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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]

High quality long-term observational records are essential to ensure appropriate and reliable trend detection of tropospheric ozone. However, the necessity for 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., 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 for 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]

The generalized extreme value distribution, generalized Pareto distribution, Pareto type-I distribution, Pareto type-II distribution, Burr distribution, log-logistic distribution, Fréchet distribution, log-normal distribution, log-Cauchy distribution, Lévy distribution, and Dagum distribution are fitted to four data-sets consisting of surface-level ozone for Weaverville, California (WVR); Tundra Lab, Niwot Ridge, Colorado (TUN); South Pole, Antarctica (SPO); and Mauna Loa, Hawaii (MLO) sites in the USA. Based on the probability distribution with the smallest Akaike information criterion, Bayesian information criterion, corrected Akaike information criterion, mean square error, mean absolute deviation, and maximum deviation values and probability-probability plots, the Dagum distribution emerged as the best fitting distribution for Weaverville, the Burr distribution emerged as the best fitting distribution for Niwot Ridge and Mauna Loa, and the log-Cauchy distribution emerged as the best fitting distribution for the South Pole site. The value at risk for each of the ozone sites is given and a backtest based on the Kupiec test is used to verify the appropriateness of the selected distributions. [ABSTRACT FROM AUTHOR]

In Switzerland, two ground-based ozone microwave radiometers are operated in the vicinity of each other (ca. 40 km): the GROund-based Millimeter-wave Ozone Spectrometer (GROMOS) in Bern (Institute of Applied Physics) and the Stratospheric Ozone MOnitoring RAdiometer (SOMORA) in Payerne (MeteoSwiss). Recently, their calibration and retrieval algorithms have been fully harmonized, and updated time series are now available since 2009. Using these harmonized ozone time series, we investigate and cross-validate the strato–mesospheric ozone diurnal cycle derived from the two instruments and compare it with various model-based datasets: the dedicated GEOS-GMI Diurnal Ozone Climatology (GDOC) based on the Goddard Earth Observing System (GEOS-5) general circulation model, the Belgian Assimilation System for Chemical ObsErvations (BASCOE) – a chemical transport model driven by ERA5 dynamics, and a set of free-running simulations from the Whole Atmosphere Community Climate Model (WACCM). Overall, the two instruments show very similar ozone diurnal cycles at all seasons and pressure levels, and the models compare well with each other. There is a good agreement between the models and the measurements at most seasons and pressure levels, and the largest discrepancies can be explained by the limited vertical resolution of the microwave radiometers. However, as in a similar study over Mauna Loa, some discrepancies remain near the stratopause, at the transition region between ozone daytime accumulation and depletion. We report similar delays in the onset of the modelled ozone diurnal depletion in the lower mesosphere. Using the newly harmonized time series of GROMOS and SOMORA radiometers, we present the first observations of short-term (sub-monthly) ozone diurnal cycle variability at mid-latitudes. The short-term variability is observed in the upper stratosphere during wintertime, when the mean monthly cycle has a small amplitude and when the dynamics are more important. This is shown in the form of strong enhancements of the diurnal cycle, reaching up to 4–5 times the amplitude of the mean monthly cycle. We show that BASCOE is able to capture some of these events, and we present a case study of one such event following the minor sudden stratospheric warming of January 2015. Our analysis of this event supports the conclusions of a previous modelling study, attributing regional variability of the ozone diurnal cycle to regional anomalies in nitrogen oxide (NO x) concentrations. However, we also find periods with an enhanced diurnal cycle that do not show much change in NO x and where other processes might be dominant (e.g. atmospheric tides). Given its importance, we believe that the short-term variability of the ozone diurnal cycle should be further investigated over the globe, for instance using the BASCOE model. [ABSTRACT FROM AUTHOR]

In Switzerland, two ground-based ozone microwave radiometers are operated in the vicinity of each other (ca. 40 km): GROMOS in Bern (Institute of Applied Physics) and SOMORA in Payerne (MeteoSwiss). Recently, their calibration and retrieval algorithms have been fully harmonized and updated time series are now available since 2009. Using these harmonized ozone time series, we investigate and cross-validate the strato-mesospheric ozone diurnal cycle derived from the two instruments and compare it with various model-based datasets: a dedicated GDOC ozone diurnal cycle climatology based on the Goddard Earth Observing System (GEOS-5) general circulation model, the Belgian Assimilation System for Chemical ObsErvations (BASCOE) a chemical transport model driven by ERA5 dynamics, and a set of free-running simulations from the Whole Atmosphere Community Climate Model (WACCM). Overall, the two instruments show very similar ozone diurnal cycles at all seasons and pressure levels and the models compare well with each other. There is a good agreement between the models and the measurements at most seasons and pressure levels and the largest discrepancies can be explained by the limited vertical resolution of the microwave radiometers. However, as in a similar study over Mauna Loa, some discrepancies remain near the stratopause, at the transition region between ozone daytime accumulation and depletion. We report similar delays in the onset of the modelled ozone diurnal depletion in the lower mesosphere. Using the newly harmonized time series of GROMOS and SOMORA radiometers, we present the first observations of short-term (sub-monthly) ozone diurnal cycle variability at mid-latitudes. The short-term variability is observed in the upper stratosphere during wintertime, when the mean monthly cycle has a small amplitude and when the dynamics is more important. It is shown in the form of strong enhancements of the diurnal cycle, reaching up to 4–5 times the amplitude of the mean monthly cycle. We show that BASCOE is able to capture some of these events and present a case study of one such event following the minor sudden stratospheric warming of January 2015. Our analysis of this event supports the conclusions of a previous modelling study, attributing regional variability of the ozone diurnal cycle to regional anomalies in nitrogen oxide (NO x ) concentrations. However, we also find period with enhanced diurnal cycle that do not show much change in NO x and where other processes might be dominant (e.g. atmospheric tides). Given its importance, we believe that the short-term variability of the ozone diurnal cycle should be further investigated over the globe, for instance using the BASCOE model. [ABSTRACT FROM AUTHOR]

A potent greenhouse gas and biological irritant, tropospheric ozone is also the primary source of atmospheric hydroxyl radicals, which remove numerous hazardous trace gases from the atmosphere. Tropospheric ozone levels have increased in spring at remote sites in the mid-latitudes of the Northern Hemisphere over the past few decades; this increase has been attributed to a growth in Asian precursor emissions. In contrast, 40 years of continuous measurements at Mauna Loa Observatory in Hawaii reveal little change in tropospheric ozone levels during spring (March-April), but a rise in autumn (September-October). Here we examine the contribution of decadal shifts in atmospheric circulation patterns to decadal variability in tropospheric ozone levels at Mauna Loa using a suite of chemistry-climate model simulations. We show that the flow of ozone-rich air from Eurasia towards Hawaii during spring weakened in the 2000s as a result of La-Niña-like decadal cooling in the eastern equatorial Pacific Ocean. During autumn, in contrast, the flow of ozone-rich air from Eurasia to Hawaii strengthened in the mid-1990s onwards, coincident with the positive phase of the Pacific-North American pattern. We suggest that these shifts in atmospheric circulation patterns can reconcile observed trends in tropospheric ozone levels at Mauna Loa and the northern mid-latitudes in recent decades. We conclude that decadal variability in atmospheric circulation patterns needs to be considered when attributing observed changes in tropospheric ozone levels to human-induced trends in precursor emissions. [ABSTRACT FROM AUTHOR]