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Fitzgerald, N. and Kirkpatrick, J. (2020) Macquarie Island air temperature lapse rates and cloud cover, 2014-2016, Ver. 1, Australian Antarctic Data Centre - doi:10.26179/qssa-7590, Accessed: 2021-06-25
Macquarie Island air temperature lapse rates and cloud cover, 2014-2016
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Australian Antarctic Data Centre, Australia
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Air temperature lapse rates vary geographically and temporally. Sub-Antarctic Macquarie Island provides an opportunity to compare lapse rates between windward and leeward slopes in a hyper-oceanic climate. Development of orographic cloud is expected to modify lapse rates, given the theoretical shift between dry and saturated adiabatic lapse rates that occurs with condensation of water vapour.
This dataset is part of a PhD project examining vegetation patterns and drivers on Macquarie Island. Data loggers were placed along an east-west altitudinal transect across the narrow axis of Macquarie Island to record air temperature from August 2014 to March 2016.A random sample of digital photographs from the AAD webcam at Macquarie Island Station was used to classify cloud base level as observed from the Station.
This dataset includes air temperature data from LogTag loggers, analysis of near surface atmospheric lapse rates, observations of cloud cover from webcam images and relevant data supplied by Bureau of Meteorology used in analysis.

Reference: Fitzgerald, N. B., and Kirkpatrick, J. B. (2020). Air temperature lapse rates and cloud cover in a hyper-oceanic climate. Antarctic Science, 14.

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The LogTag recorder located at the BoM weather station had a median air temperature of 5.4°C compared to the BoM median of 5.1°C for the same period. LogTag data were consistent with the station at lower air temperatures but show some deviation at higher temperatures, at which the logger frequently had higher readings than the BoM temperature probe. LogTag readings that notably exceeded synchronous BoM readings occurred in the daytime in summer, most often when cloud cover (measured in oktas at the BoM station) was low, which suggests the LogTag unit was heated above ambient air temperature by direct solar radiation. We identified outliers in the LogTag data using median absolute deviation (MAD) and accounted for the asymmetric distribution of outliers (i.e. mostly high temperatures) by calculating MAD separately for values lower and higher than the median. As the median temperature varies seasonally, we applied MAD monthly to each logger dataset. Outliers, defined as those values greater than two deviations from the median, were replaced with no data (n = 5833 or 4.9% of the data). Datasets with outliers removed showed little difference in median values from the original datasets.


These data are publicly available for download from the provided URL.

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  • fitzgerald, nicholas (INVESTIGATOR)
  • kirkpatrick, james (INVESTIGATOR)

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