Jacob L. Høyer

Detection of deep overflows with satellite altimetry

In-situ observations downstream of the sills in the Faroe Bank Channel and Denmark Strait show large temperature and current variability of the overflows with time-scales of a few days. These fluctuations are associated with meso-scale eddies and have an impact on sea surface height. This is confirmed by observations from the TOPEX/POSEIDON and ERS 1+2 satellites that show substantial enhancement of eddy kinetic energy and sea level variability immediately down-stream of the sills.

Simulations of the snow covered sea ice surface temperature and microwave effective temperature

The snow surface on thick multiyear sea ice in winter is on average colder than the air because of the negative radiation balance. Beneath the snow surface there is a strong temperature gradient in winter with increasing temperatures towards the ice—water interface temperature at the freezing point around –1.8 ◦C. The sea ice surface temperature and the thermal microwave brightness temperature were simulated using a combination of thermodynamic and microwave emission models. The simulations indicate that the physical snow—ice interface temperature or alternatively the 6 GHz effective temperature have a good correlation with the effective temperature at the temperature sounding channels near 50 GHz. The complete correlation matrix based on the simulations for physical and effective temperatures is given. The physical snow—ice interface temperature is related to the brightness temperature at 6 GHz vertical polarization as expected. However, the emissivity factor normally used when converting brightness temperature to the ice temperature is dependent on the ice temperature. The simulations indicate that a simple model may be used to derive the snow-ice interface temperature from satellite AMSR 6 GHz measurements.

Sea Surface Temperature Climate Data Record for the North Sea and Baltic Sea

AbstractA 30-yr climate data record (CDR) of sea surface temperature (SST) has been produced with daily gap-free analysis fields for the North Sea and the Baltic Sea region from 1982 to 2012 by combining the Pathfinder AVHRR satellite data record with the Along-Track Scanning Radiometer (ATSR) Reprocessing for Climate (ARC) dataset and with in situ observations. A dynamical bias correction scheme adjusts the Pathfinder observations toward the ARC and in situ observations. Largest Pathfinder–ARC differences are found in the summer months, when the Pathfinder observations are up to 0.4°C colder than the ARC observations on average. Validation against independent in situ observations shows a very stable performance of the data record, with a mean difference of −0.06°C compared to moored buoys and a 0.46°C standard deviation of the differences. The mean annual biases of the SST CDR are small for all years, with a negligible temporal trend when compared against drifting and moored buoys. Analysis of the SST CD...

Blending of satellite and tide gauge sea level observations and its assimilation in a storm surge model of the North Sea and Baltic Sea

Coastal storm surge forecasts are typically derived from dedicated hydrodynamic model systems, relying on Numerical Weather Prediction (NWP) inputs. Uncertainty in the NWP wind field affects both the preconditioning and the forecast of sea level. Traditionally, tide gauge data have been used to limit preconditioning errors, providing point information. Here we utilize coastal satellite altimetry sea level observations. Careful processing techniques allow data to be retrieved up to 3 km from the coast, combining 1 Hz and 20 Hz data. The use of satellite altimetry directly is limited to times when the satellite passes over the area of interest. Instead, we use a stationary blending method developed by Madsen et al. (2007) to relate the coastal satellite altimetry with corresponding tide gauge measurements, allowing generation of sea level maps whenever tide gauge data are available. We apply the method in the North Sea and Baltic Sea, including the coastal zone, and test it for operational nowcasting and hindcasting of the sea level. The feasibility to assimilate the blended product into a hydrodynamic model is assessed, using the ensemble optimal interpolation method. A 2 year test simulation shows decreased sea level root mean square error of 7–43% and improved correlation by 1–23% in all modeled areas, when validated against independent tide gauges, indicating the feasibility to limit preconditioning errors for storm surge forecasting, using a relatively cost effective assimilation scheme.

Progress in satellite remote sensing for studying physical processes at the ocean surface and its borders with the atmosphere and sea ice

Physical oceanography is the study of physical conditions, processes and variables within the ocean, including temperature–salinity distributions, mixing of the water column, waves, tides, currents and air–sea interaction processes. Here we provide a critical review of how satellite sensors are being used to study physical oceanography processes at the ocean surface and its borders with the atmosphere and sea ice. The paper begins by describing the main sensor types that are used to observe the oceans (visible, thermal infrared and microwave) and the specific observations that each of these sensor types can provide. We then present a critical review of how these sensors and observations are being used to study: (i) ocean surface currents, (ii) storm surges, (iii) sea ice, (iv) atmosphere–ocean gas exchange and (v) surface heat fluxes via phytoplankton. Exciting advances include the use of multiple sensors in synergy to observe temporally varying Arctic sea ice volume, atmosphere–ocean gas fluxes, and the potential for four-dimensional water circulation observations. For each of these applications we explain their relevance to society, review recent advances and capability, and provide a forward look at future prospects and opportunities. We then more generally discuss future opportunities for oceanography-focused remote sensing, which includes the unique European Union Copernicus programme, the potential of the International Space Station and commercial miniature satellites. The increasing availability of global satellite remote-sensing observations means that we are now entering an exciting period for oceanography. The easy access to these high quality data and the continued development of novel platforms is likely to drive further advances in remote sensing of the ocean and atmospheric systems.

Using a 1‐D model to reproduce the diurnal variability of SST

A wide range of applications, from air-sea interaction studies to fisheries and biological modeling, need accurate, high resolution SST which requires that the diurnal signal is known; for many applications diurnal estimates are necessary and should be included in blended SST products. A widely preferred approach to bridge the gap between in situ and remotely sensed measurements and obtain diurnal warming estimates at large spatial scales, is modeling of the upper ocean temperature. This study uses the 1 dimensional General Ocean Turbulence Model (GOTM) to resolve diurnal signals identified from satellite SSTs and in situ measurements. Focus is given on testing and validation of different parameterizations of the basic physical processes known to influence the generation of a warm surface layer. GOTM is tested and validated using in situ measurements obtained at three locations, two in the Atlantic Ocean and one in the Baltic Sea, where different oceanographic and atmospheric conditions occur, in order to obtain an insight into its general performance. It is found that the model, with a 9 band solar absorption model rather than the standard 2 band scheme, performs well when using 3-hourly NWP forcing fields and is able to resolve daily SST variability seen both from satellite and in situ measurements. As such, and due to its low computational cost, it is proposed as a candidate model for diurnal variability estimates. This article is protected by copyright. All rights reserved.

Deep ocean currents detected with satellite altimetry

Overflows of dense and cold bottom water through Denmark Strait and the Faroe Bank Channel are associated with enhanced meso-scale current variability associated with eddies. These fluctuations can be detected through enhanced sea surface height variability in TOPEX/Poseidon and European Remote Sensing ERS-1/2 satellite altimeter data. The increased variability coincides with the overflow plumes and has a maximum about 50 km downstream of the Faroe Bank Channel. In Denmark Strait, enhanced variability extends 150 km downstream from the sill, with a width of 50‐100 km. There is good agreement between the variability seen by the different satellites. The satellite-observed variability is also in good agreement with in situ observations of temperature and salinity and output from three-dimensional models of the overflow. Sea surface height data show seasonal variability that may be associated with annual variations of the overflow strength.

Ocean tide effects on GRACE gravimetry

Initial studies of the effects of ocean tides on the GRACE gravity field are. Based on the predicted accuracy estimates associated with GRACE errors in the ocean tide modeling are evaluated using gravity anomalies filtered at harmonic degrees 50 and 80 respectively. The results of the analysis show that the ocean tides and the ocean tide loading are important to consider in analysis of GRACE data. Furthermore, the loading is important to consider. The current ocean tide models are not accurate enough to correct GRACE data. Furthermore, the atmospheric tides may give significant errors in the ocean tide model if altimetry corrected for inverted barometer effects is used.

Optimal Estimation of Sea Surface Temperature from AMSR-E

The Optimal Estimation (OE) technique is developed within the European Space Agency Climate Change Initiative (ESA-CCI) to retrieve subskin Sea Surface Temperature (SST) from AQUA’s Advanced Microwave Scanning Radiometer—Earth Observing System (AMSR-E). A comprehensive matchup database with drifting buoy observations is used to develop and test the OE setup. It is shown that it is essential to update the first guess atmospheric and oceanic state variables and to perform several iterations to reach an optimal retrieval. The optimal number of iterations is typically three to four in the current setup. In addition, updating the forward model, using a multivariate regression model is shown to improve the capability of the forward model to reproduce the observations. The average sensitivity of the OE retrieval is 0.5 and shows a latitudinal dependency with smaller sensitivity for cold waters and larger sensitivity for warmer waters. The OE SSTs are evaluated against drifting buoy measurements during 2010. The results show an average difference of 0.02 K with a standard deviation of 0.47 K when considering the 64% matchups, where the simulated and observed brightness temperatures are most consistent. The corresponding mean uncertainty is estimated to 0.48 K including the in situ and sampling uncertainties. An independent validation against Argo observations from 2009 to 2011 shows an average difference of 0.01 K, a standard deviation of 0.50 K and a mean uncertainty of 0.47 K, when considering the best 62% of retrievals. The satellite versus in situ discrepancies are highest in the dynamic oceanic regions due to the large satellite footprint size and the associated sampling effects. Uncertainty estimates are available for all retrievals and have been validated to be accurate. They can thus be used to obtain very good retrieval results. In general, the results from the OE retrieval are very encouraging and demonstrate that passive microwave observations provide a valuable alternative to infrared satellite observations for retrieving SST.

Impact of assimilation of sea-ice surface temperatures on a coupled ocean and sea-ice model

We establish a methodology for assimilating satellite observations of ice surface temperature (IST) into a coupled ocean and sea-ice model. The method corrects the 2 meter air temperature based on the difference between the modelled and the observed IST. Thus the correction includes biases in the surface forcing and the ability of the model to convert incoming parameters at the surface to a net heat flux. A multi-sensor, daily, gap-free surface temperature analysis has been constructed over the Arctic region. This study revealed challenges estimating the ground truth based on buoys measuring IST, as the quality of the measurement varied from buoy to buoy. With these precautions we find a cold temperature bias in the remotely sensed data, and a warm bias in the modelled data relative to ice mounted buoy temperatures, prior to assimilation. As a consequence, this study weighted the modelled IST and the observed IST equally in the correction. The impact of IST was determined for experiments with and without the assimilation of IST and sea-ice concentration. We find that assimilation of remotely sensed data results in a cooling of IST, which improves the timing of the snow melt onset. The improved snow cover in spring is only based on observations from one buoy, thus additional good quality observations could strengthen the conclusions. The ice cover and the sea-ice thickness are increased, primarily in the experiment without sea-ice concentration assimilation.