Towards a Satellite-Based Sea Ice Climate Data Record

1. 0 20 40 60 80 100 Concentration (%) U21A-0801 Towards a Satellite-Based Sea Ice Climate Data Record Walter N. Meier1, Florence Fetterer1, Julienne Stroeve1, Donald J. Cavalieri2, Claire L. Parkinson2, Josefino C. Comiso2, and Ronald Weaver1 1National Snow and Ice Data Center, University of Colorado, Boulder, CO 80309 2NASA Goddard Space Flight Center, Greenbelt, MD 20771 http://polynya.gsfc.nasa.gov http://nsidc.org Introduction Sea Ice Products Potential Climate Data Records The NASA Team (NT) and Bootstrap (BT) algorithms provide complete information on the ice cover throughout the year (Figure 1). While care is taken to insure each algorithm is internally consistent via inter-sensor calibration and quality control techniques, the two algorithms yield different results, as illustrated in Figure 2. The NT algorithm typically shows less ice than the BT, particularly during summer. Each algorithm uses a different approach to estimate sea ice cover, including different passive microwave channels and different reference brightness temperatures (Tb). For example, the NT algorithm uses brightness temperature ratios to remove the effect of physical temperature; the BT algorithm uses seasonally-adjusted reference Tbs to improve performance during summer melt. Each method has advantages and disadvantages and compromises must be made because the spatial, temporal, and spectral resolution cannot resolve all features of the complex sea ice surface. This is particularly true for thin ice, whose emissivity varies with thickness depending on the frequency. This can cause considerable uncertainties in such regions because the algorithms are based on general conditions over the whole hemisphere, which is dominated overall by thicker ice. Each algorithm makes different compromises, as discussed in a joint study by the NASA Goddard authors [9].The key features of each algorithm product is that they both use internally consistent methods, consistent microwave frequencies, and consistent quality control. Sea ice plays an important role in climate by reflecting incoming solar radiation, modifying the salinity of the upper ocean during ice growth and melt, and insulating the ocean from the atmosphere. Thus a reliable, complete, and consistent climate data record of sea ice extent and area is important for climate studies. Good quality satellite records of sea ice from passive microwave sensors dating back to 1978 already exist. These records have been used to track interannual variability in both the Arctic and the Antarctic [1, 2]. In the Arctic, a significant downward trend in summer ice cover has been detected [3]. These records are among the longest and most consistent from satellites. However, there are estimates from several algorithms and no single algorithm has proven to be superior under all ice conditions. Thus, there is still potential to create higher quality estimates to obtain a unified sea ice climate data record. Differences in sea ice records are further illuminated when looking at other sea ice data records for September monthly means (Figure 3) from a variety of time series, described in Table 1. The Goddard and NSIDC data sets are pure passive microwave products from the NASA Team or Bootstrap and are more consistent. The NIC [6], Hadley [7], and ESMR Merged [8] datasets are combined products using different sources through time to create longer time series, but this leads to differences in data quality through time and possible inconsistencies in the time series. This is most notable in the NIC and Hadley data sets after 1996. The addition of Radarsat SAR as a source allowed more thin ice to be detected and increased estimates from the NIC charts compared to earlier years. The Hadley time series is based on passive microwave fields, adjusted by the 1973-1994 NIC climatology to correct the passive microwave summer low-bias; however, after 1996, the Hadley data set switched from the Goddard NT product to NCEP sea ice fields. Thus, the NIC and Hadley post-1996 fields are possibly more accurate than the pure passive microwave, but they are not consistent with their earlier period. The change in the Hadley product is particularly evident where the 1987-2002 decadal trend in the Arctic is actually positive (Table 1), inconsistent with all other time series. March September Arctic Antarctic Background Figure 3: Arctic September minimum sea ice extent from various sources. Goddard NT 2004 & 2005 estimated from the NSIDC Sea Ice Index. February September Physical Basis for Retrieval: The character of naturally emitted microwave radiation is particularly sensitive to the phase of water. In particular, the solid phase of sea ice is generally distinct from the liquid ocean waters allowing sea ice to be distinguished from the open ocean using satellite passive microwave data. There are several complicating factors such as emission from melting snow or meltwater on the ice surface, wind-roughened ocean, and water in the atmosphere. These factors lead to uncertainties in retrievals of sea ice cover. Passive Microwave Sensors:The first satellite-borne passive microwave sensor was the Electrically Scanning Microwave Radiometer (ESMR), launched in 1972. However, this was a single-channel radiometer and had several major data gaps, limiting the usefulness of its sea ice products for climate studies. The launch of the Scanning Multichannel Microwave Radiometer (SMMR) in 1978 marked the beginning of near-continuous coverage of sea ice that continued through the launch of a series of Special Sensor Microwave/Imagers (SSM/I) beginning in 1987 through to the present. Since 2002, the NASA EOS Advanced Microwave Scanning Radiometer (AMSR-E) sensor, with more channels, increased spatial resolution, and enhanced algorithms, has yielded improved sea ice estimates. Sea Ice Algorithms:Several algorithms have been developed to obtain estimates of sea ice from the observed brightness temperatures. Two of the most commonly used algorithms, the NASA Team [4] and Bootstrap [5], are presented here. Both algorithms were developed at the NASA Goddard Space Flight Center and the timeseries of sea ice conditions from the algorithms are archived at the National Snow and Ice Data Center (NSIDC). Sea Ice Products: Time series of sea ice extent (total ocean area containing at least 15% ice), sea ice area (total area covered by ice), and sea ice concentration (percentage of area covered by ice) have been produced for both algorithms that span the entire multichannel radiometer period, November 1978 – present. These products are already quite mature and contain numerous quality control enhancements, including inter-sensor calibration to assure a consistent timeseries. The products also filter contamination from mixed land-ocean pixels and weather effects (from atmospheric emission and/or wind-roughening of the ocean). Missing data are filled in through spatial and temporal interpolation to provide a complete timeseries. The resulting daily and monthly estimates of sea ice cover, as well as climatologies, browse imagery, and other ancillary data are available from NSIDC at: http://nsidc.org/data/seaice/. Browse images, animations, and monthly mean data for the NASA Team algorithm are also available from NSIDC’s Sea Ice Index pages, http://nsidc.org/data/seaice_index/. Other sea ice data products include operational ice charts from the U.S. National Ice Center [6] and other national ice services, model input-based fields such as the Hadley Center climatology [7], and fused fields such as a combined ESMR-SMMR-SSM/I-NIC timeseries [8]; these use a variety of sources to provide complete fields for their given purposes. Figure 1: Bootstrap 1978-2003 monthly mean sea ice concentration fields for the Arctic (top row) and Antarctic (bottom row) for the month of annual maximum (March for Arctic, September for Antarctic) and minimum (September for Arctic, February for Antarctic) ice cover. Table 1: Data sources for Figure 4. NT = NASA Team, BT = Bootstrap. Time period varies for each, but all products overlap for 1987-2002. Decadal trends are for September means. Figure 2: Timeseries of Arctic (left) and Antarctic (right) monthly sea ice extent from the Bootstrap and NASA Team (Nov. 1978 – Sep. 2005) algorithms; Jan. 2004 – Sep. 2005 NASA Team values from the Sea Ice Index, all other values from the NASA Goddard SMMR-SSM/I Timeseries. Tick marks represent January of each year. References Conclusion [1] Gloersen, P., C.L. Parkinson, D.J. Cavalieri, J. Comiso, and H.J. Zwally, 1999. Spatial distribution of trends and seasonality in the hemispheric sea ice covers: 1978-1996, J. Geophys. Res., 104(C9), 20,827-20,836. [2] Parkinson, C.L., D.J. Cavalieri, P. Gloersen, H.J. Zwally, and J.C. Comiso, 1999. Arctic sea ice extents, areas, and trends, 1978-1996, J. Geophys. Res., 104(C9), 20,837-20,856. [3] Stroeve, J.C., M.C. Serreze, F. Fetterer, T. Arbetter, W.N. Meier, J. Maslanik, and K. Knowles, 2005. Tracking the Arctic’s shrinking ice cover: Another extreme minimum in 2004, Geophys. Res. Lett., 32, L04501, doi: 10.1029/2004GL021810. [4] Cavalieri, D.J., P. Gloersen, and W.J. Campbell, 1984. Determination of sea ice parameters with the Nimbus 7 SMMR, J. Geophys. Res., 89(C3), 5355-5369. [5] Comiso, J., 1986. Characteristics of Arctic winter sea-ice from passive microwave and infrared observations, J. Geophys. Res., 91(C1), 975-994. [6] Dedrick, K., K. Partington, M. Van Woert, C. Bertoia, and D. Benner, 2001. U.S. National/Naval Ice Center Digital Sea Ice Data and Climatology, Can. J. Remote Sensing, 27(5), 457-475. [7] Rayner, N.A., D.E. Parker, E.B. Horton, C.K. Folland, L.V. Alexander, D.P. Rowell, E.C. Kent, and A. Kaplan. 2003. Global analysis of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. Journal of Geophysical Research. 108 (D14). doi:10.1029/2002JD002670. [8] Cavalieri, D.J., C.L. Parkinson, and K.Y. Vinnikov, 2003. 30-year satellite record reveals contrasting Arctic and Antarctic variability, Geophys. Res. Lett., 30(18), 1970, doi: 10.1029/2003GL018031. [9] Comiso, J.C., D.J. Cavalieri, C.L. Parkinson, and P. Gloersen, 1997. Passive microwave algorithms for sea ice concentration: A comparison of two techniques, Rem. Sens. Env., 60, 357-384. [10] National Research Council, 2004. Climate data records from environmental satellites, National Academies Press, Washington, DC, 116 pp. [11] Meier, W.N., 2005. Comparison of passive microwave ice concentration algorithm retrievals with AVHRR imagery in the Arctic peripheral seas, IEEE Trans. Geosci. and Remote Sens., 43(6), 1324-1337. A climate data record of sea ice cover should be one consistent, quality-controlled data set that is able to track climate variability and change [10]. As of now there is no single clear-cut superior sea ice data product. Data fusion approaches, similar to that used in the NIC, Hadley, and ESMR Merged timeseries, can be used to produce an optimal data product. The newer AMSR-E, with higher resolution and higher-quality sensor properties, can further improve the SMMR-SSM/I timeseries as we approach the NPOESS era.Great care must be taken to avoid inconsistencies due to differing data sources and different processing methods, as demonstrated by the NIC and Hadley time series. However, intelligent data fusion of the data sets, with strict standards for consistency, could yield high quality time series. Another important component of a climate data record is a quality assessment. There have been several studies of the errors in the various passive microwave algorithm products, e.g. [11], but there is not yet a data product with complete quality flags. This is the next step toward a satellite-based sea ice climate data record.