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Abstract The interior of Dronning Maud Land (DML) in East Antarctica is one of the most data-sparse regions of Antarctica for studying climate change. A monthly mean near-surface temperature dataset for the last 30 years has been compiled from the historical records from automatic weather stations (AWSs) at three sites in the region (Mizuho, Relay Station, and Dome Fuji). Multiple AWSs have been installed along the route to Dome Fuji since the 1990s, and observations have continued to the present day. The use of passive-ventilated radiation shields for the temperature sensors at the AWSs may have caused a warm bias in the temperature measurements, however, due to insufficient ventilation in the summer, when solar radiation is high and winds are low. In this study, these warm biases are quantified by comparison with temperature measurements with an aspirated shield and subsequently removed using a regression model. Systematic error resulting from changes in the sensor height due to accumulating snow was insignificant in our study area. Several other systematic errors occurring in the early days of the AWS systems were identified and corrected. After the corrections, multiple AWS records were integrated to create a time series for each station. The percentage of missing data over the three decades was 21% for Relay Station and 28% for Dome Fuji. The missing rate at Mizuho was 49%, more than double that at Relay Station. These new records allow for the study of temperature variability and change in DML, where climate change has so far been largely unexplored. Significance StatementAntarctic climate change has been studied using temperature data at staffed stations. The staffed stations, however, are mainly located on the Antarctic Peninsula and in the coastal regions. Climate change is largely unknown in the Antarctic plateau, particularly in the western sector of the East Antarctic Plateau in areas such as the interior of Dronning Maud Land (DML). To fill the data gap, this study presents a new dataset of monthly mean near-surface climate data using historical observations from three automatic weather stations (AWSs). This dataset allows us to study temperature variability and change over a data-sparse region where climate change has been largely unexplored.
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The AntAWS dataset: a compilation of Antarctic automatic weather station observations
Abstract. A new meteorological dataset derived from records of Antarctic automatic weather stations (here called the AntAWS dataset) at 3 h, daily and monthly resolutions including quality control information is presented here. This dataset integrates the measurements ofair temperature, air pressure, relative humidity, and wind speed anddirection from 267 Antarctic AWSs obtained from 1980 to 2021. The AWS spatial distribution remains heterogeneous, with the majority of instrumentslocated in near-coastal areas and only a few inland on the East Antarctic Plateau. Among these 267 AWSs, 63 have been operating for more than 20 years and 27 of them in excess of more than 30 years. Of the fivemeteorological parameters, the measurements of air temperature have the bestcontinuity and the highest data integrity. The overarching aim of thiscomprehensive compilation of AWS observations is to make these data easilyand widely accessible for efficient use in local, regional and continentalstudies; it may be accessed at https://doi.org/10.48567/key7-ch19 (Wang et al., 2022). This dataset isinvaluable for improved characterization of the surface climatology acrossthe Antarctic continent, to improve our understanding of Antarctic surfacesnow–atmosphere interactions including precipitation events associated with atmospheric rivers and to evaluate regional climate models ormeteorological reanalysis products.
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- PAR ID:
- 10420949
- Date Published:
- Journal Name:
- Earth System Science Data
- Volume:
- 15
- Issue:
- 1
- ISSN:
- 1866-3516
- Page Range / eLocation ID:
- 411 to 429
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.5194/essd-15-411-2023
