IOMASA SEA ICE DEVELOPMENTS

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1 IOMASA SEA ICE DEVELOPMENTS Soren Andersen, Rasmus Tonboe, Morten Lind Danish Meteorological Institute, Lyngbyvej 100,DK-2100 Copenhagen O Georg Heygster, Christian Melsheimer, University of Bremen, Germany Leif Toudal, DTU, Denmark Harald Schyberg, Frank Tveter, met.no, Norway Per Dahlgren, Tomas Lundelius, Nils Gustafsson SMHI, Sweden ABSTRACT Sensitivity studies show that the radiometer ice concentration estimate can be biased by +10% by anomalous atmospheric emissivity and -20% by anomalous ice surface emissivity. The aim of the sea ice activities in EU 5th FP project IOMASA is to improve sea ice concentration estimates at higher spatial resolution. The project is in the process of facilitating an ice concentration observing system through validation and a better understanding of the microwave radiative transfer of the sea ice and overlying snow layers. By use of a novel modelling approach, it is possible to better detect and determine the circumstances that may lead to anomalous sea ice concentration retrieval as well as to assess and possibly minimize the sensitivities of the retrieval system. Through an active partnership with the SAF on Ocean and Sea Ice, a prototype system will be implemented as an experimental product chain in order to shorten the loop from development to operational processing. The presentation will present the developments and examples of the new retrievals and finally give an outlook to the future perspectives of the system. 1. INTRODUCTION At present, the polar regions belong to the regions of which the least information is available about the current and predicted states of surface and atmosphere. Because of sparse observations, we only have a rough quality weather forecasts for northern Europe, and ice charts for the ice frequented waters of the European Arctic. The objective of IOMASA started in October 2002 is to improve our knowledge about the Arctic atmosphere by using satellite information which is continuously available, but currently not exploited. This progress will be achieved through an integrated approach involving the following 4 points (the outcomes of each point serving to improve the other ones): (1) remote sensing of atmospheric parameters temperature, humidity and cloud liquid water over sea and land ice, (2) improved remote sensing of sea ice with more accurate and higher resolved ice concentrations (percentage of ice-covered sea surface), (3) improving numerical weather prediction (NWP) models by assimilating the results of the points 1 and 2. We expect from IOMASA progress in the fields of (1) weather forecast for northern Europe, (2) ice charts for the ice frequented waters of the European Arctic, and (3) the estimation of the fraction of open water in the higher Arctic which is very important for the total heat budget of the region, affecting both local and regional weather and climate. A closed ice surface reduces the surface heat fluxes by a factor 100 to 1000 compared to open ocean [Moritz, 1988]. Thanks to the involvement by the OSISAF and HIRLAM communities and direct access to their systems, a fast turnaround from scientific result to operational application is expected.

2 Figure 1: Overview of the IOMASA approach. While the project is thus aiming broadly at improvement of the Arctic observing system, this paper will focus on the achievements obtained and expected in the field of sea ice remote sensing. This activity has been dominated by the need for an improved understanding of processes taking place in the system of sea ice and superimposed snow layers. The paper will present observational as well as theoretical evaluations of the problems encountered and will present results directly applicable to the OSISAF sea ice products. 2. OBSERVING SURFACE EFFECTS Figure 2: OSISAF concentration products from Feb 18 th (left) and 28 th (right) 2005, see text. The OSISAF uses a hybrid approach involving the Bootstrap [Comiso, 1986] and Nasa Team [Cavalieri et al., 1984] algorithms such that the Bootstrap algorithm is assigned highest weight in the low ice concentration regime and vice versa [Andersen, 2000]. During the routine validation of the OSISAF products

3 it has been observed that especially in the high ice concentration regime in areas such as the Greenland East Coast and the Barents Sea, the OSISAF product tends to underestimate the concentration. The Nasa Team algorithm has been reported to be particularly sensitive to layering effects, such as crusts, in the snow and sea ice surfaces [Comiso et al, 1997; Markus and Cavalieri, 2000]. An important process leading to the formation of layering is the acceleration of snow and ice metamorphism through successive melt and refreeze in the ice and snow. However, only few have previously reported on the warm spells involved in such processes during winter [Drinkwater et al., 1995; Voss, 2002]. Figure 2 shows a recent example before and after a warm spell in Western Greenland in 20 Feb It is seen how the ice concentrations along the Greenland West coast remain depressed even on 28 Feb, when temperatures have long returned to freezing. SAR data and navigational ice charts confirm that this depression is erroneous. During IOMASA, Tonboe et al. (2004) studied such warm spells intensively. From the examination of numerous such events it was confirmed that the Nasa Team algorithm was particularly sensitive over both first-year (FY) and multi year (MY) sea ice surfaces, while the bootstrap algorithm remained virtually immune. More importantly, the effects tended to linger for several weeks in the Nasa Team retrievals whereas with the Near 90 GHz algorithm the effects typically subsided in the course of a day. It was also observed that the spectral gradient between 19 and 37 GHz V polarized brightness temperatures (GR) showed a characteristic jump on refreeze. Detecting rapid changes in GR together with high Bootstrap ice concentration retrievals turned out as an efficient indicator of the refreeze stage. The scale of the problem was analyzed on a 4 year Arctic time series. The frequency of occurrence as well as size of event (>15 000km2) is shown in figure 3. This analysis underpins the extent of the problems. Warm air spells and temporary ice surface melt over sea ice in the Arctic occur often during autumn and winter (Oct. - Apr.) especially along the ice edge. The events occur weekly on small and intermediate scales ( km2) and occasionally also on larger scales ( km2). The large events can penetrate deep into the central Arctic Ocean. Figure 3: The frequency and size of sea ice surface melt during autumn and winter MODELLING RADIATIVE TRANSFER OF SEA ICE AND SNOW In order to explore the processes taking place in the sea ice and snow system, a copy of the MEMLS (Microwave Emission Model for Layered Snow-packs) system [Wiesmann & Mätzler, 1999] was obtained from the University of Bern. MEMLS has been developed for snow on land and therefore it was necessary to add extensions to include the description of sea ice scattering and dielectrics [Tonboe et al., 2005]. In lack of adequate in-situ validation data, the model was run with a snow/ice profile based on observed profiles made by Polarstern on 23rd March 2003 in the Barents Sea and compared to coincident AMSR-E data. The result is shown in Figure 4.

4 Figure 4: MEMLS simulation (lines) with measured AMSR-E brightness temperatures (points) superimposed. Vertical polarizations are shown with solid line and asterisks, horizontal polarizations use dashed line and diamonds. It was observed from the snow and sea ice profiles that the surface conditions were very inhomogeneous and varied drastically within very small spatial distances. The above result should therefore be taken only as a preliminary indication of the quality of the MEMLS model with sea ice extensions. A satisfactory validation requires detailed observations of the snow and ice structure with concurrent in-situ radiometric measurements. 4. SENSITIVITY STUDIES The MEMLS model allows a range of sensitivities to be explored. Here we will show results from the application of simulations to 3 commonly used ice concentration algorithms, including the Nasa Team 2 algorithm, that was developed as a fix to the surface related shortcomings of the Nasa Team algorithm. The model setup consists of 3 snow layers on top of two layers of first year ice as given in table 1. Type T[K] Density[kg/m3] Thickness[cm] PCI[mm] S[psu] Snow/ice Nearly new snow Snow Hard densified slap Snow Coarse grains Snow FY sea ice Ice FY sea ice Ice Table 1. The constructed first-year ice profile used as input to MEMLS. This setup is thought to be representative for snow packs on FY sea ice. By varying the density of the top snow layer it is possible to vary the opacity of this layer as well as to introduce a dielectric contrast against the air and densified snow below. This essentially represents layering and could be considered a likely result of melt and subsequent refreeze. A common consequence of a passing melt event is also the formation of coarse snow grains near the snow/ice interface. This is represented by varying the correlation length (a measure of grain size) of the coarse grain snow layer. The results are shown in figure 5 and reproduce the large sensitivity of the Nasa Team and comparable immunity of the Bootstrap algorithms to layering. Further the results from the Nasa Team 2 algorithm shows the effects of the implicit atmospheric correction scheme that obviously compensates some of the surface signal. Meier (2004) found Nasa Team 2 to deliver very high performance when compared to AVHRR data and visual assessments of the retrievals suggest that it eliminates virtually all the typical problems found with the original Nasa Team algorithm. However, ongoing studies within IOMASA suggest a complex dependence of the Nasa Team 2 atmospheric correction scheme on signals that obviously stem from the surface. Finally, the Near 90 GHz algorithm shows large sensitivities to layering and also somewhat to layers of coarse grains. However, due to the much lower penetration depth at 90 GHz, the algorithm may be immune to these effects given in those circumstances where the top snow

5 layer is sufficiently compact. A manifestation of this can be seen by the fact that grain size if only important in the presence of a very light upper snow layer. Figure 5: Sensitivities of the Nasa Team, Bootstrap, Nasa Team 2 and Near 90 GHz algorithms (left to right) with varying density of the upper snow layer and grain size (correlation length) of the coarse grain snow layer. An R symbol at (250kg/m 3,0.14mm) marks the reference point to which the algorithms have been adjusted to return 100% sea ice. 5. VALIDATION OF CONCENTRATION RETRIEVALS In contrast to validation over open water, where the algorithms are required to return as close to 0% as possible, the validation of sea ice retrievals in the high concentration regime is inherently difficult. This is due to the uncertainty of the true ice concentration, depending on the amount of subpixel cracks and leads generally formed by local dynamics. Over ice covered surfaces, the true ice concentration mostly remains known only within 10-20% in navigational ice analyses, which are broadly believed to be the most accurate operational source of information on the sea ice. To support the development and to help validate the different approaches, high resolved ice information is necessary. While visible and infrared sensors can contribute useful information, their use is hampered by clouds, that are very difficult to mask over ice. More importantly, by excluding cloudy situations, they will not be representative of the range of atmospheric settings that actually occur. SAR data are cloud independent, and mostly influenced by the surface wind over open water. Although

6 situations occur, where SAR data are useless for sea ice determination, the range of useful physical settings is much larger than for optical sensors. Figure 6: Example of results from the 3 steps followed in producing the IOMASA validation data. From left to right: Selection of training samples, classified image and finally masking of areas deemed unreliable by the ice analyst. Consequently, the final step in the IOMASA sea ice development efforts is a validation against classified SAR data. The classification scheme uses learning vector quantisation with a feature space composed of a selection of second order texture parameters as well as the original amplitude SAR data. It was adapted to Envisat and Radarsat data from a system for ERS SAR data implemented at University of Bremen [Kern et al., 2002]. The classification is supervised by skilled ice operators and are assessed to give ice concentrations accurate within 3% [Bøvith & Andersen, 2005]. For a given SAR scene, the procedure follows 3 steps: Specification of training data, automatic classification and masking of unreliable areas. Examples of results at each step are shown in figure 6. In effect, the scheme should be viewed as a tool for the ice operator to be able to delineate the ice and water pixels at much higher detail than is possible by hand. As such it adds spatial detail to the superior accuracy of navigational ice analyses. The validation will include around 50 SAR scenes representing all seasons and all areas of the Arctic. It is expected to deliver definitive scientific significance to the results obtained from radiative transfer modelling and time series analyses. 6. ADVANCED DEVELOPMENTS A range of more experimental sea ice developments are also in progress within IOMASA. These include: A thermodynamic model that allows to simulate and integrate the physical state of the snow pack based on meteorological information and provide input to the MEMLS radiative transfer model. A book-keeping procedure to help track parameters, such as melt events and sea ice age distributions, and advect them with satellite observed sea ice drift. Preliminary experiments presented in figure 7 have been able to produce reasonably realistic distributions of sea ice age when integrating ice drift data constrained by observed ice concentrations over 3 months. A sea ice concentration system. This is intended at least as a first step towards the inclusion of the high resolution observations made in the near 90 GHz band without sacrificing the superior signal to noise ratio delivered by the high contrast between ice and open water in the low frequency domain. In its simplest form it will consist of a low and a high resolution ice concentration field together with various flags such that the high resolution information can be used where it is less affected by atmospheric and surface anomalies. It is the ambition of IOMASA to develop rules that may govern this failover to lower resolution. Analysis of correlations and time evolution of successive orbits.

7 Figure 7: Projected state of the arctic sea ice at 31 st Dec 2000 resulting from integrating CERSAT satellite derived ice drift fields from 1 Oct to 31 st Dec Fields are 1) Volume of ridges, 2) Seawinds HH σ 0, 3) Multi year ice concentration, 4) New ice concentration. 7. CONCLUSIONS AND FUTURE DIRECTIONS As described in the previous sections, IOMASA has demonstrated unique results in terms of sea ice observation. On the purely scientific level, IOMASA has advanced the quantitative understanding of the processes taking place in the sea ice and snow system significantly. These results are likely to benefit and define the directions taken in future research towards a fuller understanding and description of the Arctic system. The possible applications embrace the full spectrum from weather prediction and atmospheric remote sensing to ocean and climate modeling. Additionally a path towards the extraction of comprehensive validation information from SAR data has been defined improving the confidence of validations over sea ice. The unique composition of the project team with participation from the OSISAF group will help ensure that the improved methods are implemented in a fashion that allows easy transfer to operations. At this stage significant problems with the OSISAF sea ice concentration algorithm have been pinpointed and a range of possible solutions are awaiting the final validation. As a concrete result IOMASA stands to deliver unprecedented scientific justification as well as a good deal of the implementation of a revised OSISAF sea ice concentration algorithm that is more insensitive to the surface effects suffered by the current algorithm. Another intriguing possibility arises from the integration of NWP models in the project. This allows to verify the value of the achieved results directly and to help provide directions for future efforts. Optimal sea ice retrievals are found to play an important role in the assimilation of microwave sounding data over sea ice as well as in new surface schemes allowing the description fractional surfaces within a model grid cell. The period of January 2005 will serve as a demonstration period for the integration of the IOMASA developments.

8 Looking beyond IOMASA it is hoped that initiatives e.g. during the International Polar Year will gather enough momentum to be able to deliver the direct measurements of sea ice and snow that are needed to validate and further advance the radiative transfer and thermodynamic models. Also with the expansion of spectral coverage of space borne microwave observations, it is likely that the objective of using information from the near 90 GHz band operationally with a lesser sacrifice of the signal to noise ratio is approaching. Developments within the OSISAF are already exploring the possibility of using the additional information provided by the SSMIS instrument to compensate the influence of clouds. This is currently a dominating factor in limiting the performance of high resolution ice concentration algorithms. While IOMASA has demonstrated improvements that will impact sea ice retrieval during most of the year, there has not been resources to specifically investigate the multitude of problems that need to be solved during the 3-4 summer months. We trust, however, that the improved theoretical understanding of the processes taking place in the snow and ice system can help in a long term effort towards improving summer sea ice retrievals. 8. ACKNOWLEDGEMENTS The authors wish to thank Christian Mätzler of the University of Bern for providing the MEMLS code, Lars Kaleschke of the University of Bremen for providing code for a SAR ice classification system for ERS SAR as well as CERSAT at Ifremer for providing sea ice drift fields from Seawinds. 9. REFERENCES Andersen, S. (2000) Evaluation of SSM/I sea ice algorithms for use in the SAF on Ocean and Sea Ice. DMI Scientific Report 00-10, Danish Meteorological Institute, Copenhagen. Bøvith, T., S. Andersen (2005) Sea Ice Concentration from Single-Polarized SAR data using Second-Order Grey Level Statistics and Learning Vector Quantization. DMI Scientific Report, in print. Cavalieri, D.J, P. Gloersen, & W. J. Cambell (1984). Determination of sea ice parameters with the NIMBUS 7 SMMR. Journal of Geophysical Research 89(D4), Comiso, J.C. (1986). Characteristics of arctic winter sea ice from satellite multispectral microwave observations. Journal of Geophysical Research 91(C1), Comiso J.C, D.J. Cavalieri, C.L. Parkinson, P. Gloersen, (1997) Passive microwave algorithms for sea ice concentration: A comparison of two techniques. Remote Sens. Environ, 60, Drinkwater, M. R., R. Hosseinmostafa, & P. Gogeneni (1995). C-band backscatter measurements of winter sea-ice in Weddell Sea, Antarctica. International Journal of Remote Sensing 15(17), Kern, S., L. Kaleschke, and D.A. Clausi (2003). A comparison of two 85GHz SSM/I ice concentration algorithms with AVHRR and ERS-2 SAR imagery, IEEE Trans. Geosc. Rem. Sens., 41(10), Markus, T.; D.J. Cavalieri (2000) An enhancement of the NASA Team sea ice algorithm. IEEE Transactions on. Geoscience. and Remote. Sensing., 38(, 3), Moritz, R.E. (1988). The ice budget of the Greenland Sea. PhD thesis, University of Washington Technical Report APL-UW TR8812. Meier, W. (2004) Comparison of passive microwave ice concentration algorithm retrievals with AVHRR imagery in Arctic peripheral seas. IEEE Transactions on. Geoscience. and Remote. Sensing, In print. Tonboe R., S. Andersen, L. Toudal (2004). On winter surface melt induced sea ice emissivity changes around Greenland and the impact on the computed ice concentration using radiometer algorithms. Remote Sens. Environ Submitted. Tonboe R., G. Heygster, L. Toudal, S. Andersen (2005). Sea Ice Emission Modelling. In Mätzler (Ed.): Radiative transfer models for microwave radiometry. In print. Voss, S. (2002). Synergetische Charakterisierung von Meereis mit SSM/I- und Scatterometerdaten. (Ph.D.- thesis in German), Berichte aus dem Institut für Umweltphysik, Band 15, Universität Bremen, Logos Verlag: Berlin. Wiesmann, A. & C. Mätzler (1999). Microwave emission model of layered snowpacks. Remote Sensing of Environment 70,