scholarly journals Technical Note: Calculating state dependent equilibrium climate sensitivity from palaeodata

2016 ◽  
Author(s):  
Peter Köhler ◽  
Lennert B. Stap ◽  
Anna S. von der Heydt ◽  
Bas de Boer ◽  
Roderik S. W. van de Wal
Keyword(s):  
Climate Sensitivity ◽  
Radiative Forcing ◽  
Linear Case ◽  
Scatter Plot ◽  
Theoretical Understanding ◽  
State Dependency ◽  
Surface Temperature Change ◽  
Equilibrium Climate Sensitivity ◽  
State Dependent ◽  
Non Linear

Abstract. The evidence from both data and models indicate that specific equilibrium climate sensitivity S[X] – the global annual mean surface temperature change (∆Tg) as a response to a change in radiative forcing X (∆R[X]) – is state dependent. Such a state dependency implies that the best fit in the scatter plot of ∆Tg versus ∆R[X] is not a linear regression, but for instance a higher order polynomial. While for the conventional linear case the slope (gradient) of the regression is correctly interpreted as the specific equilibrium climate sensitivity S[X], the interpretation is not straightforward in the non-linear case. We here elaborate how such a state dependent scatter plot needs to be interpreted, and provide a theoretical understanding how to calculate S[X] in the non-linear case.

scholarly journals On the state dependency of the equilibrium climate sensitivity during the last 5 million years

2015 ◽  
Vol 11 (12) ◽  
pp. 1801-1823 ◽  
Author(s):  
P. Köhler ◽  
B. de Boer ◽  
A. S. von der Heydt ◽  
L. B. Stap ◽  
R. S. W. van de Wal
Keyword(s):  
Sea Level ◽  
Climate Sensitivity ◽  
Radiative Forcing ◽  
Ice Sheet ◽  
State Dependence ◽  
The State ◽  
Data Set ◽  
State Dependency ◽  
Equilibrium Climate Sensitivity ◽  
Background Climate

Abstract. It is still an open question how equilibrium warming in response to increasing radiative forcing – the specific equilibrium climate sensitivity S – depends on background climate. We here present palaeodata-based evidence on the state dependency of S, by using CO2 proxy data together with a 3-D ice-sheet-model-based reconstruction of land ice albedo over the last 5 million years (Myr). We find that the land ice albedo forcing depends non-linearly on the background climate, while any non-linearity of CO2 radiative forcing depends on the CO2 data set used. This non-linearity has not, so far, been accounted for in similar approaches due to previously more simplistic approximations, in which land ice albedo radiative forcing was a linear function of sea level change. The latitudinal dependency of ice-sheet area changes is important for the non-linearity between land ice albedo and sea level. In our set-up, in which the radiative forcing of CO2 and of the land ice albedo (LI) is combined, we find a state dependence in the calculated specific equilibrium climate sensitivity, S[CO2,LI], for most of the Pleistocene (last 2.1 Myr). During Pleistocene intermediate glaciated climates and interglacial periods, S[CO2,LI] is on average ~ 45 % larger than during Pleistocene full glacial conditions. In the Pliocene part of our analysis (2.6–5 Myr BP) the CO2 data uncertainties prevent a well-supported calculation for S[CO2,LI], but our analysis suggests that during times without a large land ice area in the Northern Hemisphere (e.g. before 2.82 Myr BP), the specific equilibrium climate sensitivity, S[CO2,LI], was smaller than during interglacials of the Pleistocene. We thus find support for a previously proposed state change in the climate system with the widespread appearance of northern hemispheric ice sheets. This study points for the first time to a so far overlooked non-linearity in the land ice albedo radiative forcing, which is important for similar palaeodata-based approaches to calculate climate sensitivity. However, the implications of this study for a suggested warming under CO2 doubling are not yet entirely clear since the details of necessary corrections for other slow feedbacks are not fully known and the uncertainties that exist in the ice-sheet simulations and global temperature reconstructions are large.

scholarly journals On the state-dependency of the equilibrium climate sensitivity during the last 5 million years

2015 ◽  
Vol 11 (4) ◽  
pp. 3019-3069 ◽  
Author(s):  
P. Köhler ◽  
B. de Boer ◽  
A. S. von der Heydt ◽  
L. B. Stap ◽  
R. S. W. van de Wal
Keyword(s):  
Sea Level ◽  
Climate Sensitivity ◽  
Radiative Forcing ◽  
Ice Sheet ◽  
The State ◽  
Data Set ◽  
State Dependency ◽  
Equilibrium Climate Sensitivity ◽  
Open Question ◽  
Background Climate

Abstract. A still open question is how equilibrium warming in response to increasing radiative forcing – the specific equilibrium climate sensitivity S – is depending on background climate. We here present paleo-data based evidence on the state-dependency of S, by using CO2 proxy data together with 3-D ice-sheet model-based reconstruction of land ice albedo over the last 5 million years (Myr). We find that the land-ice albedo forcing depends non-linearly on the background climate, while any non-linearity of CO2 radiative forcing depends on the CO2 data set used. This non-linearity was in similar approaches not accounted for due to previously more simplistic approximations of land-ice albedo radiative forcing being a linear function of sea level change. Important for the non-linearity between land-ice albedo and sea level is a latitudinal dependency in ice sheet area changes.In our setup, in which the radiative forcing of CO2 and of the land-ice albedo (LI) is combined, we find a state-dependency in the calculated specific equilibrium climate sensitivity S[CO2,LI] for most of the Pleistocene (last 2.1 Myr). During Pleistocene intermediate glaciated climates and interglacial periods S[CO2,LI] is on average ∼ 45 % larger than during Pleistocene full glacial conditions. In the Pliocene part of our analysis (2.6–5 Myr BP) the CO2 data uncertainties prevents a well-supported calculation for S[CO2,LI], but our analysis suggests that during times without a large land-ice area in the Northern Hemisphere (e.g. before 2.82 Myr BP) the specific equilibrium climate sensitivity S[CO2,LI] was smaller than during interglacials of the Pleistocene. We thus find support for a previously proposed state-change in the climate system with the wide appearance of northern hemispheric ice sheets. This study points for the first time to a so far overlooked non-linearity in the land-ice albedo radiative forcing, which is important for similar paleo data-based approaches to calculate climate sensitivity. However, the implications of this study for a suggested warming under CO2 doubling are not yet entirely clear since the necessary corrections for other slow feedbacks are in detail unknown and the still existing uncertainties in the ice sheet simulations and global temperature reconstructions are large.

scholarly journals A lower and more constrained estimate of climate sensitivity using updated observations and detailed radiative forcing time series

2013 ◽  
Vol 4 (2) ◽  
pp. 785-852 ◽  
Author(s):  
R. B. Skeie ◽  
T. Berntsen ◽  
M. Aldrin ◽  
M. Holden ◽  
G. Myhre
Keyword(s):  
Time Series ◽  
Temperature Change ◽  
Climate Sensitivity ◽  
Radiative Forcing ◽  
Heat Content ◽  
Ocean Heat Content ◽  
Balance Model ◽  
Surface Temperature Change ◽  
Near Surface ◽  
Posterior Mean

Abstract. The equilibrium climate sensitivity (ECS) is constrained based on observed near-surface temperature change, changes in ocean heat content (OHC) and detailed radiative forcing (RF) time series from pre-industrial times to 2010 for all main anthropogenic and natural forcing mechanism. The RF time series are linked to the observations of OHC and temperature change through an energy balance model and a stochastic model, using a Bayesian approach to estimate the ECS and other unknown parameters from the data. For the net anthropogenic RF the posterior mean in 2010 is 2.1 W m−2 with a 90% credible interval (C.I.) of 1.3 to 2.8 W m−2, excluding present day total aerosol effects (direct + indirect) stronger than −1.7 W m−2. The posterior mean of the ECS is 1.8 °C with 90% C.I. ranging from 0.9 to 3.2 °C which is tighter than most previously published estimates. We find that using 3 OHC data sets simultaneously substantially narrows the range in ECS, while using only one set and similar time periods can produce comparable results as previously published estimates including the heavy tail in the probability function. The use of additional 10 yr of data for global mean temperature change and ocean heat content data narrow the probability density function of the ECS. In addition when data only until year 2000 is used the estimated mean of ECS is 20% higher. Explicitly accounting for internal variability widens the 90% C.I. for the ECS by 60%, while the mean ECS only becomes slightly higher.

scholarly journals Cloud Feedbacks from CanESM2 to CanESM5.0 and their Influence on Climate Sensitivity

2021 ◽  
Author(s):  
John G. Virgin ◽  
Christopher G. Fletcher ◽  
Jason N. S. Cole ◽  
Knut von Salzen ◽  
Toni Mitovski
Keyword(s):  
Climate Sensitivity ◽  
Radiative Forcing ◽  
Coupled Model ◽  
Sea Surface ◽  
System Model ◽  
Surface Temperature Change ◽  
Cloud Optical Depth ◽  
Cloud Feedbacks ◽  
Low Cloud ◽  
Underlying Causes

Abstract. The newest iteration of the Canadian Earth System Model (CanESM5.0.3) has an Effective Climate Sensitivity (ECS) of 5.65 kelvin, which is a 54 % increase relative to the model's previous version (CanESM2 – 3.67 K), and the highest sensitivity of all current models participating in the sixth phase of the coupled model inter-comparison project (CMIP6). Here, we explore the underlying causes behind CanESM5's increased ECS via comparison of forcing and feedbacks between CanESM2 and CanESM5. We find only modest differences in radiative forcing as a response to CO2 between model versions. Through the use of cloud area fraction output and radiative kernels, we find that more positive shortwave cloud feedbacks – particularly with regards to low clouds across the equatorial pacific, as well as sub/extratropical free troposphere cloud optical depth – are the dominant contributors to CanESM5's increased climate sensitivity. Additional simulations with prescribed sea surface temperatures reveal that the spatial pattern of surface temperature change explains the pattern of change in low cloud fraction, but does not fully explain the increased ECS in CanESM5. The results from CanESM5 are consistent with increased ECS in several other CMIP6 models, which has been primarily attributed to changes in shortwave cloud feedbacks.

scholarly journals Equilibrium climate sensitivity above 5 °C plausible due to state-dependent cloud feedback

2020 ◽  
Vol 13 (11) ◽  
pp. 718-721
Author(s):  
Jenny Bjordal ◽  
Trude Storelvmo ◽  
Kari Alterskjær ◽  
Tim Carlsen
Keyword(s):  
Climate Sensitivity ◽  
Cloud Feedback ◽  
Equilibrium Climate Sensitivity ◽  
State Dependent

scholarly journals Climate sensitivity estimates – sensitivity to radiative forcing time series and observational data

2018 ◽  
Vol 9 (2) ◽  
pp. 879-894 ◽  
Author(s):  
Ragnhild Bieltvedt Skeie ◽  
Terje Berntsen ◽  
Magne Aldrin ◽  
Marit Holden ◽  
Gunnar Myhre
Keyword(s):  
Time Series ◽  
Climate Sensitivity ◽  
Radiative Forcing ◽  
Heat Content ◽  
Deep Ocean ◽  
Mean Value ◽  
Transient Climate Response ◽  
Surface Temperature Change ◽  
Near Surface ◽  
The Mean

Abstract. Inferred effective climate sensitivity (ECSinf) is estimated using a method combining radiative forcing (RF) time series and several series of observed ocean heat content (OHC) and near-surface temperature change in a Bayesian framework using a simple energy balance model and a stochastic model. The model is updated compared to our previous analysis by using recent forcing estimates from IPCC, including OHC data for the deep ocean, and extending the time series to 2014. In our main analysis, the mean value of the estimated ECSinf is 2.0 ∘C, with a median value of 1.9 ∘C and a 90 % credible interval (CI) of 1.2–3.1 ∘C. The mean estimate has recently been shown to be consistent with the higher values for the equilibrium climate sensitivity estimated by climate models. The transient climate response (TCR) is estimated to have a mean value of 1.4 ∘C (90 % CI 0.9–2.0 ∘C), and in our main analysis the posterior aerosol effective radiative forcing is similar to the range provided by the IPCC. We show a strong sensitivity of the estimated ECSinf to the choice of a priori RF time series, excluding pre-1950 data and the treatment of OHC data. Sensitivity analysis performed by merging the upper (0–700 m) and the deep-ocean OHC or using only one OHC dataset (instead of four in the main analysis) both give an enhancement of the mean ECSinf by about 50 % from our best estimate.

scholarly journals Cloud Feedbacks from CanESM2 to CanESM5.0 and their influence on climate sensitivity

2021 ◽  
Vol 14 (9) ◽  
pp. 5355-5372
Author(s):  
John G. Virgin ◽  
Christopher G. Fletcher ◽  
Jason N. S. Cole ◽  
Knut von Salzen ◽  
Toni Mitovski
Keyword(s):  
Climate Sensitivity ◽  
Radiative Forcing ◽  
Surface Albedo ◽  
Coupled Model ◽  
Cloud Feedback ◽  
Surface Temperature Change ◽  
Cloud Optical Depth ◽  
Cloud Feedbacks ◽  
Low Cloud ◽  
Underlying Causes

Abstract. The newest iteration of the Canadian Earth System Model (CanESM5.0.3) has an effective climate sensitivity (EffCS) of 5.65 K, which is a 54 % increase relative to the model's previous version (CanESM2 – 3.67 K), and the highest sensitivity of all current models participating in the sixth phase of the coupled model inter-comparison project (CMIP6). Here, we explore the underlying causes behind CanESM5's increased EffCS via comparison of forcing and feedbacks between CanESM2 and CanESM5. We find only modest differences in radiative forcing as a response to CO2 between model versions. We find small increases in the surface albedo and longwave cloud feedback, as well as a substantial increase in the SW cloud feedback in CanESM5. Through the use of cloud area fraction output and cloud radiative kernels, we find that more positive low and non-low shortwave cloud feedbacks – particularly with regards to low clouds across the equatorial Pacific, as well as subtropical and extratropical free troposphere cloud optical depth – are the dominant contributors to CanESM5's increased climate sensitivity. Additional simulations with prescribed sea surface temperatures reveal that the spatial pattern of surface temperature change exerts controls on the magnitude and spatial distribution of low-cloud fraction response but does not fully explain the increased EffCS in CanESM5. The results from CanESM5 are consistent with increased EffCS in several other CMIP6 models, which has been primarily attributed to changes in shortwave cloud feedbacks.

scholarly journals Climate sensitivity estimates – sensitivity to radiative forcing time series and observational data

2017 ◽  
Author(s):  
Ragnhild Bieltvedt Skeie ◽  
Terje Berntsen ◽  
Magne Aldrin ◽  
Marit Holden ◽  
Gunnar Myhre
Keyword(s):  
Time Series ◽  
Climate Sensitivity ◽  
Radiative Forcing ◽  
Heat Content ◽  
Deep Ocean ◽  
Balance Model ◽  
Data Set ◽  
Surface Temperature Change ◽  
Near Surface ◽  
The Mean

Abstract. Inferred Effective Climate Sensitivity (ECSinf) is estimated using a method combining radiative forcing (RF) time series and several series of observed ocean heat content (OHC) and near-surface temperature change in a Bayesian-framework using a simple energy balance model and a stochastic model. The model is updated compared to our previous analysis by using recent forcing estimates from IPCC, including OHC data for the deep ocean, and extending the time series to 2014. The mean value of the estimated ECSinf is 2.0 °C, with a 90 % credible interval of 1.2–3.1 °C. The mean estimate has recently been shown to be consistent with the higher values for the equilibrium climate sensitivity estimated by climate models. We show a strong sensitivity of the estimated ECSinf to the choice of a priori RF time series, excluding pre-1950 data and the treatment of OHC data. Sensitivity analysis performed by merging the upper (0–700 m) and the deep ocean OHC or using only one OHC data set (instead of four in the main analysis), both give an enhancement the mean ECSinf by about 50 % from our best estimate.

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