Introducing surface waves in a coupled wave‐atmosphere regional climate model: Impact on atmospheric mixing length

Rutgersson, Anna; Nilsson, Erik; Kumar, Rajesh

doi:10.1029/2012jc007940

Cited by 25 publications

(33 citation statements)

References 31 publications

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“…The important wave data for the RCA model is obtained by a two-dimensional ocean wave spectrum and may involve parameters like wave height and period as well as roughness length. The applied coupled RCA4-WAM setup is similar to that described by Wu et al (2015) and Rutgersson et al (2012) only exchanging the roughness length.…”

Section: Rca4-wammentioning

confidence: 99%

Characteristics of convective snow bands along the Swedish east coast

Jeworrek

Dieterich

et al. 2017

Earth Syst. Dynam.

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Abstract. Convective snow bands develop in response to a cold air outbreak from the continent or the frozen sea over the open water surface of lakes or seas. The comparatively warm water body triggers shallow convection due to increased heat and moisture fluxes. Strong winds can align with this convection into wind-parallel cloud bands, which appear stationary as the wind direction remains consistent for the time period of the snow band event, delivering enduring snow precipitation at the approaching coast. The statistical analysis of a dataset from an 11-year high-resolution atmospheric regional climate model (RCA4) indicated 4 to 7 days a year of moderate to highly favourable conditions for the development of convective snow bands in the Baltic Sea region. The heaviest and most frequent lake effect snow was affecting the regions of Gävle and Västervik (along the Swedish east coast) as well as Gdansk (along the Polish coast). However, the hourly precipitation rate is often higher in Gävle than in the Västervik region. Two case studies comparing five different RCA4 model setups have shown that the Rossby Centre atmospheric regional climate model RCA4 provides a superior representation of the sea surface with more accurate sea surface temperature (SST) values when coupled to the ice-ocean model NEMO as opposed to the forcing by the ERA-40 reanalysis data. The refinement of the resolution of the atmospheric model component leads, especially in the horizontal direction, to significant improvement in the representation of the mesoscale circulation process as well as the local precipitation rate and area by the model.

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Section: Rca4-wammentioning

confidence: 99%

Characteristics of convective snow bands along the Swedish east coast

Jeworrek

Dieterich

et al. 2017

Earth Syst. Dynam.

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“…The coupled RCAÁWAM system is similar to that described by Rutgersson et al (2010Rutgersson et al ( , 2012.…”

Section: Coupled Model System and Measurementsmentioning

confidence: 79%

The impact of waves and sea spray on modelling storm track and development

Wu¹,

Rutgersson²,

Larsén³

2015

Tellus A: Dynamic Meteorology and Oceanography

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A B S T R A C TIn high wind speed conditions, sea spray generated by intensely breaking waves greatly influences the wind stress and heat fluxes. Measurements indicate that the drag coefficient decreases at high wind speeds. The sea spray generation function (SSGF), an important term of wind stress parameterisation at high wind speeds, is usually treated as a function of wind speed/friction velocity. In this study, we introduce a wave-state-dependent SSGF and wave-age-dependent Charnock number into a high wind speedÁwind stress parameterisation. The newly proposed wind stress parameterisation and sea spray heat flux parameterisation were applied to an atmosphereÁwave coupled model to study the mid-latitude storm development of six storm cases. Compared with measurements from the FINO1 platform in the North Sea, the new wind stress parameterisation can reduce wind speed simulation errors in the high wind speed range. Considering only sea spray impact on wind stress (and not on heat fluxes) will intensify the storms (in terms of minimum sea level pressure and maximum wind speed), but has little effect on the storm tracks. Considering the impact of sea spray on heat fluxes only (not on wind stress) can improve the model performance regarding air temperature, but it has little effect on the storm intensity and storm track performance. If the impact of sea spray on both the wind stress and heat fluxes is taken into account, the model performs best in all experiments for minimum sea level pressure, maximum wind speed and air temperature.

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“…Accordingly, swell waves can influence wind stress, wind speed profiles, atmospheric mixing, heat fluxes, etc. [ Veron et al ., ; Semedo et al ., ; Carlsson et al ., ; Högström et al ., ; Smedman et al ., ; Rutgersson et al ., ].…”

Section: Introductionmentioning

confidence: 99%

“…[], Rutgersson et al . [] introduced the swell influence into an E – l turbulence scheme ( E is the turbulent kinetic energy and l is the mixing length). In the modified E – l turbulence scheme, a wave contribution parameter is added to the parameterization of the atmospheric mixing length to introduce the enhanced mixing due to swell waves.…”

Section: Introductionmentioning

confidence: 99%

Swell impact on wind stress and atmospheric mixing in a regional coupled atmosphere‐wave model

Rutgersson

Larsén³

2016

JGR Oceans

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Over the ocean, the atmospheric turbulence can be significantly affected by swell waves.Change in the atmospheric turbulence affects the wind stress and atmospheric mixing over swell waves. In this study, the influence of swell on atmospheric mixing and wind stress is introduced into an atmospherewave-coupled regional climate model, separately and combined. The swell influence on atmospheric mixing is introduced into the atmospheric mixing length formula by adding a swell-induced contribution to the mixing. The swell influence on the wind stress under wind-following swell, moderate-range wind, and nearneutral and unstable stratification conditions is introduced by changing the roughness length. Five year simulation results indicate that adding the swell influence on atmospheric mixing has limited influence, only slightly increasing the near-surface wind speed; in contrast, adding the swell influence on wind stress reduces the near-surface wind speed. Introducing the wave influence roughness length has a larger influence than does adding the swell influence on mixing. Compared with measurements, adding the swell influence on both atmospheric mixing and wind stress gives the best model performance for the wind speed. The influence varies with wave characteristics for different sea basins. Swell occurs infrequently in the studied area, and one could expect more influence in high-swell-frequency areas (i.e., low-latitude ocean). We conclude that the influence of swell on atmospheric mixing and wind stress should be considered when developing climate models.

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Introducing surface waves in a coupled wave‐atmosphere regional climate model: Impact on atmospheric mixing length

Cited by 25 publications

References 31 publications

Characteristics of convective snow bands along the Swedish east coast

Characteristics of convective snow bands along the Swedish east coast

The impact of waves and sea spray on modelling storm track and development

Swell impact on wind stress and atmospheric mixing in a regional coupled atmosphere‐wave model

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