Webinar - Improving national reporting of greenhouse emissions using atmospheric observations

Photo of smog in the city with words Improving national reporting of greenhouse emissions using atmospheric observations December 16th Online
Thursday, 16 December 2021 - 12:30pm to 1:15pm
Online

Estimating emissions from atmospheric observations, commonly referred to as 'top-down' or inverse modelling, is a relatively new area of science. Greenhouse and ozone-depleting gases are particularly aligned with inverse modelling because they are, by their very nature, long-lived in the atmosphere. Through continued funding from the UK government, the Met Office working with the University of Bristol have provided 'top-down' emission estimates for a comprehensive set of Greenhouse and ozone-depleting gases since 2003, to complement and compare to the mandated 'bottom-up' national emissions inventories.

The UK was the first country to report these results annually as part of their national submission to the United Nations Framework Convention on Climate Change. This work is gaining more and more interest as countries set ambitious targets to reduce emissions and move to net-zero, as verifying progress towards these targets is an attractive option.

In this webinar, Alistair Manning will present the work they're undertaking comparing top-down and bottom-up emissions inventories and how both approaches can work in synergy to gain a better understanding of national emissions.


Guest speaker

Tim Flach

 

Alistair Manning has worked at the Met Office for many years following a degree in mathematics and a PhD in computational modelling. Their work in the Met Office has always evolved around the area of atmospheric dispersion modelling. Initially they focussed on air quality and emergency response but then moved, in 2000, into the science of estimating emissions from atmospheric observations, commonly referred to as 'top-down' or inverse modelling.

They have been involved in more than 150 publications, the vast majority related to inverse modelling, for example Manning et. al. ACP 2021; Lunt et. al. ACP 2021; Montzka et. al. Nature 2021; Park et. al. Nature 2021.

 

Who to contact

Katherine Karr

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