Detecting Greenhouse-Gas-Induced Climate Change with an Optimal Fingerprint Method

Hegerl, Gabriele C.; Storch, Hans von; Hasselmann, Klaus; Santer, Benjamin D.; Cubasch, Ulrich; Jones, Phil

doi:10.1175/1520-0442(1996)009<2281:dggicc>2.0.co;2

Cited by 332 publications

(296 citation statements)

References 28 publications

Supporting

Mentioning

273

Contrasting

Unclassified

Order By: Relevance

“…A climate change detection strategy (Hegerl et al 1996) with only this regional variable perhaps is not reasonable, however, and has not been attempted.…”

Section: Discussionmentioning

confidence: 99%

Agreement between Observed Rainfall Trends and Climate Change Simulations in the Southwest of Europe

González-Rouco¹,

Heyen

Zorita

et al. 2000

J. Climate

View full text Add to dashboard Cite

The lowest spatial scale at which current climate models are considered to be skillful is on the order of 1000 km because of resolution and computer capabilities. The estimation of the regional changes caused by anthropogenic emissions of greenhouse gases and aerosols therefore is problematic. Here a statistical downscaling scheme is used to study the relationship between large-scale sea level pressure and regional precipitation in southwestern Europe, both in observed data and in outputs from a general circulation model (GCM) forced with increasing levels of greenhouse gases and sulfate aerosols. The results indicate that the GCM does reproduce the main aspects of the large-to local-scale coupled variability. Furthermore, these large-to local-scale relationships remain stable in the scenario simulations. The GCM runs predict increases of advection of oceanic air masses to the Iberian Peninsula that will produce a slight decrease of precipitation amounts in the north coast and the opposite effect in the rest of the territory, with values that could reach 10 mm decade Ϫ1 in the south. In the homogenized historical records, the obtained pattern of change is very similar. These results support estimations of future regional trends simulated by the GCM under future emission scenarios.

show abstract

“…A climate change detection strategy (Hegerl et al 1996) with only this regional variable perhaps is not reasonable, however, and has not been attempted.…”

Section: Discussionmentioning

confidence: 99%

Agreement between Observed Rainfall Trends and Climate Change Simulations in the Southwest of Europe

González-Rouco¹,

Heyen

Zorita

et al. 2000

J. Climate

View full text Add to dashboard Cite

show abstract

“…from long control simulations). To avoid bias in the estimate of the covariance of the scaling factors, b i (Hegerl et al 1996(Hegerl et al , 1997, a second statistically independent estimate of the covariance matrix is used to determine the uncertainty in the scaling factors (e.g. from an independent segment of the control simulation).…”

Section: Constraints On Future Climate Change Based On Past Climate Cmentioning

confidence: 99%

Ensemble climate predictions using climate models and observational constraints

Stott

Forest

2007

Phil. Trans. R. Soc. A.

View full text Add to dashboard Cite

Two different approaches are described for constraining climate predictions based on observations of past climate change. The first uses large ensembles of simulations from computationally efficient models and the second uses small ensembles from state-of-the-art coupled ocean-atmosphere general circulation models. Each approach is described and the advantages of each are discussed. When compared, the two approaches are shown to give consistent ranges for future temperature changes. The consistency of these results, when obtained using independent techniques, demonstrates that past observed climate changes provide robust constraints on probable future climate changes. Such probabilistic predictions are useful for communities seeking to adapt to future change as well as providing important information for devising strategies for mitigating climate change.

show abstract

“…The reason for 'high confidence' is that tropical temperature emergence has already been seen in observations 6,10,11 and in many previous studies examining climate simulations 3,4,6,11,12,13,14,15,16,17,18 , none of which were cited by M13. While projections of emergence times are clearly important for estimating a wide range of impacts (as demonstrated for food security 17 , biodiversity hotspots 18 and ocean biogeochemistry 19 ), they must be quantified within a framework that incorporates climate variability, as illustrated in the large body of literature that has already examined this issue.…”

mentioning

confidence: 99%

Uncertainties in the timing of unprecedented climates

Hawkins

Anderson

Diffenbaugh

et al. 2014

Nature

View full text Add to dashboard Cite

The question of when the signal of climate change will emerge from the background noise of climate variability -the 'time of emergence' -is potentially important for adaptation planning. Mora et al. 1 (M13) presented precise projections of the time of emergence of unprecedented regional climates. However, their methodology produces artificially early dates at which specific regions will permanently experience unprecedented climates and artificially low uncertainty in those dates everywhere. This overconfidence could impair the effectiveness of climate risk management decisions 2 .Any human-induced changes in climate will be modulated by natural fluctuations of the oceans and atmosphere (e.g. El Niño events). These fluctuations occur randomly and independently, in both reality and individual modelbased projections, and act to obscure the climate change signal 3,4,5 . M13 discuss projections of when changes in climate emerge permanently above the levels of such fluctuations (a metric first considered by ref. 6). However, by ignoring the irreducible limits imposed by these same random fluctuations, M13 express their emergence dates with too much certainty.Several methodological oversights contribute to the erroneous uncertainty quantification. Firstly, M13 ignore the possibility that emergence dates before the end of the simulations are not permanent deviations from the historical range 6 (termed 'pseudo-emergence'). In many regions where emergence has not occurred by the year 2100, M13 even artificially set the emergence date to equal 2100. This oversight produces several effects, including: (i) early and overconfident estimates of regional temperature emergence, and (ii) implausible emergence dates for precipitation of exactly 2100 with zero uncertainty almost everywhere.Secondly, M13 estimate precision of regional emergence timing using the standard error of the ensemble mean (σ/√N), where N(=39) is the number of simulations and σ is their standard deviation. While the estimate of the ensemble-mean becomes more precise with larger ensemble size, natural fluctuations of the climate (such as El Niño) dictate that the future evolution of climate will not behave like the mean, but as a single realization from a range of outcomes 5,7 . The use of σ/√N greatly underestimates 8 this irreducible uncertainty, as well as the climate-response uncertainty given by the inter-model spread, and is therefore inappropriate for use in

show abstract

Detecting Greenhouse-Gas-Induced Climate Change with an Optimal Fingerprint Method

Cited by 332 publications

References 28 publications

Agreement between Observed Rainfall Trends and Climate Change Simulations in the Southwest of Europe

Agreement between Observed Rainfall Trends and Climate Change Simulations in the Southwest of Europe

Ensemble climate predictions using climate models and observational constraints

Uncertainties in the timing of unprecedented climates

Contact Info

Product

Resources

About