Remote Sensing

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19 pages, 5572 KiB

Open AccessArticle

Air–Sea Interaction in the Central Mediterranean Sea: Assessment of Reanalysis and Satellite Observations

by Salvatore Marullo, Jaime Pitarch, Marco Bellacicco, Alcide Giorgio di Sarra, Daniela Meloni, Francesco Monteleone, Damiano Sferlazzo, Vincenzo Artale and Rosalia Santoleri

Remote Sens. 2021, 13(11), 2188; https://doi.org/10.3390/rs13112188 - 3 Jun 2021

Cited by 6 | Viewed by 3501

Abstract

Air–sea heat fluxes are essential climate variables, required for understanding air–sea interactions, local, regional and global climate, the hydrological cycle and atmospheric and oceanic circulation. In situ measurements of fluxes over the ocean are sparse and model reanalysis and satellite data can provide [...] Read more.

Air–sea heat fluxes are essential climate variables, required for understanding air–sea interactions, local, regional and global climate, the hydrological cycle and atmospheric and oceanic circulation. In situ measurements of fluxes over the ocean are sparse and model reanalysis and satellite data can provide estimates at different scales. The accuracy of such estimates is therefore essential to obtain a reliable description of the occurring phenomena and changes. In this work, air–sea radiative fluxes derived from the SEVIRI sensor onboard the MSG satellite and from ERA5 reanalysis have been compared to direct high quality measurements performed over a complete annual cycle at the ENEA oceanographic observatory, near the island of Lampedusa in the Central Mediterranean Sea. Our analysis reveals that satellite derived products overestimate in situ direct observations of the downwelling short-wave (bias of 6.1 W/m²) and longwave (bias of 6.6 W/m²) irradiances. ERA5 reanalysis data show a negligible positive bias (+1.0 W/m²) for the shortwave irradiance and a large negative bias (−17 W/m²) for the longwave irradiance with respect to in situ observations. ERA5 meteorological variables, which are needed to calculate the air–sea heat flux using bulk formulae, have been compared with in situ measurements made at the oceanographic observatory. The two meteorological datasets show a very good agreement, with some underestimate of the wind speed by ERA5 for high wind conditions. We investigated the impact of different determinations of heat fluxes on the near surface sea temperature (1 m depth), as determined by calculations with a one-dimensional numerical model, the General Ocean Turbulence Model (GOTM). The sensitivity of the model to the different forcing was measured in terms of differences with respect to in situ temperature measurements made during the period under investigation. All simulations reproduced the true seasonal cycle and all high frequency variabilities. The best results on the overall seasonal cycle were obtained when using meteorological variables in the bulk formulae formulations used by the model itself. The derived overall annual net heat flux values were between +1.6 and 40.4 W/m², depending on the used dataset. The large variability obtained with different datasets suggests that current determinations of the heat flux components and, in particular, of the longwave irradiance, need to be improved. The ENEA oceanographic observatory provides a complete, long-term, high resolution time series of high quality in situ observations. In the future, more similar sites worldwide will be needed for model and satellite validations and to improve the determination of the air–sea exchange and the understanding of related processes. Full article

(This article belongs to the Special Issue Remote Sensing of Air-Sea Fluxes)

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Figure 1

Figure 1
Pictorial view of the air–sea heat exchange components. Full article ">Figure 2
The Oceanographic Observatory (OO, left panel) and its position near Lampedusa Island. AO indicates the position of the Atmospheric observatory. The Lampedusa island picture was taken from the International Space Station (picture ISS 024-E-10246; see acknowledgments). Full article ">Figure 3
Comparison between key meteorological variables contributing to air–sea heat flux estimates from ERA5 and from in situ observations. Air temperature (a), dew point temperature (b), atmospheric pressure at sea level (c), wind intensity (d), sea surface temperature (e). The (hourly) SST was inferred from the “skin” SST by adding the mean value of the difference with the “subskin” (0.17 °C) to make it more comparable with the sensor measurement at 1 m depth. Dot color indicates data density, increasing from white to black. Data refer to the period 3 June 2017–3 June 2018. Boxes within each plot include statistics of differences between ERA5 estimates and in situ measurements. Negative bias values indicate underestimates of the reanalysis. Full article ">Figure 4
Comparison between hourly shortwave (a) and longwave (b) irradiance estimated by ERA5 and corresponding values measured on the buoy. Dot color indicates data density, increasing from white to black. Data refer to the period 4 June 2017–3 June 2018. Units are W/m2. Boxes within each plot include statistics of differences between ERA5 estimates and in situ measurements of shortwave and longwave irradiance. Full article ">Figure 5
Comparison between hourly shortwave (a) and longwave (b) irradiances estimated by SEVIRI and the corresponding values measured on the buoy. Dot color indicates data density, increasing from white to black. Data refer to the period June 2017–3 June 2018. Units are W/m2. Boxes within each plot include statistics of differences between satellite estimates and in situ measurements of shortwave and longwave irradiances. Full article ">Figure 6
In situ CTD casts made from 3 June 2017 at 14:00 UTC to 4 June 2017 at 07:56:00 UTC close to the Oceanographic Observatory. Dots represent individual CTD casts. The black lines represent the average profiles for temperature (a) and salinity (b) used as initial condition for the simulation. Full article ">Figure 7
Comparison between in situ measurements of water temperature at 1 m depth (black curves in (a–c)) and GOTM simulated water temperatures at the same depth obtained as follows: (a) Simulations using air–sea heat and momentum fluxes computed by the model at each time step, using bulk formulae with ERA5 meteorological parameters (experiment 1, red line) or bulk formulae with in situ meteorological parameters (experiment 2, blue line). (b) Simulations imposing air–sea heat and momentum fluxes obtained by ERA5 (experiment 3, red line), by ERA5 except for the shortwave irradiance, which is from in situ measurements (experiment 4, blue line) and by ERA5 except for the shortwave irradiance, which is estimated by SEVIRI (experiment 5, green line). (c) Simulations imposing air–sea heat and momentum fluxes obtained by ERA5 (experiment 3, red line), by ERA5 except for the longwave irradiance, which is form in situ observations at the buoy (experiment 6, blue line), by ERA5 except for the longwave irradiance, which is estimated by SEVIRI (experiment 7, green line). The differences between measured and simulated temperatures at 1 m depth are reported in panels d, e, f, for: (d) Experiment 1 (red) and experiment 2 (blue). (e) Experiment 3 (red), experiment 4 (blue) and experiment 5 (green). (f) Experiment 3 (red), experiment 6 (blue) and experiment 7 (green). (g) Ocean heat flux loss (latent + sensible + net longwave) computed by the model for experiment 1 (red) and experiment 2 (blue). (h) Shortwave component of the heat fluxes from in situ measurements (blue), ERA5 (red) and SEVIRI (green). (i) Ocean heat flux loss (latent+sensible+net longwave) for experiment 3 (red), experiment 6 (blue) and experiment 7 (green) A 10 days moving average filter has been applied to curves in panels (g–i) to enhance readability. Full article ">Figure 8
ERA5 minus buoy wind speed (a) and ERA5 minus buoy heat loss (Latent + Sensible + net Longwave) as a function of the in situ measured wind speed (b). Averages and standard deviations over 1 m/s intervals are shown as blue squares and red bars, respectively. Full article ">

28 pages, 72526 KiB

Open AccessArticle

Emerging Pattern of Wind Change over the Eurasian Marginal Seas Revealed by Three Decades of Satellite Ocean-Surface Wind Observations

by Lisan Yu

Remote Sens. 2021, 13(9), 1707; https://doi.org/10.3390/rs13091707 - 28 Apr 2021

Cited by 3 | Viewed by 3428

Abstract

This study provides the first full characterization of decadal changes of surface winds over 10 marginal seas along the Eurasian continent using satellite wind observations. During the three decades (1988–2018), surface warming has occurred in all seas at a rate more pronounced in [...] Read more.

This study provides the first full characterization of decadal changes of surface winds over 10 marginal seas along the Eurasian continent using satellite wind observations. During the three decades (1988–2018), surface warming has occurred in all seas at a rate more pronounced in the South European marginal seas (0.4–0.6 °C per decade) than in the monsoon-influenced North Indian and East Asian marginal seas (0.1–0.2 °C per decade). However, surface winds have not strengthened everywhere. On a basin average, winds have increased over the marginal seas in the subtropical/mid-latitudes, with the rate of increase ranging from 11 to 24 cms⁻¹ per decade. These upward trends reflect primarily the accelerated changes in the 1990s and have largely flattened since 2000. Winds have slightly weakened or remained little changed over the marginal seas in the tropical monsoonal region. Winds over the Red Sea and the Persian Gulf underwent an abrupt shift in the late 1990s that resulted in an elevation of local wind speeds. The varying relationships between wind and SST changes suggest that different marginal seas have responded differently to environmental warming and further studies are needed to gain an improved understanding of climate change on a regional scale. Full article

(This article belongs to the Special Issue Remote Sensing of Air-Sea Fluxes)

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23 pages, 4825 KiB

Open AccessArticle

Air-Sea Interactions over Eddies in the Brazil-Malvinas Confluence

by Ronald Souza, Luciano Pezzi, Sebastiaan Swart, Fabrício Oliveira and Marcelo Santini

Remote Sens. 2021, 13(7), 1335; https://doi.org/10.3390/rs13071335 - 31 Mar 2021

Cited by 19 | Viewed by 3917

Abstract

The Brazil–Malvinas Confluence (BMC) is one of the most dynamical regions of the global ocean. Its variability is dominated by the mesoscale, mainly expressed by the presence of meanders and eddies, which are understood to be local regulators of air-sea interaction processes. The [...] Read more.

The Brazil–Malvinas Confluence (BMC) is one of the most dynamical regions of the global ocean. Its variability is dominated by the mesoscale, mainly expressed by the presence of meanders and eddies, which are understood to be local regulators of air-sea interaction processes. The objective of this work is to study the local modulation of air-sea interaction variables by the presence of either a warm (ED1) and a cold core (ED2) eddy, present in the BMC, during September to November 2013. The translation and lifespans of both eddies were determined using satellite-derived sea level anomaly (SLA) data. Time series of satellite-derived surface wind data, as well as these and other meteorological variables, retrieved from ERA5 reanalysis at the eddies’ successive positions in time, allowed us to investigate the temporal modulation of the lower atmosphere by the eddies’ presence along their translation and lifespan. The reanalysis data indicate a mean increase of 78% in sensible and 55% in latent heat fluxes along the warm eddy trajectory in comparison to the surrounding ocean of the study region. Over the cold core eddy, on the other hand, we noticed a mean reduction of 49% and 25% in sensible and latent heat fluxes, respectively, compared to the adjacent ocean. Additionally, a field campaign observed both eddies and the lower atmosphere from ship-borne observations before, during and after crossing both eddies in the study region during October 2013. The presence of the eddies was imprinted on several surface meteorological variables depending on the sea surface temperature (SST) in the eddy cores. In situ oceanographic and meteorological data, together with high frequency micrometeorological data, were also used here to demonstrate that the local, rather than the large scale forcing of the eddies on the atmosphere above, is, as expected, the principal driver of air-sea interaction when transient atmospheric systems are stable (not actively varying) in the study region. We also make use of the in situ data to show the differences (biases) between bulk heat flux estimates (used on atmospheric reanalysis products) and eddy covariance measurements (taken as “sea truth”) of both sensible and latent heat fluxes. The findings demonstrate the importance of short-term changes (minutes to hours) in both the atmosphere and the ocean in contributing to these biases. We conclude by emphasizing the importance of the mesoscale oceanographic structures in the BMC on impacting local air-sea heat fluxes and the marine atmospheric boundary layer stability, especially under large scale, high-pressure atmospheric conditions. Full article

(This article belongs to the Special Issue Remote Sensing of Air-Sea Fluxes)

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15 pages, 4302 KiB

Open AccessArticle

Mesoscale Temporal Wind Variability Biases Global Air–Sea Gas Transfer Velocity of CO₂ and Other Slightly Soluble Gases

by Yuanyuan Gu, Gabriel G. Katul and Nicolas Cassar

Remote Sens. 2021, 13(7), 1328; https://doi.org/10.3390/rs13071328 - 31 Mar 2021

Cited by 3 | Viewed by 2857

Abstract

The significance of the water-side gas transfer velocity for air–sea CO₂ gas exchange (k) and its non-linear dependence on wind speed (

U

) is well accepted. What remains a subject of inquiry are biases associated with the form of the non-linear [...] Read more.

The significance of the water-side gas transfer velocity for air–sea CO₂ gas exchange (k) and its non-linear dependence on wind speed (

U

) is well accepted. What remains a subject of inquiry are biases associated with the form of the non-linear relation linking k to U (hereafter labeled as f(

U

), where f(.) stands for an arbitrary function of

U

), the distributional properties of

U

(treated as a random variable) along with other external factors influencing k, and the time-averaging period used to determine k from

U

. To address the latter issue, a Taylor series expansion is applied to separate f(

U

) into a term derived from time-averaging wind speed (labeled as

⟨ U ⟩,

where

⟨ . ⟩

indicates averaging over a monthly time scale) as currently employed in climate models and additive bias corrections that vary with the statistics of

U

. The method was explored for nine widely used f(

U

) parameterizations based on remotely-sensed 6-hourly global wind products at 10 m above the sea-surface. The bias in k of monthly estimates compared to the reference 6-hourly product was shown to be mainly associated with wind variability captured by the standard deviation σ

σ_{U}

around

⟨ U ⟩

or, more preferably, a dimensionless coefficient of variation

I_{u}

= σ

σ_{U}

/

⟨ U ⟩

. The proposed correction outperforms previous methodologies that adjusted k when using

⟨ U ⟩

only. An unexpected outcome was that upon setting

I_{u}^{2}

= 0.15 to correct biases when using monthly wind speed averages, the new model produced superior results at the global and regional scale compared to prior correction methodologies. Finally, an equation relating

I_{u}^{2}

to the time-averaging interval (spanning from 6 h to a month) is presented to enable other sub-monthly averaging periods to be used. While the focus here is on CO₂, the theoretical tactic employed can be applied to other slightly soluble gases. As monthly and climatological wind data are often used in climate models for gas transfer estimates, the proposed approach provides a robust scheme that can be readily implemented in current climate models. Full article

(This article belongs to the Special Issue Remote Sensing of Air-Sea Fluxes)

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28 pages, 25436 KiB

Open AccessArticle

Twenty-Seven Years of Scatterometer Surface Wind Analysis over Eastern Boundary Upwelling Systems

by Abderrahim Bentamy, Semyon A. Grodsky, Gildas Cambon, Pierre Tandeo, Xavier Capet, Claude Roy, Steven Herbette and Antoine Grouazel

Remote Sens. 2021, 13(5), 940; https://doi.org/10.3390/rs13050940 - 3 Mar 2021

Cited by 10 | Viewed by 3130

Abstract

More than twelve satellite scatterometers have operated since 1992 through the present, providing the main source of surface wind vector observations over global oceans. In this study, these scatterometer winds are used in combination with radiometers and synthetic aperture radars (SAR) for the [...] Read more.

More than twelve satellite scatterometers have operated since 1992 through the present, providing the main source of surface wind vector observations over global oceans. In this study, these scatterometer winds are used in combination with radiometers and synthetic aperture radars (SAR) for the better determination and characterization of high spatial and temporal resolution of regional surface wind parameters, including wind speed and direction, wind stress components, wind stress curl, and divergence. In this paper, a 27-year-long (1992–2018) 6-h satellite wind analysis with a spatial resolution of 0.125° in latitude and longitude is calculated using spatial structure functions derived from high-resolution SAR data. The main objective is to improve regional winds over three major upwelling regions (the Canary, Benguela, and California regions) through the use of accurate and homogenized wind observations and region-specific spatial and temporal wind variation structure functions derived from buoy and SAR data. The long time series of satellite wind analysis over the California upwelling, where a significant number of moorings is available, are used for assessing the accuracy of the analysis. The latter is close to scatterometer wind retrieval accuracy. This assessment shows that the root mean square difference between collocated 6-h satellite wind analysis and buoys is lower than 1.50 and 1.80 m s⁻¹ for offshore and nearshore locations, respectively. The temporal correlation between buoy and satellite analysis winds exceeds 0.90. The analysis accuracy is lower for 1992–1999 when satellite winds were mostly retrieved from ERS-1 and/or ERS-2 scatterometers. To further assess the improvement brought by this new wind analysis, its data and data from three independent products (ERA5, CMEMS, and CCMP) are compared with purely scatterometer winds over the Canary and Benguela regions. Even though the four products are generally similar, the new satellite analysis shows significant improvements, particularly in the upwelling areas. Full article

(This article belongs to the Special Issue Remote Sensing of Air-Sea Fluxes)

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22 pages, 10015 KiB

Open AccessFeature PaperArticle

The Impact of the Madden–Julian Oscillation on Cyclone Amphan (2020) and Southwest Monsoon Onset

by Heather L. Roman-Stork and Bulusu Subrahmanyam

Remote Sens. 2020, 12(18), 3011; https://doi.org/10.3390/rs12183011 - 16 Sep 2020

Cited by 15 | Viewed by 3480

Abstract

Cyclone Amphan was an exceptionally strong tropical cyclone in the Bay of Bengal that achieved a minimum central pressure of 907 mb during its active period in May 2020. In this study, we analyzed the oceanic and surface atmospheric conditions leading up to [...] Read more.

Cyclone Amphan was an exceptionally strong tropical cyclone in the Bay of Bengal that achieved a minimum central pressure of 907 mb during its active period in May 2020. In this study, we analyzed the oceanic and surface atmospheric conditions leading up to cyclogenesis, the impact of this storm on the Bay of Bengal, and how the processes that led to cyclogenesis, such as the Madden–Julian Oscillation (MJO) and Amphan itself, in turn impacted southwest monsoon preconditioning and onset. To accomplish this, we took a multiparameter approach using a combination of near real time satellite observations, ocean model forecasts, and reanalysis to better understand the processes involved. We found that the arrival of a second downwelling Kelvin wave in the equatorial Bay of Bengal, coupled with elevated upper ocean heat content and the positioning of the convective phase of the MJO, helped to create the conditions necessary for cyclogenesis, where the northward-propagating branch of the MJO acted as a trigger for cyclogenesis. This same MJO event, in conjunction with Amphan, heavily contributed atmospheric moisture to the southeastern Arabian Sea and established low-level westerlies that allowed for the southwest monsoon to climatologically onset on June 1. Full article

(This article belongs to the Special Issue Remote Sensing of Air-Sea Fluxes)

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17 pages, 2701 KiB

Open AccessArticle

FluxSat: Measuring the Ocean–Atmosphere Turbulent Exchange of Heat and Moisture from Space

by Chelle L. Gentemann, Carol Anne Clayson, Shannon Brown, Tong Lee, Rhys Parfitt, J. Thomas Farrar, Mark Bourassa, Peter J. Minnett, Hyodae Seo, Sarah T. Gille and Victor Zlotnicki

Remote Sens. 2020, 12(11), 1796; https://doi.org/10.3390/rs12111796 - 3 Jun 2020

Cited by 24 | Viewed by 9884

Abstract

Recent results using wind and sea surface temperature data from satellites and high-resolution coupled models suggest that mesoscale ocean–atmosphere interactions affect the locations and evolution of storms and seasonal precipitation over continental regions such as the western US and Europe. The processes responsible [...] Read more.

Recent results using wind and sea surface temperature data from satellites and high-resolution coupled models suggest that mesoscale ocean–atmosphere interactions affect the locations and evolution of storms and seasonal precipitation over continental regions such as the western US and Europe. The processes responsible for this coupling are difficult to verify due to the paucity of accurate air–sea turbulent heat and moisture flux data. These fluxes are currently derived by combining satellite measurements that are not coincident and have differing and relatively low spatial resolutions, introducing sampling errors that are largest in regions with high spatial and temporal variability. Observational errors related to sensor design also contribute to increased uncertainty. Leveraging recent advances in sensor technology, we here describe a satellite mission concept, FluxSat, that aims to simultaneously measure all variables necessary for accurate estimation of ocean–atmosphere turbulent heat and moisture fluxes and capture the effect of oceanic mesoscale forcing. Sensor design is expected to reduce observational errors of the latent and sensible heat fluxes by almost 50%. FluxSat will improve the accuracy of the fluxes at spatial scales critical to understanding the coupled ocean–atmosphere boundary layer system, providing measurements needed to improve weather forecasts and climate model simulations. Full article

(This article belongs to the Special Issue Remote Sensing of Air-Sea Fluxes)

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20 pages, 10393 KiB

Open AccessArticle

CYGNSS Surface Heat Flux Product Development

by Juan A. Crespo, Derek J. Posselt and Shakeel Asharaf

Remote Sens. 2019, 11(19), 2294; https://doi.org/10.3390/rs11192294 - 1 Oct 2019

Cited by 30 | Viewed by 7215

Abstract

Ocean surface heat fluxes play a significant role in the genesis and evolution of various marine-based atmospheric phenomena, from the synoptic scale down to the microscale. While in-situ measurements from buoys and flux towers will continue to be the standard in regard to [...] Read more.

Ocean surface heat fluxes play a significant role in the genesis and evolution of various marine-based atmospheric phenomena, from the synoptic scale down to the microscale. While in-situ measurements from buoys and flux towers will continue to be the standard in regard to surface heat flux estimates, they commonly have significant gaps in temporal and spatial coverage. Previous and current satellite missions have filled these gaps; though they may not observe the fluxes directly, they can measure the variables needed (wind speed, temperature and humidity) to estimate latent and sensible heat fluxes. However, current remote sensing instruments have their own limitations, such as infrequent coverage, signals attenuated by precipitation or both. The Cyclone Global Navigation Satellite System (CYGNSS) mission overcomes these limitations over the tropical and subtropical oceans by providing improved coverage in nearly all weather conditions. While CYGNSS (Level 2) primarily estimates surface winds, when coupled with observations or estimates of temperature and humidity from reanalysis data, it can provide estimates of latent and sensible heat fluxes along its orbit. This paper describes the development of the Surface Heat Flux Product for the CYGNSS mission, its current results and expected improvements and changes in future releases. Full article

(This article belongs to the Special Issue Remote Sensing of Air-Sea Fluxes)

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