Robert Parker1, Gerd Folberth2, Chris Jones2, Nicola Gedney2, Fiona O’Connor2, Alistair Sellar2 and Andy Wiltshire2 

 1 NCEO and UKESM core group, 2 Met Office Hadley Centre 

Climate models are beginning to incorporate processes and feedbacks related to biogeochemical cycles, becoming “Earth system” models (ESMs). How such processes interact with each other and the physical climate is highly uncertain but has a large potential to impact upon climate projections. 

Methane (CH4) is the second most important greenhouse gas after carbon dioxide and a tropospheric ozone precursor, with potential for climate mitigation and air pollution abatement due to its relatively short lifetime. 

ESMs participating in the Climate Model Intercomparison Project Phase 6 (CMIP6) are driven via CH4 concentrations (Eyring et al. 2016) rather than prescribing and/or generating their own emissions and interactively coupling them to the model atmosphere. The reason for this is that we currently have too little understanding of all the relevant processes and their couplings/feedbacks, especially in relation to accurately modelling the methane lifetime. This leads to large uncertainties in climate predictions or prevents important processes from being included in simulations due to the sensitivity of their feedbacks. 

The CH4 emission from natural wetlands is the most significant uncertainty in the global CH4 budget (Kirschke et al. (2013), Saunois et al. (2019)) and further work is needed to better understand the processes by which wetlands emit CH4 and how these emissions are likely to respond to changes in climate. As shown in Gedney et al. (2019), wetland feedbacks are expected to be significant over the next century. When compared to simulations with no feedbacks, they are found to increase atmospheric concentrations by a further 25% and lead to an additional temperature increase of up to 5.5%. Other CH4 feedbacks such as temperature, humidity, permafrost thaw and methane hydrates also all play a role and need to be considered in future projections.  

The above highlights the need to begin incorporating these processes into ESMs. We detail the changes currently under development at the Met Office Hadley Centre to achieve this with UKESM1 (Sellar et al., 2019) and provide some initial, promising, results. 

Rather than using the prescribed methane concentrations as standard in CMIP6, we replaced these with explicit prescribed emission sources (e.g. anthropogenic, fire), included CH4 surface removal and coupled the wetland scheme in JULES with the UKCA atmospheric composition model (O’Connor et al., 2014). 

To include methane surface emissions from wetlands and to account for the coupling between the land surface and the atmosphere via wetland methane emissions we implemented the coupling between the wetland scheme described in Gedney et al. (2019) and the atmospheric composition component UKCA as outlined in Archibald et al. (2019). 

JULES wetland CH4 flux into the atmosphere (FCH4, Gedney et al., 2019) is dependent on wetland fraction fw, available substrate (NPP) and temperature:

where Tsoil (K ) and T0 are mean top 1m soil and reference (273.16K) temperatures respectively. K is tuned to the contemporary, global total wetland flux, where current estimates range between 127–227 Tg(CH4) yr−1 (Saunois et al., 2016). In our model the present-day decadal mean (2000-2009) global total methane emissions from wetlands amount to 198 Tg(CH4) yr-1. The Q10(T) factor represents the reaction rate increase following a 10K temperature increase.

We performed a 30-year test run with the pre-industrial control configuration. From this control experiment we derived a 30-year average monthly mean climatology of methane surface exchange fluxes for each gridbox. In essence, this CH4 surface exchange flux climatology represents the “missing” sources and sinks that are required in our emissions-driven configuration to reproduce exactly the default UKESM1 configuration with prescribed methane surface concentrations, assuming that the UKESM1 default configuration represents the “correct” state with respect to methane. The aim of this step is to remove any systematic bias in the initial state. 

Figure 1 shows some initial comparisons between the standard concentration-driven simulations and the same simulations run in emission-driven mode where the CH4 emissions are interactively coupled to the rest of the model. The CH4 surface concentrations for the emission-driven results are found to be similar to the concentration-driven simulations for both the historical and different future scenarios. A reasonably consistent offset between the two is maintained from the late 1900s throughout the future projections in both cases. As pointed out by Gedney et al., 2019, limited knowledge of contemporary global wetland emissions restricts model calibration and the accuracy of the modelled methane lifetime can significantly affect the simulated distribution. Through further evaluation and calibration, the differences to the concentration-driven simulations would expect to be reduced.

Figure 1: Global annual mean methane surface concentration in ppbv. Black: concentration-driven simulations; red: emissions-driven simulations. Included are three ensemble members from historic simulations (1850-2014) and one ensemble member each from two future scenarios (2014-2100). 

 

Figure 2 shows a global map of 10year (1985-1995) CH4 surface mole fractions from the historical emission-driven simulation. It should be noted that in the concentration-driven mode, the data would be entirely homogenous with a flat value of 1714 ppb. In contrast, the emission-driven mode is able to successfully generate a reasonable CH4 spatial distribution with features such as wetlands, fires and anthropogenic emissions all evident.  

Figure 2: 10-year average annual mean CH4 surface mole fractions (1985-1995) for the emission-driven simulation. 


In conclusion, we present here the first promising results from the implementation of a fully-interactive CH
4 scheme within UKESM1.  

Future work will focus on evaluating these simulations, using a range of satellite observations and ancillary datasets, and improving the consistency between the concentration-driven results and those driven by prescribed and interactive emissions. Ultimately this will allow the emission-driven simulations to be routinely used for future climate projections, providing valuable insights into how CH4 concentrations may respond and contribute to a changing climate. 

 

References 

Archibald, A. T., et al.: Description and evaluation of the UKCA stratosphere-troposphere chemistry scheme (StratTrop vn 1.0) implemented in UKESM1, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-246, in review, 2019. 

Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016. 

Gedney, N., Huntingford C., Comyn-Platt E. and Wiltshire A., Significant feedbacks of wetland methane release on climate change and the causes of their uncertainty, Environ. Res. Lett. 14 084027, https://doi.org/10.1088/1748-9326/ab2726, 2019 

Kirschke, S., Bousquet, P., Ciais, P. et al. Three decades of global methane sources and sinks. Nature Geosci6, 813–823 (2013) https://doi.org/10.1038/ngeo1955  

O’Connor, F. M., Evaluation of the new UKCA climate-composition model – Part 2: The Troposphere, Geosci. Model Dev., 7, pp. 41–91, https://doi.org/10.5194/gmd-7-41-2014, 2014 

Saunois, M., et al.: The global methane budget 2000–2012, Earth Syst. Sci. Data, 8, 697–751, https://doi.org/10.5194/essd-8-697-2016, 2016. 

Saunois, M., et al.: The Global Methane Budget 2000–2017, Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2019-128, in review, 2019 

Sellar, A. A., Jones,C.G., Mulcahy, J., Tang, Y., Yool,A., & Wiltshire, A. et al., UKESM1: Description and evaluation of the UK Earth System Model. Journal of Advances in Modeling Earth Systems, 11, pp 2–46. https://doi.org/10.1029/2019MS001739, 2019 

–>

Download this Newsletter as a PDF (2015KB)