Effective forcing in CMIP5 assuming nonconstant feedback parameter and linear response

2020 ◽  
Author(s):  
Hege-Beate Fredriksen
Keyword(s):  
Time Scales ◽  
Climate Sensitivity ◽  
Climate Models ◽  
Temperature Response ◽  
Previous Method ◽  
Cmip5 Models ◽  
Nonlinear Feedback ◽  
Geophysical Research ◽  
Linear Temperature ◽  
Feedback Parameter

<p>We investigate a new algorithm for estimating time-evolving global forcing in climate models. The method is an extension of a previous method by Forster et al. (2013), but here we also allow for a globally nonlinear feedback. We assume the nonlinearity of this global feedback can be explained as a time-scale dependence, associated with linear temperature responses to the forcing on different time scales, as in Proistosescu and Huybers (2017). With this method we obtain stronger forcing estimates than previously believed for the representative concentration pathway experiments in CMIP5 models. The reason for the higher future forcing is that the global feedback has a higher magnitude at the smaller time scales than at the longer time scales – this is closely related to provided arguments for the equilibrium climate sensitivity showing changes with time.</p><p>We find also that the linear temperature response to our new forcing predicts the modelled response quite well, although the response is a little overestimated for some CMIP5 models. Finally, we discuss the assumptions made in our study, and consistency of our assumptions with other leading hypotheses for why the global feedback is nonlinear.</p><p> </p><p>References:</p><p>Forster, P. M., T. Andrews, P. Good, J. M. Gregory, L. S. Jackson, and M. Zelinka (2013), Evaluating adjusted forcing and model spread for historical and future scenarios in the cmip5 generation of climate models, Journal of Geophysical Research, 118, 1139–1150, doi:10.1002/jgrd.50174.</p><p>Proistosescu, C., and P. J. Huybers (2017), Slow climate mode reconciles historical and model-based estimates of climate sensitivity, Sci. Adv., 3, e1602, 821, doi:10.1126/sciadv.1602821</p>

Reexamining the Relationship between Climate Sensitivity and the Southern Hemisphere Radiation Budget in CMIP Models

2015 ◽  
Vol 28 (23) ◽  
pp. 9298-9312 ◽  
Author(s):  
Kevin M. Grise ◽  
Lorenzo M. Polvani ◽  
John T. Fasullo
Keyword(s):  
Southern Ocean ◽  
Southern Hemisphere ◽  
Climate Sensitivity ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Radiation Budget ◽  
Cmip5 Models ◽  
Net Radiation ◽  
Subtropical Clouds

Abstract Recent efforts to narrow the spread in equilibrium climate sensitivity (ECS) across global climate models have focused on identifying observationally based constraints, which are rooted in empirical correlations between ECS and biases in the models’ present-day climate. This study reexamines one such constraint identified from CMIP3 models: the linkage between ECS and net top-of-the-atmosphere radiation biases in the Southern Hemisphere (SH). As previously documented, the intermodel spread in the ECS of CMIP3 models is linked to present-day cloud and net radiation biases over the midlatitude Southern Ocean, where higher cloud fraction in the present-day climate is associated with larger values of ECS. However, in this study, no physical explanation is found to support this relationship. Furthermore, it is shown here that this relationship disappears in CMIP5 models and is unique to a subset of CMIP models characterized by unrealistically bright present-day clouds in the SH subtropics. In view of this evidence, Southern Ocean cloud and net radiation biases appear inappropriate for providing observationally based constraints on ECS. Instead of the Southern Ocean, this study points to the stratocumulus-to-cumulus transition regions of the SH subtropical oceans as key to explaining the intermodel spread in the ECS of both CMIP3 and CMIP5 models. In these regions, ECS is linked to present-day cloud and net radiation biases with a plausible physical mechanism: models with brighter subtropical clouds in the present-day climate show greater ECS because 1) subtropical clouds dissipate with increasing CO2 concentrations in many models and 2) the dissipation of brighter clouds contributes to greater solar warming of the surface.

scholarly journals On the Climate Sensitivity and Historical Warming Evolution in Recent Coupled Model Ensembles

2020 ◽  
Author(s):  
Clare Marie Flynn ◽  
Thorsten Mauritsen
Keyword(s):  
Climate Sensitivity ◽  
Climate Models ◽  
Coupled Model ◽  
Cmip5 Models ◽  
Antarctic Sea Ice ◽  
Intercomparison Project ◽  
Future Warming ◽  
Model Treatment ◽  
Systematic Shift ◽  
Model Ensembles

Abstract. The Earth's equilibrium climate sensitivity (ECS) to a doubling of atmospheric CO2, along with the transient 35 climate response (TCR) and greenhouse gas emissions pathways, determines the amount of future warming. Coupled climate models have in the past been important tools to estimate and understand ECS. ECS estimated from Coupled Model Intercomparison Project Phase 5 (CMIP5) models lies between 2.0 and 4.7 K (mean of 3.2 K), whereas in the latest CMIP6 the spread has increased: 1.8–5.5 K (mean of 3.7 K), with 5 out of 25 models exceeding 5 K. It is thus pertinent to understand the causes underlying this shift. Here we compare the CMIP5 and CMIP6 model ensembles, and find a systematic shift between CMIP eras to be unexplained as a process of random sampling from modeled forcing and feedback distributions. Instead, shortwave feedbacks shift towards more positive values, in particular over the Southern Ocean, driving the shift towards larger ECS values in many of the models. These results suggest that changes in model treatment of mixed-phase cloud processes and changes to Antarctic sea ice representation are likely causes of the shift towards larger ECS. Somewhat surprisingly, CMIP6 models exhibit less historical warming than CMIP5 models; the evolution of the warming suggests, however, that several of the models apply too strong aerosol cooling resulting in too weak mid 20th Century warming compared to the instrumental record.

scholarly journals The Pliocene Model Intercomparison Project Phase 2: large-scale climate features and climate sensitivity

2020 ◽  
Vol 16 (6) ◽  
pp. 2095-2123 ◽  
Author(s):  
Alan M. Haywood ◽  
Julia C. Tindall ◽  
Harry J. Dowsett ◽  
Aisling M. Dolan ◽  
Kevin M. Foley ◽  
...  
Keyword(s):  
Climate Sensitivity ◽  
Large Scale ◽  
Climate Models ◽  
Temperature Response ◽  
Surface Air Temperature ◽  
System Response ◽  
Phase 2 ◽  
Model Intercomparison ◽  
Intergovernmental Panel ◽  
Intercomparison Project

Abstract. The Pliocene epoch has great potential to improve our understanding of the long-term climatic and environmental consequences of an atmospheric CO2 concentration near ∼400 parts per million by volume. Here we present the large-scale features of Pliocene climate as simulated by a new ensemble of climate models of varying complexity and spatial resolution based on new reconstructions of boundary conditions (the Pliocene Model Intercomparison Project Phase 2; PlioMIP2). As a global annual average, modelled surface air temperatures increase by between 1.7 and 5.2 ∘C relative to the pre-industrial era with a multi-model mean value of 3.2 ∘C. Annual mean total precipitation rates increase by 7 % (range: 2 %–13 %). On average, surface air temperature (SAT) increases by 4.3 ∘C over land and 2.8 ∘C over the oceans. There is a clear pattern of polar amplification with warming polewards of 60∘ N and 60∘ S exceeding the global mean warming by a factor of 2.3. In the Atlantic and Pacific oceans, meridional temperature gradients are reduced, while tropical zonal gradients remain largely unchanged. There is a statistically significant relationship between a model's climate response associated with a doubling in CO2 (equilibrium climate sensitivity; ECS) and its simulated Pliocene surface temperature response. The mean ensemble Earth system response to a doubling of CO2 (including ice sheet feedbacks) is 67 % greater than ECS; this is larger than the increase of 47 % obtained from the PlioMIP1 ensemble. Proxy-derived estimates of Pliocene sea surface temperatures are used to assess model estimates of ECS and give an ECS range of 2.6–4.8 ∘C. This result is in general accord with the ECS range presented by previous Intergovernmental Panel on Climate Change (IPCC) Assessment Reports.

scholarly journals Effective radiative forcing and adjustments in CMIP6 models

2020 ◽  
Vol 20 (16) ◽  
pp. 9591-9618 ◽  
Author(s):  
Christopher J. Smith ◽  
Ryan J. Kramer ◽  
Gunnar Myhre ◽  
Kari Alterskjær ◽  
William Collins ◽  
...  
Keyword(s):  
Climate Sensitivity ◽  
Radiative Forcing ◽  
Climate Models ◽  
Coupled Model ◽  
Internal Variability ◽  
Cmip5 Models ◽  
Model Intercomparison ◽  
Aerosol Forcing ◽  
Intercomparison Project ◽  
Effective Radiative Forcing

Abstract. The effective radiative forcing, which includes the instantaneous forcing plus adjustments from the atmosphere and surface, has emerged as the key metric of evaluating human and natural influence on the climate. We evaluate effective radiative forcing and adjustments in 17 contemporary climate models that are participating in the Coupled Model Intercomparison Project (CMIP6) and have contributed to the Radiative Forcing Model Intercomparison Project (RFMIP). Present-day (2014) global-mean anthropogenic forcing relative to pre-industrial (1850) levels from climate models stands at 2.00 (±0.23) W m−2, comprised of 1.81 (±0.09) W m−2 from CO2, 1.08 (± 0.21) W m−2 from other well-mixed greenhouse gases, −1.01 (± 0.23) W m−2 from aerosols and −0.09 (±0.13) W m−2 from land use change. Quoted uncertainties are 1 standard deviation across model best estimates, and 90 % confidence in the reported forcings, due to internal variability, is typically within 0.1 W m−2. The majority of the remaining 0.21 W m−2 is likely to be from ozone. In most cases, the largest contributors to the spread in effective radiative forcing (ERF) is from the instantaneous radiative forcing (IRF) and from cloud responses, particularly aerosol–cloud interactions to aerosol forcing. As determined in previous studies, cancellation of tropospheric and surface adjustments means that the stratospherically adjusted radiative forcing is approximately equal to ERF for greenhouse gas forcing but not for aerosols, and consequentially, not for the anthropogenic total. The spread of aerosol forcing ranges from −0.63 to −1.37 W m−2, exhibiting a less negative mean and narrower range compared to 10 CMIP5 models. The spread in 4×CO2 forcing has also narrowed in CMIP6 compared to 13 CMIP5 models. Aerosol forcing is uncorrelated with climate sensitivity. Therefore, there is no evidence to suggest that the increasing spread in climate sensitivity in CMIP6 models, particularly related to high-sensitivity models, is a consequence of a stronger negative present-day aerosol forcing and little evidence that modelling groups are systematically tuning climate sensitivity or aerosol forcing to recreate observed historical warming.

Evaluating parametric sensitivity of climate feedbacks in CNRM-CM6

2021 ◽  
Author(s):  
Saloua Peatier ◽  
Benjamin Sanderson ◽  
Laurent Terray
Keyword(s):  
Climate Sensitivity ◽  
General Circulation ◽  
Temperature Response ◽  
Coupled Model ◽  
General Circulation Models ◽  
Future Climate Change ◽  
Climate Feedbacks ◽  
Geophysical Research ◽  
Physics Ensemble ◽  
Earth’S Climate

<p>The global surface temperature response to CO2 doubling (Equilibrium Climate Sensitivity or ECS) is a key uncertain parameter determining the extent of future climate change. Sherwood et al. (2020) estimated the ECS to be within [2.6K - 4.5K], but in the Coupled Model Intercomparison Project phase 6 (CMIP6), 1/3 of the General Circulation Models (GCMs) show ECS exceeding 4.5K (Zelinka et al., 2020). CNRM-CM6-1 is one of these models, with an ECS of 4.9K. In this paper, we sampled 30 atmospheric parameters of CNRM-CM6-1 and produced a Perturbed Physics Ensemble (PPE) of atmospheric-only simulations to explore the feedback parameters diversity and the climatological plausibility of the members. This PPE showed a comparable  range of feedback parameters to the multi-model archive, from 0.8 W.m-2/K to 1.8 W.m-2/K. Emulators of climatological performance and feedback parameters were used together with  observational datasets to search for optimal model configurations conditional on different net climate feedbacks. The climatological constraints considered here did not themselves rule out the higher end ECS values of 5K and above. An optimal subset of parameter configurations were chosen to sample the range of ECS allowing the assessment of feedback constraints in future fully coupled experiments.</p><p> </p><p><strong>References :</strong></p><p>Sherwood, S. C., Webb, M. J., Annan, J. D., Armour, K. C., Forster, P. M., Hargreaves, J. C., ... & Zelinka, M. D. (2020). An assessment of Earth's climate sensitivity using multiple lines of evidence. Reviews of Geophysics, 58(4), e2019RG000678.</p><p>Zelinka, M. D., Myers, T. A., McCoy, D. T., Po‐Chedley, S., Caldwell, P. M., Ceppi, P., ... & Taylor, K. E. (2020). Causes of higher climate sensitivity in CMIP6 models. Geophysical Research Letters, 47(1), e2019GL085782.</p><p><br><br></p>

scholarly journals On the Emergent Constraints of Climate Sensitivity

2018 ◽  
Vol 31 (2) ◽  
pp. 863-875 ◽  
Author(s):  
Xin Qu ◽  
Alex Hall ◽  
Anthony M. DeAngelis ◽  
Mark D. Zelinka ◽  
Stephen A. Klein ◽  
...  
Keyword(s):  
Climate Sensitivity ◽  
Climate Models ◽  
Selection Process ◽  
Temperature Response ◽  
Cloud Feedback ◽  
Emergent Constraints ◽  
Low Cloud ◽  
Model Ensembles ◽  
Statistical Connection ◽  
Emergent Constraint

Differences among climate models in equilibrium climate sensitivity (ECS; the equilibrium surface temperature response to a doubling of atmospheric CO2) remain a significant barrier to the accurate assessment of societally important impacts of climate change. Relationships between ECS and observable metrics of the current climate in model ensembles, so-called emergent constraints, have been used to constrain ECS. Here a statistical method (including a backward selection process) is employed to achieve a better statistical understanding of the connections between four recently proposed emergent constraint metrics and individual feedbacks influencing ECS. The relationship between each metric and ECS is largely attributable to a statistical connection with shortwave low cloud feedback, the leading cause of intermodel ECS spread. This result bolsters confidence in some of the metrics, which had assumed such a connection in the first place. Additional analysis is conducted with a few thousand artificial metrics that are randomly generated but are well correlated with ECS. The relationships between the contrived metrics and ECS can also be linked statistically to shortwave cloud feedback. Thus, any proposed or forthcoming ECS constraint based on the current generation of climate models should be viewed as a potential constraint on shortwave cloud feedback, and physical links with that feedback should be investigated to verify that the constraint is real. In addition, any proposed ECS constraint should not be taken at face value since other factors influencing ECS besides shortwave cloud feedback could be systematically biased in the models.

scholarly journals Comment on "Scaling regimes and linear/nonlinear responses of last millennium climate to volcanic and solar forcing" by S. Lovejoy and C. Varotsos

2016 ◽  
Author(s):  
Kristoffer Rypdal ◽  
Martin Rypdal
Keyword(s):  
Time Scales ◽  
Power Law ◽  
Linear Response ◽  
Nonlinear Response ◽  
Climate Models ◽  
Temperature Response ◽  
Internal Variability ◽  
Strong Response ◽  
Volcanic Forcing ◽  
Temperature Signal

Abstract. Lovejoy and Varotsos (L&V) analyse the temperature response to solar, volcanic, and solar plus volcanic, forcing in the Zebiak-Cane (ZC) model, and to solar and solar plus volcanic forcing in the GISS-E2-R model. By a simple wavelet filtering technique they conclude that the responses in the ZC model combine subadditively on time scales from 50 to 1000 yr. Nonlinear response on shorter time scales is claimed by analysis of intermittencies in the forcing and the temperature signal for both models. The analysis of additivity in the ZC model suffers from a confusing presentation of results based on an invalid approximation, and from ignoring the effect of internal variability. We present a test without this approximation which is not able to reject the linear response hypothesis, even without accounting for internal variability. We also demonstrate that internal variability will appear as subadditivity if it is not accounted for. The analysis of intermittencies is based on a mathematical corollary stating that the intermittencies of forcing and response is the same if the response is linear. We argue that there are at least three different factors that may invalidate the application of this corollary for these data. First, the corollary is valid only for a power-law response function. This implies a strong response on centennial time scales, which the authors claim does not take place in these models. Second, it assumes power-law scaling of structure functions of forcing as well as temperature signal, which is not the case for these data. And third, the internal variability, which is strong at least on the short time scales, will exert an influence temperature intermittence which is independent of the forcing. We demonstrate by a synthetic example that the differences in intermittencies observed by L&Veasily can be accounted for by these effects under the assumption of a linear response. Our conclusion is that the analysis performed by L&V does not present valid evidence for a nonlinear response in the global temperature in these climate models.

scholarly journals On the climate sensitivity and historical warming evolution in recent coupled model ensembles

2020 ◽  
Vol 20 (13) ◽  
pp. 7829-7842 ◽  
Author(s):  
Clare Marie Flynn ◽  
Thorsten Mauritsen
Keyword(s):  
Climate Sensitivity ◽  
Climate Models ◽  
Coupled Model ◽  
Cmip5 Models ◽  
Transient Climate Response ◽  
Antarctic Sea Ice ◽  
Intercomparison Project ◽  
Future Warming ◽  
Model Treatment ◽  
Model Ensembles

Abstract. The Earth's equilibrium climate sensitivity (ECS) to a doubling of atmospheric CO2, along with the transient climate response (TCR) and greenhouse gas emissions pathways, determines the amount of future warming. Coupled climate models have in the past been important tools to estimate and understand ECS. ECS estimated from Coupled Model Intercomparison Project Phase 5 (CMIP5) models lies between 2.0 and 4.7 K (mean of 3.2 K), whereas in the latest CMIP6 the spread has increased to 1.8–5.5 K (mean of 3.7 K), with 5 out of 25 models exceeding 5 K. It is thus pertinent to understand the causes underlying this shift. Here we compare the CMIP5 and CMIP6 model ensembles and find a systematic shift between CMIP eras to be unexplained as a process of random sampling from modeled forcing and feedback distributions. Instead, shortwave feedbacks shift towards more positive values, in particular over the Southern Ocean, driving the shift towards larger ECS values in many of the models. These results suggest that changes in model treatment of mixed-phase cloud processes and changes to Antarctic sea ice representation are likely causes of the shift towards larger ECS. Somewhat surprisingly, CMIP6 models exhibit less historical warming than CMIP5 models, despite an increase in TCR between CMIP eras (mean TCR increased from 1.7 to 1.9 K). The evolution of the warming suggests, however, that several of the CMIP6 models apply too strong aerosol cooling, resulting in too weak mid-20th century warming compared to the instrumental record.

Revisiting the relationship between dynamical sensitivity and climate sensitivity in the Southern hemisphere

2020 ◽  
Author(s):  
Thomas Wood ◽  
Amanda Maycock ◽  
Christine McKenna ◽  
Andreas Chrysanthou ◽  
John Fyfe ◽  
...  
Keyword(s):  
Southern Hemisphere ◽  
Climate Sensitivity ◽  
Climate Models ◽  
Stratospheric Ozone ◽  
Storm Track ◽  
Temperature Gradients ◽  
Cmip5 Models ◽  
Positive Trend ◽  
Equilibrium Climate Sensitivity ◽  
The Relationship

<p>The Southern Annular Mode (SAM) is the dominant mode of midlatitude atmospheric circulation variability in the Southern hemisphere. In the future, the SAM trend is expected to be the net result of opposing effects from increasing greenhouse gases (GHG) and ozone recovery. Different greenhouse gas scenarios, which induce different rates of surface and atmospheric temperature change, are therefore associated with different future SAM trends (Barnes et al., 2014). Since the magnitude of warming due to GHGs is an important component of this response, one might expect to find a relationship between equilibrium climate sensitivity (ECS) and future Southern hemisphere circulation trends. In CMIP5, the relationship between the SAM and the level of tropospheric warming across models was found to be strongest in the summer and autumn and could explain around 20% of the intermodel spread (Grise and Polvani, 2014). The spread is more strongly correlated with differences in meridional temperature gradients (Harvey et al., 2014).</p><p>Many of the latest CMIP6 models show a larger equilibrium climate sensitivity (ECS) of up to ~5.5 K (Forster et al., 2019) compared to a maximum of ~4.7 K in CMIP5. This raises the important question of how a higher level of warming affects projections of the SH midlatitude circulation. In this study, we examine the response of the SAM in CMIP6 models and quantify its relationship to ECS and temperature gradients. Our starting hypothesis is that stronger surface warming will induce a larger increase in tropical free tropospheric temperatures, and hence all being equal, a larger tropics-to-pole temperature gradient and a more positive SAM trend. However, results show that despite the higher level of warming in the CMIP6 models, there is a smaller positive trend in SAM index than in CMIP5 indicating a different relationship between warming and midlatitude circulation trends in CMIP6. We attempt to explain potential reasons for these differences.</p><p><strong>References:</strong></p><p>Barnes, E.A., N.W. Barnes, and L.M. Polvani, 2014: Delayed Southern Hemisphere Climate Change Induced by Stratospheric Ozone Recovery, as Projected by the CMIP5 Models. J. Climate, 27, 852–867, https://doi.org/10.1175/JCLI-D-13-00246.1</p><p>Forster, P.M., Maycock, A.C., McKenna, C.M. et al. (2019), Latest climate models confirm need for urgent mitigation. Nat. Clim. Chang. (2019) doi:10.1038/s41558-019-0660-0</p><p>Grise, K. M., and Polvani, L. M. (2014), Is climate sensitivity related to dynamical sensitivity? A Southern Hemisphere perspective, Geophys. Res. Lett., 41, 534– 540, doi:10.1002/2013GL058466.</p><p>Harvey, B.J., Shaffrey, L.C. & Woollings, T.J. (2014) Equator­-to-­pole temperature differences and the extra­tropical storm track responses of the CMIP5 climate models, Clim Dyn, 43: 1171. https://doi.org/10.1007/s00382-013-1883-9</p>

scholarly journals Intermodel Spread in the Pattern Effect and Its Contribution to Climate Sensitivity in CMIP5 and CMIP6 Models

2020 ◽  
Vol 33 (18) ◽  
pp. 7755-7775 ◽  
Author(s):  
Yue Dong ◽  
Kyle C. Armour ◽  
Mark D. Zelinka ◽  
Cristian Proistosescu ◽  
David S. Battisti ◽  
...  
Keyword(s):  
Time Scales ◽  
Climate Sensitivity ◽  
Atmospheric Model ◽  
Warm Pool ◽  
Cmip5 Models ◽  
Fast Time ◽  
Pattern Effect ◽  
Surface Warming ◽  
Early Portion ◽  
Radiative Feedbacks

AbstractRadiative feedbacks depend on the spatial patterns of sea surface temperature (SST) and thus can change over time as SST patterns evolve—the so-called pattern effect. This study investigates intermodel differences in the magnitude of the pattern effect and how these differences contribute to the spread in effective equilibrium climate sensitivity (ECS) within CMIP5 and CMIP6 models. Effective ECS in CMIP5 estimated from 150-yr-long abrupt4×CO2 simulations is on average 10% higher than that estimated from the early portion (first 50 years) of those simulations, which serves as an analog for historical warming; this difference is reduced to 7% on average in CMIP6. The (negative) net radiative feedback weakens over the course of the abrupt4×CO2 simulations in the vast majority of CMIP5 and CMIP6 models, but this weakening is less dramatic on average in CMIP6. For both ensembles, the total variance in the effective ECS is found to be dominated by the spread in radiative response on fast time scales, rather than the spread in feedback changes. Using Green’s functions derived from two AGCMs shows that the spread in feedbacks on fast time scales may be primarily due to differences in atmospheric model physics, whereas the spread in feedback evolution is primarily governed by differences in SST patterns. Intermodel spread in feedback evolution is well explained by differences in the relative warming in the west Pacific warm-pool regions for the CMIP5 models, but this relation fails to explain differences across the CMIP6 models, suggesting that a stronger sensitivity of extratropical clouds to surface warming may also contribute to feedback changes in CMIP6.

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