Correlation methods in fingerprint detection studies

This investigation addresses two general issues regarding the role of pattern similarity statistics in greenhouse warming detection studies: normalization, and the relative merits of centered versus uncentered statistics. A pattern correlation statistic is used to search for the greenhouse warming signals predicted by five different models in the observed records of land and ocean surface temperature changes. Two forms of this statistic were computed: R (t), which makes use of nonnormalized data, and (t), which employs point-wise normalized data in order to focus the search on regions where the signal-to-noise ratio is large. While there are no trends in the R (t) time series, the time series of (t) show large positive trends. However, it is not possible to infer from the (t) results that the observed pattern of temperature change is, in fact, becoming increasingly similar to the model-predicted signal. This is because point-wise normalization of the observed and simulated mean change fields by a single common field introduces a common factor effect, which means that the quantities being compared should show some similarity a priori. This does not necessarily make normalization inapplicable, because the detection test involves seeking a trend in the similarity statistic. We show, however, that trends in (t) must arise almost completely from the observed data, and cannot be an indicator of increasing observed data/signal similarity. We also compare the information provided by centered statistics such as R(t) and the uncentered C(t) statistic introduced by Barnett. We show that C(t) may be expressed as the weighted sum of two terms, one proportional to R(t) and the other proportional to the observed spatial mean. For near-surface temperatures, the spatial average term dominates over the R(t) term. In this case the use of C(t) is equivalent to the use of spatial-mean temperature. We conclude that at present, the most informative pattern correlation statistic for detection purposes is R(t), the standard product-moment correlation coefficient between the observed and model fields. Our failure to find meaningful trends in R(t) may be due to the fact that the signal is being obscured by the background noise of natural variability, and/or because of incorrect model signals or sensitivities.     

A climate change simulation starting from 1935

Due to restrictions in the available computing resources and a lack of suitable observational data, transient climate change experiments with global coupled ocean-atmosphere models have been started from an initial state at equilibrium with the present day forcing. The historical development of greenhouse gas forcing from the onset of industrialization until the present has therefore been neglected. Studies with simplified models have shown that this cold start error leads to a serious underestimation of the anthropogenic global warming. In the present study, a 150-year integration has been carried out with a global coupled ocean-atmosphere model starting from the greenhouse gas concentration observed in 1935, i.e., at an early time of industrialization. The model was forced with observed greenhouse gas concentrations up to 1985, and with the equivalent C0₂ concentrations stipulated in Scenario A (Business as Usual) of the Intergovernmental Panel on Climate Change from 1985 to 2085. The early starting date alleviates some of the cold start problems. The global mean near surface temperature change in 2085 is about 0.3 K (ca. 10%) higher in the early industrialization experiment than in an integration with the same model and identical Scenario A greenhouse gas forcing, but with a start date in 1985. Comparisons between the experiments with early and late start dates show considerable differences in the amplitude of the regional climate change patterns, particularly for sea level. The early industrialization experiment can be used to obtain a first estimate of the detection time for a greenhouse-gas-induced near-surface temperature signal. Detection time estimates are obtained using globally and zonally averaged data from the experiment and a long control run, as well as principal component time series describing the evolution of the dominant signal and noise modes. The latter approach yields the earliest detection time (in the decade 1990–2000) for the time-evolving near-surface temperature signal. For global-mean temperatures or for temperatures averaged between 45°N and 45°S, the signal detection times are in the decades 2015–2025 and 2005–2015, respectively. The reduction of the cold start error in the early industrialization experiment makes it possible to separate the near-surface temperature signal from the noise about one decade earlier than in the experiment starting in 1985. We stress that these detection times are only valid in the context of the coupled model's internally-generated natural variability, which possibly underestimates low frequency fluctuations and does not incorporate the variance associated with changes in external forcing factors, such as anthropogenic sulfate aerosols, solar variability or volcanic dust.     

Towards the detection and attribution of an anthropogenic effect on climate

It has been hypothesized recently that regional-scale cooling caused by anthropogenic sulfate aerosols may be partially obscuring a warming signal associated with changes in greenhouse gas concentrations. Here we use results from model experiments in which sulfate and carbon dioxide have been varied individually and in combination in order to test this hypothesis. We use centered [R (t)] and uncentered [C (t)] pattern similarity statistics to compare observed time-evolving surface temperature change patterns with the model-predicted equilibrium signal patterns. We show that in most cases, the C (t) statistic reduces to a measure of observed global-mean temperature changes, and is of limited use in attributing observed climate changes to a specific causal mechanism. We therefore focus on R (t), which is a more useful statistic for discriminating between forcing mechanisms with different pattern signatures but similar rates of global mean change. Our results indicate that over the last 50 years, the summer (JJA) and fall (SON) observed patterns of near-surface temperature change show increasing similarity to the model-simulated response to combined sulfate aerosol/CO₂ forcing. At least some of this increasing spatial congruence occurs in areas where the real world has cooled. To assess the significance of the most recent trends in R (t) and C (t), we use data from multi-century control integrations performed with two different coupled atmosphere-ocean models, which provide information on the statistical behavior of 'unforced' trends in the pattern correlation statistics. For the combined sulfate aerosol/CO₂ experiment, the 50-year R (t) trends for the JJA and SON signals are highly significant. Results are robust in that they do not depend on the choice of control run used to estimate natural variability noise properties. The R (t) trends for the CO₂-only signal are not significant in any season. C (t) trends for signals from both the CO₂-only and combined forcing experiments are highly significant in all seasons and for all trend lengths (except for trends over the last 10 years), indicating large global-mean changes relative to the two natural variability estimates used here. The caveats regarding the signals and natural variability noise which form the basis of this study are numerous. Nevertheless, we have provided first evidence that both the largest-scale (global-mean) and smaller-scale (spatial anomalies about the global mean) components of a combined CO₂/anthropogenic sulfate aerosol signal are identifiable in the observed near-surface air temperature data. If the coupled-model noise estimates used here are realistic, we can be highly confident that the anthropogenic signal that we have identified is distinctly different from internally generated natural variability noise. The fact that we have been able to detect the detailed spatial signature in response to combined CO₂ and sulfate aerosol forcing, but not in response to CO₂ forcing alone, suggests that some of the regional-scale background noise (against which we were trying to detect a CO₂-only signal) is in fact part of the signal of a sulfate aerosol effect on climate. The large effect of sulfate aerosols found in this study demonstrates the importance of their inclusion in experiments designed to simulate past and future climate change. Received: 10 November 1994 / Accepted: 19 July 1995     

A probabilistic quantification of the anthropogenic component of twentieth century global warming

This paper examines in detail the statement in the 2007 IPCC Fourth Assessment Report that “Most of the observed increase in global average temperatures since the mid-twentieth century is very likely due to the observed increase in anthropogenic greenhouse gas concentrations”. We use a quantitative probabilistic analysis to evaluate this IPCC statement, and discuss the value of the statement in the policy context. For forcing by greenhouse gases (GHGs) only, we show that there is a greater than 90 % probability that the expected warming over 1950–2005 is larger than the total amount (not just “most”) of the observed warming. This is because, following current best estimates, negative aerosol forcing has substantially offset the GHG-induced warming. We also consider the expected warming from all anthropogenic forcings using the same probabilistic framework. This requires a re-assessment of the range of possible values for aerosol forcing. We provide evidence that the IPCC estimate for the upper bound of indirect aerosol forcing is almost certainly too high. Our results show that the expected warming due to all human influences since 1950 (including aerosol effects) is very similar to the observed warming. Including the effects of natural external forcing factors has a relatively small impact on our 1950–2005 results, but improves the correspondence between model and observations over 1900–2005. Over the longer period, however, externally forced changes are insufficient to explain the early twentieth century warming. We suggest that changes in the formation rate of North Atlantic Deep Water may have been a significant contributing factor.     

Relative detectability of greenhouse-gas and aerosol climate change signals

 The use of pattern correlations to compare observed temperature changes with predicted anthropogenic effects has greatly increased our confidence in the reality of these effects. Here we use synthetic observed data to determine the expected behavior of the pattern correlation statistic, R(t), and hence clarify some results obtained in previous studies. We show that, for the specific case considered here (near-surface temperature changes), even with a perfectly-known signal, expected values of R(t) currently should be only of order 0.3–0.5, as observed; that R(t) may show markedly non-linear variations in time; that the CO₂-alone signal pattern should be difficult to detect today primarily because of data coverage deficiencies; and why the signal due to combined CO₂-aerosol forcing is easier to detect than either the CO₂-alone or aerosol-alone signals. Finally, we show that little is to be gained at present by searching for a time-dependent signal compared with a representative constant signal pattern. Received: 24 June 1996/Revised: 3 March 1998     

Time-dependent greenhouse warming computations with a coupled ocean-atmosphere model

Climate changes during the next 100 years caused by anthropogenic emissions of greenhouse gases have been simulated for the Intergovernmental Panel on Climate Change Scenarios A (business as usual) and D (accelerated policies) using a coupled ocean-atmosphere general circulation model. In the global average, the near-surface temperature rises by 2.6 K in Scenario A and by 0.6 K in Scenario D. The global patterns of climate change for both IPCC scenarios and for a third step-function 2 x CO₂ experiment were found to be very similar. The warming delay over the oceans is larger than found in simulations with atmospheric general circulation models coupled to mixed-layer models, leading to a more pronounced land-sea contrast and a weaker warming (and in some regions even an initial cooling) in the Southern Ocean. During the first forty years, the global warming and sea level rise due to the thermal expansion of the ocean are significantly slower than estimated previously from box-diffusion-upwelling models, but the major part of this delay can be attributed to the previous warming history prior to the start of present coupled ocean-atmosphere model integration (cold start).     

Transiting exoplanets from the CoRoT space mission Resolving the nature of transit candidates for the LRa03 and SRa03 fields

CoRoT is a space telescope which aims at studying internal structure of stars and detecting extrasolar planets. We present here a list of transits detected in the light curves of stars observed by CoRoT in two fields in the anti-center direction: the LRa03 one observed during 148 days from 3 October 2009 to 1 March 2010 followed by the SRa03 one from the 5 March 2010 to the 29 March 2010 during 25 days. 5329 light curves for the LRa03 field and 4169 for the SRa03 field were analyzed by the detection team of CoRoT. Then some of the selected exoplanetary candidates have been followed up from the ground. In the LRa03 field, 19 exoplanet candidates have been found, 8?remain unsolved. No secured planet has been found yet. In the SRa03 field, there were 11 exoplanetary candidates among which 6 cases remain unsolved and 3 planets have been found: CoRoT-18b, CoRoT-19b, CoRoT-20b.     

人为强迫和自然强迫对近年来对流层顶高度变化的贡献

观测表明,自1979年以来对流层顶（平流层与对流层之间的界限）高度增加了数百米。在气候模式实验中,等量的增加也是显而易见的。气候模式实验表明,人为因素导致臭氧和均匀混合温室气体的变化,对1979-1999年对流层顶高度增加的影响占所有因素的80％。臭氧对平流层的冷却作用和均匀混合的温室气体对对流层的加热作用,使对流层顶高度增加。