Internal Variability and Disequilibrium Confound Estimates of Climate Sensitivity From Observations

Marvel, Kate; Pincus, Robert; Schmidt, Gavin A.; Miller, Ron

doi:10.1002/2017gl076468

Cited by 50 publications

(83 citation statements)

References 26 publications

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“…In models, it is usually derived from simulations of greenhouse warming at equilibrium or in response to a large abrupt forcing (i.e. high signal-to-noise ratio; Marvel et al 2018). It has been recognised that the feedbacks to an early and increasing warming of the climate system in response to increasing greenhouse gases may not be identical to those to equilibrated greenhouse warming.…”

Section: Greenhouse Gas Attributable Effective Climate Sensitivity (Smentioning

confidence: 99%

Observational constraints on the effective climate sensitivity from the historical period

Tokarska

Hegerl

Schurer

et al. 2020

Environ. Res. Lett.

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The observed warming in the atmosphere and ocean can be used to estimate the climate sensitivity linked to present-day feedbacks, which is referred to as the effective climate sensitivity (S hist ). However, such an estimate is affected by uncertainty in the radiative forcing, particularly aerosols, over the historical period. Here, we make use of detection and attribution techniques to derive the surface air temperature and ocean warming that can be attributed directly to greenhouse gas increases. These serve as inputs to a simple energy budget to infer the likelihood of S hist in response to observed greenhouse gases increases over two time periods (1862-2012 and 1955-2012). The benefit of using greenhouse gas attributable quantities is that they are not subject to uncertainties in the aerosol forcing (other than uncertainty in the attribution to greenhouse gas versus aerosol forcing not captured by the multi-model aerosol response pattern). The resulting effective climate sensitivity estimate, S hist , ranges from 1.3°C to 3.1°C (5%-95% range) over the full instrumental period (1862-2012) for our best estimate, and gets slightly wider when considering further uncertainties. This estimate increases to 1.7°C-4.6°C if using the shorter period . We also evaluate the climate model simulated surface air temperature and ocean heat content increase in response to greenhouse gas forcing over the same periods, and compare them with the observationally-constrained values. We find that that the ocean warming simulated in greenhouse gas only simulations in models considered here is consistent with that attributed to greenhouse gas increases from observations, while one model simulates more greenhouse gas-induced surface air warming than observed. However, other models with sensitivity outside our range show greenhouse gas warming that is consistent with that attributed in observations, emphasising that feedbacks during the historical period may differ from the feedbacks at CO 2 doubling and from those at true equilibrium.

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Section: Greenhouse Gas Attributable Effective Climate Sensitivity (Smentioning

confidence: 99%

Observational constraints on the effective climate sensitivity from the historical period

Tokarska

Hegerl

Schurer

et al. 2020

Environ. Res. Lett.

Self Cite

View full text Add to dashboard Cite

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“…The relationship between global surface temperature change and the Earth's radiative response—a measure of the radiative feedbacks in the system and a key determinant of the Earth's climate sensitivity—can vary on timescales of decades to millennia. Thus, feedbacks governing warming over the observed historical record may be different from those acting on the Earth's long‐term climate sensitivity to rising greenhouse gas concentrations (e.g., Armour, ; Gregory & Andrews, ; Marvel et al, ; Proistosescu & Huybers, ; Silvers et al, ; Zhou et al, ). This is in contrast to decades of studies that explicitly or implicitly assume that the relationship between historical temperature change and energy budget variations provides a direct constraint on long‐term climate sensitivity (e.g., Gregory et al, ; Otto et al, ).…”

Section: Introductionmentioning

confidence: 99%

Accounting for Changing Temperature Patterns Increases Historical Estimates of Climate Sensitivity

Andrews

Gregory

Paynter

et al. 2018

Geophysical Research Letters

146

204

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Eight atmospheric general circulation models (AGCMs) are forced with observed historical (1871–2010) monthly sea surface temperature and sea ice variations using the Atmospheric Model Intercomparison Project II data set. The AGCMs therefore have a similar temperature pattern and trend to that of observed historical climate change. The AGCMs simulate a spread in climate feedback similar to that seen in coupled simulations of the response to CO2 quadrupling. However, the feedbacks are robustly more stabilizing and the effective climate sensitivity (EffCS) smaller. This is due to a pattern effect, whereby the pattern of observed historical sea surface temperature change gives rise to more negative cloud and longwave clear‐sky feedbacks. Assuming the patterns of long‐term temperature change simulated by models, and the radiative response to them, are credible; this implies that existing constraints on EffCS from historical energy budget variations give values that are too low and overly constrained, particularly at the upper end. For example, the pattern effect increases the long‐term Otto et al. (2013, https://doi.org/10.1038/ngeo1836) EffCS median and 5–95% confidence interval from 1.9 K (0.9–5.0 K) to 3.2 K (1.5–8.1 K).

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“…Their presence is tied to a combination of large-scale atmospheric circulation and sea surface temperatures (SSTs), which affect temperature and moisture differences between the surface and the free troposphere (e.g., Bretherton et al, 2013;Klein and Hartmann, 1993). While the underlying processes are not fully understood, recent observationally based studies confirm that low-cloud cover (LCC) and SST are negatively correlated (e.g., McCoy et al, 2017;Myers and Norris, 2015;Qu et al, 2015). Therefore, in a warming world, all else being equal, marine boundary layer clouds are expected to dissipate somewhat, which will result in more incoming solar radiation, reinforcing the surface warming through a positive feedback.…”

Section: Introductionmentioning

confidence: 99%

Evaluating models' response of tropical low clouds to SST forcings using CALIPSO observations

et al. 2019

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Abstract. Recent studies have shown that, in response to a surface warming, the marine tropical low-cloud cover (LCC) as observed by passive-sensor satellites substantially decreases, therefore generating a smaller negative value of the top-of-the-atmosphere (TOA) cloud radiative effect (CRE). Here we study the LCC and CRE interannual changes in response to sea surface temperature (SST) forcings in the GISS model E2 climate model, a developmental version of the GISS model E3 climate model, and in 12 other climate models, as a function of their ability to represent the vertical structure of the cloud response to SST change against 10 years of CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) observations. The more realistic models (those that satisfy the observational constraint) capture the observed interannual LCC change quite well (ΔLCC/ΔSST=-3.49±1.01 % K−1 vs. ΔLCC/ΔSSTobs=-3.59±0.28 % K−1) while the others largely underestimate it (ΔLCC/ΔSST=-1.32±1.28 % K−1). Consequently, the more realistic models simulate more positive shortwave (SW) feedback (ΔCRE/ΔSST=2.60±1.13 W m−2 K−1) than the less realistic models (ΔCRE/ΔSST=0.87±2.63 W m−2 K−1), in better agreement with the observations (ΔCRE/ΔSSTobs=3±0.26 W m−2 K−1), although slightly underestimated. The ability of the models to represent moist processes within the planetary boundary layer (PBL) and produce persistent stratocumulus (Sc) decks appears crucial to replicating the observed relationship between clouds, radiation and surface temperature. This relationship is different depending on the type of low clouds in the observations. Over stratocumulus regions, cloud-top height increases slightly with SST, accompanied by a large decrease in cloud fraction, whereas over trade cumulus (Cu) regions, cloud fraction decreases everywhere, to a smaller extent.

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Internal Variability and Disequilibrium Confound Estimates of Climate Sensitivity From Observations

Cited by 50 publications

References 26 publications

Observational constraints on the effective climate sensitivity from the historical period

Observational constraints on the effective climate sensitivity from the historical period

Accounting for Changing Temperature Patterns Increases Historical Estimates of Climate Sensitivity

Evaluating models' response of tropical low clouds to SST forcings using CALIPSO observations

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