Derivation of turbulent surface fluxes — An iterative flux-profile method allowing arbitrary observing heights

Launiainen, Jouko; Vihma, Timo

doi:10.1016/0266-9838(90)90021-w

Cited by 91 publications

(83 citation statements)

References 42 publications

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“…The form for CD follows the results given by Smith (1980) and Large and Pond (1980). Values for the Stanton and Dalton numbers were based on the ratio C HE /C D from Launiainen (1983); the C HE value above agrees well with that for a comprehensive oceanic data set published by Large and Pond (1982). For the universal functions, the so-called Businger-Dyer-type form (Businger et al, 1971;Dyer and Hicks, 1970;Dyer, 1974;Högström, 1988) was used for the unstable region and Holtslag and de Bruin's (1988) form was adopted for the stable stratification.…”

Section: Data a Observationssupporting

confidence: 64%

“…That the observations of wind speed, temperature and moisture were not made on the Polarstern at the same height level, leads to another internal iteration procedure. A detailed methodological description of the solution procedure, and the basis for the physical choices of the roughness lengths and universal functions including the necessary algorithms are given in Launiainen and Vihma (1990). For reference, the formulae used yield, for a wind speed of 8 m s" 1 and neutral stratification, the bulk transfer coefficients C D (10) = 1.32 χ 10" 3 and C HE (l0) = 1.15 χ 10~3 referred to a 10-m height.…”

Section: Data a Observationsmentioning

confidence: 99%

“…An air mass with temperatures, moisture and velocities corresponding to those upstream of the front was allowed to be modified by setting the water temperature equal to that observed downwind of the front. The initial temperature profile for the model Air-Sea Interaction in Areas of Thermal Marine Fronts / 605 was determined for the lowest 100 m using the observations and a flux-profile scheme (Launiainen and Vihma, 1990). The results were rather insensitive to the initial profile above 100 m in short-period simulations.…”

Section: Model Simulations Of Air-mass Modificationmentioning

confidence: 99%

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On the air‐sea interaction in areas of thermal marine fronts in the Greenland Sea

1991

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Section: Data a Observationssupporting

confidence: 64%

Section: Data a Observationsmentioning

confidence: 99%

Section: Model Simulations Of Air-mass Modificationmentioning

confidence: 99%

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On the air‐sea interaction in areas of thermal marine fronts in the Greenland Sea

1991

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“…Launiainen and Vihma, 1990;Jordan et al, 1999;Andreas et al, 2004;Reijmer et al, 2004), it has not been fully validated over sea ice. Andreas (2002) tested (6.6) with data from the literature, but these data were collected over snowcovered ground and glaciers.…”

Section: Finding the Scalar Roughness Lengthsmentioning

confidence: 99%

Parametrizing turbulent exchange over summer sea ice and the marginal ice zone

Andreas

Horst

Grachev

et al. 2010

Q.J.R. Meteorol. Soc.

157

188

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The surface of the Arctic Ocean in summer is a mix of sea ice and water in both leads and melt ponds. Here we use data collected at multiple sites during the year-long experiment to study the Surface Heat Budget of the Arctic Ocean (SHEBA) to develop a bulk turbulent flux algorithm for predicting the surface fluxes of momentum and sensible and latent heat over the Arctic Ocean during summer from readily measured or modelled quantities. The distinctive aerodynamic feature of summer sea ice is that the leads and melt ponds create vertical ice faces that the wind can push against; momentum transfer to the surface is thus enhanced through form drag. In effect, summer sea ice behaves aerodynamically like the marginal ice zone, which is another surface that consists of sea ice and water. In our bulk flux algorithm, we therefore combine our SHEBA measurements of the neutral-stability drag coefficient at a reference height of 10 m, C DN10 , with similar measurements from marginal ice zones that have been reported in the literature to create a unified parametrization for C DN10 for summer sea ice and for any marginal ice zone. This parametrization predicts C DN10 from a second-order polynomial in ice concentration. Our bulk flux algorithm also includes expressions for the roughness lengths for temperature and humidity, introduces new profile stratification corrections for stable stratification, and effectively eliminates the singularities that often occur in iterative flux algorithms for very light winds. In summary, this new algorithm seems capable of estimating the friction velocity u

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“…We refer to this estimation as MODIS2km. The turbulent fluxes of sensible and latent heat were calculated by an iterative bulk approach (Launiainen and Vihma, 1990) based on the MoninObukhov similarity theory. Thereby, the turbulent exchange coefficient C H is a function of stability and of the roughness length for momentum and for heat (Doms et al, 2011).…”

Section: Estimation Of Sea-ice Productionmentioning

confidence: 99%

Quantification of ice production in Laptev Sea polynyas and its sensitivity to thin-ice parameterizations in a regional climate model

et al. 2016

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Abstract. The quantification of sea-ice production in the Laptev Sea polynyas is important for the Arctic sea-ice budget and the heat loss to the atmosphere. We estimated the ice production for the winter season 2007/2008 (NovemberApril) based on simulations with the regional climate model COSMO-CLM at a horizontal resolution of 5 km and compared it to remote sensing estimates. A reference and five sensitivity simulations were performed with different assumptions on grid-scale and subgrid-scale ice thickness considered within polynyas, using a tile approach for fractional sea ice. In addition, the impact of heat loss on the atmospheric boundary layer was investigated.About 29.1 km 3 of total winter ice production was estimated for the reference simulation, which varies by up to +124 % depending on the thin-ice assumptions. For the most realistic assumptions based on remote sensing of ice thickness the ice production increases by +39 %. The use of the tile approach enlarges the area and enhances the magnitude of the heat loss from polynyas up to +110 % if subgrid-scale open water is assumed and by +20 % for realistic assumptions. This enhanced heat loss causes in turn higher ice production rates and stronger impact on the atmospheric boundary layer structure over the polynyas. The study shows that ice production is highly sensitive to the thin-ice parameterizations for fractional sea-ice cover. In summary, realistic ice production estimates could be retrieved from our simulations. Neglecting subgrid-scale energy fluxes might considerably underestimate the ice production in coastal polynyas, such as in the Laptev Sea, with possible consequences on the Arctic sea-ice budget.

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Derivation of turbulent surface fluxes — An iterative flux-profile method allowing arbitrary observing heights

Cited by 91 publications

References 42 publications

On the air‐sea interaction in areas of thermal marine fronts in the Greenland Sea

On the air‐sea interaction in areas of thermal marine fronts in the Greenland Sea

Parametrizing turbulent exchange over summer sea ice and the marginal ice zone

Quantification of ice production in Laptev Sea polynyas and its sensitivity to thin-ice parameterizations in a regional climate model

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