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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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GRC Weather Station Data, San Salvador Island, Bahamas
To monitor meteorologic conditions on San Salvador Island throughout the duration of our well and lake instrumentation campaigns (see associated datasets), we installed an automatic weather station (AWS) at the Gerace Research Centre (GRC) located on the island's northern shore. The GRC weather station was equipped with a HOBO U30 Data Logger that recorded sensor measurements at a 15-minute sampling rate from November 2017 through October 2019. The AWS measured air temperature, and relative humidity with a Temperature/RH Smart Sensor (S-WSB-M003) installed within a solar radiation shield to prevent overheating. Rainfall was measured with a HOBO/Onset Rain Gauge Smart Sensor that using a tipping bucket mechanism mounted on a stainless steel shaft with brass bearings within aluminum housing to monitor rainfall rates up to 12.7 cm per hour. Atmospheric pressure was measured using a Barometric Pressure Smart Sensor within weatherproof housing with an accuracy of +/- 3.0 mbar, a resolution of 1.0 mbar, and a measurement range of 660-1070 mbar. Incoming shortwave solar radiation was measured with a silicon pyranometer (Solar Radiation Smart Sensor) mounted onto the weather station using the Onset Light Sensor Bracket. Data gaps due to sensor failure or proceeding sensor addition to the weather station producing null values are filled with "NaN" (i.e., not a number).
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- Award ID(s):
- 1743383
- PAR ID:
- 10501113
- Publisher / Repository:
- HydroShare
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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