The 25-Day-Period Large-Scale Oscillations in the Argentine Basin Revisited

Abstract From sea surface height measurements made by the Ocean Topography Experiment (TOPEX)/Poseidon satellite, Fu et al. found and described large-scale oscillations at the period of 25 days in the Argentine Basin of the South Atlantic Ocean. These oscillations were previously hinted at by in situ observations. Only the extensive space–time sampling capability of TOPEX/Poseidon, however, was able to give a complete description of the phenomenon as a counterclockwise-rotating dipole centered at 45°S, 317°E over the Zapiola Rise. Fu et al. also undertook theoretical and numerical studies to suggest that the phenomenon is a resonantly excited barotropic normal mode of the locally closed f/H contour. In a simulation study, however, they also found that the space–time smoothing scheme employed would probably lower the amplitude of the estimated phenomenon by 30%–40%. By reprocessing the data using a different method and showing the amplitude to be almost 2 times as large, in this note it is confirmed that this is indeed the case. The original 5-yr study has also been extended to nearly 10 yr, demonstrating that the same phenomenon has persisted for almost 10 yr.

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High resolution 3-D temperature and salinity fields derived from in situ and satellite observations

Ocean Science ◽

10.5194/os-8-845-2012 ◽

2012 ◽

Vol 8 (5) ◽

pp. 845-857 ◽

Cited By ~ 107

Author(s):

S. Guinehut ◽

A.-L. Dhomps ◽

G. Larnicol ◽

P.-Y. Le Traon

Keyword(s):

High Resolution ◽

Surface Temperature ◽

Sea Surface Temperature ◽

Large Scale ◽

Sea Surface ◽

Temperature Fields ◽

Global Ocean ◽

In Situ Observations ◽

Temperature Observations

Abstract. This paper describes an observation-based approach that efficiently combines the main components of the global ocean observing system using statistical methods. Accurate but sparse in situ temperature and salinity profiles (mainly from Argo for the last 10 yr) are merged with the lower accuracy but high-resolution synthetic data derived from satellite altimeter and sea surface temperature observations to provide global 3-D temperature and salinity fields at high temporal and spatial resolution. The first step of the method consists in deriving synthetic temperature fields from altimeter and sea surface temperature observations, and salinity fields from altimeter observations, through multiple/simple linear regression methods. The second step of the method consists in combining the synthetic fields with in situ temperature and salinity profiles using an optimal interpolation method. Results show the revolutionary nature of the Argo observing system. Argo observations now allow a global description of the statistical relationships that exist between surface and subsurface fields needed for step 1 of the method, and can constrain the large-scale temperature and mainly salinity fields during step 2 of the method. Compared to the use of climatological estimates, results indicate that up to 50% of the variance of the temperature fields can be reconstructed from altimeter and sea surface temperature observations and a statistical method. For salinity, only about 20 to 30% of the signal can be reconstructed from altimeter observations, making the in situ observing system essential for salinity estimates. The in situ observations (step 2 of the method) further reduce the differences between the gridded products and the observations by up to 20% for the temperature field in the mixed layer, and the main contribution is for salinity and the near surface layer with an improvement up to 30%. Compared to estimates derived using in situ observations only, the merged fields provide a better reconstruction of the high resolution temperature and salinity fields. This also holds for the large-scale and low-frequency fields thanks to a better reduction of the aliasing due to the mesoscale variability. Contribution of the merged fields is then illustrated to describe qualitatively the temperature variability patterns for the period from 1993 to 2009.

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High Resolution 3-D temperature and salinity fields derived from in situ and satellite observations

Ocean Science Discussions ◽

10.5194/osd-9-1313-2012 ◽

2012 ◽

Vol 9 (2) ◽

pp. 1313-1347 ◽

Cited By ~ 10

Author(s):

S. Guinehut ◽

A.-L. Dhomps ◽

G. Larnicol ◽

P.-Y. Le Traon

Keyword(s):

High Resolution ◽

Surface Temperature ◽

Sea Surface Temperature ◽

Large Scale ◽

Satellite Observations ◽

Sea Surface ◽

Temperature Fields ◽

In Situ Observations ◽

Temperature Observations

Abstract. This paper describes an observation-based approach that combines efficiently the main components of the global ocean observing system using statistical methods. Accurate but sparse in situ temperature and salinity profiles (mainly from Argo for the last 10 years) are merged with the lower accuracy but high-resolution synthetic data derived from altimeter and sea surface temperature satellite observations to provide global 3-D temperature and salinity fields at high temporal and spatial resolution. The first step of the method consists in deriving synthetic temperature fields from altimeter and sea surface temperature observations and salinity fields from altimeter observations through multiple/simple linear regression methods. The second step of the method consists in combining the synthetic fields with in situ temperature and salinity profiles using an optimal interpolation method. Results show the revolution of the Argo observing system. Argo observations now allow a global description of the statistical relationships that exist between surface and subsurface fields needed for step 1 of the method and can constrain the large-scale temperature and mainly salinity fields during step 2 of the method. Compared to the use of climatological estimates, results indicate that up to 50 % of the variance of the temperature fields can be reconstructed from altimeter and sea surface temperature observations and a statistical method. For salinity, only about 20 to 30 % of the signal can be reconstructed from altimeter observations, making the in situ observing system mandatory for salinity estimates. The in situ observations (step 2 of the method) reduce additionally the error by up to 20 % for the temperature field in the mixed layer and the main contribution is for salinity and the near surface layer with an improvement up to 30 %. Compared to estimates derived using in situ observations only, the merged fields provide a better reconstruction of the high resolution temperature and salinity fields. This also holds for the large-scale and low-frequency fields thanks to a better reduction of the aliasing due to the mesoscale variability. Contribution of the merged fields is then illustrated to qualitatively describe the temperature variability patterns for the 1993 to 2009 time period.

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MESSENGER observations of planetary ion enhancements at Mercury’s northern magnetospheric cusp during Flux Transfer Event Showers

10.21203/rs.3.rs-1138782/v1 ◽

2021 ◽

Author(s):

Weijie Sun ◽

James Slavin ◽

Anna Milillo ◽

Ryan Dewey ◽

Stefano Orsini ◽

...

Keyword(s):

Solar Wind ◽

Large Scale ◽

Transfer Event ◽

Order Of Magnitude ◽

In Situ Observations ◽

Flux Transfer ◽

Magnetospheric Cusp ◽

Upward Moving ◽

Entry Layer

Abstract At Mercury, several processes can release ions and neutrals out of the planet’s surface. Here we present enhancements of dayside planetary ions in the solar wind entry layer during flux transfer event (FTE) “showers” near Mercury’s northern magnetospheric cusp. In this entry layer, solar wind ions are accelerated and move downward (i.e. planetward) toward the cusps, which sputter upward-moving planetary ions within 1 minute. The precipitation rate is enhanced by an order of magnitude during FTE showers and the neutral density of the exosphere can vary by >10% due to this FTE-driven sputtering. These in situ observations of enhanced planetary ions in the entry layer likely correspond to an escape channel of Mercury’s planetary ions, and the large-scale variations of the exosphere observed on minute-timescales by ground-based telescopes. Comprehensive, future multi-point measurements made by BepiColombo will greatly enhance our understanding of the processes contributing to Mercury’s dynamic exosphere and magnetosphere.

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Sea surface salinity reemergence in an updated North Atlantic in-situ salinity data set

Journal of Climate ◽

10.1175/jcli-d-20-0840.1 ◽

2021 ◽

pp. 1-49

Author(s):

Claude Frankignoul ◽

Elodie Kestenare ◽

Gilles Reverdin

Keyword(s):

Large Scale ◽

Cold Season ◽

Sea Surface ◽

Sea Surface Salinity ◽

Surface Salinity ◽

Data Set ◽

Salinity Data ◽

Deep Mixed Layer ◽

Geostrophic Advection

AbstractMonthly sea surface salinity (SSS) fields are constructed from observations, using objective mapping on a 1°x1° grid in the Atlantic between 30°S and 50°N in the 1970-2016 period in an update of the data set of Reverdin et al. (2007). Data coverage is heterogeneous, with increased density in 2002 when Argo floats become available, high density along Voluntary Observing Ship lines, and low density south of 10°S. Using lag correlation, the seasonal reemergence of SSS anomalies is investigated between 20°N and 50°N in 5°x5° boxes during the 1993-2016 period, both locally and remotely following the displacements of the deep mixed-layer waters estimated from virtual float trajectories derived from the daily AVISO surface geostrophic currents. Although SSS data are noisy, local SSS reemergence is detected in about half of the boxes, notably in the northeast and southeast, while little reemergence is seen in the central and part of the eastern subtropical gyre. In the same period, sea surface temperature (SST) reemergence is found only slightly more frequently, reflecting the short data duration. However, taking geostrophic advection into account degrades the detection of remote SSS and even SST reemergence. When anomalies are averaged over broader areas, robust evidence of a second and third SSS reemergence peak is found in the northeastern and southeastern parts of the domain, indicating long cold-season persistence of large-scale SSS anomalies, while only a first SST reemergence is seen. An oceanic reanalysis is used to confirm that the correlation analysis indeed reflects the reemergence of subsurface salinity anomalies.

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

Remote Sensing ◽

10.3390/rs12162554 ◽

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.

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What Is the Impact of Additional Tropical Observations on a Modern Data Assimilation System?

Monthly Weather Review ◽

10.1175/mwr-d-18-0120.1 ◽

2019 ◽

Vol 147 (7) ◽

pp. 2433-2449

Author(s):

Laura C. Slivinski ◽

Gilbert P. Compo ◽

Jeffrey S. Whitaker ◽

Prashant D. Sardeshmukh ◽

Jih-Wang A. Wang ◽

...

Keyword(s):

El Niño ◽

Large Scale ◽

El Nino ◽

Mean Square ◽

Observational Network ◽

In Situ Observations ◽

The Mean ◽

The Impact ◽

Assimilation System

Abstract Given the network of satellite and aircraft observations around the globe, do additional in situ observations impact analyses within a global forecast system? Despite the dense observational network at many levels in the tropical troposphere, assimilating additional sounding observations taken in the eastern tropical Pacific Ocean during the 2016 El Niño Rapid Response (ENRR) locally improves wind, temperature, and humidity 6-h forecasts using a modern assimilation system. Fields from a 50-km reanalysis that assimilates all available observations, including those taken during the ENRR, are compared with those from an otherwise-identical reanalysis that denies all ENRR observations. These observations reveal a bias in the 200-hPa divergence of the assimilating model during a strong El Niño. While the existing observational network partially corrects this bias, the ENRR observations provide a stronger mean correction in the analysis. Significant improvements in the mean-square fit of the first-guess fields to the assimilated ENRR observations demonstrate that they are valuable within the existing network. The effects of the ENRR observations are pronounced in levels of the troposphere that are sparsely observed, particularly 500–800 hPa. Assimilating ENRR observations has mixed effects on the mean-square difference with nearby non-ENRR observations. Using a similar system but with a higher-resolution forecast model yields comparable results to the lower-resolution system. These findings imply a limited improvement in large-scale forecast variability from additional in situ observations, but significant improvements in local 6-h forecasts.

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ROMA (Rank-Ordered Multifractal Analyses) of intermittency in space plasmas – a brief tutorial review

Nonlinear Processes in Geophysics ◽

10.5194/npg-17-545-2010 ◽

2010 ◽

Vol 17 (5) ◽

pp. 545-551 ◽

Cited By ~ 11

Author(s):

T. Chang ◽

C. C. Wu ◽

J. Podesta ◽

M. Echim ◽

H. Lamy ◽

...

Keyword(s):

Large Scale ◽

Auroral Zone ◽

Space Plasmas ◽

Dynamic Interactions ◽

Stochastic Media ◽

Mhd Simulations ◽

Field Fluctuations ◽

In Situ Observations ◽

Magnetospheric Cusp

Abstract. Intermittent fluctuations are the consequence of the dynamic interactions of multiple coherent or pseudo-coherent structures of varied sizes in the stochastic media (Chang, 1999). We briefly review here a recently developed technique, the Rank-Ordered Multifractal Analysis (ROMA), which is both physically explicable and quantitatively accurate in deciphering the multifractal characteristics of such intermittent structures (Chang and Wu, 2008). The utility of the method is demonstrated using results obtained from large-scale 2-D MHD simulations as well as in-situ observations of magnetic field fluctuations from the interplanetary and magnetospheric cusp regions, and the broadband electric field oscillations from the auroral zone.

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Evaluating model-simulated and satellite-derived SM using in situ observations under different environment conditions in China

10.5194/egusphere-egu2020-2819 ◽

2020 ◽

Author(s):

Yuanyuan Wang ◽

Guicai Li

Keyword(s):

Land Surface ◽

Large Scale ◽

Microwave Remote Sensing ◽

Ground Truth ◽

Original Data ◽

Observation Data ◽

Monitoring Networks ◽

The Future ◽

In Situ Observations

Soil moisture (SM) is a key variable in understanding the climate system through its controls on the land surface energy and water budget. Large scale SM products have become increasingly available thanks to development in microwave remote sensing and land surface modeling. Comprehensive assessments on the reliability of satellite-derived and model-simulated SM products are essential for their improvement and application. In this research, the active, passive and combined Climate Change Initiative (CCI V04.2) SM products and the China Land Data Assimilation System (CLDAS V2.0) SM products were evaluated by comparing with in situ observed data over three networks in China: Hebi, Naqu and Heihe. The three sites have different environmental conditions and sensor densities, providing observations covering more than 2 years. Four statistic scores were calculated: R (considering both original data and anomalies), Bias, RMSE, ubRMSE. TC (Triple Collocation) analysis was also carried out in which uncertainties in observations are taken into account. Results indicate that the performance of the two SM products varies between the monitoring networks. For Naqu site, both products show good performance, with CCI-SM showing slightly higher R and lower ubRMSE. For Hebi site, CLDAS-SM performs better than CCI-SM, whereas for Heihe site, CLDAS-SM performs worse than CCI-SM. The expected uncertainty (0.04 m3/m3) can be achieved in Naqu and Heihe site by CCI-SM, and in Hebi and Naqu site by CLDAS-SM, which is quite encouraging. The two products agree in terms of sign of the Bias value, which is positive in Hebi and negative in Naqu and Heihe. Among all the three networks, Heihe site exhibits the lowest accuracy due to its complicated terrain and heterogeneous land surface. Ranom of CLDAS-SM in Heihe is close to 0, indicating its inability to capture short term variability. TC results reveal that for Naqu site the observation data have quite good qualities, while for Hebi site CLDAS-SM is more approximate to &#8216;ground truth&#8217; than in situ observations, suggesting a refinement of network maybe needed in the future. Overall, the two products are complementary. CLDAS-SM performs better in populated area (e.g., Hebi) where meteorological forcing is more accurate and CCI-SM performs better in remote areas (Naqu, Heihe) where RFI is usually low. More reliable validation networks are needed in the future to comprehensively understand the advantages and disadvantage of the two SM products in China.

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An Observing System Simulation Experiment for the Calibration and Validation of the Surface Water Ocean Topography Sea Surface Height Measurement Using In Situ Platforms

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-17-0076.1 ◽

2018 ◽

Vol 35 (2) ◽

pp. 281-297 ◽

Cited By ~ 24

Author(s):

Jinbo Wang ◽

Lee-Lueng Fu ◽

Bo Qiu ◽

Dimitris Menemenlis ◽

J. Thomas Farrar ◽

...

Keyword(s):

Surface Water ◽

Simulation Experiment ◽

System Simulation ◽

Sea Surface ◽

Station Keeping ◽

Calibration And Validation ◽

Wavenumber Spectrum ◽

Ocean Topography ◽

Observing System Simulation Experiment

AbstractThe wavenumber spectrum of sea surface height (SSH) is an important indicator of the dynamics of the ocean interior. While the SSH wavenumber spectrum has been well studied at mesoscale wavelengths and longer, using both in situ oceanographic measurements and satellite altimetry, it remains largely unknown for wavelengths less than ~70 km. The Surface Water Ocean Topography (SWOT) satellite mission aims to resolve the SSH wavenumber spectrum at 15–150-km wavelengths, which is specified as one of the mission requirements. The mission calibration and validation (CalVal) requires the ground truth of a synoptic SSH field to resolve the targeted wavelengths, but no existing observational network is able to fulfill the task. A high-resolution global ocean simulation is used to conduct an observing system simulation experiment (OSSE) to identify the suitable oceanographic in situ measurements for SWOT SSH CalVal. After fixing 20 measuring locations (the minimum number for resolving 15–150-km wavelengths) along the SWOT swath, four instrument platforms were tested: pressure-sensor-equipped inverted echo sounders (PIES), underway conductivity–temperature–depth (UCTD) sensors, instrumented moorings, and underwater gliders. In the context of the OSSE, PIES was found to be an unsuitable tool for the target region and for SSH scales 15–70 km; the slowness of a single UCTD leads to significant aliasing by high-frequency motions at short wavelengths below ~30 km; an array of station-keeping gliders may meet the requirement; and an array of moorings is the most effective system among the four tested instruments for meeting the mission’s requirement. The results shown here warrant a prelaunch field campaign to further test the performance of station-keeping gliders.

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Blending Sea Surface Temperatures from Multiple Satellites and In Situ Observations for Coastal Oceans

Journal of Atmospheric and Oceanic Technology ◽

10.1175/2009jtecho592.1 ◽

2009 ◽

Vol 26 (7) ◽

pp. 1415-1426 ◽

Cited By ~ 61

Author(s):

Yi Chao ◽

Zhijin Li ◽

John D. Farrara ◽

Peter Hung

Keyword(s):

Coastal Upwelling ◽

Sea Surface ◽

High Temporal Resolution ◽

Potential Applications ◽

Independent Observations ◽

Ocean Flows ◽

In Situ Observations ◽

Continuous Fields ◽

Mean Square Errors

Abstract A two-dimensional variational data assimilation (2DVAR) method for blending sea surface temperature (SST) data from multiple observing platforms is presented. This method produces continuous fields and has the capability of blending multiple satellite and in situ observations. In addition, it allows specification of inhomogeneous and anisotropic background correlations, which are common features of coastal ocean flows. High-resolution (6 km in space and 6 h in time) blended SST fields for August 2003 are produced for a region off the California coast to demonstrate and evaluate the methodology. A comparison of these fields with independent observations showed root-mean-square errors of less than 1°C, comparable to the errors in conventional SST observations. The blended SST fields also clearly reveal the finescale spatial and temporal structures associated with coastal upwelling, demonstrating their utility in the analysis of finescale flows. With the high temporal resolution, the blended SST fields are also used to describe the diurnal cycle. Potential applications of this SST blending methodology in other coastal regions are discussed.

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