Near-surface meteorology during the Arctic Summer Cloud Ocean Study (ASCOS):  evaluation of reanalyses and global climate models

Boer, Gijs de; Shupe, Matthew D.; Caldwell, Peter; Bauer, Susanne E.; Persson, Ola; Boyle, J. S.; Kelley, M.; Klein, Stephen A.; Tjernström, Michael

doi:10.5194/acp-14-427-2014

Cited by 42 publications

(41 citation statements)

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“…In addition, the complete SEB contains turbulent fluxes as well, which can play an important role in energy exchanges between surface and atmosphere (Curry et al, 2000;Van Den Broeke et al, 2006;de Boer et al, 2014) and in mass-related processes such as sublimation (Thiery et al, 2012), in addition to the radiative fluxes considered here. Since turbulent fluxes cannot be retrieved from CloudSat and CALIPSO observations, these are not included in the present study.…”

Section: Discussionmentioning

confidence: 99%

Improving satellite-retrieved surface radiative fluxes in polar regions using a smart sampling approach

et al. 2016

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Abstract. The surface energy budget (SEB) of polar regions is key to understanding the polar amplification of global climate change and its worldwide consequences. However, despite a growing network of ground-based automatic weather stations that measure the radiative components of the SEB, extensive areas remain where no ground-based observations are available. Satellite remote sensing has emerged as a potential solution to retrieve components of the SEB over remote areas, with radar and lidar aboard the CloudSat and CALIPSO satellites among the first to enable estimates of surface radiative long-wave (LW) and short-wave (SW) fluxes based on active cloud observations. However, due to the small swath footprints, combined with a return cycle of 16 days, questions arise as to how CloudSat and CALIPSO observations should be optimally sampled in order to retrieve representative fluxes for a given location. Here we present a smart sampling approach to retrieve downwelling surface radiative fluxes from CloudSat and CALIPSO observations for any given land-based point-of-interest (POI) in polar regions. The method comprises a spatial correction that allows the distance between the satellite footprint and the POI to be increased in order to raise the satellite sampling frequency. Sampling frequency is enhanced on average from only two unique satellite overpasses each month for limited-distance sampling < 10 km from the POI, to 35 satellite overpasses for the smart sampling approach. This reduces the root-meansquare errors on monthly mean flux estimates compared to ground-based measurements from 23 to 10 W m −2 (LW) and from 43 to 14 W m −2 (SW). The added value of the smart sampling approach is shown to be largest on finer temporal resolutions, where limited-distance sampling suffers from severely limited sampling frequencies. Finally, the methodology is illustrated for Pine Island Glacier (Antarctica) and the Greenland northern interior. Although few ground-based observations are available for these remote areas, important climatic changes have been recently reported. Using the smart sampling approach, 5-day moving average time series of downwelling LW and SW fluxes are demonstrated. We conclude that the smart sampling approach may help to reduce the observational gaps that remain in polar regions to further refine the quantification of the polar SEB.

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Section: Discussionmentioning

confidence: 99%

Improving satellite-retrieved surface radiative fluxes in polar regions using a smart sampling approach

et al. 2016

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“…Nearsurface temperatures are kept constant at 263.48 K until 5 h to allow adequate time for model spin-up, after which they are forced to warm linearly, in hourly increments, to 265.66 K at approximately 11 h 20 min. This warming profile was artificially constructed based on approximated ERA-Interim (ECMWF reanalysis; Dee et al, 2011) 2 m temperature variations over the ocean in the Svalbard archipelago, close to the sea ice, during a cold-air outbreak (23 March 2013; see Young et al, 2016, 2017, andFig. S1 in the Supplement for further details).…”

Section: Model Set-upmentioning

confidence: 99%

“…Heterogeneous primary ice formation is represented using the D10 parameterisation with aerosol number concentrations calculated during the study by Young et al (2017). Previous studies (Harrington et al, 1999;Harrington and Olsson, 2001;Prenni et al, 2007;Morrison et al, 2012;de Boer et al, 2011;Young et al, 2017) have shown that the lifetime of springtime single-layer mixed-phase clouds at high latitudes is strongly dependent on N ice . Here, a lower (N ice = D10 × 0.5) and higher (N ice = D10 × 2) threshold are implemented to change the number concentration of modelled ice, and snow, particles.…”

Section: Test 3: Ice Number Concentrationmentioning

confidence: 99%

Relating large-scale subsidence to convection development in Arctic mixed-phase marine stratocumulus

2018

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Abstract. Large-scale subsidence, associated with highpressure systems, is often imposed in large-eddy simulation (LES) models to maintain the height of boundary layer (BL) clouds. Previous studies have considered the influence of subsidence on warm liquid clouds in subtropical regions; however, the relationship between subsidence and mixedphase cloud microphysics has not specifically been studied. For the first time, we investigate how widespread subsidence associated with synoptic-scale meteorological features can affect the microphysics of Arctic mixed-phase marine stratocumulus (Sc) clouds. Modelled with LES, four idealised scenarios -a stable Sc, varied droplet (N drop ) or ice (N ice ) number concentrations, and a warming surface (representing motion southwards) -were subjected to different levels of subsidence to investigate the cloud microphysical response. We find strong sensitivities to large-scale subsidence, indicating that high-pressure systems in the ocean-exposed Arctic regions have the potential to generate turbulence and changes in cloud microphysics in any resident BL mixedphase clouds.Increased cloud convection is modelled with increased subsidence, driven by longwave radiative cooling at cloud top and rain evaporative cooling and latent heating from snow growth below cloud. Subsidence strengthens the BL temperature inversion, thus reducing entrainment and allowing the liquid-and ice-water paths (LWPs, IWPs) to increase. Through increased cloud-top radiative cooling and subsequent convective overturning, precipitation production is enhanced: rain particle number concentrations (N rain ), incloud rain mass production rates, and below-cloud evaporation rates increase with increased subsidence.Ice number concentrations (N ice ) play an important role, as greater concentrations suppress the liquid phase; therefore, N ice acts to mediate the strength of turbulent overturning promoted by increased subsidence. With a warming surface, a lack of -or low -subsidence allows for rapid BL turbulent kinetic energy (TKE) coupling, leading to a heterogeneous cloud layer, cloud-top ascent, and cumuli formation below the Sc cloud. In these scenarios, higher levels of subsidence act to stabilise the Sc layer, where the combination of these two forcings counteract one another to produce a stable, yet dynamic, cloud layer.

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“…A high temporal and spatial resolution and a detailed microphysics scheme appear to be essential for representing the boundary layer and the gradients of various parameters, such as liquid water content, properly (Wesslén et al, 2014;de Boer et al, 2014;Solomon et al, 2015;Sotiropoulou et al, 2016). Several studies have also found that Arctic mixed-phase clouds, and their surface radiative effects, are sensitive to changes in CCN concentrations.…”

Section: Introductionmentioning

confidence: 99%

Modelling micro- and macrophysical contributors to the dissipation of an Arctic mixed-phase cloud during the Arctic Summer Cloud Ocean Study (ASCOS)

et al. 2017

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Abstract. The Arctic climate is changing; temperature changes in the Arctic are greater than at midlatitudes, and changing atmospheric conditions influence Arctic mixedphase clouds, which are important for the Arctic surface energy budget. These low-level clouds are frequently observed across the Arctic. They impact the turbulent and radiative heating of the open water, snow, and sea-ice-covered surfaces and influence the boundary layer structure. Therefore the processes that affect mixed-phase cloud life cycles are extremely important, yet relatively poorly understood. In this study, we present sensitivity studies using semi-idealized large eddy simulations (LESs) to identify processes contributing to the dissipation of Arctic mixed-phase clouds. We found that one potential main contributor to the dissipation of an observed Arctic mixed-phase cloud, during the Arctic Summer Cloud Ocean Study (ASCOS) field campaign, was a low cloud droplet number concentration (CDNC) of about 2 cm −3 . Introducing a high ice crystal concentration of 10 L −1 also resulted in cloud dissipation, but such high ice crystal concentrations were deemed unlikely for the present case. Sensitivity studies simulating the advection of dry air above the boundary layer inversion, as well as a modest increase in ice crystal concentration of 1 L −1 , did not lead to cloud dissipation. As a requirement for small droplet numbers, pristine aerosol conditions in the Arctic environment are therefore considered an important factor determining the lifetime of Arctic mixed-phase clouds.

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Near-surface meteorology during the Arctic Summer Cloud Ocean Study (ASCOS): evaluation of reanalyses and global climate models

Cited by 42 publications

References 50 publications

Improving satellite-retrieved surface radiative fluxes in polar regions using a smart sampling approach

Improving satellite-retrieved surface radiative fluxes in polar regions using a smart sampling approach

Relating large-scale subsidence to convection development in Arctic mixed-phase marine stratocumulus

Modelling micro- and macrophysical contributors to the dissipation of an Arctic mixed-phase cloud during the Arctic Summer Cloud Ocean Study (ASCOS)

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