Presentations

 

ACTRIS meeting in Rennes 13-16 May

Presented first results in the session on Long-term trends and variability of the atmospheric composition :

ACTRIS_Rennes_2024.pptx

 

DORA midterm evaluation 10 June

Presented results, progress, and prospects of the project in front of the committee of DORA

DORA_midterm.pptx

 

NDACC/IRWG-TCCON-COCCON annual meeting 8-12 July

Presented results and progress of the DORA project in the NDACC/IRWG session

NDACC_IRWG_2024.pptx

 

Quadrennial Ozone Symposium in Boulder, Colorado 14-19 July

Accepted talk in the session on Stratospheric Ozone Science

QOS_Boulder.pptx

 

Project results

Sodankyla ozone time series

In the Arctic, we have 6 FTIR stations measuring with the NDACC specifications, however we have 1 additional FTIR available from TCCON at Sodankyla. Measures in different spectral region (3040 cm-1) so the retrieval strategy needs to be adapted. The used retrieval strategy is based on the strategy by Zhou et al. (2020) in Xianghe, China and García et al. (2014) in Izaña, Spain. Using this, we are able to provide total column data and 2 partial columns (DOFS is around 2.5) from 2012 onwards of a completely new ozone time series product.

Sod_TIK_pro.png

Representativeness study

Within DORA we analyzed the representativeness of the ozone measurements for the Arctic. An obvious limitation of ground-based observations of ozone trends is the representativeness of these sparse data: Are the detected trends at a specific site representative for ozone behavior at a regional scale? How does the network of ground-based stations improve the representativeness of ozone trends in the Arctic, as compared to a single site? For the sites that are found representative of the same area in the Arctic, we will later construct merged time series of ozone anomalies and study the impact on the detection of the trends (e.g., reduced uncertainties due to higher sampling), by applying our MLR model on the anomalies.

To tackle these questions of representativeness we use model reanalysis data from CAMS (Copernicus Atmosphere Monitoring Service) which supply ozone data every 3 hours from 2003 to 2023. The CAMS ozone data combines model data and observations to give gridded monthly means time series (0.75°x0.75°) for 25 different pressure levels and is taken from https://ads.atmosphere.copernicus.eu/datasets/cams-global-reanalysis-eac4?tab=overview .

For the spatial representativeness follow the methodology of Weatherhead et al. (2017), who were using satellite data instead of reanalysis. Here we calculate the correlation of each station with the other spatial points within the Arctic. This is done based on the anomalies calculated in each partial and total column.

The first step is to convert the provided mass-mixing ratios of CAMS to column densities (in DU) using

NA/Mair/g*rm*Δp/DU

(where NA is Avogadro’s constant, Mair is molecular mass of air, g is the gravitational acceleration, rm is mass mixing ratio of ozone, Δp is pressure difference between layer boundaries, and DU is a conversion number 2.6867e20 molec/cm2).

In a second step the vertical profiles of CAMS are integrated into partial columns which approximate the columns as calculated for the ground-based measurements by summing over the column densities of the levels within these partial column layers. These are defined in pressure levels from 250-70hPa, 70-10hPa, and 10-1hPa for the stratospheric columns.

The third step is to calculate the anomaly from the ozone timeseries by subtracting and dividing each column value by the mean value of its corresponding month in the year.

Lastly, we iterate over each point of the CAMS grid to compare the anomalies of that point to the anomalies at the location of one of the measurement sites. This gives us a map of the Pearson correlation coefficient at each point of the CAMS grid which is different for each station.

r_eur_an_tc.png

The same calculations are performed by looking at the anomalies for of separate months and of 4 different seasons. The same data of anomalies is kept, but the correlation is calculated only based on the data within one season. These are defined as DJF (December, January, February), MAM (March, April, May), JJA (June, July, August), and SON (September, October, November).

Merging datasets

The CAMS correlation analysis is used to decide which ground-based measurements to merge into one merged dataset. We decide to merge stations if the Pearson correlation coefficient exceeds a value of 0.85 for high correlation or 0.7 for good correlation. This allows us to define several regions within the Arctic which are highly correlated and thus eligible for merging. These regions are ‘Canada’ (Alert sonde, Eureka FTIR, Eureka sonde, Thule FTIR, Resolute sonde), Scandinavia (Ny Alesund FTIR, Ny Alesund sonde, Kiruna FTIR, Sodankyla FTIR, Sodankyla sonde), North Europe (Kiruna FTIR, Sodankyla FTIR, Sodankyla sonde, Harestua FTIR, St. Petersburg FTIR), South Europe (Harestua FTIR, Lerwick sonde), and Lapland (Kiruna FTIR, Sodankyla FTIR, Sodankyla sonde).

The merging is done in a similar way as for satellite measurements in Sofieva et al. (2021). For each month we take the median value for the anomalies of all included measurements. Through this method we provide merged anomalies of multiple partial and total columns for several correlated regions in the Arctic. The figure below shows an example of all anomalies overlaid which is used for merging the 'Canadian' sites.

anomalies_canada.png

 

Trend analysis

The trends are calculated using a Multiple-Linear Regression method including proxies to capture the natural variability of ozone. Below are some resulting figures from the analysis.

TC_trends.jpeg

Total column trends for 'Canadian' sites.

 

US_merged_trends.jpeg

Merged trends in the upper stratosphere.