Characterisation and surface radiative impact of Arctic low clouds from the IAOOS field experiment

Maillard, Julia; Ravetta, François; Raut, Jean-Christophe; Mariage, Vincent; Pelon, Jacques

doi:10.5194/acp-21-4079-2021

Cited by 5 publications

(5 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Both datasets lead to very similar results with a discrepancy of only 4% (Table 1). Using a direct inversion of the lidar backscatter signals obtained from the IAOOS buoys during all drifts, Maillard et al (2021) estimated much lower cloud optical depths (< 3). It however does not reveal a disagreement with our findings as the values reported by Maillard et al (2021) only focus on the first low-level cloud layer detected by the lidar, ignoring opaque clouds (that could completely attenuate lidar signals) or multilayered clouds.…”

Section: Comparison Of the Values Of The Cloud Optical Depth From The Two Methodsmentioning

confidence: 99%

“…This study will mostly focus on the lidar measurements acquired from January to June 2015 onboard three buoys during the N-ICE field experiment. They have already been used to investigate the occurrence and distribution of winter to summertime clouds and aerosols in the high Arctic Ocean (Di Biagio et al, 2018Biagio et al, , 2020Maillard et al, 2021). The lidar emits a laser at a wavelength of 808 nm and the field of view of the receiver is ∼ 1.4×10 −6 sr. Measurements were performed 2 to 4 times a day with a 10-minute averaging sequence for each profile.…”

Section: Iaoos Lidar Data Aboard Buoysmentioning

confidence: 99%

“…where S c is the lidar ratio in the cloud layer and β max is the maximal value of the backscatter coefficient β within a vertical profile. The limit of that approach is that it permits the determination of τ only when z t can be identified, which generally occurs as long as the cloud optical depth does not reach τ ≈ 3 (Maillard et al, 2021). This approach is therefore not suitable to provide an accurate estimate of τ for opaque clouds, but it offers the opportunity to provide a rough information on the optical properties of the cloud and to determine whether τ IAOOS is larger or lower than the threshold value τ max (θ) seen in Fig.…”

Section: Determination Of the Cloud Optical Depth From The Iaoos Lidar : τ Iaoosmentioning

confidence: 99%

“…Satellitebased remote sensing instruments documenting clouds, such as CALIOP aboard CALIPSO (Lacour et al, 2017) or CloudSat (Stephens et al, 2018) are limited to latitudes below 82 • N because of the satellite flight path (Winker et al, 2009). To respond to the need for more observations in the Arctic and compensate the lack of satellite observations at the highest latitudes, multiple buoys have been deployed in the Arctic Ocean between 2014 and 2019 as part of the IAOOS (Ice Atmosphere arctic Ocean Operating System) project (Mariage et al, 2017;Maillard et al, 2021). Buoys have also limitations, e.g.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Radiative fluxes in the High Arctic region derived from ground-based lidar measurements onboard drifting buoys

Loyer¹,

Raut²,

Biagio

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract. The Arctic is facing drastic climate changes that are not correctly represented by state-of-the-art models because of complex feedbacks between radiation, clouds and sea-ice surfaces. A better understanding of the surface energy budget requires radiative measurements that are limited in time and space in the High Arctic (> 80° N) and mostly obtained through specific expeditions. Six years of lidar observations onboard buoys drifting in the Arctic Ocean above 83° N have been carried out as part of the IAOOS (Ice Atmosphere arctic Ocean Operating System) project. The objective of this study is to investigate the possibility to extent the IAOOS dataset to provide estimates of the shortwave (SW) and longwave (LW) surface irradiances from lidar measurements on drifting buoys. Our approach relies on the use of the STREAMER radiative transfer model to estimate the downwelling SW scattered radiances from the background noise measured by lidar. Those radiances are then used to derive estimates of the cloud optical depths. In turn, the knowledge of the cloud optical depth enables to estimate the SW and LW (using additional IAOOS measured information) downwelling irradiances at the surface. The method was applied to the IAOOS buoy measurements in spring 2015, and retrieved cloud optical depths were compared to those derived from radiative irradiances measured during the N-ICE (Norwegian Young Sea Ice Experiment) campaign at the meteorological station, in the vicinity of the drifting buoys. Retrieved and measured SW and LW irradiances were then compared. Results showed overall good agreement. Cloud optical depths were estimated with a rather large dispersion of about 47 %. LW irradiances showed a fairly small dispersion (within 5 W m−2), with a corrigible residual bias (3 W m−2). The estimated uncertainty of the SW irradiances was 4 %. But, as for the cloud optical depth, the SW irradiances showed the occurrence of a few outliers, that may be due to a short lidar sequence acquisition time (no more than four times 10 mn per day), possibly not long enough to smooth out cloud heterogeneity. The net SW and LW irradiances are retrieved within 13 W m−2.

show abstract

Section: Comparison Of the Values Of The Cloud Optical Depth From The Two Methodsmentioning

confidence: 99%

Section: Iaoos Lidar Data Aboard Buoysmentioning

confidence: 99%

Section: Determination Of the Cloud Optical Depth From The Iaoos Lidar : τ Iaoosmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Radiative fluxes in the High Arctic region derived from ground-based lidar measurements onboard drifting buoys

Loyer¹,

Raut²,

Biagio

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Cloud‐sea ice interactions is exciting to study due to their complexity but still poorly understood. Therefore, it has been the focus of several recent studies (e.g., Kay & Gettelman, 2009; Li et al., 2023; Maillard et al., 2021; Morrison et al., 2018; Monroe et al., 2021; Taylor & Monroe, 2023; Taylor et al., 2015; Yu et al., 2019). Air‐sea coupling during non‐summer season promotes the formation of low‐level liquid clouds above open water in response to sea ice loss (Kay & Gettelman, 2009).…”

Section: Introductionmentioning

confidence: 99%

Surface Cloud Warming Increases as Late Fall Arctic Sea Ice Cover Decreases

Arouf,

Chepfer,

Kay

et al. 2024

Geophysical Research Letters

View full text Add to dashboard Cite

During the Arctic night, clouds regulate surface energy budgets through longwave warming alone. During fall, any increase in low‐level clouds will increase surface cloud warming and could potentially delay sea ice formation. While an increase in clouds due to fall sea ice loss has been observed, quantifying the surface warming is observationally challenging. Here, we use a new observational data set of surface cloud warming at instantaneous 330 m × 90 m spatial resolution. By instantaneously co‐locating surface cloud warming and sea ice observations in regions where sea ice varies, we find October large surface cloud warming values (>80 W m−2) are much more frequent (∼+50%) over open water than over sea ice. Notably, in November large surface cloud warming values (>80 W m−2) occur more frequently (∼+200%) over open water than over sea ice. These results suggest more surface warming caused by low‐level opaque clouds in the future as open water persists later into the fall.

show abstract

Modulation of Boundary-Layer Stability and the Surface Energy Budget by a Local Flow in Central Alaska

Maillard

Ravetta

Raut

et al. 2022

Boundary-Layer Meteorol

View full text Add to dashboard Cite

The pre-ALPACA (Alaskan Layered Pollution And Chemical Analysis) 2019 winter campaign took place in Fairbanks, Alaska, in November–December 2019. One objective of the campaign was to study the life-cycle of surface-based temperature inversions and the associated surface energy budget changes. Several instruments, including a 4-component radiometer and sonic anemometer were deployed in the open, snow-covered University of Alaska Fairbanks (UAF) Campus Agricultural Field. A local flow from a connecting valley occurs at this site. This flow is characterized by locally elevated wind speeds (greater than 3 m s$$^{-1}$$ - 1 ) under clear-sky conditions and a north-westerly direction. It is notably different to the wind observed at the airport more than 3.5 km to the south-west. The surface energy budget at the UAF Field site exhibits two preferential modes. In the first mode, turbulent sensible heat and net longwave fluxes are close to 0 W m$$^{-2}$$ - 2 , linked to the presence of clouds and generally low winds. In the second, the net longwave flux is around − 50 W m$$^{-2}$$ - 2 and the turbulent sensible heat flux is around 15 W m$$^{-2}$$ - 2 , linked to clear skies and elevated wind speeds. The development of surface-based temperature inversions at the field is hindered compared to the airport because the local flow sustains vertical mixing. In this second mode the residual of the surface energy budget is large, possibly due to horizontal temperature advection.

show abstract

Characterisation and surface radiative impact of Arctic low clouds from the IAOOS field experiment

Cited by 5 publications

References 56 publications

Radiative fluxes in the High Arctic region derived from ground-based lidar measurements onboard drifting buoys

Radiative fluxes in the High Arctic region derived from ground-based lidar measurements onboard drifting buoys

Surface Cloud Warming Increases as Late Fall Arctic Sea Ice Cover Decreases

Modulation of Boundary-Layer Stability and the Surface Energy Budget by a Local Flow in Central Alaska

Contact Info

Product

Resources

About