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).
Municipal solid waste (MSW) compost is increasingly used in agriculture as a soil conditioner but also as a fertilizer. Proponents of this practice consider it an important recycling tool since MSW ...would otherwise be landfilled and critics are concerned with its often elevated metal concentrations. Large amounts of MSW compost are frequently used in agriculture to meet crop N requirements and for the addition of organic matter. The main concern is loading the soil with metals that can result in increased metal content of crops. Furthermore, in some cases, metals and excess nutrients can move through the soil profile into groundwater. Municipal solid waste compost has also been reported to have high salt concentrations, which can inhibit plant growth and negatively affect soil structure. A review of relevant agricultural studies is presented as well as recommendations for improving MSW compost quality. Its safe use in agriculture can be ensured with source separation (or triage of MSW to be composted) as well as the development and implementation of comprehensive industry standards.
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
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 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.
We have extended the GENIE‐1 Earth system model to include a representation of sedimentary stratigraphy and the preservation of biogenic carbonates delivered to the ocean floor. This has enabled us ...to take a novel approach in diagnosing modern marine carbon cycling: assimilating observation of the calcium carbonate (CaCO3) content of deep‐sea sediments with an ensemble Kalman filter. The resulting calibrated model predicts a mean surface sediment content (32.5 wt%) close to the observed value (34.8 wt%), and a global burial rate of CaCO3 in deep sea sediments of 0.121 PgC yr−1, in line with recent budget estimates of 0.10−0.14 PgC yr−1. We employ the GENIE‐1 model in quantifying the multimillennial‐scale fate of fossil fuel CO2 emitted to the atmosphere. In the absence of any interaction between ocean and sediments, an equilibrium partitioning of CO2 is reached within ∼1000 years of emissions ceasing, with 34% (645 ppm) remaining in the atmosphere out of a total fossil fuel burn of 4173 PgC. An additional 12% of CO2 emissions (223 ppm) are sequestered as bicarbonate ions (HCO3−) by reaction with deep‐sea carbonates (“seafloor CaCO3 neutralization”) on a timescale of ∼1.7 ka. Excess of carbonate weathering on land over deep‐sea burial results in a further net transformation of 14% of CO2 emissions (261 ppm) into HCO3− (“terrestrial CaCO3 neutralization”) on a timescale of ∼8.3 ka. We have also assessed the importance of a changing climate in modulating the stabilization of atmospheric CO2 through ocean‐sediment interaction. Increased ocean stratification suppresses particulate organic carbon export, which in turn enhances seafloor CaCO3 preservation. The resulting reduction in the sequestration of fossil fuel CO2 represents a new positive feedback on millennial‐scale climate change.
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