Evaluation of a Roughness Length Model and Sea Surface Properties with Data from the Baltic Sea

Carlsson, Björn; Papadimitrakis, Y.; Rutgersson, Anna

doi:10.1175/2010jpo4340.1

Cited by 5 publications

(5 citation statements)

References 66 publications

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“…Moreover, estimating the directional difference requires extra information (the wind direction) to be obtained from the wind field or from a spectral partitioning of the spectrum. Such partitioning would also be required for other wave parameters, such as the energy ratio of swell and local wind sea [ Carlsson et al , 2010]. Both partitioning and wind information can be avoided by using the wave directional spreading.…”

Section: Resultsmentioning

confidence: 99%

Wind and waves in extreme hurricanes

Holthuijsen¹,

Powell²,

Pietrzak³

2012

J. Geophys. Res.

270

297

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[1] Waves breaking at the ocean surface are important to the dynamical, chemical and biological processes at the air-sea interface. The traditional view is that the white capping and aero-dynamical surface roughness increase with wind speed up to a limiting value. This view is fundamental to hurricane forecasting and climate research but it has never been verified at extreme winds. Here we show with observations that at high wind speeds white caps remain constant and at still higher wind speeds are joined, and increasingly dominated, by streaks of foam and spray. At surface wind speeds of $40 m/s the streaks merge into a white out, the roughness begins to decrease and a high-velocity surface jet begins to develop. The roughness reduces to virtually zero by $80 m/s wind speed, rendering the surface aero-dynamically extremely smooth in the most intense part of extreme (or major) hurricanes (wind speed > 50 m/s). A preliminary assessment shows that cross swell, dominant in large regions of hurricanes, allows the roughness under high wind conditions to increase considerably before it reduces to the same low values.

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Section: Resultsmentioning

confidence: 99%

Wind and waves in extreme hurricanes

Holthuijsen¹,

Powell²,

Pietrzak³

2012

J. Geophys. Res.

270

297

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show abstract

“…In this case, the model seems particularly sensitive to changes in the parameter c_sea, describing the surface area density of the waves over sea, used for the calculation of roughness length. The roughness of the sea surface steers the exchange of momentum, moisture and heat between ocean and atmosphere (Carlsson et al, 2010;Vickers and Mahrt, 2010). Thévenot et al (2016) demonstrated that an increase in sea surface roughness may generate higher momentum fluxes, impacting low-level atmospheric dynamics, particularly affecting wind speeds, and that a proper representation of the sea surface roughness may lead to a better localization of heavy precipitation.…”

Section: Model Behaviour For Different Subregionsmentioning

confidence: 99%

“…These regions are characterized by particularly stable stratified atmospheric conditions. For these, the model has already proved to be highly sensitive to tkhmin (Cerenzia et al, 2014;Buzzi et al, 2011), producing excessive mixing during periods with highly stable stratification and a consequent overestimation of temperatures. Basically, higher values of tkhmin produce exaggerated mixing, leading to more cloud formation, more similarly to observations, that otherwise the model is not able to reproduce.…”

Section: Model Behaviour For Different Subregionsmentioning

confidence: 99%

Exploring the parameter space of the COSMO-CLM v5.0 regional climate model for the Central Asia CORDEX domain

Russo

Sørland

Kirchner³

et al. 2020

Geosci. Model Dev.

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Abstract. The parameter uncertainty of a climate model represents the spectrum of the results obtained by perturbing its empirical and unconfined parameters used to represent subgrid-scale processes. In order to assess a model's reliability and to better understand its limitations and sensitivity to different physical processes, the spread of model parameters needs to be carefully investigated. This is particularly true for regional climate models (RCMs), whose performance is domain dependent. In this study, the parameter space of the Consortium for Small-scale Modeling CLimate Mode (COSMO-CLM) RCM is investigated for the Central Asia Coordinated Regional Climate Downscaling Experiment (CORDEX) domain, using a perturbed physics ensemble (PPE) obtained by performing 1-year simulations with different parameter values. The main goal is to characterize the parameter uncertainty of the model and to determine the most sensitive parameters for the region. Moreover, the presented experiments are used to study the effect of several parameters on the simulation of selected variables for subregions characterized by different climate conditions, assessing by which degree it is possible to improve model performance by properly selecting parameter inputs in each case. Finally, the paper explores the model parameter sensitivity over different domains, tackling the question of transferability of an RCM model setup to different regions of study. Results show that only a subset of model parameters present relevant changes in model performance for different parameter values. Importantly, for almost all parameter inputs, the model shows an opposite behaviour among different clusters and regions. This indicates that conducting a calibration of the model against observations to determine optimal parameter values for the Central Asia domain is particularly challenging: in this case, the use of objective calibration methods is highly necessary. Finally, the sensitivity of the model to parameter perturbation for Central Asia is different than the one observed for Europe, suggesting that an RCM should be retuned, and its parameter uncertainty properly investigated, when setting up model experiments for different domains of study.

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“…Roughness length is determined from c_sea and the roughness of the sea surface guides the exchange of momentum, moisture, and heat between ocean and atmosphere. Therefore, an increase in c_sea may induce higher magnitude momentum fluxes, modifying low-level atmospheric dynamics, specifically affecting wind speed and heavy precipitation [77,78] . Differential heating of the land surface causes regional uncertainties which stimulate moist convection by liberating latent heat.…”

Section: Physical Interpretation Of Parameter Sensitivitymentioning

confidence: 99%

Evaluation of COSMO-CLM Model Parameter Sensitivity in the Study of Extreme Events across the Eastern Region of India

Bal,

Kirchner

2024

j. of atmos. sci. res.

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The present study aims to identify the parameters from the Consortium for Small-scale Modelling in CLimate Mode (COSMO-CLM) regional climate model that strongly controls the prediction of extreme events over West Bengal and the adjoining areas observed between 2013 to 2018. Metrics, namely Performance Score (PS) screen out the most persuasive parameter on model output. Additionally, the Performance Index (PI) measure the reliability of the model and Skill Score (SS) establishes the model performance against the reference simulation leading to the optimization of the model for a given variable. In this study, parameter screening for four output variables such as 2m-temperature, surface latent heat flux, precipitation and cloud cover of COSMO-CLM is accomplished. For heat wave simulations, 2m-temperature and surface latent heat flux are explored whereas cloud cover and precipitation are examined for extreme rainfall events. A total of 25 adjustable parameters representing the following parameterization schemes: turbulence, land surface process, microphysics, convection, radiation and soil. Out of the six parameterization schemes, the scaling factor of the laminar boundary layer for heat (rlam_heat) and the ratio of laminar scaling factors for heat over sea and land (rat_sea) from the land surface process is sensitive to SLH, TP. The exponent to get the effective surface area (e_surf) from the land surface has a large impact on 2m-temperature. A few parameters from microphysics (cloud ice threshold for auto conversion), convection (mean entrainment rate for shallow convection) and radiation (parameter for computing the amount of cloud cover in saturated conditions) play a significant role in producing TP, and TCC fields. It is evident from the results that the parameter sensitivities on model performance depend on the choice of the meteorological field. Furthermore, in almost all input model parameters, the model performance reveals the opposite character in different domains for a given meteorological field.

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Evaluation of a Roughness Length Model and Sea Surface Properties with Data from the Baltic Sea

Cited by 5 publications

References 66 publications

Wind and waves in extreme hurricanes

Wind and waves in extreme hurricanes

Exploring the parameter space of the COSMO-CLM v5.0 regional climate model for the Central Asia CORDEX domain

Evaluation of COSMO-CLM Model Parameter Sensitivity in the Study of Extreme Events across the Eastern Region of India

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