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Temporal Variography for the Evaluation of Atmospheric Carbon Dioxide Monitoring

Since 1958, the Mauna Loa Observatory (MLO) has continuously monitored carbon dioxide variations using nondispersive infrared sensors, with the Keeling curve as an early indicator of the anthropogenic contribution to… Click to show full abstract

Since 1958, the Mauna Loa Observatory (MLO) has continuously monitored carbon dioxide variations using nondispersive infrared sensors, with the Keeling curve as an early indicator of the anthropogenic contribution to global atmospheric carbon dioxide. The increasing CO2 levels are alarming and have led to international agreements that promote cleaner industrial activities. However, any change in global behavior would not immediately cause detectable changes in the MLO data; the extent to which global and long-term trends are conflated with local and short-term variations remains unclear. Hence the current article verifies the performance of the sampling and measurement systems of MLO, using existing data published within the months of October and November 2020, which comply with the temporal continuity requirements of chronostatistics. It has been determined that the components of the MLO air including carbon dioxide are well mixed due to their particular location. Beyond this, the variographic analysis distinguishes between small (<5%) and large (>10%) variability contributions due to sampling, including graphical depictions of MLO data. Coupled with the precision of the method being better than 0.2 ppm, it has been determined that the sampling and measurement protocols are highly suitable to meet the objective of representing CO2 fluctuations over time. The variographic application also manages to quantify short-term variabilities resulting from the local processes of the region where the observatory is located. The results support the furthering of multiscaled temporal analysis of atmospheric CO2, and potentially the incorporation of CO2 variographic parameters into empirical and semiempirical climate models.

Keywords: atmospheric carbon; temporal variography; carbon; carbon dioxide; variography evaluation

Journal Title: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Year Published: 2022

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