Keeling Curve: Result, Interpretation &amp; Global Monitoring

Ostrowski, Augustyn

doi:10.35935/edr/14.3834

Cited by 1 publication

(1 citation statement)

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“…The data from both monitoring centers showed an annual increase in the concentration of CO2 [14,17] , which originated what is now known as the Keeling curve, which graphs the concentration of carbon dioxide in the atmosphere from periodic measurements in both observatories. The Keeling curve was published for the first time in 1960, providing the first evidence of a sustained increase in carbon dioxide levels in the atmosphere due to anthropogenic emissions [18] . Several studies within different fields have been conducted by employing information captured in the Mauna Loa Observatory (MLO).…”

Section: Introductionmentioning

confidence: 99%

Temporal Variography for the Evaluation of Atmospheric Carbon Dioxide Monitoring

Torres¹,

Toro²,

Gálvez³

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Since 1958, the Mauna Loa Observatory (MLO) has continuously monitored carbon dioxide variations using nondispersive infrared (NDIR) 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 term trends are conflated with local and short-term variations remains unclear. Hence the current paper 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 semi-empirical climate models.

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Section: Introductionmentioning

confidence: 99%