2 results
Search Results
Now showing 1 - 2 of 2
- ItemEmulating atmosphere-ocean and carbon cycle models with a simpler model, MAGICC6 - Part 2: Applications(München : European Geopyhsical Union, 2011) Meinshausen, M.; Raper, S.C.B.; Wigley, T.M.L.Intercomparisons of coupled atmosphere-ocean general circulation models (AOGCMs) and carbon cycle models are important for galvanizing our current scientific knowledge to project future climate. Interpreting such intercomparisons faces major challenges, not least because different models have been forced with different sets of forcing agents. Here, we show how an emulation approach with MAGICC6 can address such problems. In a companion paper (Meinshausen et al., 2011a), we show how the lower complexity carbon cycle-climate model MAGICC6 can be calibrated to emulate, with considerable accuracy, globally aggregated characteristics of these more complex models. Building on that, we examine here the Coupled Model Intercomparison Project's Phase 3 results (CMIP3). If forcing agents missed by individual AOGCMs in CMIP3 are considered, this reduces ensemble average temperature change from pre-industrial times to 2100 under SRES A1B by 0.4 °C. Differences in the results from the 1980 to 1999 base period (as reported in IPCC AR4) to 2100 are negligible, however, although there are some differences in the trajectories over the 21st century. In a second part of this study, we consider the new RCP scenarios that are to be investigated under the forthcoming CMIP5 intercomparison for the IPCC Fifth Assessment Report. For the highest scenario, RCP8.5, relative to pre-industrial levels, we project a median warming of around 4.6 °C by 2100 and more than 7 °C by 2300. For the lowest RCP scenario, RCP3-PD, the corresponding warming is around 1.5 °C by 2100, decreasing to around 1.1 °C by 2300 based on our AOGCM and carbon cycle model emulations. Implied cumulative CO2 emissions over the 21st century for RCP8.5 and RCP3-PD are 1881 GtC (1697 to 2034 GtC, 80% uncertainty range) and 381 GtC (334 to 488 GtC), when prescribing CO2 concentrations and accounting for uncertainty in the carbon cycle. Lastly, we assess the reasons why a previous MAGICC version (4.2) used in IPCC AR4 gave roughly 10% larger warmings over the 21st century compared to the CMIP3 average. We find that forcing differences and the use of slightly too high climate sensitivities inferred from idealized high-forcing runs were the major reasons for this difference.
- ItemEvaluation of biospheric components in earth system models using modern and palaeo-observations: The state-of-the-art(München : European Geopyhsical Union, 2013) Foley, A.M.; Dalmonech, D.; Friend, A.D.; Aires, F.; Archibald, A.T.; Bartlein, P.; Bopp, L.; Chappellaz, J.; Cox, P.; Edwards, N.R.; Feulner, G.; Friedlingstein, P.; Harrison, S.P.; Hopcroft, P.O.; Jones, C.D.; Kolassa, J.; Levine, J.G.; Prentice, I.C.; Pyle, J.; Vázquez Riveiros, N.; Wolff, E.W.; Zaehle, S.Earth system models (ESMs) are increasing in complexity by incorporating more processes than their predecessors, making them potentially important tools for studying the evolution of climate and associated biogeochemical cycles. However, their coupled behaviour has only recently been examined in any detail, and has yielded a very wide range of outcomes. For example, coupled climate–carbon cycle models that represent land-use change simulate total land carbon stores at 2100 that vary by as much as 600 Pg C, given the same emissions scenario. This large uncertainty is associated with differences in how key processes are simulated in different models, and illustrates the necessity of determining which models are most realistic using rigorous methods of model evaluation. Here we assess the state-of-the-art in evaluation of ESMs, with a particular emphasis on the simulation of the carbon cycle and associated biospheric processes. We examine some of the new advances and remaining uncertainties relating to (i) modern and palaeodata and (ii) metrics for evaluation. We note that the practice of averaging results from many models is unreliable and no substitute for proper evaluation of individual models. We discuss a range of strategies, such as the inclusion of pre-calibration, combined process- and system-level evaluation, and the use of emergent constraints, that can contribute to the development of more robust evaluation schemes. An increasingly data-rich environment offers more opportunities for model evaluation, but also presents a challenge. Improved knowledge of data uncertainties is still necessary to move the field of ESM evaluation away from a "beauty contest" towards the development of useful constraints on model outcomes.