scholarly journals Effects of sea surface winds on marine aerosols characteristics and impacts on longwave radiative forcing over the Arabian Sea

2008 ◽  
Vol 8 (4) ◽  
pp. 15855-15899 ◽  
Author(s):  
Vijayakumar S. Nair ◽  
S. Suresh Babu ◽  
S. K. Satheesh ◽  
K. Krishna Moorthy
Keyword(s):  
Wind Speed ◽  
Radiative Forcing ◽  
Arabian Sea ◽  
Near Infrared ◽  
Surface Wind ◽  
Sea Surface ◽  
Surface Winds ◽  
Near Surface ◽  
Sea Surface Winds ◽  
Direct Radiative Forcing

Abstract. Collocated measurements of spectral aerosol optical depths (AODs), total and BC mass concentrations, and number size distributions of near surface aerosols, along with sea surface winds, made onboard a scientific cruise over southeastern Arabian Sea, are used to delineate the effects of changes in the wind speed on aerosol properties and its implication on the shortwave and longwave radiative forcing. The results indicated that an increase in the sea-surface wind speed from calm to moderate (<1 to 8 m s−1) values results in a selective increase of the particle concentrations in the size range 0.5 to 5 μm, leading to significant changes in the size distribution, increase in the mass concentration, decrease in the BC mass fraction, a remarkable increase in AODs in the near infrared and a flattening of the AOD spectrum. The consequent increase in the longwave direct radiative forcing almost entirely offsets the corresponding increase in the short wave direct radiative forcing (or even overcompensates) at the top of the atmosphere; while the surface forcing is offset by about 50%.

scholarly journals Arabian sea surface winds and ocean transports determined from ERS-1 scatterometer

1998 ◽  
Vol 103 (C4) ◽  
pp. 7799-7805 ◽  
Author(s):  
David Halpern ◽  
Michael H. Freilich ◽  
Robert A. Weller
Keyword(s):  
Arabian Sea ◽  
Sea Surface ◽  
Surface Winds ◽  
Sea Surface Winds

scholarly journals Parametric Estimation of the Stochastic Dynamics of Sea Surface Winds

2014 ◽  
Vol 71 (9) ◽  
pp. 3465-3483 ◽  
Author(s):  
William F. Thompson ◽  
Adam H. Monahan ◽  
Daan Crommelin
Keyword(s):  
Time Series ◽  
Stochastic Dynamics ◽  
Surface Wind ◽  
Estimation Procedure ◽  
Parametric Estimation ◽  
Sea Surface ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Surface Winds ◽  
Sea Surface Winds

Abstract In this study, the parameters of a stochastic–dynamical model of sea surface winds are estimated from long time series of sea surface wind observational data. The model was introduced by A. H. Monahan, who developed an idealized model from a highly simplified representation of the momentum budget of a surface atmospheric layer of fixed depth. Such estimation of model parameters is challenging, in particular for a multivariate model with nonlinear terms as is considered here. The authors use a method developed recently by Crommelin and Vanden-Eijnden, which approaches the estimation problem variationally, finding the spectrally “best fit” stochastic differential equation to a time series of observations. While the estimation procedure assumes forcing that is white in time, observed time series are generally better approximated as forced by red noise. Using a red-noise-forced linear system, the authors first show that the estimation procedure can still be used to estimate model parameters. Because the assumption of white noise is violated, these estimates lead to model autocorrelation functions that differ from the observed time series. Application of the estimation procedure to the wind data is further complicated by the fact that the boundary layer model is inconsistent with certain observed features of the wind. When these mismatches between the model and observations are accounted for, the estimation procedure generally results in parameter estimates consistent with the climatological features of the associated meteorological fields. Important exceptions to this result are the layer thickness and layer-top eddy diffusivity, which are poorly estimated where the vector winds are close to Gaussian.

scholarly journals Temporal Filtering Enhances the Skewness of Sea Surface Winds

2018 ◽  
Vol 31 (14) ◽  
pp. 5695-5706 ◽  
Author(s):  
Adam H. Monahan
Keyword(s):  
Time Scales ◽  
Surface Wind ◽  
Cutoff Frequency ◽  
Sea Surface ◽  
Generic Model ◽  
Wind Component ◽  
Surface Winds ◽  
Near Surface ◽  
Non Gaussian ◽  
Equatorial Band

The component of the sea surface wind in the along-mean wind direction is known to display pronounced skewness at many locations over the ocean. A recent study by Proistosescu et al. found that the skewness of daily 850-hPa air temperature measured by radiosondes is typically reduced by bandpass filtering. This behavior was also shown to be characteristic of correlated additive–multiplicative (CAM) noise, which has been proposed as a generic model for non-Gaussian variability in the atmosphere and ocean. The present study shows that if the cutoff frequency is not too low, the skewness of the along-mean wind component is enhanced by low-pass filtering, particularly in the equatorial band and in the midlatitude storm tracks. The filter time scale beyond which skewness is systematically reduced by filtering is of the daily to synoptic scale, except in a narrow equatorial band where it is of subseasonal to seasonal time scales. This behavior is reproduced in an idealized stochastic model of the near-surface winds, in which key parameters are the characteristic time scales of the nonlinear dynamics and of the noise. These results point toward more general approaches for assessing the relative importance of multiplicative noise or dynamical nonlinearities in producing non-Gaussian structure in atmospheric and oceanic fields.

scholarly journals Can We See the Wind? Statistical Downscaling of Historical Sea Surface Winds in the Subarctic Northeast Pacific

2012 ◽  
Vol 25 (5) ◽  
pp. 1511-1528 ◽  
Author(s):  
Adam H. Monahan
Keyword(s):  
Wind Speed ◽  
Sea Surface ◽  
Wind Component ◽  
Surface Winds ◽  
Northeast Pacific ◽  
Predictive Skill ◽  
Sea Surface Winds ◽  
Mean Wind Speed ◽  
Mean Vector ◽  
Vector Wind

The statistical predictability of wintertime (December–February) monthly-mean sea surface winds (both vector wind components and wind speed) in the subarctic northeast Pacific off the west coast of Canada is considered, in the context of surface wind downscaling. Predictor fields (zonal wind, meridional wind, wind speed, and temperature) are shown to carry predictive information on the large scales (both vertical and horizontal) that are well simulated by numerical weather prediction and global climate models. It is found that, in general, the monthly mean vector wind components are more predictable by indices of the large-scale flow than by the monthly mean wind speed, with no systematic vertical variation in predictive skill for either across the depth of the troposphere. The difference in predictive skill between monthly-mean vector wind components and wind speed is interpreted in terms of an idealized model of the vector wind speed probability distribution, which demonstrates that for the conditions in the subarctic northeast Pacific, the sensitivity of mean wind speed to the standard deviations of vector wind component fluctuations (which are not well predicted) is greater than that to the mean vector wind components. It is demonstrated that this sensitivity is state dependent, and it is suggested that monthly mean wind speeds may be inherently more predictable in regions where the sensitivity to the vector wind component means is greater than that to the standard deviations. It is also demonstrated that daily wind fluctuations (both vector wind and wind speed) are generally more predictable than monthly-mean variability, and that monthly averages of the predicted daily winds generally represent the monthly-mean surface winds better than the predictions directly from monthly mean predictors.

scholarly journals Impact of Satellite Data Assimilation in Atmospheric Reanalysis on the Marine Wind and Wave Climate

2016 ◽  
Vol 29 (17) ◽  
pp. 6351-6361 ◽  
Author(s):  
Wataru Sasaki
Keyword(s):  
Wind Speed ◽  
Data Assimilation ◽  
Southern Ocean ◽  
Satellite Data ◽  
North Pacific ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Surface Winds ◽  
Near Surface ◽  
The Impact

Abstract This study investigated the impact of assimilating satellite data into atmospheric reanalyses on trends in ocean surface winds and waves. Two experiments were performed using a numerical wave model forced by near-surface winds: one derived from the Japanese 55-year Reanalysis (JRA-55; experiment A) and the other derived from JRA-55 using assimilated conventional observations only (JRA-55C; experiment B). The results showed that the satellite data assimilation reduced upward trends of the annual mean of wave energy flux (WEF) in the midlatitude North Pacific and southern ocean (30°–60°S), south of Australia, from 1959 to 2012. It was also found that the assimilation of scatterometer winds reduced the near-surface wind speed in the midlatitude North Pacific after the mid-1990s, which resulted in the reduced trend in WEF from 1959 to 2012. By contrast, assimilation of the satellite radiances for 1973–94 increased near-surface wind speed in the southern ocean, south of Australia, whereas the assimilation of the scatterometer winds after the mid-1990s reduced wind speed. The latter led to the reduced trend in WEF south of Australia from 1959 to 2012.

scholarly journals The Impact of Atmosphere–Ocean–Wave Coupling on the Near-Surface Wind Speed in Forecasts of Extratropical Cyclones

2021 ◽  
Author(s):  
Emanuele S. Gentile ◽  
Suzanne L. Gray ◽  
Janet F. Barlow ◽  
Huw W. Lewis ◽  
John M. Edwards
Keyword(s):  
Wind Speed ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Sea Surface ◽  
Model Systems ◽  
Sea State ◽  
Near Surface ◽  
Extreme Surface ◽  
Wind Speeds ◽  
The Impact

AbstractAccurate modelling of air–sea surface exchanges is crucial for reliable extreme surface wind-speed forecasts. While atmosphere-only weather forecast models represent ocean and wave effects through sea-state independent parametrizations, coupled multi-model systems capture sea-state dynamics by integrating feedbacks between the atmosphere, ocean and wave model components. Here, we investigate the sensitivity of extreme surface wind speeds to air–sea exchanges at the kilometre scale using coupled and uncoupled configurations of the Met Office’s UK Regional Coupled Environmental Prediction system. The case period includes the passage of extra-tropical cyclones Helen, Ali, and Bronagh, which brought maximum gusts of 36 m s$$^{-1}$$ - 1 over the UK. Compared with the atmosphere-only results, coupling to the ocean decreases the domain-average sea-surface temperature by up to 0.5 K. Inclusion of coupling to waves reduce the 98th percentile 10-m wind speed by up to 2 m s$$^{-1}$$ - 1 as young, growing wind waves reduce the wind speed by increasing the sea-surface aerodynamic roughness. Impacts on gusts are more modest, with local reductions of up to 1 m s$$^{-1}$$ - 1 , due to enhanced boundary-layer turbulence which partially offsets air–sea momentum transfer. Using a new drag parametrization based on the Coupled Ocean–Atmosphere Response Experiment 4.0 parametrization, with a cap on the neutral drag coefficient and reduction for wind speeds exceeding 27 m s$$^{-1}$$ - 1 , the atmosphere-only model achieves equivalent impacts on 10-m wind speeds and gusts as from coupling to waves. Overall, the new drag parametrization achieves the same 20% improvement in forecast 10-m wind-speed skill as coupling to waves, with the advantage of saving the computational cost of the ocean and wave models.

Evaluation of reanalysis and satellite-based sea surface winds using in situ measurements from Chinese Antarctic Expeditions

2013 ◽  
Vol 24 (3) ◽  
pp. 147
Author(s):  
Ming LI ◽  
Qinghua YANG ◽  
Jiechen ZHAO ◽  
Lin ZHANG ◽  
Chunhua LI ◽  
...  
Keyword(s):  
In Situ Measurements ◽  
Sea Surface ◽  
Surface Winds ◽  
Sea Surface Winds
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