Covariability of Surface Wind and Stress Responses to Sea Surface Temperature Fronts

2012 ◽  
Vol 25 (17) ◽  
pp. 5916-5942 ◽  
Author(s):  
Larry W. O’Neill ◽  
Dudley B. Chelton ◽  
Steven K. Esbensen
Keyword(s):  
Stress Response ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Stress Responses ◽  
Large Scale ◽  
Surface Wind ◽  
Nonlinear Function ◽  
Algebraic Manipulation ◽  
Sea Surface ◽  
Earth Observing System

Abstract The responses of surface wind and wind stress to spatial variations of sea surface temperature (SST) are investigated using satellite observations of the surface wind from the Quick Scatterometer (QuikSCAT) and SST from the Advanced Microwave Scanning Radiometer on the Advanced Microwave Scanning Radiometer for Earth Observing System (EOS) (AMSR-E) Aqua satellite. This analysis considers the 7-yr period June 2002–May 2009 during which both instruments were operating. Attention is focused in the Kuroshio, North and South Atlantic, and Agulhas Return Current regions. Since scatterometer wind stresses are computed solely as a nonlinear function of the scatterometer-derived 10-m equivalent neutral wind speed (ENW), qualitatively similar responses of the stress and ENW to SST are expected. However, the responses are found to be more complicated on the oceanic mesoscale. First, the stress and ENW are both approximately linearly related to SST, despite a nonlinear relationship between them. Second, the stress response to SST is 2 to 5 times stronger during winter compared to summer, while the ENW response to SST exhibits relatively little seasonal variability. Finally, the stress response to SST can be strong in regions where the ENW response is weak and vice versa. A straightforward algebraic manipulation shows that the stress perturbations are directly proportional to the ENW perturbations multiplied by a nonlinear function of the ambient large-scale ENW. This proportionality explains why both the stress and ENW depend linearly on the mesoscale SST perturbations, while the dependence of the stress perturbations on the ambient large-scale ENW explains both the seasonal pulsing and the geographic variability of the stress response to SST compared with the less variable ENW response.

scholarly journals Understanding the 2017 warm event in North Tropical Atlantic

2020 ◽  
Author(s):  
Ana Trindade ◽  
Marta Matín-Rey ◽  
Marcos Portabella ◽  
Eleftheria Exarchou ◽  
Pablo Ortega ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Large Scale ◽  
Temporal Frequency ◽  
Surface Wind ◽  
Sea Surface ◽  
Earth Model ◽  
Tropical Atlantic ◽  
Ocean Wind ◽  
North Tropical Atlantic

<p>The Atlantic Ocean has suffered tremendous warming during recent decades as a consequence of anthropogenic forcing, modulated by the natural low frequency variability. Special attention should be paid to the high temporal frequency of warm interannual events in the North Tropical Atlantic (NTA) since the early 2000s, resulting in the most intense hurricane seasons on record (Hallam et al., 2017; Lim et al., 2018; Murakami et al., 2018; Klotzbach et al., 2018; Camp et al., 2018). Moreover, NTA sea surface temperature anomalies during boreal spring have been suggested as a potential precursor to the Equatorial Mode (Foltz and McPhaden, 2010ab; Burmeister et al., 2016; Martín-Rey and Lazar, 2019; Martín-Rey et al., 2019).<strong> </strong></p><p>This study aims to investigate the development of the 2017 NTA spring-summer warming event, which was the strongest of the last decade, as well as the importance of an accurate ocean forcingin the simulation of this event. For such purpose, a set of four simulations using distinct ocean wind forcing products, namely from the EC-Earth model, ERA-Interim (ERAi) reanalysis and a new ERAi-corrected ocean wind product (ERAstar), have been performed and analysed.The latter consists of average geolocated scatterometer-based corrections applied to ERAi output (Trindade et al., 2019).In this sense, ERAstar includes some of the physical processes missing or misrepresented by ERA-i, and corrects for large-scale NWP parameterization and dynamical errors.</p><p>The air-sea processes underlying the onset and development of the warm 2017 NTA event and the wave activity present in the equatorial Atlantic will be explored to determine their possible connection with the equatorial sea surface temperature variability. Furthermore, the comparison between the different experiments allows us to validate the new surface wind dataset and evaluate the importance of accurate, high-resolution ocean forcing in the representation of tropical Atlantic variability.</p>

scholarly journals Sea surface wind perturbations over the Kashevarov Bank of the Okhotsk Sea: a satellite study

2011 ◽  
Vol 29 (2) ◽  
pp. 393-399
Author(s):  
T. I. Tarkhova ◽  
M. S. Permyakov ◽  
E. Yu. Potalova ◽  
V. I. Semykin
Keyword(s):  
Wind Speed ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Surface Wind ◽  
Sea Surface ◽  
Cold Spot ◽  
Okhotsk Sea ◽  
Autumn Period ◽  
Sea Surface Wind ◽  
Spot Center

Abstract. Sea surface wind perturbations over sea surface temperature (SST) cold anomalies over the Kashevarov Bank (KB) of the Okhotsk Sea are analyzed using satellite (AMSR-E and QuikSCAT) data during the summer-autumn period of 2006–2009. It is shown, that frequency of cases of wind speed decreasing over a cold spot in August–September reaches up to 67%. In the cold spot center SST cold anomalies reached 10.5 °C and wind speed lowered down to ~7 m s−1 relative its value on the periphery. The wind difference between a periphery and a centre of the cold spot is proportional to SST difference with the correlations 0.5 for daily satellite passes data, 0.66 for 3-day mean data and 0.9 for monthly ones. For all types of data the coefficient of proportionality consists of ~0.3 m s−1 on 1 °C.

scholarly journals Statistical Downscaling Forecasts for Winter Monsoon Precipitation in Malaysia Using Multimodel Output Variables

2010 ◽  
Vol 23 (1) ◽  
pp. 17-27 ◽  
Author(s):  
Liew Juneng ◽  
Fredolin T. Tangang ◽  
Hongwen Kang ◽  
Woo-Jin Lee ◽  
Yap Kok Seng
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Winter Season ◽  
Peninsular Malaysia ◽  
Sea Surface ◽  
Asia Pacific ◽  
Forecast Data ◽  
Multimodel Ensembles

Abstract This paper compares the skills of four different forecasting approaches in predicting the 1-month lead time of the Malaysian winter season precipitation. Two of the approaches are based on statistical downscaling techniques of multimodel ensembles (MME). The third one is the ensemble of raw GCM forecast without any downscaling, whereas the fourth approach, which provides a baseline comparison, is a purely statistical forecast based solely on the preceding sea surface temperature anomaly. The first multimodel statistical downscaling method was developed by the Asia-Pacific Economic Cooperation (APEC) Climate Center (APCC) team, whereas the second is based on the canonical correlation analysis (CCA) technique using the same predictor variables. For the multimodel downscaling ensemble, eight variables from seven operational GCMs are used as predictors with the hindcast forecast data spanning a period of 21 yr from 1983/84 to 2003/04. The raw GCM forecast ensemble tends to have higher skills than the baseline skills of the purely statistical forecast that relates the dominant modes of observed sea surface temperature variability to precipitation. However, the downscaled MME forecasts have higher skills than the raw GCM products. In particular, the model developed by APCC showed significant improvement over the peninsular Malaysia region. This is attributed to the model’s ability to capture regional and large-scale predictor signatures from which the additional skills originated. Overall, the results showed that the appropriate downscaling technique and ensemble of various GCM forecasts could result in some skill enhancement, particularly over peninsular Malaysia, where other models tend to have lower or no skills.

scholarly journals Adjusting for Desert-Dust-Related Biases in a Climate Data Record of Sea Surface Temperature

2020 ◽  
Vol 12 (16) ◽  
pp. 2554
Author(s):  
Christopher J. Merchant ◽  
Owen Embury
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Large Scale ◽  
Dust Aerosol ◽  
Sea Surface ◽  
Climate Data ◽  
Desert Dust ◽  
Data Record ◽  
Climate Data Record

Atmospheric desert-dust aerosol, primarily from north Africa, causes negative biases in remotely sensed climate data records of sea surface temperature (SST). Here, large-scale bias adjustments are deduced and applied to the v2 climate data record of SST from the European Space Agency Climate Change Initiative (CCI). Unlike SST from infrared sensors, SST measured in situ is not prone to desert-dust bias. An in-situ-based SST analysis is combined with column dust mass from the Modern-Era Retrospective analysis for Research and Applications, Version 2 to deduce a monthly, large-scale adjustment to CCI analysis SSTs. Having reduced the dust-related biases, a further correction for some periods of anomalous satellite calibration is also derived. The corrections will increase the usability of the v2 CCI SST record for oceanographic and climate applications, such as understanding the role of Arabian Sea SSTs in the Indian monsoon. The corrections will also pave the way for a v3 climate data record with improved error characteristics with respect to atmospheric dust aerosol.

Sign in / Sign up

Export Citation Format

Share Document