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- ItemSimilarity estimators for irregular and age-uncertain time series(München : European Geopyhsical Union, 2014) Rehfeld, K.; Kurths, J.Paleoclimate time series are often irregularly sampled and age uncertain, which is an important technical challenge to overcome for successful reconstruction of past climate variability and dynamics. Visual comparison and interpolation-based linear correlation approaches have been used to infer dependencies from such proxy time series. While the first is subjective, not measurable and not suitable for the comparison of many data sets at a time, the latter introduces interpolation bias, and both face difficulties if the underlying dependencies are nonlinear. In this paper we investigate similarity estimators that could be suitable for the quantitative investigation of dependencies in irregular and age-uncertain time series. We compare the Gaussian-kernel-based cross-correlation (gXCF, Rehfeld et al., 2011) and mutual information (gMI, Rehfeld et al., 2013) against their interpolation-based counterparts and the new event synchronization function (ESF). We test the efficiency of the methods in estimating coupling strength and coupling lag numerically, using ensembles of synthetic stalagmites with short, autocorrelated, linear and nonlinearly coupled proxy time series, and in the application to real stalagmite time series. In the linear test case, coupling strength increases are identified consistently for all estimators, while in the nonlinear test case the correlation-based approaches fail. The lag at which the time series are coupled is identified correctly as the maximum of the similarity functions in around 60–55% (in the linear case) to 53–42% (for the nonlinear processes) of the cases when the dating of the synthetic stalagmite is perfectly precise. If the age uncertainty increases beyond 5% of the time series length, however, the true coupling lag is not identified more often than the others for which the similarity function was estimated. Age uncertainty contributes up to half of the uncertainty in the similarity estimation process. Time series irregularity contributes less, particularly for the adapted Gaussian-kernel-based estimators and the event synchronization function. The introduced link strength concept summarizes the hypothesis test results and balances the individual strengths of the estimators: while gXCF is particularly suitable for short and irregular time series, gMI and the ESF can identify nonlinear dependencies. ESF could, in particular, be suitable to study extreme event dynamics in paleoclimate records. Programs to analyze paleoclimatic time series for significant dependencies are included in a freely available software toolbox.
- ItemHistorical greenhouse gas concentrations for climate modelling (CMIP6)(München : European Geopyhsical Union, 2017) Meinshausen, Malte; Vogel, Elisabeth; Nauels, Alexander; Lorbacher, Katja; Meinshausen, Nicolai; Etheridge, David M.; Fraser, Paul J.; Montzka, Stephen A.; Rayner, Peter J.; Trudinger, Cathy M.; Krummel, Paul B.; Beyerle, Urs; Canadell, Josep G.; Daniel, John S.; Enting, Ian G.; Law, Rachel M. Law; Lunder, Chris R.; O'Doherty, Simon; Prinn, Ron G.; Reimann, Stefan; Rubino, Mauro; Velders, Guus J.M.; Vollmer, Martin K.; Wang, Ray H.J.; Weiss, RayAtmospheric greenhouse gas (GHG) concentrations are at unprecedented, record-high levels compared to the last 800000 years. Those elevated GHG concentrations warm the planet and – partially offset by net cooling effects by aerosols – are largely responsible for the observed warming over the past 150 years. An accurate representation of GHG concentrations is hence important to understand and model recent climate change. So far, community efforts to create composite datasets of GHG concentrations with seasonal and latitudinal information have focused on marine boundary layer conditions and recent trends since the 1980s. Here, we provide consolidated datasets of historical atmospheric concentrations (mole fractions) of 43 GHGs to be used in the Climate Model Intercomparison Project – Phase 6 (CMIP6) experiments. The presented datasets are based on AGAGE and NOAA networks, firn and ice core data, and archived air data, and a large set of published studies. In contrast to previous intercomparisons, the new datasets are latitudinally resolved and include seasonality. We focus on the period 1850–2014 for historical CMIP6 runs, but data are also provided for the last 2000 years. We provide consolidated datasets in various spatiotemporal resolutions for carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O), as well as 40 other GHGs, namely 17 ozone-depleting substances, 11 hydrofluorocarbons (HFCs), 9 perfluorocarbons (PFCs), sulfur hexafluoride (SF6), nitrogen trifluoride (NF3) and sulfuryl fluoride (SO2F2). In addition, we provide three equivalence species that aggregate concentrations of GHGs other than CO2, CH4 and N2O, weighted by their radiative forcing efficiencies. For the year 1850, which is used for pre-industrial control runs, we estimate annual global-mean surface concentrations of CO2 at 284.3ppm, CH4 at 808.2ppb and N2O at 273.0ppb. The data are available at https://esgf-node.llnl.gov/search/input4mips/ and http://www.climatecollege.unimelb.edu.au/cmip6. While the minimum CMIP6 recommendation is to use the global- and annual-mean time series, modelling groups can also choose our monthly and latitudinally resolved concentrations, which imply a stronger radiative forcing in the Northern Hemisphere winter (due to the latitudinal gradient and seasonality).
- ItemGreenland ice sheet model parameters constrained using simulations of the Eemian Interglacial(München : European Geopyhsical Union, 2011) Robinson, A.; Calov, R.; Ganopolski, A.Using a new approach to force an ice sheet model, we performed an ensemble of simulations of the Greenland Ice Sheet evolution during the last two glacial cycles, with emphasis on the Eemian Interglacial. This ensemble was generated by perturbing four key parameters in the coupled regional climate-ice sheet model and by introducing additional uncertainty in the prescribed "background" climate change. The sensitivity of the surface melt model to climate change was determined to be the dominant driver of ice sheet instability, as reflected by simulated ice sheet loss during the Eemian Interglacial period. To eliminate unrealistic parameter combinations, constraints from present-day and paleo information were applied. The constraints include (i) the diagnosed present-day surface mass balance partition between surface melting and ice discharge at the margin, (ii) the modeled present-day elevation at GRIP; and (iii) the modeled elevation reduction at GRIP during the Eemian. Using these three constraints, a total of 360 simulations with 90 different model realizations were filtered down to 46 simulations and 20 model realizations considered valid. The paleo constraint eliminated more sensitive melt parameter values, in agreement with the surface mass balance partition assumption. The constrained simulations resulted in a range of Eemian ice loss of 0.4–4.4 m sea level equivalent, with a more likely range of about 3.7–4.4 m sea level if the GRIP δ18O isotope record can be considered an accurate proxy for the precipitation-weighted annual mean temperatures.
- ItemSimulation of the last glacial cycle with a coupled climate ice-sheet model of intermediate complexity(München : European Geopyhsical Union, 2010) Ganopolski, A.; Calov, R.; Claussen, M.A new version of the Earth system model of intermediate complexity, CLIMBER-2, which includes the three-dimensional polythermal ice-sheet model SICOPOLIS, is used to simulate the last glacial cycle forced by variations of the Earth's orbital parameters and atmospheric concentration of major greenhouse gases. The climate and ice-sheet components of the model are coupled bi-directionally through a physically-based surface energy and mass balance interface. The model accounts for the time-dependent effect of aeolian dust on planetary and snow albedo. The model successfully simulates the temporal and spatial dynamics of the major Northern Hemisphere (NH) ice sheets, including rapid glacial inception and strong asymmetry between the ice-sheet growth phase and glacial termination. Spatial extent and elevation of the ice sheets during the last glacial maximum agree reasonably well with palaeoclimate reconstructions. A suite of sensitivity experiments demonstrates that simulated ice-sheet evolution during the last glacial cycle is very sensitive to some parameters of the surface energy and mass-balance interface and dust module. The possibility of a considerable acceleration of the climate ice-sheet model is discussed.
- ItemGlacial CO 2 cycle as a succession of key physical and biogeochemical processes(München : European Geopyhsical Union, 2012) Brovkin, V.; Ganopolski, A.; Archer, D.; Munhoven, G.During glacial-interglacial cycles, atmospheric CO2 concentration varied by about 100 ppmv in amplitude. While testing mechanisms that have led to the low glacial CO2 level could be done in equilibrium model experiments, an ultimate goal is to explain CO2 changes in transient simulations through the complete glacial-interglacial cycle. The computationally efficient Earth System model of intermediate complexity CLIMBER-2 is used to simulate global biogeochemistry over the last glacial cycle (126 kyr). The physical core of the model (atmosphere, ocean, land and ice sheets) is driven by orbital changes and reconstructed radiative forcing from greenhouses gases, ice, and aeolian dust. The carbon cycle model is able to reproduce the main features of the CO2 changes: a 50 ppmv CO2 drop during glacial inception, a minimum concentration at the last glacial maximum 80 ppmv lower than the Holocene value, and an abrupt 60 ppmv CO2 rise during the deglaciation. The model deep ocean δ13C also resembles reconstructions from deep-sea cores. The main drivers of atmospheric CO2 evolve in time: changes in sea surface temperatures and in the volume of bottom water of southern origin control atmospheric CO2 during the glacial inception and deglaciation; changes in carbonate chemistry and marine biology are dominant during the first and second parts of the glacial cycle, respectively. These feedback mechanisms could also significantly impact the ultimate climate response to the anthropogenic perturbation.