Empirical data for: Performance evaluation of UKESM1 for surface ozone across the pan-tropics

This data set contains ozone concentrations from monitoring stations in the tropics, grouped into 'sites' which match the resolution of modelled simulations from UKESM1.

The files give ozone concentrations in nmol mol-1:

site info: gives information about the site such as gridcell latitude and longitude, and an identifier index that is used in the other csv files

site daily: daily mean ozone concentrations for each monitoring station at each site and their anomalies from the monthly mean ozone concentration

site daily max: maximum daily ozone concentration in each month at each site

site diel: monthly mean diel cycles in ozone concentration at each site

site monthly mean: a time series of monthly mean ozone concentrations and a monthly climatology for each site 

site monthly DOR: a time series of monthly mean diurnal ozone range and a monthly climatology for each site

Additionally, this database contains daily data from three sites (Barro Colorado, Yangambi and Daintree). Please see the Data Use Policy before downloading these files:

Yangambi_daily.csv

Daintree_daily.csv

Barro_colorado_daily.csv

publication abstract:

 Surface ozone monitoring sites in the tropics are limited, despite the risk that surface ozone poses to human health, tropical forest, and crop productivity. Atmospheric chemistry models allow us to assess ozone exposure in unmonitored locations and evaluate the potential influence of changing policies and climate on air quality, human health, and ecosystem integrity. Here, we utilise in situ ozone measurements from ground-based stations in the pan-tropics to evaluate ozone from the UK Earth system model, UKESM1, with a focus on remote sites. The study includes ozone data from areas with limited previous data, notably Tropical South America, central Africa, and tropical North Australia. Evaluating UKESM1 against observations beginning in 1987 onwards, we show that UKESM1 is able to capture changes in surface ozone concentration at different temporal resolutions, albeit with a systematic high bias of 18.1 nmol mol-1 on average. We use the Diurnal Ozone Range (DOR) as a metric for evaluation and find that UKESM1 captures the observed DOR (mean bias of 2.7 nmol mol-1 and RMSE of 7.1 nmol mol-1) and the trend in DOR with location and season. Results from this study demonstrate the applicability of hourly output from UKESM1 for human and ecosystem health-based impact assessments, increase confidence in model projections, and highlight areas that would benefit from further observations. Indeed, hourly surface ozone data has been crucial to this study, and we encourage other modelling groups to include hourly surface ozone output as a default.