Climate sensitivity constrained by temperature reconstructions over the past seven centuries

The magnitude and impact of future global warming depends on the sensitivity of the climate system to changes in greenhouse gas concentrations. The commonly accepted range for the equilibrium global mean temperature change in response to a doubling of the atmospheric carbon dioxide concentration, termed climate sensitivity, is 1.5-4.5 K (ref. 2). A number of observational studies, however, find a substantial probability of significantly higher sensitivities, yielding upper limits on climate sensitivity of 7.7 K to above 9 K (refs 3-8). Here we demonstrate that such observational estimates of climate sensitivity can be tightened if reconstructions of Northern Hemisphere temperature over the past several centuries are considered. We use large-ensemble energy balance modelling and simulate the temperature response to past solar, volcanic and greenhouse gas forcing to determine which climate sensitivities yield simulations that are in agreement with proxy reconstructions. After accounting for the uncertainty in reconstructions and estimates of past external forcing, we find an independent estimate of climate sensitivity that is very similar to those from instrumental data. If the latter are combined with the result from all proxy reconstructions, then the 5-95 per cent range shrinks to 1.5-6.2 K, thus substantially reducing the probability of very high climate sensitivity.     

(In)consistency of airsea heat flux estimates; ocean heat uptake anomalies and top of atmosphere radiation budgets

This paper introduces the consistency between top of atmosphere （TOA） imbalances and ocean heat uptake, and the inconsistency between ocean heat uptake estimates and flux climatologies, and then gives some recommendations and outlook.     

Living systems analysis and neoclassical economics: Towards a new economics paradigm

This paper points out the flaws in the neoclassical economics paradigm which invalidate it as an economic policy tool and place it in the realm of illusion. An evolutionary paradigm would be more appropriate. The paper outlines how the primary regulatory processes in living systems analysis (LSA) can be applied to both the design of configurations of resources in the secondary regulatory processes and the creation of new resources so as to optimize the output of the economic system as a whole. It is the degree of government intervention which is important, and how it is integrated, not whether there will or will not be intervention.     

Emancipatory practice through the Sky River Project

Timothy Kennedy went to an Eskimo village in Alaska as a VISTA volunteer to perform community service for 2 years. He remained through 11 years, building participatory community development projects. As he became accepted by the Eskimo villagers, he got them involved in using videotape technology to build community solidarity and then to communicate more effectively with government officials and politicians about the needs and wishes of their community. So as to make clear that this was not a documentary project directed by a professional film maker, Kennedy got villagers involved in all stages of the process of videotaping and also in determining who and how their tapes should be seen by authorities. The Sky River Project achieved three major changes in governmental policies for the Eskimos. As the videotapes were shown in other Eskimo villages, who shared the same needs and wishes, the process generated intervillage communication and collaboration on various projects.     

Improved Classification for Compositional Data Using the <Emphasis Type="Italic">α</Emphasis>-transformation

In compositional data analysis, an observation is a vector containing nonnegative values, only the relative sizes of which are considered to be of interest. Without loss of generality, a compositional vector can be taken to be a vector of proportions that sum to one. Data of this type arise in many areas including geology, archaeology, biology, economics and political science. In this paper we investigate methods for classification of compositional data. Our approach centers on the idea of using the α-transformation to transform the data and then to classify the transformed data via regularized discriminant analysis and the k-nearest neighbors algorithm. Using the α-transformation generalizes two rival approaches in compositional data analysis, one (when α=1) that treats the data as though they were Euclidean, ignoring the compositional constraint, and another (when α = 0) that employs Aitchison’s centered log-ratio transformation. A numerical study with several real datasets shows that whether using α = 1 or α = 0 gives better classification performance depends on the dataset, and moreover that using an intermediate value of α can sometimes give better performance than using either 1 or 0.