English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We present an improved tagging method, which describes the combined effect of emissions of various species from individual emission categories, e.g. the impact of both, nitrogen oxides and non-methane hydrocarbon emissions on ozone. This method is applied to two simplified chemistry schemes, which represent the main characteristics of atmospheric ozone chemistry. Analytical solutions are presented for this tagging approach. In the past, besides tagging approaches, sensitivity methods were used, which estimate the contributions from individual sources based on differences in two simulations, a base case and a simulation with a perturbation in the respective emission category. We apply both methods to our simplified chemical systems and demonstrate that potentially large errors (factor of 2) occur with the sensitivity method, which depend on the degree of linearity of the chemical system. This error depends on two factors, the ability to linearise the chemical system around a base case, and second the completeness of the contributions, which means that all contributions should principally add up to 100%. For some chemical regimes the first error can be minimised by employing only small perturbations of the respective emission, e.g. 5%. The second factor depends on the chemical regime and cannot be minimized by a specific experimental set-up. It is inherent to the sensitivity method. Since a complete tagging algorithm for global chemistry models is difficult to achieve, we present two error metrics, which can be applied for sensitivity methods in order to estimate the potential error of this approach for a specific application

On the attribution of contributions of atmospheric trace gases to emissions in atmospheric model applications