Climate variance envelope and non-stationary couplings in climate-plankton interactions
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Climate system
Envelope (radar)
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Abstract. Terrestrial vegetation influences climate by modifying the radiative-, momentum-, and hydrologic-balance. This paper contributes to the ongoing debate on the question whether positive biogeophysical feedbacks between vegetation and climate may lead to multiple equilibria in vegetation and climate and consequent abrupt regime shifts. Several modelling studies argue that vegetation-climate feedbacks at local to regional scales could be strong enough to establish multiple states in the climate system. An Earth Model of Intermediate Complexity, PlaSim, is used to investigate the resilience of the climate system to vegetation disturbance at regional to global scales. We hypothesize that by starting with two extreme initialisations of biomass, positive vegetation-climate feedbacks will keep the vegetation-atmosphere system within different attraction domains. Indeed, model integrations starting from different initial biomass distributions diverged to clearly distinct climate-vegetation states in terms of abiotic (precipitation and temperature) and biotic (biomass) variables. Moreover, we found that between these states there are several other steady states which depend on the scale of perturbation. From here global susceptibility maps were made showing regions of low and high resilience. The model results suggest that mainly the boreal and monsoon regions have low resiliences, i.e. instable biomass equilibria, with positive vegetation-climate feedbacks in which the biomass induced by a perturbation is further enforced. The perturbation did not only influence single vegetation-climate cell interactions but also caused changes in spatial patterns of atmospheric circulation due to neighbouring cells constituting in spatial vegetation-climate feedbacks. Large perturbations could trigger an abrupt shift of the system towards another steady state. Although the model setup used in our simulation is rather simple, our results stress that the coupling of feedbacks at multiple scales in vegetation-climate models is essential and urgent to understand the system dynamics for improved projections of ecosystem responses to anthropogenic changes in climate forcing.
Alternative stable state
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Abstract The stochastic character of short time scale atmospheric variables induce unpredictable fluctuations in the climatic system. These fluctuations are treated here as a “forced noise level” to be taken into account in climate modeling. A simple statistical model based on recent work by Hasselmann (1976) is used to estimate the resulting error in the calculated means of climate variables over finite intervals. This error may be large for short record lengths, as illustrated for the sea surface temperature. Some consequences relevant to joint ocean-atmosphere models and climate change experiments are discussed.
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Forcing (mathematics)
Component (thermodynamics)
Climate system
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The key to understanding the link between climate variability and the biosphere are feedbacks of the ocean and land surfaces to atmospheric variability. Generalizations of the Hasselman random forcing model continue to provide a framework for much of the observed climate variability. Zero dimension ocean‐atmosphere and land‐atmosphere coupled models reveal the timescales of mutual adjustment and hence the amplitude of variability for a given forcing. The coupling in both models introduces longer timescales than in any uncoupled model and hence illustrates how the coupled systems act to amplify climate variability. The common feature of both models is a quantity that is conserved between the surface and the atmosphere. For the atmosphere‐ocean model, this is energy, whereas for the atmosphere‐land model, the conserved quantity is water. These conservative quantities represent the projection of climate variability onto the slowest system mode, an inference that readily generalizes to modes of climate variability with spatial structure and hence they provide a useful focus for diagnosis of observations and complex model simulations.
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Climatic variability
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The precise mechanisms driving Arctic amplification are still under debate. Previous attribution methods based on top-of-atmosphere energy budgets have assumed all forcings and feedbacks lead to vertically-uniform temperature changes, with any departures from this collected into the lapse-rate feedback. We propose an alternative attribution method using a single column model that accounts for the forcing-dependence of high latitude lapse-rate changes. We test this method in an idealized General Circulation Model (GCM), finding that, even though the column-integrated carbon dioxide (CO2) forcing and water vapor feedback are stronger in the tropics, they contribute to polar-amplified surface warming as they lead to bottom-heavy warming in high latitudes. A separation of atmospheric temperature changes into local and remote contributors shows that, in the absence of polar surface forcing (e.g., sea-ice retreat), changes in energy transport are primarily responsible for the polar amplified pattern of warming. The addition of surface forcing substantially increases polar surface warming and reduces the contribution of atmospheric dry static energy transport. This physically-based attribution method can be applied to comprehensive GCMs to provide a clearer view of the mechanisms behind Arctic amplification.
Forcing (mathematics)
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Transient climate simulation
Forcing (mathematics)
Climate commitment
Abrupt climate change
Climate state
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Equilibrium climate sensitivity is a key predictor of climate change. However, it is not very well constrained, either by climate models or by observational data. The reasons for this include strong internal variability and forcing on many timescales. In practice, this means that the 'equilibrium' will only be relative to fixing the slow feedback processes before comparing palaeoclimate sensitivity estimates with estimates from model simulations. In addition, information from the late Pleistocene ice age cycles indicates that the climate cycles between cold and warm regimes, and the climate sensitivity varies considerably between regime because of fast feedback processes changing relative strength and timescales over one cycle. In this paper, we consider climate sensitivity for quite general climate dynamics. Using a conceptual Earth system model of Gildor and Tziperman (A sea ice climate switch mechanism for the 100-kyr glacial cycles. J Geophys Res 2001; 106: 9117–33) (with Milankovich forcing and dynamical ocean biogeochemistry), we explore various ways of quantifying the state dependence of climate sensitivity from unperturbed and perturbed model time series. Even without considering any perturbation, we suggest that climate sensitivity can be usefully thought of as a distribution that quantifies variability within the 'climate attractor'. On the 'climate attractor', there is a strong dependence on climate state or more specifically on the 'climate regime' where fast processes are approximately in equilibrium. We also consider perturbations by instantaneous doubling of CO2 and similarly find a strong dependence on the climate state using our approach.
Climate state
Transient climate simulation
Forcing (mathematics)
Climate commitment
Abrupt climate change
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Predictability
SIGNAL (programming language)
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