INFO3RS / CCM-SOCOL

OzoneINFO3RS / CCM-SOCOL

Overview

The project INFO3RS aims to investigate the future evolution of Total Column Ozone (TCO) in Switzerland. To support the trend analysis of the development of the ozone layer, additional data sources to ground-based measurements are analysed. The global Chemistry-Climate Model, CCM-SOCOL, developed at PMOD/WRC, and the SBUV satellite data represent an independent data source to potentially enhance the statistical significance of future trends. INFO3RS is funded by the MeteoSwiss GAW-CH programme.

Comparison of Model and Observational Data

The question: “How and when can we detect the recovery of the ozone layer over Switzerland?”, is the main aspect summarising the overall objective of the INFO3RS project, funded by the Swiss Global Atmosphere Watch Programme (GAW-CH). “How” indicates the method of observing the Total Column Ozone (TCO) such as:

• Ground-based observations with Dobson or Brewer networks and novel array spectroradiometer systems.

• TCO estimates with CCM-SOCOL (Stenke et al., 2013).

• Space-borne satellite TCO retrieval, such as solar backscatter UV (SBUV) (Bartia et al., 2013).

In order to increase the available TCO data points with additional data sources, the CCM-SOCOL output of the free running mode (1932 – 2015) and the “nudged” mode (1980 – 2015) are verified with ground-based measurements from the long-term Arosa ozone time-series (Staehelin et al., 2018) for 1932 – 2015.

Figure 1. Annual mean Total Column Ozone (TCO) estimates from four different data sources over Arosa, Switzerland.

The “Nudged” mode means that the stratospheric temperature, wind fields and atmospheric pressure are driven using re-analysis data from the ERA-Interim database. Data from satellite observations are a merged composite of daily ozone values, publicly available from NASA, and are aggregated to annual means for 1980 – 2015.

Figure 1 shows the temporal course of the four different methods to estimate TCO over Arosa. The CCM-SOCOL model calculations and satellite data show systematic biases of up to 30 DU for the CCM-SOCOL free-running and ~10 DU for the satellite and “nudged” CCM-SOCOL runs. Both the satellite and “nudged” CCM-SOCOL data correctly follow the annual variability as well as the climatological trend from 1980 to 1990. The free-running CCM-SOCOL mode, however, only correctly reproduced the long-term climatology, while the annual variations fluctuate randomly, resulting in a poor correlation. For this model calculation, only one realisation of the free-running mode is analysed; free-running models have their own, independent internal variability, and are not ‘nudged’. All methods show a similar variability of the ozone layer of ~8 DU or 2.5%.

On a monthly time-scale, the correlations within the four methods are significantly better. Figure 2 shows the comparison of the three TCO estimates related to the ground-based measurements. The high correlation coefficients indicate that the seasonal variation of the Arosa time-series is reproduced well by the independent data and therefore, model calculations can be used to potentially remove the autocorrelation of the seasonal trend to enhance the statistical significance of ozone trends over Arosa.

The improvement of the trend analysis, considering the effect of natural variability, measurement uncertainty, calibration uncertainty and impact of the instrument movement from Arosa to Davos will be further investigated in the project using the verified data source.

Figure 2. Correlation of CCM-SOCOL model output and SBUV satellite TCO data on a monthly time-scale compared to the Arosa time-series.

References

Bartia P. K., et al., (2013), Solar Backscatter UV (SBUV) total ozone and profile algorithm, Atmos. Meas. Tech., 6, 2533–2548, https://doi.org/10.5194/amt-6-2533-2013.
Staehelin et al., (2018), Stratospheric ozone measurements at Arosa (Switzerland): history and scientific relevance, Atmos. Chem. Phys., 18, 6567–6584, https://doi.org/10.5194/acp-18-6567-2018.
Stenke A., et al., (2013), The SOCOL version 3.0 chemistry-climate model: description, evaluation, and implications from an advanced transport algorithm, Geosci. Model Dev., 6, 1407–1427, https://doi.org/10.5194/gmd-6-1407-2013.
For further information please contact: Dr. Luca Egli