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- ItemThe future sea-level contribution of the Greenland ice sheet: A multi-model ensemble study of ISMIP6(Katlenburg-Lindau : Copernicus, 2020) Goelzer, Heiko; Nowicki, Sophie; Payne, Anthony; Larour, Eric; Seroussi, Helene; Lipscomb, William H.; Gregory, Jonathan; Abe-Ouchi, Ayako; Shepherd, Andrew; Simon, Erika; Agosta, Cécile; Alexander, Patrick; Aschwanden, Andy; Barthel, Alice; Calov, Reinhard; Chambers, Christopher; Choi, Youngmin; Cuzzone, Joshua; Dumas, Christophe; Edwards, Tamsin; Felikson, Denis; Fettweis, Xavier; Golledge, Nicholas R.; Greve, Ralf; Humbert, Angelika; Huybrechts, Philippe; Le clec'h, Sebastien; Lee, Victoria; Leguy, Gunter; Little, Chris; Lowry, Daniel P.; Morlighem, Mathieu; Nias, Isabel; Quiquet, Aurelien; Rückamp, Martin; Schlegel, Nicole-Jeanne; Slater, Donald A.; Smith, Robin S.; Straneo, Fiammetta; Tarasov, Lev; van de Wal, Roderik; van den Broeke, MichielThe Greenland ice sheet is one of the largest contributors to global mean sea-level rise today and is expected to continue to lose mass as the Arctic continues to warm. The two predominant mass loss mechanisms are increased surface meltwater run-off and mass loss associated with the retreat of marine-terminating outlet glaciers. In this paper we use a large ensemble of Greenland ice sheet models forced by output from a representative subset of the Coupled Model Intercomparison Project (CMIP5) global climate models to project ice sheet changes and sea-level rise contributions over the 21st century. The simulations are part of the Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6).We estimate the sea-level contribution together with uncertainties due to future climate forcing, ice sheet model formulations and ocean forcing for the two greenhouse gas concentration scenarios RCP8.5 and RCP2.6. The results indicate that the Greenland ice sheet will continue to lose mass in both scenarios until 2100, with contributions of 90-50 and 32-17mm to sea-level rise for RCP8.5 and RCP2.6, respectively. The largest mass loss is expected from the south-west of Greenland, which is governed by surface mass balance changes, continuing what is already observed today. Because the contributions are calculated against an unforced control experiment, these numbers do not include any committed mass loss, i.e. mass loss that would occur over the coming century if the climate forcing remained constant. Under RCP8.5 forcing, ice sheet model uncertainty explains an ensemble spread of 40 mm, while climate model uncertainty and ocean forcing uncertainty account for a spread of 36 and 19 mm, respectively. Apart from those formally derived uncertainty ranges, the largest gap in our knowledge is about the physical understanding and implementation of the calving process, i.e. the interaction of the ice sheet with the ocean. © Author(s) 2020.
- ItemComparison of particle number size distribution trends in ground measurements and climate models(Katlenburg-Lindau : EGU, 2022) Leinonen, Ville; Kokkola, Harri; Yli-Juuti, Taina; Mielonen, Tero; Kühn, Thomas; Nieminen, Tuomo; Heikkinen, Simo; Miinalainen, Tuuli; Bergman, Tommi; Carslaw, Ken; Decesari, Stefano; Fiebig, Markus; Hussein, Tareq; Kivekäs, Niku; Krejci, Radovan; Kulmala, Markku; Leskinen, Ari; Massling, Andreas; Mihalopoulos, Nikos; Mulcahy, Jane P.; Noe, Steffen M.; van Noije, Twan; O'Connor, Fiona M.; O'Dowd, Colin; Olivie, Dirk; Pernov, Jakob B.; Petäjä, Tuukka; Seland, Øyvind; Schulz, Michael; Scott, Catherine E.; Skov, Henrik; Swietlicki, Erik; Tuch, Thomas; Wiedensohler, Alfred; Virtanen, Annele; Mikkonen, SanttuDespite a large number of studies, out of all drivers of radiative forcing, the effect of aerosols has the largest uncertainty in global climate model radiative forcing estimates. There have been studies of aerosol optical properties in climate models, but the effects of particle number size distribution need a more thorough inspection. We investigated the trends and seasonality of particle number concentrations in nucleation, Aitken, and accumulation modes at 21 measurement sites in Europe and the Arctic. For 13 of those sites, with longer measurement time series, we compared the field observations with the results from five climate models, namely EC-Earth3, ECHAM-M7, ECHAM-SALSA, NorESM1.2, and UKESM1. This is the first extensive comparison of detailed aerosol size distribution trends between in situ observations from Europe and five earth system models (ESMs). We found that the trends of particle number concentrations were mostly consistent and decreasing in both measurements and models. However, for many sites, climate models showed weaker decreasing trends than the measurements. Seasonal variability in measured number concentrations, quantified by the ratio between maximum and minimum monthly number concentration, was typically stronger at northern measurement sites compared to other locations. Models had large differences in their seasonal representation, and they can be roughly divided into two categories: for EC-Earth and NorESM, the seasonal cycle was relatively similar for all sites, and for other models the pattern of seasonality varied between northern and southern sites. In addition, the variability in concentrations across sites varied between models, some having relatively similar concentrations for all sites, whereas others showed clear differences in concentrations between remote and urban sites. To conclude, although all of the model simulations had identical input data to describe anthropogenic mass emissions, trends in differently sized particles vary among the models due to assumptions in emission sizes and differences in how models treat size-dependent aerosol processes. The inter-model variability was largest in the accumulation mode, i.e. sizes which have implications for aerosol-cloud interactions. Our analysis also indicates that between models there is a large variation in efficiency of long-range transportation of aerosols to remote locations. The differences in model results are most likely due to the more complex effect of different processes instead of one specific feature (e.g. the representation of aerosol or emission size distributions). Hence, a more detailed characterization of microphysical processes and deposition processes affecting the long-range transport is needed to understand the model variability.