Anomalous Variability in Antarctic Sea Ice Extents During the 1960s With the Use of Nimbus Data

Gallaher, D. W.; Campbell, G. G.; Meier, Walter N.

doi:10.1109/jstars.2013.2264391

Cited by 23 publications

(29 citation statements)

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“…However, several modeling studies, such as those used in the phase 5 Coupled Model Intercomparison Project (CMIP5), have suggested that the sea ice increase over the last 36 years remains within the range of intrinsic of internal variability (e.g., Bitz and Polvani, 2012;Turner et al, 2013;Mahlstein et al, 2013;Polvani and Smith, 2013;Swart and Fyfe, 2013). Earlier satellite data from the 1960s and 1970s and data from ship observations suggest periods of high and low sea ice extent and thus high natural variability (Meier et al, 2013b;Gallaher et al, 2014). Further evidence comes from ice core climate records, which suggest that the climate variability observed in the Antarctic during the last 50 years remains within the range of natural variability seen over the last several hundred to thousand years (Thomas et al, 2013;Steig et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

“…Also, recent sea ice assessments from early satellite images from the Nimbus program of the late 1960s indicate a similarly high but variable SIE to that observed over (Meier et al, 2013bGallaher et al, 2014). Mapping of the September 1964 ice edge indicates that ice extent likely exceeded both the 2012 and 2013 record monthly-average maxima, at 19.7 ± 0.3 million km 2 .…”

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confidence: 83%

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Mapping and assessing variability in the Antarctic marginal ice zone, pack ice and coastal polynyas in two sea ice algorithms with implications on breeding success of snow petrels

et al. 2016

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Abstract. Sea ice variability within the marginal ice zone (MIZ) and polynyas plays an important role for phytoplankton productivity and krill abundance. Therefore, mapping their spatial extent as well as seasonal and interannual variability is essential for understanding how current and future changes in these biologically active regions may impact the Antarctic marine ecosystem. Knowledge of the distribution of MIZ, consolidated pack ice and coastal polynyas in the total Antarctic sea ice cover may also help to shed light on the factors contributing towards recent expansion of the Antarctic ice cover in some regions and contraction in others. The long-term passive microwave satellite data record provides the longest and most consistent record for assessing the proportion of the sea ice cover that is covered by each of these ice categories. However, estimates of the amount of MIZ, consolidated pack ice and polynyas depend strongly on which sea ice algorithm is used. This study uses two popular passive microwave sea ice algorithms, the NASA Team and Bootstrap, and applies the same thresholds to the sea ice concentrations to evaluate the distribution and variability in the MIZ, the consolidated pack ice and coastal polynyas. Results reveal that the seasonal cycle in the MIZ and pack ice is generally similar between both algorithms, yet the NASA Team algorithm has on average twice the MIZ and half the consolidated pack ice area as the Bootstrap algorithm. Trends also differ, with the Bootstrap algorithm suggesting statistically significant trends towards increased pack ice area and no statistically significant trends in the MIZ. The NASA Team algorithm on the other hand indicates statistically significant positive trends in the MIZ during spring. Potential coastal polynya area and amount of broken ice within the consolidated ice pack are also larger in the NASA Team algorithm. The timing of maximum polynya area may differ by as much as 5 months between algorithms. These differences lead to different relationships between sea ice characteristics and biological processes, as illustrated here with the breeding success of an Antarctic seabird.

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Section: Discussionmentioning

confidence: 99%

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confidence: 83%

Mapping and assessing variability in the Antarctic marginal ice zone, pack ice and coastal polynyas in two sea ice algorithms with implications on breeding success of snow petrels

et al. 2016

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“…(Sea ice area is nominally different from SIE in that it includes regions of 0-15% sea ice concentration, but tests comparing the SIE and sea ice area data reveal no noticeable difference for the season of interest.) Additionally, point estimates of SIE from the Nimbus 1, 2, and 3 satellite missions [Meier et al, 2013a;Gallaher et al, 2014] were used. The Nimbus 1 mission covered a 3 week period in September 1964, Nimbus 2 was operational from May to August 1966, and Nimbus 3 data are available for the entire May 1969 to January 1970.…”

Section: Sie Datamentioning

confidence: 99%

“…Studies have suggested that multidecadal variability may explain some of the observed trends [Fan et al, 2014], with a particular role for atmospheric teleconnections to the tropical Pacific [Ding et al, 2011], but this is hard to test in the satellite record, and useful insight may be gained from extending the sea ice record further back than 1979. Estimates of sea ice areal cover from the early NIMBUS satellite missions now provide Southern Ocean SIE as far back as 1964 [Meier et al, 2013a;Gallaher et al, 2014]. Gagne et al [2015] compared the NIMBUS 1 estimate of September 1964 Southern Ocean SIE with both models and passive microwave data and found that although there has been a slight decrease over the 50 year period, the change was not significant in the context of simulated internal variability.…”

Section: Introductionmentioning

confidence: 99%

Century‐scale perspectives on observed and simulated Southern Ocean sea ice trends from proxy reconstructions

et al. 2016

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Since 1979 when continuous satellite observations began, Southern Ocean sea ice cover has increased, whilst global coupled climate models simulate a decrease over the same period. It is uncertain whether the observed trends are anthropogenically forced or due to internal variability, or whether the apparent discrepancy between models and observations can be explained by internal variability. The shortness of the satellite record is one source of this uncertainty, and a possible solution is to use proxy reconstructions, which extend the analysis period but at the expense of higher observational uncertainty. In this work, we evaluate the utility for change detection of 20th century Southern Ocean sea ice proxies. We find that there are reliable proxies for the East Antarctic, Amundsen, Bellingshausen and Weddell sectors in late winter, and for the Weddell Sea in late autumn. Models and reconstructions agree that sea ice extent in the East Antarctic, Amundsen and Bellingshausen Seas has decreased since the early 1970s, consistent with an anthropogenic response. However, the decrease is small compared to internal variability, and the change is not robustly detectable. We also find that optimal fingerprinting filters out much of the uncertainty in proxy reconstructions. The Ross Sea is a confounding factor, with a significant increase in sea ice since 1979 that is not captured by climate models; however, existing proxy reconstructions of this region are not yet sufficiently reliable for formal change detection.

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“…The authors have provided 1964 Arctic sea ice extents in [13] and 1960s Antarctic sea ice estimates in [14]. The analysis of the Nimbus I, II and III missions for the Arctic sea ice extent data are consistent with the 1979 start of the modern satellite record, and lend more context for the strong downward trend since 1979.…”

Section: Sea Ice Extent Values Resultsmentioning

confidence: 72%

The process of bringing dark data to light: The rescue of the early Nimbus satellite data

Gallaher

Campbell

Meier

et al. 2015

GeoResJ

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a b s t r a c tMyriad environmental satellite missions are currently orbiting the earth. The comprehensive monitoring by these sensors provide scientists, policymakers, and the public critical information on the earth's weather and climate system. The state of the art technology of our satellite monitoring system is the legacy of the first environment satellites, the Nimbus systems launched by NASA in the mid-1960s. Such early data can extend our climate record and provide important context in longer-term climate changes. However, the data was stowed away and, over the years, largely forgotten. It was nearly lost before its value was recognized and attempts to recover the data were undertaken. This paper covers what it took the authors to recover, navigate and reprocess the data into modern formats so that it could be used as a part of the satellite climate record. The procedures to recover the Nimbus data, from both film and tape, could be used by other data rescue projects, however the algorithms presented will tend to be Nimbus specific. Data rescue projects are often both difficult and time consuming but the data they bring back to the science community makes these efforts worthwhile.

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Anomalous Variability in Antarctic Sea Ice Extents During the 1960s With the Use of Nimbus Data

Cited by 23 publications

References 8 publications

Mapping and assessing variability in the Antarctic marginal ice zone, pack ice and coastal polynyas in two sea ice algorithms with implications on breeding success of snow petrels

Mapping and assessing variability in the Antarctic marginal ice zone, pack ice and coastal polynyas in two sea ice algorithms with implications on breeding success of snow petrels

Century‐scale perspectives on observed and simulated Southern Ocean sea ice trends from proxy reconstructions

The process of bringing dark data to light: The rescue of the early Nimbus satellite data

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