scholarly journals Extreme sensitivity and climate tipping points

2020 ◽  
Author(s):  
Anna von der Heydt ◽  
Peter Ashwin
Keyword(s):  
Climate Sensitivity ◽  
Climate Models ◽  
Tipping Point ◽  
Climate System ◽  
Tipping Points ◽  
Equilibrium Climate Sensitivity ◽  
Future Global Warming ◽  
Wide Range ◽  
Extreme Sensitivity ◽  
Global Mean

<p>The equilibrium climate sensitivity (ECS) is widely used as a measure for possible future global warming. It has been determined from a wide range of climate models, observations and palaeoclimate records, however, it still remains relatively unconstrained. In particular, large values of warming as a consequence of atmospheric greenhouse gas increase cannot be excluded, with some of the most recent state-of-the-art climate models (CMIP6) supporting (much) more warming than previous generations of climate models. Moreover, a number of tipping elements have been identified within the climate system, some of which may affect the global mean temperature. Therefore, it is interesting to explore how the climate systems response (e.g. ECS) behaves when the system is close to a tipping point. <br>A climate state close to a tipping point will have a degenerate linear response to perturbations, which can be associated with extreme values of the equilibrium climate sensitivity (ECS). In this talk we contrast linearized ('instantaneous') with fully nonlinear geometric ('two-point') notions of ECS, in both presence and absence of tipping points. For a stochastic energy balance model of the global mean surface temperature with two stable regimes, we confirm that tipping events cause the appearance of extremes in both notions of ECS. Moreover, multiple regimes with different mean sensitivities are visible in the two-point ECS. We confirm some of our findings in a physics-based multi-box model of the climate system.</p><p><strong>Reference</strong><br>P. Ashwin and A. S. von der Heydt (2019), Extreme Sensitivity and Climate Tipping Points, J. Stat. Phys.  <strong>370</strong>, 1166–24. http://doi.org/10.1007/s10955-019-02425-x.</p>

scholarly journals Another look at climate sensitivity

2010 ◽  
Vol 17 (2) ◽  
pp. 113-122 ◽  
Author(s):  
I. Zaliapin ◽  
M. Ghil
Keyword(s):  
Climate Sensitivity ◽  
Large Amplitude ◽  
Climate Models ◽  
Climate Science ◽  
Climate System ◽  
Balance Model ◽  
Tipping Points ◽  
Amplitude Response ◽  
Classical Energy ◽  
Sensitive Dependence

Abstract. We revisit a recent claim that the Earth's climate system is characterized by sensitive dependence to parameters; in particular, that the system exhibits an asymmetric, large-amplitude response to normally distributed feedback forcing. Such a response would imply irreducible uncertainty in climate change predictions and thus have notable implications for climate science and climate-related policy making. We show that equilibrium climate sensitivity in all generality does not support such an intrinsic indeterminacy; the latter appears only in essentially linear systems. The main flaw in the analysis that led to this claim is inappropriate linearization of an intrinsically nonlinear model; there is no room for physical interpretations or policy conclusions based on this mathematical error. Sensitive dependence nonetheless does exist in the climate system, as well as in climate models – albeit in a very different sense from the one claimed in the linear work under scrutiny – and we illustrate it using a classical energy balance model (EBM) with nonlinear feedbacks. EBMs exhibit two saddle-node bifurcations, more recently called "tipping points," which give rise to three distinct steady-state climates, two of which are stable. Such bistable behavior is, furthermore, supported by results from more realistic, nonequilibrium climate models. In a truly nonlinear setting, indeterminacy in the size of the response is observed only in the vicinity of tipping points. We show, in fact, that small disturbances cannot result in a large-amplitude response, unless the system is at or near such a point. We discuss briefly how the distance to the bifurcation may be related to the strength of Earth's ice-albedo feedback.

scholarly journals Multivariate Estimations of Equilibrium Climate Sensitivity from Short Transient Warming Simulations

2021 ◽  
Author(s):  
Robbin Bastiaansen ◽  
Henk Dijkstra ◽  
Anna von der Heydt
Keyword(s):  
Time Series ◽  
Climate Sensitivity ◽  
Climate Models ◽  
Global Climate ◽  
Equilibrium Temperature ◽  
Global Climate Models ◽  
Equilibrium Climate Sensitivity ◽  
Wide Range ◽  
Short Time

<p>One of the most used metrics to gauge the effects of climate change is the equilibrium climate sensitivity, defined as the long-term (equilibrium) temperature increase resulting from instantaneous doubling of atmospheric CO2. Since global climate models cannot be fully equilibrated in practice, extrapolation techniques are used to estimate the equilibrium state from transient warming simulations. Because of the abundance of climate feedbacks – spanning a wide range of temporal scales – it is hard to extract long-term behaviour from short-time series; predominantly used techniques are only capable of detecting the single most dominant eigenmode, thus hampering their ability to give accurate long-term estimates. Here, we present an extension to those methods by incorporating data from multiple observables in a multi-component linear regression model. This way, not only the dominant but also the next-dominant eigenmodes of the climate system are captured, leading to better long-term estimates from short, non-equilibrated time series.</p>

scholarly journals Estimates of climate system properties incorporating recent climate change

2018 ◽  
Vol 4 (1/2) ◽  
pp. 19-36 ◽  
Author(s):  
Alex G. Libardoni ◽  
Chris E. Forest ◽  
Andrei P. Sokolov ◽  
Erwan Monier
Keyword(s):  
Climate Change ◽  
Surface Temperature ◽  
21St Century ◽  
Climate Sensitivity ◽  
Temperature Rise ◽  
Heat Content ◽  
Climate System ◽  
Model Parameters ◽  
Aerosol Forcing ◽  
Global Mean

Abstract. Historical time series of surface temperature and ocean heat content changes are commonly used metrics to diagnose climate change and estimate properties of the climate system. We show that recent trends, namely the slowing of surface temperature rise at the beginning of the 21st century and the acceleration of heat stored in the deep ocean, have a substantial impact on these estimates. Using the Massachusetts Institute of Technology Earth System Model (MESM), we vary three model parameters that influence the behavior of the climate system: effective climate sensitivity (ECS), the effective ocean diffusivity of heat anomalies by all mixing processes (Kv), and the net anthropogenic aerosol forcing scaling factor. Each model run is compared to observed changes in decadal mean surface temperature anomalies and the trend in global mean ocean heat content change to derive a joint probability distribution function for the model parameters. Marginal distributions for individual parameters are found by integrating over the other two parameters. To investigate how the inclusion of recent temperature changes affects our estimates, we systematically include additional data by choosing periods that end in 1990, 2000, and 2010. We find that estimates of ECS increase in response to rising global surface temperatures when data beyond 1990 are included, but due to the slowdown of surface temperature rise in the early 21st century, estimates when using data up to 2000 are greater than when data up to 2010 are used. We also show that estimates of Kv increase in response to the acceleration of heat stored in the ocean as data beyond 1990 are included. Further, we highlight how including spatial patterns of surface temperature change modifies the estimates. We show that including latitudinal structure in the climate change signal impacts properties with spatial dependence, namely the aerosol forcing pattern, more than properties defined for the global mean, climate sensitivity, and ocean diffusivity.

scholarly journals Dansgaard-Oeschger events: tipping points in the climate system

2012 ◽  
Vol 8 (5) ◽  
pp. 4269-4294 ◽  
Author(s):  
A. A. Cimatoribus ◽  
S. S. Drijfhout ◽  
V. Livina ◽  
G. van der Schrier
Keyword(s):  
High Resolution ◽  
Abrupt Change ◽  
Ice Core ◽  
Tipping Point ◽  
Statistical Properties ◽  
Climate System ◽  
Tipping Points ◽  
External Forcing ◽  
Observational Constraint ◽  
Climate Fluctuations

Abstract. The largest variability in temperature over the last sixty thousand years is connected with Dansgaard-Oeschger events. Various prototype models have been proposed to explain these rapid climate fluctuations, but until now no observational constraint has been forwarded to choose between different theories. We assess the bimodality of the system reconstructing the topology of the multi-dimensional attractor over which the climate system evolves. Furthermore, we show that Dansgaard-Oeschger events are compatible with the crossing of a tipping point in the climate system. We use high-resolution ice core isotope data to investigate the statistical properties of the climate fluctuations in the period before the onset of the abrupt change. We find that the statistics are consistent with the switches between two different climate equilibrium states in response to a changing external forcing.

scholarly journals FaIRv2.0.0: a generalized impulse response model for climate uncertainty and future scenario exploration

2021 ◽  
Vol 14 (5) ◽  
pp. 3007-3036
Author(s):  
Nicholas J. Leach ◽  
Stuart Jenkins ◽  
Zebedee Nicholls ◽  
Christopher J. Smith ◽  
John Lynch ◽  
...  
Keyword(s):  
Greenhouse Gas ◽  
Impulse Response ◽  
Climate Models ◽  
Global Climate ◽  
Climate System ◽  
System Response ◽  
Common Denominator ◽  
Response Model ◽  
Wide Range ◽  
Complex Models

Abstract. Here we present an update to the FaIR model for use in probabilistic future climate and scenario exploration, integrated assessment, policy analysis, and education. In this update we have focussed on identifying a minimum level of structural complexity in the model. The result is a set of six equations, five of which correspond to the standard impulse response model used for greenhouse gas (GHG) metric calculations in the IPCC's Fifth Assessment Report, plus one additional physically motivated equation to represent state-dependent feedbacks on the response timescales of each greenhouse gas cycle. This additional equation is necessary to reproduce non-linearities in the carbon cycle apparent in both Earth system models and observations. These six equations are transparent and sufficiently simple that the model is able to be ported into standard tabular data analysis packages, such as Excel, increasing the potential user base considerably. However, we demonstrate that the equations are flexible enough to be tuned to emulate the behaviour of several key processes within more complex models from CMIP6. The model is exceptionally quick to run, making it ideal for integrating large probabilistic ensembles. We apply a constraint based on the current estimates of the global warming trend to a million-member ensemble, using the constrained ensemble to make scenario-dependent projections and infer ranges for properties of the climate system. Through these analyses, we reaffirm that simple climate models (unlike more complex models) are not themselves intrinsically biased “hot” or “cold”: it is the choice of parameters and how those are selected that determines the model response, something that appears to have been misunderstood in the past. This updated FaIR model is able to reproduce the global climate system response to GHG and aerosol emissions with sufficient accuracy to be useful in a wide range of applications and therefore could be used as a lowest-common-denominator model to provide consistency in different contexts. The fact that FaIR can be written down in just six equations greatly aids transparency in such contexts.

scholarly journals FaIRv2.0.0: a generalised impulse-response model for climate uncertainty and future scenario exploration

2020 ◽  
Author(s):  
Nicholas J. Leach ◽  
Stuart Jenkins ◽  
Zebedee Nicholls ◽  
Christopher J. Smith ◽  
John Lynch ◽  
...  
Keyword(s):  
Greenhouse Gas ◽  
Impulse Response ◽  
Climate Models ◽  
Climate System ◽  
System Response ◽  
Common Denominator ◽  
Response Model ◽  
Wide Range ◽  
Complex Models ◽  
Additional Equation

Abstract. Here we present an update to the FaIR model for use in probabilistic future climate and scenario exploration, integrated assessment, policy analysis and education. In this update we have focussed on identifying a minimum level of structural complexity in the model. The result is a set of six equations, five of which correspond to the standard Impulse Response model used for greenhouse gas (GHG) metric calculations in the IPCC's fifth assessment report, plus one additional physically-motivated additional equation to represent state-dependent feedbacks on the response timescales of each greenhouse gas cycle. This additional equation is necessary to reproduce non-linearities in the carbon cycle apparent in both Earth System Models and observations. These six equations are transparent and sufficiently simple that the model is able to be written in standard tabular data analysis packages, such as Excel; increasing the potential user base considerably. However, we demonstrate that the equations are flexible enough to be tuned to emulate the behaviour of several key processes within more complex models from CMIP6. The model is exceptionally quick to run, making it ideal for integrating large probabilistic ensembles. We apply a constraint based on the current estimates of the global warming trend to a one million member ensemble, using the constrained ensemble to make scenario dependent projections and infer ranges for properties of the climate system. Through these analyses, we reaffirm that simple climate models (unlike more complex models) are not themselves intrinsically biased hot or cold: it is the choice of parameters and how those are selected that determines the model response, something that appears to have been misunderstood in the past. This updated FaIR model is able to reproduce the global climate system response to GHG and aerosol emissions with sufficient accuracy to be useful in a wide range of applications; and therefore could be used as a lowest common denominator model to provide consistency in different contexts. The fact that FaIR can be written down in just six equations greatly aids transparency in such contexts.

scholarly journals Could the Pliocene constrain the Equilibrium Climate Sensitivity?

2016 ◽  
Author(s):  
J. C. Hargreaves ◽  
J. D. Annan
Keyword(s):  
Carbon Dioxide ◽  
Boundary Conditions ◽  
Climate Sensitivity ◽  
Atmospheric Carbon Dioxide ◽  
Climate Models ◽  
Climate Model ◽  
Equilibrium Climate Sensitivity ◽  
Model Experiments ◽  
First Time ◽  
Industrial State

Abstract. The mid-PlioceneWarm Period (mPWP) is the most recent interval in which atmospheric carbon dioxide was substantially higher than in modern pre-industrial times. It is, therefore, a potentially valuable target for testing the ability of climate models to simulate climates warmer than the pre-industrial state. The recent Pliocene model inter-comparison Project (PlioMIP) presented boundary conditions for the mPWP, and a protocol for climate model experiments. Here we analyse results from the PlioMIP and, for the first time, discuss the potential for this interval to usefully constrain the equilibrium climate sensitivity. We present an estimate of 1.8–3.6 °C, but there are considerable uncertainties surrounding the analysis. We consider the extent to which these uncertainties may be lessened in the next few years.

scholarly journals Equilibrium Climate Sensitivity Estimated by Equilibrating Climate Models

2020 ◽  
Vol 47 (4) ◽  
Author(s):  
Maria Rugenstein ◽  
Jonah Bloch‐Johnson ◽  
Jonathan Gregory ◽  
Timothy Andrews ◽  
Thorsten Mauritsen ◽  
...  
Keyword(s):  
Climate Sensitivity ◽  
Climate Models ◽  
Equilibrium Climate Sensitivity

Discrepancy in radiative feedbacks between models and observations tied to models inability to reproduce historical surface temperature patterns over the tropical Indo-Pacific.

2020 ◽  
Author(s):  
Cristian Proistosescu ◽  
Yue Dong ◽  
Malte Stuecker ◽  
Kyle Armour ◽  
Robb Wills ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Climate Sensitivity ◽  
Climate Models ◽  
Atmospheric Model ◽  
Natural Variability ◽  
Forced Response ◽  
Large Ensemble ◽  
Radiative Feedback ◽  
Wide Range ◽  
Radiative Feedbacks

<p>How much Earth warms in response to radiative forcing is determined by the net radiative feedback, which quantifies how much more energy is radiated to space for a given increase in surface temperature.  Estimates from present day observations of temperature and earth's energetic imbalance yield a strongly negative radiative feedback, or, equivalently, a very low climate sensitivity, which lies outside the range of climate sensitivity in coupled climate models. This discrepancy in radiative feedbacks can be linked to discrepancies between models and observations in the pattern of historical sea-surface temperature (SST) anomalies driving tropical atmospheric circulation and radiative damping.  Indeed, we find that an atmospheric model (CAM5) forced with observed SSTs yields a net feedback that is consistent with observational estimates, but up to three times more negative than that from the same period (2000-2017) in historical simulations where the same atmospheric model is coupled to a dynamical ocean model (CESM1). </p><p>To understand the role natural variability can play in this discrepancy, we compare the radiative feedbacks generated by the observed pattern of SSTs to those within the CESM1 large ensemble over the same period. The large ensemble produces a wide range of feedbacks due to internal variability alone. Yet, global radiative feedbacks (cloud feedbacks in particular) generated by observed warming patterns are far outside the range of natural variability in the large ensemble. Using both a Green's function approach, as well as a simple metric based on the East-West tropical pacific gradient, we show that none of the control simulations of CMIP5 climate models can generate sufficiently large natural variability to explain the discrepancy between models and observations. We conclude that the discrepancy in SST patterns, and the resulting discrepancy in radiative feedbacks, is caused by an deficiency in models' ability to simulate either natural variabilty or the forced response over the recent historical period. We will also show preliminary analysis from CMIP6 simulations.</p>

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