دورية أكاديمية
Technical note: Challenges in detecting free tropospheric ozone trends in a sparsely sampled environment.
| العنوان: | Technical note: Challenges in detecting free tropospheric ozone trends in a sparsely sampled environment. |
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| المؤلفون: | Chang, Kai-Lan, Cooper, Owen R., Gaudel, Audrey, Petropavlovskikh, Irina, Effertz, Peter, Morris, Gary, McDonald, Brian C. |
| المصدر: | Atmospheric Chemistry & Physics; 2024, Vol. 24 Issue 10, p6197-6218, 22p |
| مصطلحات موضوعية: | TROPOSPHERIC ozone, INFERENTIAL statistics, SAMPLING errors, TIME series analysis, OZONE, TROPOSPHERE |
| مصطلحات جغرافية: | HAWAII, MAUNA Loa (Hawaii Island, Hawaii) |
| مستخلص: | High-quality long-term observational records are essential to ensure appropriate and reliable trend detection of tropospheric ozone. However, the necessity of maintaining high sampling frequency, in addition to continuity, is often under-appreciated. A common assumption is that, so long as long-term records (e.g., a span of a few decades) are available, (1) the estimated trends are accurate and precise, and (2) the impact of small-scale variability (e.g., weather) can be eliminated. In this study, we show that the undercoverage bias (e.g., a type of sampling error resulting from statistical inference based on sparse or insufficient samples, such as once-per-week sampling frequency) can persistently reduce the trend accuracy of free tropospheric ozone, even if multi-decadal time series are considered. We use over 40 years of nighttime ozone observations measured at Mauna Loa, Hawaii (representative of the lower free troposphere), to make this demonstration and quantify the bias in monthly means and trends under different sampling strategies. We also show that short-term meteorological variability remains a cause of an inflated long-term trend uncertainty. To improve the trend precision and accuracy due to sampling bias, two remedies are proposed: (1) a data variability attribution of colocated meteorological influence can efficiently reduce estimation uncertainty and moderately reduce the impact of sparse sampling, and (2) an adaptive sampling strategy based on anomaly detection enables us to greatly reduce the sampling bias and produce more accurate trends using fewer samples compared to an intense regular sampling strategy. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: | Complementary Index |
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