss along the transect due to erosion and sublimation as between 53 and 68 %. Until 7 May 2020, we suggest a cumulative snowfall of 98–114 mm.
Location: United States of America
No related grants have been discovered for MATTHEW SHUPE.