We assess evidence relevant to Earth's equilibrium climate sensitivity per doubling of atmospheric CO2, characterized by an effective sensitivity S . This evidence includes feedback process ...understanding, the historical climate record, and the paleoclimate record. An S value lower than 2 K is difficult to reconcile with any of the three lines of evidence. The amount of cooling during the Last Glacial Maximum provides strong evidence against values of S greater than 4.5 K. Other lines of evidence in combination also show that this is relatively unlikely. We use a Bayesian approach to produce a probability density (PDF) for S given all the evidence, including tests of robustness to difficult‐to‐quantify uncertainties and different priors. The 66% range is 2.6‐3.9 K for our Baseline calculation, and remains within 2.3‐4.5 K under the robustness tests; corresponding 5‐95% ranges are 2.3‐4.7 K, bounded by 2.0‐5.7 K (although such high‐confidence ranges should be regarded more cautiously). This indicates a stronger constraint on S than reported in past assessments, by lifting the low end of the range. This narrowing occurs because the three lines of evidence agree and are judged to be largely independent, and because of greater confidence in understanding feedback processes and in combining evidence. We identify promising avenues for further narrowing the range in S , in particular using comprehensive models and process understanding to address limitations in the traditional forcing‐feedback paradigm for interpreting past changes.
Some recent compilations of proxy data both on land and ocean (MARGO Project Members, 2009; Bartlein et al., 2011; Shakun et al., 2012), have provided a new opportunity for an improved assessment of ...the overall climatic state of the Last Glacial Maximum. In this paper, we combine these proxy data with the ensemble of structurally diverse state of the art climate models which participated in the PMIP2 project (Braconnot et al., 2007) to generate a spatially complete reconstruction of surface air (and sea surface) temperatures. We test a variety of approaches, and show that multiple linear regression performs well for this application. Our reconstruction is significantly different to and more accurate than previous approaches and we obtain an estimated global mean cooling of 4.0 ± 0.8 °C (95% CI).
Reliability of the CMIP3 ensemble Annan, J. D.; Hargreaves, J. C.
Geophysical research letters,
January 2010, Letnik:
37, Številka:
2
Journal Article
Recenzirano
We consider paradigms for interpretation and analysis of the CMIP3 ensemble of climate model simulations. The dominant paradigm in climate science, of an ensemble sampled from a distribution centred ...on the truth, is contrasted with the paradigm of a statistically indistinguishable ensemble, which has been more commonly adopted in other fields. This latter interpretation (which gives rise to a natural probabilistic interpretation of ensemble output) leads to new insights about the evaluation of ensemble performance. Using the well‐known rank histogram method of analysis, we find that the CMIP3 ensemble generally provides a rather good sample under the statistically indistinguishable paradigm, although it appears marginally over‐dispersive and exhibits some modest biases. These results contrast strongly with the incompatibility of the ensemble with the truth‐centred paradigm. Thus, our analysis provides for the first time a sound theoretical foundation, with empirical support, for the probabilistic use of multi‐model ensembles in climate research.
The Coupled Model Intercomparison Project phase 3 (CMIP3) multimodel ensemble has been widely utilized for climate research and prediction, but the properties and behavior of the ensemble are not yet ...fully understood. Here, some investigations are undertaken into various aspects of the ensemble’s behavior, in particular focusing on the performance of the multimodel mean. This study presents an explanation of this phenomenon in the context of the statistically indistinguishable paradigm and also provides a quantitative analysis of the main factors that control how likely the mean is to outperform the models in the ensemble, both individually and collectively. The analyses lend further support to the usage of the paradigm of a statistically indistinguishable ensemble and indicate that the current ensemble size is too small to adequately sample the space from which the models are drawn.
We present a selection of methodologies for using the palaeo-climate model component of the Coupled Model Intercomparison Project (Phase 5) (CMIP5) to attempt to constrain future climate projections ...using the same models. The constraints arise from measures of skill in hindcasting palaeo-climate changes from the present over three periods: the Last Glacial Maximum (LGM) (21 000 yr before present, ka), the mid-Holocene (MH) (6 ka) and the Last Millennium (LM) (850-1850 CE). The skill measures may be used to validate robust patterns of climate change across scenarios or to distinguish between models that have differing outcomes in future scenarios. We find that the multi-model ensemble of palaeo-simulations is adequate for addressing at least some of these issues. For example, selected benchmarks for the LGM and MH are correlated to the rank of future projections of precipitation/temperature or sea ice extent to indicate that models that produce the best agreement with palaeo-climate information give demonstrably different future results than the rest of the models. We also explore cases where comparisons are strongly dependent on uncertain forcing time series or show important non-stationarity, making direct inferences for the future problematic. Overall, we demonstrate that there is a strong potential for the palaeo-climate simulations to help inform the future projections and urge all the modelling groups to complete this subset of the CMIP5 runs.
We investigate the relationship between the Last Glacial Maximum (LGM) and climate sensitivity across the PMIP2 multi‐model ensemble of GCMs, and find a correlation between tropical temperature and ...climate sensitivity which is statistically significant and physically plausible. We use this relationship, together with the LGM temperature reconstruction of Annan and Hargreaves (2012), to generate estimates for the equilibrium climate sensitivity. We estimate the equilibrium climate sensitivity to be about 2.5°C with a high probability of being under 4°C, though these results are subject to several important caveats. The forthcoming PMIP3/CMIP5 models were not considered in this analysis, as very few LGM simulations are currently available from these models. We propose that these models will provide a useful validation of the correlation presented here.
Key Points
Climate sensitivity is estimated using data and models from the LGM
The best estimate is about 2.5C with a high probability of being under 4C
Consistent results are obtained with Bayesian and Frequentist methods
The observed dramatic decrease in September sea ice extent (SIE) has been widely discussed in the scientific literature. Though there is qualitative agreement between observations and ensemble ...members of the Third Coupled Model Intercomparison Project (CMIP3), it is concerning that the observed trend (1979-2010) is not captured by any ensemble member. The potential sources of this discrepancy include: observational uncertainty, physical model limitations and vigorous natural climate variability. The latter has received less attention and is difficult to assess using the relatively short observational sea ice records. In this study multi-centennial pre-industrial control simulations with five CMIP3 climate models are used to investigate the role that the Arctic oscillation (AO), the Atlantic multi-decadal oscillation (AMO) and the Atlantic meridional overturning circulation (AMOC) play in decadal sea ice variability. Further, we use the models to determine the impact that these sources of variability have had on SIE over both the era of satellite observation (1979-2010) and an extended observational record (1953-2010). There is little evidence of a relationship between the AO and SIE in the models. However, we find that both the AMO and AMOC indices are significantly correlated with SIE in all the models considered. Using sensitivity statistics derived from the models, assuming a linear relationship, we attribute 0.5-3.1% decade of the 10.1% decade decline in September SIE (1979-2010) to AMO driven variability.
Climate sensitivity has been subjectively estimated to be likely to lie in the range of 1.5–4.5°C, and this uncertainty contributes a substantial part of the total uncertainty in climate change ...projections over the coming century. Objective observationally‐based estimates have so far failed to improve on this upper bound, with many estimates even suggesting a significant probability of climate sensitivity exceeding 6°C. In this paper, we show how it is possible to greatly reduce this uncertainty by using Bayes' Theorem to combine several independent lines of evidence. Based on some conservative assumptions regarding the value of independent estimates, we conclude that climate sensitivity is very unlikely (<5% probability) to exceed 4.5°C. We cannot assign a significant probability to climate sensitivity exceeding 6°C without making what appear to be wholly unrealistic exaggerations about the uncertainties involved. This represents a significant lowering of the previously‐estimated bound.
The equilibrium climate response to anthropogenic forcing has long been one of the dominant, and therefore most intensively studied, uncertainties in predicting future climate change. As a result, ...many probabilistic estimates of the climate sensitivity (
S
) have been presented. In recent years, most of them have assigned significant probability to extremely high sensitivity, such as
P
(
S
> 6
C
) > 5%. In this paper, we investigate some of the assumptions underlying these estimates. We show that the popular choice of a uniform prior has unacceptable properties and cannot be reasonably considered to generate meaningful and usable results. When instead reasonable assumptions are made, much greater confidence in a moderate value for
S
is easily justified, with an upper 95% probability limit for
S
easily shown to lie close to 4°C, and certainly well below 6°C. These results also impact strongly on projected economic losses due to climate change.
We review progress in model and proxy-based reconstruction of the surface temperature field of the Last Glacial Maximum. Both approaches have converged towards a climate state substantially colder ...than the present day, with the temperature anomaly field showing strong polar amplification and land-sea contrast. The magnitudes of the large-scale changes are increasingly well-constrained, with a recent model-data synthesis generating a value of 4 °C, which suggests a moderate equilibrium climate sensitivity of about 2.5 °C. However, significant areas of uncertainty remain, particularly in the tropical sea surface temperature change. At finer sub-continental spatial scales, there is limited agreement between models and data regarding the patterns of change.
•We discuss developments in modelling and proxy-based estimation of the surface temperature field of the Last Glacial Maximum.•Recent data analyses suggest global mean temperature anomaly of around 4.0 °C below pre-industrial.•Model simulations agree reasonably well on broad scales, but poorly on a regional basis.
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