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Grant Branstator and Frank Selten


A 62-member ensemble of coupled general circulation model (GCM) simulations of the years 1940–2080, including the effects of projected greenhouse gas increases, is examined. The focus is on the interplay between the trend in the Northern Hemisphere December–February (DJF) mean state and the intrinsic modes of variability of the model atmosphere as given by the upper-tropospheric meridional wind. The structure of the leading modes and the trend are similar. Two commonly proposed explanations for this similarity are considered.

Several results suggest that this similarity in most respects is consistent with an explanation involving patterns that result from the model dynamics being well approximated by a linear system. Specifically, the leading intrinsic modes are similar to the leading modes of a stochastic model linearized about the mean state of the GCM atmosphere, trends in GCM tropical precipitation appear to excite the leading linear pattern, and the probability density functions (PDFs) of prominent circulation patterns are quasi-Gaussian.

There are, on the other hand, some subtle indications that an explanation for the similarity involving preferred states (which necessarily result from nonlinear influences) has some relevance. For example, though unimodal, PDFs of prominent patterns have departures from Gaussianity that are suggestive of a mixture of two Gaussian components. And there is some evidence of a shift in probability between the two components as the climate changes. Interestingly, contrary to the most prominent theory of the influence of nonlinearly produced preferred states on climate change, the centroids of the components also change as the climate changes. This modification of the system’s preferred states corresponds to a change in the structure of its dominant patterns. The change in pattern structure is reproduced by the linear stochastic model when its basic state is modified to correspond to the trend in the general circulation model’s mean atmospheric state. Thus, there is a two-way interaction between the trend and the modes of variability.

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Ruud Sperna Weiland, Karin van der Wiel, Frank Selten, and Dim Coumou


Persistent hot–dry or cold–wet summer weather can have significant impacts on agriculture, health, and the environment. For northwestern Europe, these weather regimes are typically linked to, respectively, blocked or zonal jet stream states. The fundamental dynamics underlying these circulation states are still poorly understood. Edward Lorenz postulated that summer circulation may be either fully or almost intransitive, implying that part of the phase space (capturing circulation variability) cannot be reached within one specific summer. If true, this would have major implications for the predictability of summer weather and our understanding of the drivers of interannual variability of summer weather. Here, we test the two Lorenz hypotheses (i.e., fully or almost intransitive) for European summer circulation, capitalizing on a newly available very large ensemble (2000 years) of present-day climate data in the fully coupled global climate model EC-Earth. Using self-organizing maps, we quantify the phase space of summer circulation and the trajectories through phase space in unprecedented detail. We show that, based on Markov assumptions, the summer circulation is strongly dependent on its initial state in early summer with the atmospheric memory ranging from 28 days up to ~45 days. The memory is particularly long if the initial state is either a blocked or a zonal flow state. Furthermore, we identify two groups of summers that are characterized by distinctly different trajectories through phase space, and that prefer either a blocked or zonal circulation state, respectively. These results suggest that intransitivity is indeed a fundamental property of the atmosphere and an important driver of interannual variability.

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DáithíA. Stone, Myles R. Allen, Frank Selten, Michael Kliphuis, and Peter A. Stott


The detection and attribution of climate change in the observed record play a central role in synthesizing knowledge of the climate system. Unfortunately, the traditional method for detecting and attributing changes due to multiple forcings requires large numbers of general circulation model (GCM) simulations incorporating different initial conditions and forcing scenarios, and these have only been performed with a small number of GCMs. This paper presents an extension to the fingerprinting technique that permits the inclusion of GCMs in the multisignal analysis of surface temperature even when the required families of ensembles have not been generated. This is achieved by fitting a series of energy balance models (EBMs) to the GCM output in order to estimate the temporal response patterns to the various forcings.

This methodology is applied to the very large Challenge ensemble of 62 simulations of historical climate conducted with the NCAR Community Climate System Model version 1.4 (CCSM1.4) GCM, as well as some simulations from other GCMs. Considerable uncertainty exists in the estimates of the parameters in fitted EBMs. Nevertheless, temporal response patterns from these EBMs are more reliable and the combined EBM time series closely mimics the GCM in the context of transient forcing. In particular, detection and attribution results from this technique appear self-consistent and consistent with results from other methods provided that all major forcings are included in the analysis.

Using this technique on the Challenge ensemble, the estimated responses to changes in greenhouse gases, tropospheric sulfate aerosols, and stratospheric volcanic aerosols are all detected in the observed record, and the responses to the greenhouse gases and tropospheric sulfate aerosols are both consistent with the observed record without a scaling of the amplitude being required. The result is that the temperature difference of the 1996–2005 decade relative to the 1940–49 decade can be attributed to greenhouse gas emissions, with a partially offsetting cooling from sulfate emissions and little contribution from natural sources.

The results support the viability of the new methodology as an extension to current analysis tools for the detection and attribution of climate change, which will allow the inclusion of many more GCMs. Shortcomings remain, however, and so it should not be considered a replacement to traditional techniques.

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