scholarly journals An observation-based scaling model for climate sensitivity estimates and global projections to 2100

2020 ◽  
Author(s):  
Raphaël Hébert ◽  
Shaun Lovejoy ◽  
Bruno Tremblay
Keyword(s):  
Climate Sensitivity ◽  
Coupled Model ◽  
Internal Variability ◽  
Forced Response ◽  
Scaling Exponent ◽  
Climate Response ◽  
Transient Climate Response ◽  
Rcp 8.5 ◽  
Historical Temperature ◽  
Inner Scale

AbstractWe directly exploit the stochasticity of the internal variability, and the linearity of the forced response to make global temperature projections based on historical data and a Green’s function, or Climate Response Function (CRF). To make the problem tractable, we take advantage of the temporal scaling symmetry to define a scaling CRF characterized by the scaling exponent H, which controls the long-range memory of the climate, i.e. how fast the system tends toward a steady-state, and an inner scale $$\tau \approx 2$$ τ ≈ 2   years below which the higher-frequency response is smoothed out. An aerosol scaling factor and a non-linear volcanic damping exponent were introduced to account for the large uncertainty in these forcings. We estimate the model and forcing parameters by Bayesian inference which allows us to analytically calculate the transient climate response and the equilibrium climate sensitivity as: $$1.7^{+0.3} _{-0.2}$$ 1 . 7 - 0.2 + 0.3   K and $$2.4^{+1.3} _{-0.6}$$ 2 . 4 - 0.6 + 1.3   K respectively (likely range). Projections to 2100 according to the RCP 2.6, 4.5 and 8.5 scenarios yield warmings with respect to 1880–1910 of: $$1.5^{+0.4}_{-0.2}K$$ 1 . 5 - 0.2 + 0.4 K , $$2.3^{+0.7}_{-0.5}$$ 2 . 3 - 0.5 + 0.7   K and $$4.2^{+1.3}_{-0.9}$$ 4 . 2 - 0.9 + 1.3   K. These projection estimates are lower than the ones based on a Coupled Model Intercomparison Project phase 5 multi-model ensemble; more importantly, their uncertainties are smaller and only depend on historical temperature and forcing series. The key uncertainty is due to aerosol forcings; we find a modern (2005) forcing value of $$[-1.0, -0.3]\, \,\,\mathrm{Wm} ^{-2}$$ [ - 1.0 , - 0.3 ] Wm - 2 (90 % confidence interval) with median at $$-0.7 \,\,\mathrm{Wm} ^{-2}$$ - 0.7 Wm - 2 . Projecting to 2100, we find that to keep the warming below 1.5 K, future emissions must undergo cuts similar to RCP 2.6 for which the probability to remain under 1.5 K is 48 %. RCP 4.5 and RCP 8.5-like futures overshoot with very high probability.

scholarly journals Context for interpreting equilibrium climate sensitivity and transient climate response from the CMIP6 Earth system models

2020 ◽  
Vol 6 (26) ◽  
pp. eaba1981 ◽  
Author(s):  
Gerald A. Meehl ◽  
Catherine A. Senior ◽  
Veronika Eyring ◽  
Gregory Flato ◽  
Jean-Francois Lamarque ◽  
...  
Keyword(s):  
Climate Sensitivity ◽  
Coupled Model ◽  
Cmip5 Models ◽  
Climate Response ◽  
Earth System ◽  
Transient Climate Response ◽  
Equilibrium Climate Sensitivity ◽  
System Models ◽  
Intercomparison Project ◽  
Earth System Models

For the current generation of earth system models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6), the range of equilibrium climate sensitivity (ECS, a hypothetical value of global warming at equilibrium for a doubling of CO2) is 1.8°C to 5.6°C, the largest of any generation of models dating to the 1990s. Meanwhile, the range of transient climate response (TCR, the surface temperature warming around the time of CO2 doubling in a 1% per year CO2 increase simulation) for the CMIP6 models of 1.7°C (1.3°C to 3.0°C) is only slightly larger than for the CMIP3 and CMIP5 models. Here we review and synthesize the latest developments in ECS and TCR values in CMIP, compile possible reasons for the current values as supplied by the modeling groups, and highlight future directions. Cloud feedbacks and cloud-aerosol interactions are the most likely contributors to the high values and increased range of ECS in CMIP6.

scholarly journals Relating climate sensitivity indices to projection uncertainty

2020 ◽  
Vol 11 (3) ◽  
pp. 721-735 ◽  
Author(s):  
Benjamin Sanderson
Keyword(s):  
Climate Sensitivity ◽  
Climate Models ◽  
Coupled Model ◽  
Internal Variability ◽  
Surprising Result ◽  
Transient Climate Response ◽  
Sensitivity Indices ◽  
Project Phases ◽  
Single Number ◽  
Relative Errors

Abstract. Can we summarize uncertainties in global response to greenhouse gas forcing with a single number? Here, we assess the degree to which traditional metrics are related to future warming indices using an ensemble of simple climate models together with results from the Coupled Model Intercomparison Project phases 5 and 6 (CMIP5 and CMIP6). We consider effective climate sensitivity (EffCS), transient climate response (TCR) at CO2 quadrupling (T140) and a proposed simple metric of temperature change 140 years after a quadrupling of carbon dioxide (A140). In a perfectly equilibrated model, future temperatures under RCP8.5 (Representative Concentration Pathway 8.5) are almost perfectly described by T140, whereas in a mitigation scenario such as RCP2.6, both EffCS and T140 are found to be poor predictors of 21st century warming, and future temperatures are better correlated with A140. We show further that T140 and EffCS calculated in full CMIP simulations are subject to errors arising from control model drift and internal variability, with greater relative errors in estimation for T140. As such, if starting from a non-equilibrated state, measured values of effective climate sensitivity can be better correlated with true TCR than measured values of TCR itself. We propose that this could be an explanatory factor in the previously noted surprising result that EffCS is a better predictor than TCR of future transient warming under RCP8.5.

Equilibrium Climate Sensitivity and Transient Climate Response biased low in historical simulations of CMIP6 models

2021 ◽  
Author(s):  
Yue Dong ◽  
Kyle C. Armour ◽  
Cristian Proistosescu ◽  
Timothy Andrews ◽  
David S. Battisti ◽  
...  
Keyword(s):  
Climate Sensitivity ◽  
Climate Response ◽  
Transient Climate Response ◽  
Equilibrium Climate Sensitivity

scholarly journals The Interpretation and Use of Biases in Decadal Climate Predictions

2014 ◽  
Vol 27 (8) ◽  
pp. 2931-2947 ◽  
Author(s):  
Ed Hawkins ◽  
Buwen Dong ◽  
Jon Robson ◽  
Rowan Sutton ◽  
Doug Smith
Keyword(s):  
Climate Model ◽  
Coupled Model ◽  
Internal Variability ◽  
Time Dependent ◽  
Start Time ◽  
Transient Climate Response ◽  
Global Mean Temperature ◽  
Decadal Climate ◽  
Highly Correlated ◽  
Hadley Centre

Abstract Decadal climate predictions exhibit large biases, which are often subtracted and forgotten. However, understanding the causes of bias is essential to guide efforts to improve prediction systems, and may offer additional benefits. Here the origins of biases in decadal predictions are investigated, including whether analysis of these biases might provide useful information. The focus is especially on the lead-time-dependent bias tendency. A “toy” model of a prediction system is initially developed and used to show that there are several distinct contributions to bias tendency. Contributions from sampling of internal variability and a start-time-dependent forcing bias can be estimated and removed to obtain a much improved estimate of the true bias tendency, which can provide information about errors in the underlying model and/or errors in the specification of forcings. It is argued that the true bias tendency, not the total bias tendency, should be used to adjust decadal forecasts. The methods developed are applied to decadal hindcasts of global mean temperature made using the Hadley Centre Coupled Model, version 3 (HadCM3), climate model, and it is found that this model exhibits a small positive bias tendency in the ensemble mean. When considering different model versions, it is shown that the true bias tendency is very highly correlated with both the transient climate response (TCR) and non–greenhouse gas forcing trends, and can therefore be used to obtain observationally constrained estimates of these relevant physical quantities.

scholarly journals Bayesian estimation of Earth's climate sensitivity and transient climate response from observational warming and heat content datasets

2021 ◽  
Vol 12 (2) ◽  
pp. 709-723
Author(s):  
Philip Goodwin ◽  
B. B. Cael
Keyword(s):  
Climate Sensitivity ◽  
Future Climate ◽  
Climate Feedback ◽  
Future Climate Change ◽  
Climate Projections ◽  
Climate Response ◽  
Ocean Heat Uptake ◽  
Transient Climate Response ◽  
Posterior Probability Distribution ◽  
The Impact

Abstract. Future climate change projections, impacts, and mitigation targets are directly affected by how sensitive Earth's global mean surface temperature is to anthropogenic forcing, expressed via the climate sensitivity (S) and transient climate response (TCR). However, the S and TCR are poorly constrained, in part because historic observations and future climate projections consider the climate system under different response timescales with potentially different climate feedback strengths. Here, we evaluate S and TCR by using historic observations of surface warming, available since the mid-19th century, and ocean heat uptake, available since the mid-20th century, to constrain a model with independent climate feedback components acting over multiple response timescales. Adopting a Bayesian approach, our prior uses a constrained distribution for the instantaneous Planck feedback combined with wide-ranging uniform distributions of the strengths of the fast feedbacks (acting over several days) and multi-decadal feedbacks. We extract posterior distributions by applying likelihood functions derived from different combinations of observational datasets. The resulting TCR distributions when using two preferred combinations of historic datasets both find a TCR of 1.5 (1.3 to 1.8 at 5–95 % range) ∘C. We find the posterior probability distribution for S for our preferred dataset combination evolves from S of 2.0 (1.6 to 2.5) ∘C on a 20-year response timescale to S of 2.3 (1.4 to 6.4) ∘C on a 140-year response timescale, due to the impact of multi-decadal feedbacks. Our results demonstrate how multi-decadal feedbacks allow a significantly higher upper bound on S than historic observations are otherwise consistent with.

scholarly journals Partitioning climate projection uncertainty with multiple large ensembles and CMIP5/6

2020 ◽  
Vol 11 (2) ◽  
pp. 491-508 ◽  
Author(s):  
Flavio Lehner ◽  
Clara Deser ◽  
Nicola Maher ◽  
Jochem Marotzke ◽  
Erich M. Fischer ◽  
...  
Keyword(s):  
Coupled Model ◽  
Internal Variability ◽  
Potential Method ◽  
Added Value ◽  
Future Climate Change ◽  
Climate Projections ◽  
Good Representation ◽  
Transient Climate Response ◽  
Large Ensembles ◽  
Method Bias

Abstract. Partitioning uncertainty in projections of future climate change into contributions from internal variability, model response uncertainty and emissions scenarios has historically relied on making assumptions about forced changes in the mean and variability. With the advent of multiple single-model initial-condition large ensembles (SMILEs), these assumptions can be scrutinized, as they allow a more robust separation between sources of uncertainty. Here, the framework from Hawkins and Sutton (2009) for uncertainty partitioning is revisited for temperature and precipitation projections using seven SMILEs and the Coupled Model Intercomparison Project CMIP5 and CMIP6 archives. The original approach is shown to work well at global scales (potential method bias < 20 %), while at local to regional scales such as British Isles temperature or Sahel precipitation, there is a notable potential method bias (up to 50 %), and more accurate partitioning of uncertainty is achieved through the use of SMILEs. Whenever internal variability and forced changes therein are important, the need to evaluate and improve the representation of variability in models is evident. The available SMILEs are shown to be a good representation of the CMIP5 model diversity in many situations, making them a useful tool for interpreting CMIP5. CMIP6 often shows larger absolute and relative model uncertainty than CMIP5, although part of this difference can be reconciled with the higher average transient climate response in CMIP6. This study demonstrates the added value of a collection of SMILEs for quantifying and diagnosing uncertainty in climate projections.

scholarly journals Forced and Unforced Decadal Behavior of the Interhemispheric SST Contrast during the Instrumental Period (1881–2012): Contextualizing the Late 1960s–Early 1970s Shift

2020 ◽  
Vol 33 (9) ◽  
pp. 3487-3509 ◽  
Author(s):  
Andrew R. Friedman ◽  
Gabriele C. Hegerl ◽  
Andrew P. Schurer ◽  
Shih-Yu Lee ◽  
Wenwen Kong ◽  
...  
Keyword(s):  
Coupled Model ◽  
Internal Variability ◽  
Forced Response ◽  
Meridional Overturning Circulation ◽  
Large Magnitude ◽  
Sst Cooling ◽  
Sst Variability ◽  
External Forcings ◽  
Sst Warming ◽  
Instrumental Period

AbstractThe sea surface temperature (SST) contrast between the Northern Hemisphere (NH) and Southern Hemisphere (SH) influences the location of the intertropical convergence zone (ITCZ) and the intensity of the monsoon systems. This study examines the contributions of external forcing and unforced internal variability to the interhemispheric SST contrast in HadSST3 and ERSSTv5 observations, and 10 models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) from 1881 to 2012. Using multimodel mean fingerprints, a significant influence of anthropogenic, but not natural, forcing is detected in the interhemispheric SST contrast, with the observed response larger than that of the model mean in ERSSTv5. The forced response consists of asymmetric NH–SH SST cooling from the mid-twentieth century to around 1980, followed by opposite NH–SH SST warming. The remaining best-estimate residual or unforced component is marked by NH–SH SST maxima in the 1930s and mid-1960s, and a rapid NH–SH SST decrease around 1970. Examination of decadal shifts in the observed interhemispheric SST contrast highlights the shift around 1970 as the most prominent from 1881 to 2012. Both NH and SH SST variability contributed to the shift, which appears not to be attributable to external forcings. Most models examined fail to capture such large-magnitude shifts in their control simulations, although some models with high interhemispheric SST variability are able to produce them. Large-magnitude shifts produced by the control simulations feature disparate spatial SST patterns, some of which are consistent with changes typically associated with the Atlantic meridional overturning circulation (AMOC).

scholarly journals The Climate Sensitivity of the Community Climate System Model Version 3 (CCSM3)

2006 ◽  
Vol 19 (11) ◽  
pp. 2584-2596 ◽  
Author(s):  
Jeffrey T. Kiehl ◽  
Christine A. Shields ◽  
James J. Hack ◽  
William D. Collins
Keyword(s):  
Carbon Dioxide ◽  
High Resolution ◽  
Surface Temperature ◽  
Climate Sensitivity ◽  
Horizontal Resolution ◽  
System Model ◽  
Climate Response ◽  
Community Climate System Model ◽  
Transient Climate Response ◽  
Climate System Model

Abstract The climate sensitivity of the Community Climate System Model (CCSM) is described in terms of the equilibrium change in surface temperature due to a doubling of carbon dioxide in a slab ocean version of the Community Atmosphere Model (CAM) and the transient climate response, which is the surface temperature change at the point of doubling of carbon dioxide in a 1% yr−1 CO2 simulation with the fully coupled CCSM. For a fixed atmospheric horizontal resolution across model versions, we show that the equilibrium sensitivity has monotonically increased across CSM1.4, CCSM2, to CCSM3 from 2.01° to 2.27° to 2.47°C, respectively. The transient climate response for these versions is 1.44° to 1.09° to 1.48°C, respectively. Using climate feedback analysis, it is shown that both clear-sky and cloudy-sky processes have contributed to the changes in transient climate response. The dependence of these sensitivities on horizontal resolution is also explored. The equilibrium sensitivity of the high-resolution (T85) version of CCSM3 is 2.71°C, while the equilibrium response for the low-resolution model (T31) is 2.32°C. It is shown that the shortwave cloud response of the high-resolution version of the CCSM3 is anomalous compared to the low- and moderate-resolution versions.

scholarly journals The inconstancy of the transient climate response parameter under increasing CO 2

2015 ◽  
Vol 373 (2054) ◽  
pp. 20140417 ◽  
Author(s):  
J. M. Gregory ◽  
T. Andrews ◽  
P. Good
Keyword(s):  
Radiative Forcing ◽  
Coupled Model ◽  
Surface Air Temperature ◽  
Deep Ocean ◽  
Climate Feedback ◽  
Climate Response ◽  
Ocean Heat Uptake ◽  
Transient Climate Response ◽  
Feedback Parameter ◽  
Response Parameter

In the Coupled Model Intercomparison Project Phase 5 (CMIP5), the model-mean increase in global mean surface air temperature T under the 1pctCO2 scenario (atmospheric CO 2 increasing at 1% yr −1 ) during the second doubling of CO 2 is 40% larger than the transient climate response (TCR), i.e. the increase in T during the first doubling. We identify four possible contributory effects. First, the surface climate system loses heat less readily into the ocean beneath as the latter warms. The model spread in the thermal coupling between the upper and deep ocean largely explains the model spread in ocean heat uptake efficiency. Second, CO 2 radiative forcing may rise more rapidly than logarithmically with CO 2 concentration. Third, the climate feedback parameter may decline as the CO 2 concentration rises. With CMIP5 data, we cannot distinguish the second and third possibilities. Fourth, the climate feedback parameter declines as time passes or T rises; in 1pctCO2, this effect is less important than the others. We find that T projected for the end of the twenty-first century correlates more highly with T at the time of quadrupled CO 2 in 1pctCO2 than with the TCR, and we suggest that the TCR may be underestimated from observed climate change.

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