Steve Rumbold1*, Colin Jones1*, Catherine Hardacre2*, Jane Mulcahy2*, Alistair Sellar2* and Ed Blockley2.

1National Centre for Atmospheric Science (NCAS), 2Met Office, * UKESM core group member

UKESM1 historical simulations for CMIP6 exhibit a cold bias in 20th century global mean surface temperatures from ~1920 to ~1995 when compared to the observed record (Figure1). 

Figure 1 – UKESM historical model (12 member ensemble) anomalies in global mean surface air temperature relative to their 1850 to 1900 mean.  These are compared to the equivalent from the measurement record as analysed in HadCRUT4 (Morice et al., 2012) and Cowtan and Way, 2014. 

The cold bias is most pronounced in the Northern Hemisphere extratropics, suggesting anthropogenic aerosols are likely a significant contributor.  Indeed, when looking close to anthropogenic SO2/SO4 emission sites, it can be seen that the concentration of near-surface sulphur dioxide in the model (SO2, which is ultimately converted into sulphate aerosol to act as a cloud condensation nuclei) is biased high compared to site measurements (Figure 2).  A likely implication of this bias is that excess SO2 is transported to remote areas, such as clean marine regions, where conversion to sulphate aerosol gives rise to a large negative radiative forcing and a cooling of the climate. 

 Figure 2 – Comparison of UKESM1 SO2 surface concentrations against EMEP site observations (Tørseth et al., 2012). Blue line is the seasonal model mean and blue shading is the range of variability for the model; black line is the seasonal observation site mean and black shading is the range of variability for the measurements (note the blue shading overlaps the black).  European winter left; summer right. 

 

To address the positive bias in atmospheric SO2 and, by association, a potentially too strong negative aerosol forcing, several improvements have been introduced to the model’s parameterization of sulphur dioxide deposition, including the correction of an error in the resistance to SO2 deposition over the ocean. The key improvements however consist of (i) introducing a sensitivity of SO2 deposition to whether the underlying land surface is wet or dry (SO2 deposits much more readily on a wet surface than a dry one, see Erisman et al., 1994a) and (ii) when the land surface is dry, making the modelled resistance to SO2 deposition depend on near surface humidity (Erisman et al. 1994b). In addition the basic resistance constants for SO2 deposition with respect to each land surface type have been updated following Zhang (2003) 

The combination of these changes leads to a reduction of lower atmosphere SO2 concentrations by around 35 to 40% across the Northern Hemisphere extratropics.  This in turn reduces the magnitude of the 1850 to 1980 aerosol effective radiative forcing (ERF) from -1.4 Wm-2 to -1.1 Wm-2 When these changes are implemented in the fully coupled model and CMIP6 historical simulations repeated, the result is a significant reduction in the surface temperature cold bias (Figure 3, left panel), with the majority of the warming relative to the standard UKESM1 simulation occurring in the Northern Hemisphere extratropics (Figure 3, right panel). 

Figure 3 – Left panel as per Figure 1, but with HadCRUT4 removed and a 4 member ensemble of UKESM1 with the SO2 modifications (UKESM1-SO2mod) included. Right panel, as left but mean for the Northern Hemisphere extra-tropics.  

The reduction in the UKESM1 cold bias, combined with a general warming of the model has beneficial impacts on other aspects of the simulated climate, for example Arctic sea ice volume (primarily thickness) is significantly reduced in the updated model, bringing simulated values much more in line with observations (Figure 4).

Figure 4 Simulation of Arctic sea ice volume from UKESM1 standard and improved SO2 deposition versions compared to PIOMAS observations (Axel et. al, 2019).

Subsequently, UKESM1 simulations, with the SO2 deposition modifications active, have been extended to 2100, using 1 of the 4 historical ensemble members, forced by ScenarioMIP SSP58.5 and SSP1-2.6 projections (O’ Neill et al. 2016).  The purpose of these runs was to check whether the improvements to the historical temperature record had any impact on future projections. From Figure 5, we can see that in the future at the global scale, UKESM1 behaves very similarly in terms of surface temperature regardless of the inclusion of the SO2 deposition modifications.  This supports the validity of the original UKESM1 ScenarioMIP projections, while highlighting the importance of SO2 deposition primarily for accurately simulating the historical surface temperature evolution. 

Figure 5 Single ensemble member historical and future scenario global mean surface temperature anomalies from 1850 to 1900 mean baseline (original UKESM1 in dotted lines; UKESM1 with improved SO2 in full lines).  Two scenarios are used (SSP5-8.5 in red; SSP1-2.6 in blue).  A 5 year running smoothing has been applied.

The current aim is to release an updated version of UKESM to the community in summer 2020 with these improvements included. 

 

References: 

Axel J. Schweiger, Kevin R. Wood, Jinlun Zhang. Arctic sea ice volume variability over 1901–2010: A model-based reconstruction. Journal of Climate (in early release) https://doi.org/10.1175/JCLI-D-19-0008.1, 2019. 

Cowtan, K.D. and Way, R.G., Global temperature reconstructions version 2, https://doi.org/10.15124/20ee85c3-f53c-4ab6-8e50-270b0ddd3686, 2014. 

Tørseth, K., Aas, W., Breivik, K., Fjæraa, A. M., Fiebig, M., Hjellbrekke, A. G., Lund Myhre, C., Solberg, S., and Yttri, K. E.: Introduction to the European Monitoring and Evaluation Programme (EMEP) and observed atmospheric composition change during 1972–2009, Atmos. Chem. Phys., 12, 5447–5481, https://doi.org/10.5194/acp-12-5447-2012, 2012. 

Erisman, J.W., Pul, A.V., and Wyers P., Parametrization of surface resistance for the quantification of atmospheric deposition of acidifying pollutants and ozone, Atmospheric Environment, Volume 28, Issue 16, 2595-2607, https://doi.org/10.1016/1352-2310(94)90433-2, 1994a. 

Erisman, J.W., van Elzakker, B.G., Mennen, M.G., Hogenkamp, J., Zwart, E., van den Beld, L., Römer, F.G., Bobbink, R., Heil, G., Raessen, M., Duyzer, J.H., Verhage, H., Wyers, G.P., Otjes, R.P., Möls, J.J., The Elspeetsche Veld experiment on surface exchange of trace gases: Summary of results, Atmospheric Environment, Volume 28, Issue 3, Pages 487-496, https://doi.org/10.1016/1352-2310(94)90126-0, 1994b. 

Morice, C.P., Kennedy, J.J., Rayner, N.A. and Jones, P.D.. Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: the HadCRUT4 dataset. Journal of Geophysical Research, 117, D08101, doi:10.1029/2011JD017187, 2012. 

O’Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J.-F., Lowe, J., Meehl, G. A., Moss, R., Riahi, K., and Sanderson, B. M.: The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6, Geosci. Model Dev., 9, 3461–3482, https://doi.org/10.5194/gmd-9-3461-2016, 2016. 

Zhang, L., Brook, J. R., and Vet, R.: A revised parameterization for gaseous dry deposition in air-quality models, Atmos. Chem. Phys., 3, 2067–2082, https://doi.org/10.5194/acp-3-2067-2003, 2003. 

 

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