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S. M. Dean and P. A. Stott

Abstract

A representative temperature record for New Zealand based on station data from 1853 onward is used in conjunction with four coupled climate models to investigate the causes of recent warming over this small midlatitude country. The observed variability over interannual and decadal time scales is simulated well by the models. The variability of simulated 50-yr trends is consistent with the very short observational record. For a simple detection analysis it is not possible to separate the observed 30- and 50-yr temperature trends from the distribution created by internal variability in the model control simulations. A pressure index that is representative of meridional flow (M1) is used to show that the models fail to simulate an observed trend to more southerly flows in the region. The strong relationship between interannual temperature variability and the M1 index in both the observations and the models is used to remove the influence of this circulation variability from the temperature records. Recent 50-yr trends in the residual temperature record cannot be explained by natural climate variations, but they are consistent with the combined climate response to anthropogenic greenhouse gas emissions, ozone depletion, and sulfate aerosols, demonstrating a significant human influence on New Zealand warming. This result highlights the effect of circulation variability on regional detection and attribution analyses. Such variability can either mask or accelerate human-induced warming in observed trends, underscoring the importance of determining the underlying forced trend, and the need to adequately capture regional circulation effects in climate models.

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Xuebin Zhang, Francis W. Zwiers, and P. A. Stott

Abstract

Using an optimal detection technique and climate change simulations produced with two versions of two GCMs, we have assessed the causes of twentieth-century temperature changes from global to regional scales. Our analysis is conducted in nine spatial domains: 1) the globe; 2) the Northern Hemisphere; four large regions in the Northern Hemispheric midlatitudes covering 30°–70°N including 3) Eurasia, 4) North America, 5) Northern Hemispheric land only, 6) the entire 30°–70°N belt; and three smaller regions over 7) southern Canada, 8) southern Europe, and 9) China. We find that the effect of anthropogenic forcing on climate is clearly detectable at global through regional scales.

The effect of combined greenhouse gases and sulfate aerosol forcing is detectable in all nine domains in annual and seasonal mean temperatures observed during the second half of the twentieth century. The effect of greenhouse gases can also be separated from that of sulfate aerosols over this period at continental and regional scales. Uncertainty in these results is larger in the smaller spatial domains. Detection is improved when an ensemble of models is used to estimate the response to anthropogenic forcing and the underlying internal variability of the climate system. Our detection results hold after removal of North Atlantic Oscillation (NAO)-related variability in temperature observations—variability that may or may not be associated with anthropogenic forcing. They also continue to hold when our estimates of natural internal climate variability are doubled.

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Donald P. Cummins, David B. Stephenson, and Peter A. Stott

Abstract

This study has developed a rigorous and efficient maximum likelihood method for estimating the parameters in stochastic energy balance models (with any k > 0 number of boxes) given time series of surface temperature and top-of-the-atmosphere net downward radiative flux. The method works by finding a state-space representation of the linear dynamic system and evaluating the likelihood recursively via the Kalman filter. Confidence intervals for estimated parameters are straightforward to construct in the maximum likelihood framework, and information criteria may be used to choose an optimal number of boxes for parsimonious k-box emulation of atmosphere–ocean general circulation models (AOGCMs). In addition to estimating model parameters the method enables hidden state estimation for the unobservable boxes corresponding to the deep ocean, and also enables noise filtering for observations of surface temperature. The feasibility, reliability, and performance of the proposed method are demonstrated in a simulation study. To obtain a set of optimal k-box emulators, models are fitted to the 4 × CO2 step responses of 16 AOGCMs in CMIP5. It is found that for all 16 AOGCMs three boxes are required for optimal k-box emulation. The number of boxes k is found to influence, sometimes strongly, the impulse responses of the fitted models.

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Stephanie C. Herring, Martin P. Hoerling, James P. Kossin, Thomas C. Peterson, and Peter A. Stott

Editors note: For easy download the posted pdf of the Explaining Extreme Events of 2014 is a very low-resolution file. A high-resolution copy of the report is available by clicking here. Please be patient as it may take a few minutes for the high-resolution file to download.

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Stephanie C. Herring, Martin P. Hoerling, James P. Kossin, Thomas C. Peterson, and Peter A. Stott
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Stephanie C. Herring, Martin P. Hoerling, James P. Kossin, Thomas C. Peterson, and Peter A. Stott
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J. A. Kettleborough, B. B. B. Booth, P. A. Stott, and M. R. Allen

Abstract

A method for estimating uncertainty in future climate change is discussed in detail and applied to predictions of global mean temperature change. The method uses optimal fingerprinting to make estimates of uncertainty in model simulations of twentieth-century warming. These estimates are then projected forward in time using a linear, compact relationship between twentieth-century warming and twenty-first-century warming. This relationship is established from a large ensemble of energy balance models.

By varying the energy balance model parameters an estimate is made of the error associated with using the linear relationship in forecasts of twentieth-century global mean temperature. Including this error has very little impact on the forecasts. There is a 50% chance that the global mean temperature change between 1995 and 2035 will be greater than 1.5 K for the Special Report on Emissions Scenarios (SRES) A1FI scenario. Under SRES B2 the same threshold is not exceeded until 2055. These results should be relatively robust to model developments for a given radiative forcing history.

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J. M. Gregory, R. J. Stouffer, S. C. B. Raper, P. A. Stott, and N. A. Rayner

Abstract

A probability distribution for values of the effective climate sensitivity, with a lower bound of 1.6 K (5th percentile), is obtained on the basis of the increase in ocean heat content in recent decades from analyses of observed interior-ocean temperature changes, surface temperature changes measured since 1860, and estimates of anthropogenic and natural radiative forcing of the climate system. Radiative forcing is the greatest source of uncertainty in the calculation; the result also depends somewhat on the rate of ocean heat uptake in the late nineteenth century, for which an assumption is needed as there is no observational estimate. Because the method does not use the climate sensitivity simulated by a general circulation model, it provides an independent observationally based constraint on this important parameter of the climate system.

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Nathan P. Gillett, Gabriele C. Hegerl, Myles R. Allen, Peter A. Stott, and Reiner Schnur

Abstract

Anthropogenic influences on surface temperature over the second half of the twentieth century are examined using output from two general circulation models (HadCM2 and ECHAM3). Optimal detection techniques involve the comparison of observed temperature changes with those simulated by a climate model, using a control integration to test the null hypothesis that all the observed changes are due to natural variability. Two recent studies have examined the influence of greenhouse gases and the direct effect of sulfate aerosol on surface temperature using output from the same two climate models but with many differences in the methods applied. Both detected overall anthropogenic influence on climate, but results on the separate detection of greenhouse gas and sulfate aerosol influences were different. This paper concludes that the main differences between the results can be explained by the season over which temperatures were averaged, the length of the climatology from which anomalies were taken, and the use of a time-evolving signal pattern as opposed to a spatial pattern of temperature trends. This demonstration of consistency increases confidence in the equivalence of the methodologies in other respects, and helps to synthesize results from the two approaches. Including information on the temporal evolution of the response to different forcings allows sulfate aerosol influence to be detected more easily in HadCM2, whereas focusing on spatial patterns gives better detectability in ECHAM3.

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Stephanie C. Herring, Nikolaos Christidis, Andrew Hoell, Martin P. Hoerling, and Peter A. Stott

Editors note: For easy download the posted pdf of the Explaining Extreme Events of 2016 is a very low-resolution file. A high-resolution copy of the report is available by clicking here. Please be patient as it may take a few minutes for the high-resolution file to download.

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