Can Silicone derive concentration timeseries, as well as emission timeseries?

[JeremyFyke]

Hi there,

First, thank you for developing the Silicone tool! I and colleagues here in Canada (Environment and Climate Change Canada, and the Ouranos Consortium for Regional Climatology) are working on a demonstration project to develop a suite of climate risk-oriented Earth System Model simulations (see minute 16 of this conference webinar recording for a 12 minute talk on this topic), forced by a probabilistic series of emissions timeseries of CO2 that we have previously developed (see here). We are interested in developing timeseries of other radiatively active gases that are consistent with our base CO2 timeseries that we have developed, which naturally led us to the very interesting Silicone tool.

However, in exploring use of Silicone, we have run across an issue that we were wondering if you had insight into. The Earth System Model we are using for our demonstration (CanESM) lacks explicit calculation of atmospheric methane/nitrous oxide, as well as aerosol chemistry. For this reason, unlike for CO2, CanESM requires these species to be input to the model code in units of concentration, rather than units of emissions. Our understanding is that for exercises like CMIP6, the MAGICC model is used to obtain this 'conversion'. Our question, prior to digging into MAGICC model usage ourselves, is: can Silicone be used to develop 'follower' CH4 (and, nitrous oxide/aerosol) concentration timeseries, given CO2 emission lead timeseries? It's not clear from my exploration and test-running of Silicone, that this is possible given the datasets one can access via Silicone. I'd be really interested to hear if you can confirm/deny this.

Any thoughts would be welcome here, and, thanks again for making the Silicone tool available for general use.

Sincerely,

Jeremy Fyke

[znicholls]

Hi Jeremy, Thanks for the nice feedback, we’re glad you like the tool. In theory you could use silicone for infilling concentrations, but I don’t think anyone has ever done it so who knows what unknown issues might crop up. My feeling would be that running a model like magicc is the same amount of work as running silicone (magicc might even be faster in some edge cases, although computation time isn’t really the issue) so you’re better off just using a tool that is well known and understood rather than opening up a new can of worms. If you go down this route, I can recommend OpenSCM-runner (it’s on GitHub). If you do use silicone, you’ll just get the correlations inherent in whatever model output you use for infilling anyway (so you’ll get something magicc or fair like, but not actually magic or fair). If you want to try silicone, downloading the AR6 WG3 output from the wg3 scenario data base will probably give you the most useful infilling database. Cheers, Zeb

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On Tue, 13 Jun 2023 at 7:30 am, Jeremy Fyke ***@***.***> wrote: Hi there, First, thank you for developing the Silicone tool! I and colleagues here in Canada (Environment and Climate Change Canada, and the Ouranos Consortium for Regional Climatology) are working on a demonstration project to develop a suite of climate risk-oriented Earth System Model simulations (see minute 16 of this <https://www.youtube.com/watch?v=byC-oeul4qU> conference webinar recording for a 12 minute talk on this topic), forced by a probabilistic series of emissions timeseries of CO2 that we have previously developed (see here <https://iopscience.iop.org/article/10.1088/1748-9326/10/11/115007/meta>). We are interested in developing timeseries of other radiatively active gases that are consistent with our base CO2 timeseries that we have developed, which naturally led us to the very interesting Silicone tool. However, in exploring use of Silicone, we have run across an issue that we were wondering if you had insight into. The Earth System Model we are using for our demonstration (CanESM) lacks explicit calculation of atmospheric methane/nitrous oxide, as well as aerosol chemistry. For this reason, unlike for CO2, CanESM requires these species to be input to the model code in units of *concentration*, rather than units of *emissions*. Our understanding is that for exercises like CMIP6, the MAGICC model is used to obtain this 'conversion'. Our question, prior to digging into MAGICC model usage ourselves, is: can Silicone be used to develop 'follower' CH4 (and, nitrous oxide/aerosol) *concentration* timeseries, given CO2 *emission* lead timeseries? It's not clear from my exploration and test-running of Silicone, that this is possible given the datasets one can access via Silicone. I'd be really interested to hear if you can confirm/deny this. Any thoughts would be welcome here, and, thanks again for making the Silicone tool available for general use. Sincerely, Jeremy Fyke — Reply to this email directly, view it on GitHub <#151>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AFUH5GZA4LO3MVL4HNCDMV3XK6C6LANCNFSM6AAAAAAZD6QW7A> . You are receiving this because you are subscribed to this thread.Message ID: ***@***.***>

[Rlamboll]

Hi Jeremy
It depends how rigorous you want to be. The complication is that concentrations

1. depend on feedbacks between many variables
2. may be integrated over time in some way.

For long-lived emissions, if you use RMS closest cruncher with a large database containing some similar CO2 timeseries and concentration series I don't see a huge problem, though I don't think many databases record the concentrations of most variables. The closest path should have a similar temperature trend, so the differences in feedback will be small and that cruncher intrinsically accounts for across-time behaviour. Aerosol concentration is strongly correlated with emissions, so the time-dependence is less intrinsically important, but has a feedback dependent on temperature. I don't think there's a rigorous way to include this feedback with the current set of crunchers, but since the temperature is strongly correlated with the across-time CO2 pathway (and you don't have an independent estimate of the other emissions types to influence this) it's probably not the worst approximation ever to also infill that with the nearest pathway. If you did have other emissions series, you could include them and weight them both with their GWP in some metric to improve the correspondence. But also, do you not require spatially explicit aerosol concentrations? What exactly do you need?
Robin