Multivariate Estimations of Equilibrium Climate Sensitivity From Short Transient Warming Simulations

Bastiaansen, Robbin; Dijkstra, Henk A.; Heydt, Anna S. von der

doi:10.1029/2020gl091090

Cited by 6 publications

(9 citation statements)

References 44 publications

(92 reference statements)

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“…First, the amount of modes S1 in Supporting Information S1. This is also in agreement with other reports that typically find two or three relevant modes (Bastiaansen et al, 2020;Caldeira & Myhrvold, 2013;Tsutsui, 2017;Proistosescu & Huybers, 2017). With 3 E M  fixed, the other parameters are fitted with nonlinear regression.…”

Section: Temporal Evolution Abrupt4×cosupporting

confidence: 89%

Projections of the Transient State‐Dependency of Climate Feedbacks

Bastiaansen

Dijkstra

Heydt

2021

Geophysical Research Letters

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Were the climate system free of feedback processes, it would be easy to predict and control the future climate (Arrhenius, 1896). Unfortunately, the climate system contains many feedback loops (Heinze et al., 2019;Von der Heydt et al., 2020). Because of this, climate change can get suppressed or enhanced, making future projections hard (Sherwood et al., 2020). Therefore, detailed knowledge of all relevant feedback processes is required to accurately assess potential future climates. However, knowledge of the current strengths of climate feedbacks is not enough. Over time, as the climate state changes, the strengths of climate feedbacks also change (Armour et al., 2013;Gregory & Andrews, 2016;Marvel et al., 2018); for instance, the albedo-increasing effect of ice is only relevant when there still is ice.As the Earth warms, the strengths of feedbacks change (Bony et al., 2006). Therefore, projections based only on current knowledge of climate feedbacks misestimate future climate change-especially the committed warming that is to come even if zero-emission is achieved (Goodwin, 2018;Marvel et al., 2018;Senior & Mitchell, 2000). To properly assess different emission scenarios, it is crucial to identify all relevant feedback mechanisms and, additionally, to quantify how their strengths change over time.For future temperature projections with global climate models, the focus lies with the following feedback processes (Cess, 1975;Klocke et al., 2013;Zelinka et al., 2020). First, the Planck radiation feedback suppresses warming due to increased outgoing radiation. Second, the lapse rate feedback also suppresses warming due to an increase in long-wave radiation escaping to space (Sinha, 1995). The third is the ice-albedo feedback that enhances warming through a less reflective Earth surface (Curry et al., 1995). Fourth is the water vapor feedback which boosts warming because of increased atmospheric water vapor content (Manabe &

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Section: Temporal Evolution Abrupt4×cosupporting

confidence: 89%

Projections of the Transient State‐Dependency of Climate Feedbacks

Bastiaansen

Dijkstra

Heydt

2021

Geophysical Research Letters

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“…Using all data of the LongRunMIP results in a climate sensitivity estimate of 6.66 K, some 20% higher than at year 50. We also use a multi-component linear regression suggested by Bastiaansen et al (2021) for short simulations using the globally-integrated ocean heat content and its time derivative as additional state variables. This method results in similar estimates as the Gregory method ignoring the first 20 years of data.…”

Section: Summary and Discussionmentioning

confidence: 99%

“…This is another example of the state dependence of the climate sensitivity. A number of improvements on Gregory (2004) method have been suggested, for example, by taking into account additional measures of the climate state (Bastiaansen et al, 2021). Synthesizing results from all lines of evidence, in its 6th Assessment Report the IPCC estimates the likely range of the ECS to be 2.5 4.5 K with a best estimate of 3 K (red bar in Fig.…”

Section: Climate Sensitivitymentioning

confidence: 99%

“…The Gregory method leverages the fact that the radiative imbalance goes to zero and the GMST stabilizes at a new value. The linear regression approach can be extended to include additional variables which fulfill these properties (Bastiaansen et al, 2021). With the resulting multi-component linear regression the ECS can potentially be estimated more robustly and with less data as more than only the dominant eigenmode can be captured.…”

Section: Climate Sensitivitymentioning

confidence: 99%

“…The MC-LR fit determines the matrix A and F := A X⇤ where the star subscript denotes the equilibrium. Bastiaansen et al (2021) use the shortwave albedo and longwave emissivity and their time derivatives as additional variables. We find that for our short time series the albedo and emissivity are too linearly dependent on GMST to be useful as the resulting matrix becomes nearly singular and the estimates of equilibrium temperature vary wildly.…”

Section: Climate Sensitivitymentioning

confidence: 99%

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AR(1) Autoregressive process of first order auftreten können. Darüber hinaus finden wir lokale Unterschiede in der Reaktion auf den Strahlungsantrieb und, bemerkenswerterweise, dass die AMOC im Gegensatz zur Simulation ohne Verwirbelung in ein instabiles Regime eintreten kann. Obwohl das CESM nur ein Modell aus einer ganzen Klasse von Erdsystemmodellen ist, gehen wir davon aus, dass unsere Ergebnisse im Allgemeinen auch für andere Modelle gelten. Zusätzlich zu den oben genannten Kapiteln wird diese Arbeit durch Kapitel 7 abgerundet, das die Forschungsergebnisse zusammenfasst, die Forschungsfragen beantwortet und die Ergebnisse kritisch reflektiert.

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Ecosystem Resilience Monitoring and Early Warning Using Earth Observation Data: Challenges and Outlook

Bathiany,

Bastiaansen,

Bastos

et al. 2024

Surv Geophys

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As the Earth system is exposed to large anthropogenic interferences, it becomes ever more important to assess the resilience of natural systems, i.e., their ability to recover from natural and human-induced perturbations. Several, often related, measures of resilience have been proposed and applied to modeled and observed data, often by different scientific communities. Focusing on terrestrial ecosystems as a key component of the Earth system, we review methods that can detect large perturbations (temporary excursions from a reference state as well as abrupt shifts to a new reference state) in spatio-temporal datasets, estimate the recovery rate after such perturbations, or assess resilience changes indirectly from stationary time series via indicators of critical slowing down. We present here a sequence of ideal methodological steps in the field of resilience science, and argue how to obtain a consistent and multi-faceted view on ecosystem or climate resilience from Earth observation (EO) data. While EO data offers unique potential to study ecosystem resilience globally at high spatial and temporal scale, we emphasize some important limitations, which are associated with the theoretical assumptions behind diagnostic methods and with the measurement process and pre-processing steps of EO data. The latter class of limitations include gaps in time series, the disparity of scales, and issues arising from aggregating time series from multiple sensors. Based on this assessment, we formulate specific recommendations to the EO community in order to improve the observational basis for ecosystem resilience research.

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Multivariate Estimations of Equilibrium Climate Sensitivity From Short Transient Warming Simulations

Cited by 6 publications

References 44 publications

Projections of the Transient State‐Dependency of Climate Feedbacks

Projections of the Transient State‐Dependency of Climate Feedbacks

Climate Variability and Response in High-Resolution Earth System Models

Ecosystem Resilience Monitoring and Early Warning Using Earth Observation Data: Challenges and Outlook

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