ALBERT

Description: A long-standing task in climate research has been to distinguish between anthropogenic climate change and natural climate variability. A prerequisite for fulfilling this task is the understanding of the relative roles of external drivers and internal variability of climate and the carbon cycle. Here, we present the first ensemble simulations over the last 1200 years with a comprehensive Earth system model including a fully interactive carbon cycle. Applying up-to-date reconstructions of external forcing including the recent low-amplitude estimates of solar variations, the ensemble simulations reproduce temperature evolutions consistent with the range of reconstructions. The 20th-century warming trend stands out against all pre-industrial trends within the ensemble. Volcanic eruptions are necessary to explain variations in pre-industrial climate such as the Little Ice Age; yet only the strongest, repeated eruptions lead to cooling trends that differ significantly from the internal variability across all ensemble members. The simulated atmospheric CO2 concentrations exhibit a stable carbon cycle over the pre-industrial era with multi-centennial variations somewhat smaller than in the observational records. Early land-cover changes have modulated atmospheric CO2 concentrations only slightly. We provide a model-based quantification of the sensitivity (termed γ) of the global carbon cycle to temperature for a variety of climate and forcing conditions. We diagnose a distinct dependence of γ on the forcing strength and time-scales involved, thus providing a possible explanation for the systematic difference in the observational estimates for different segments of the last millennium.

Description: A simple method for initializing coupled general circulation models (CGCMs) using only sea surface temperature (SST) data is comprehensively tested in an extended set of ensemble hindcasts with the Max-Planck-Institute (MPI) climate model, MPI-OM/ECHAM5. In the scheme, initial conditions for both atmosphere and ocean are generated by running the coupled model with SST nudged strongly to observations. Air–sea interaction provides the mechanism through which SST influences the subsurface. Comparison with observations indicates that the scheme is performing well in the tropical Pacific. Results from a 500-yr control run show that the model's El Niño Southern Oscillation (ENSO) variability is quite realistic, in terms of strength, structure and period. The hindcasts performed were six months long, initiated four times per year, consisted of nine ensemble members, and covered the period 1969–2001. The ensemble was generated by only varying atmospheric initial conditions, which were sampled from the initialization run to capture intraseasonal variability. At six-month lead, the model is able to capture all the major ENSO extremes of the period. However, because of poor sampling of ocean initial conditions and model deficiencies, the ensemble-mean anomaly correlation skill for Niño3 SST is only 0.6 at six-month lead. None the less, the results presented here demonstrate the potential of such a simple scheme, and provide a simple method by which SST information may be better used in more complex initialization schemes.

Description: A seasonal forecast system is presented, based on the global coupled climate model MPI-ESM as used for CMIP5 simulations. We describe the initialisation of the system and analyse its predictive skill for surface temperature. The presented system is initialised in the atmospheric, oceanic, and sea ice component of the model from reanalysis/observations with full field nudging in all three components. For the initialisation of the ensemble, bred vectors with a vertically varying norm are implemented in the ocean component to generate initial perturbations. In a set of ensemble hindcast simulations, starting each May and November between 1982 and 2010, we analyse the predictive skill. Bias-corrected ensemble forecasts for each start date reproduce the observed surface temperature anomalies at 2–4 months lead time, particularly in the tropics. Niño3.4 sea surface temperature anomalies show a small root-mean-square error and predictive skill up to 6 months. Away from the tropics, predictive skill is mostly limited to the ocean, and to regions which are strongly influenced by ENSO teleconnections. In summary, the presented seasonal prediction system based on a coupled climate model shows predictive skill for surface temperature at seasonal time scales comparable to other seasonal prediction systems using different underlying models and initialisation strategies. As the same model underlying our seasonal prediction system—with a different initialisation—is presently also used for decadal predictions, this is an important step towards seamless seasonal-to-decadal climate predictions.

Description: Sea surface temperature (SST) observations in the North Atlantic indicate the existence of strong multidecadal variability with a unique spatial structure. It is shown by means of a new global climate model, which does not employ flux adjustments, that the multidecadal SST variability is closely related to variations in the North Atlantic thermohaline circulation (THC). The close correspondence between the North Atlantic SST and THC variabilities allows, in conjunction with the dynamical inertia of the THC, for the prediction of the slowly varying component of the North Atlantic climate system. It is shown additionally that past variations of the North Atlantic THC can be reconstructed from a simple North Atlantic SST index and that future, anthropogenically forced changes in the THC can be easily monitored by observing SSTs. The latter is confirmed by another state-of-the-art global climate model. Finally, the strong multidecadal variability may mask an anthropogenic signal in the North Atlantic for some decades.

Description: Perfect model ensemble experiments are performed with five coupled atmosphere-ocean models to investigate the potential for initial-value climate forecasts on interannual to decadal time scales. Experiments are started from similar initial states and common diagnostics of predictability are used. We find that; variations in the ocean Meridional Overturning Circulation are potentially predictable on interannual to decadal time scales, a more consistent picture of the surface temperature impact of decadal variations in the MOC is now apparent, and variations of surface air temperatures in the N. Atlantic are also potentially predictable on interannual to decadal time scales, albeit with potential skill levels which are less than those seen for MOC variations. This inter-comparison represents a step forward in assessing the robustness of model estimates of potential skill and is a pre-requisite for the development of any operational forecasting system

Description: The influence of phytoplankton on the seasonal cycle and the mean global climate is investigated in a fully coupled climate model. The control experiment uses a fixed attenuation depth for shortwave radiation, while the attenuation depth in the experiment with biology is derived from phytoplankton concentrations simulated with a marine biogeochemical model coupled online to the ocean model. Some of the changes in the upper ocean are similar to the results from previous studies that did not use interactive atmospheres, for example, amplification of the seasonal cycle; warming in upwelling regions, such as the equatorial Pacific and the Arabian Sea; and reduction in sea ice cover in the high latitudes. In addition, positive feedbacks within the climate system cause a global shift of the seasonal cycle. The onset of spring is about 2 weeks earlier, which results in a more realistic representation of the seasons. Feedback mechanisms, such as increased wind stress and changes in the shortwave radiation, lead to significant warming in the midlatitudes in summer and to seasonal modifications of the overall warming in the equatorial Pacific. Temperature changes also occur over land where they are sometimes even larger than over the ocean. In the equatorial Pacific, the strength of interannual SST variability is reduced by about 10%–15% and phase locking to the annual cycle is improved. The ENSO spectral peak is broader than in the experiment without biology and the dominant ENSO period is increased to around 5 yr. Also the skewness of ENSO variability is slightly improved. All of these changes lead to the conclusion that the influence of marine biology on the radiative budget of the upper ocean should be considered in detailed simulations of the earth’s climate.

Description: This paper describes the mean ocean circulation and the tropical variability simulated by the Max Planck Institute for Meteorology (MPI-M) coupled atmosphere–ocean general circulation model (AOGCM). Results are presented from a version of the coupled model that served as a prototype for the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) simulations. The model does not require flux adjustment to maintain a stable climate. A control simulation with present-day greenhouse gases is analyzed, and the simulation of key oceanic features, such as sea surface temperatures (SSTs), large-scale circulation, meridional heat and freshwater transports, and sea ice are compared with observations. A parameterization that accounts for the effect of ocean currents on surface wind stress is implemented in the model. The largest impact of this parameterization is in the tropical Pacific, where the mean state is significantly improved: the strength of the trade winds and the associated equatorial upwelling weaken, and there is a reduction of the model’s equatorial cold SST bias by more than 1 K. Equatorial SST variability also becomes more realistic. The strength of the variability is reduced by about 30% in the eastern equatorial Pacific and the extension of SST variability into the warm pool is significantly reduced. The dominant El Niño–Southern Oscillation (ENSO) period shifts from 3 to 4 yr. Without the parameterization an unrealistically strong westward propagation of SST anomalies is simulated. The reasons for the changes in variability are linked to changes in both the mean state and to a reduction in atmospheric sensitivity to SST changes and oceanic sensitivity to wind anomalies.

Description: On seasonal time scales, ENSO prediction has become feasible in an operational framework in recent years. On decadal to multidecadal time scales, the variability of the oceanic circulation is assumed to provide a potential for climate prediction. To investigate the decadal predictability of the coupled atmosphere–ocean general circulation model (AOGCM) European Centre-Hamburg model version 5/Max Planck Institute Ocean Model (ECHAM5/MPI-OM), a 500-yr-long control integration and “perfect model” predictability experiments are analyzed. The results show that the sea surface temperatures (SSTs) of the North Atlantic, Nordic Seas, and Southern Ocean exhibit predictability on multidecadal time scales. Over the ocean, the predictability of surface air temperature (SAT) is very similar to that of SST. Over land, there is little evidence of decadal predictability of SAT except for some small maritime-influenced regions of Europe. The AOGCM produces predictable signals in lower-tropospheric temperature and precipitation over the North Atlantic, but not in sea level pressure.