Communication of the role of natural variability in future North American climate

Deser, Clara; Knutti, Reto; Solomon, Susan; Phillips, Adam S.

doi:10.1038/nclimate1562

Cited by 737 publications

(671 citation statements)

References 20 publications

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“…While radiative-forced response will become increasingly important, deviations from the forced response are substantial at any given time, especially on regional scales 19 . Quantitative tools like our POGA-H are crucial to attribute the causes of regional climate anomalies 17 .…”

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confidence: 99%

Recent global-warming hiatus tied to equatorial Pacific surface cooling

Kosaka

Xie

2013

Nature

1,451

1,100

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mentioning

confidence: 99%

Recent global-warming hiatus tied to equatorial Pacific surface cooling

Kosaka

Xie

2013

Nature

1,451

1,100

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“…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.…”

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confidence: 99%

“…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 emergence estimates.…”

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confidence: 99%

Uncertainties in the timing of unprecedented climates

Hawkins

Anderson

Diffenbaugh

et al. 2014

Nature

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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

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“…S4). This interval included the noise term N (0, σ ), and captured the observed year-to-year variations as well as model differences (Deser et al, 2012). We assumed that the multi-model ensemble spread for any given year could approximately represent the typical year-to-year variance, which meant that the 95th percentile forμ, which we henceforth refer to asμ 95 , could be used as a proxy for the value to be exceeded once in 20 years (Benestad, 2016) (the 20-year event has a probability of 0.05 (1/20) of occurring in a given year, and the 95th percentile represents a limit that only 5 % (1 in 20) of the distribution exceeds).…”

Section: The Empirical-statistical Modelmentioning

confidence: 99%

“…Nevertheless, RCMs have been used to study precipitation extremes (e.g. Frei et al, 2006), although the heavy computational demands have limited analysis to a small number of GCMs which means that the ensembles do not provide a realistic range of possible outcomes associated with natural variability and model uncertainty (Deser et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Simple and approximate estimations of future precipitation return values

Benestad

Parding

Mezghani

et al. 2017

Nat. Hazards Earth Syst. Sci.

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Abstract. We present estimates of future 20-year return values for 24 h precipitation based on multi-model ensembles of temperature projections and a crude method to quantify how warmer conditions may influence precipitation intensity. Our results suggest an increase by as much as 40-50 % projected for 2100 for a number of locations in Europe, assuming the high Representative Concentration Pathway (RCP) 8.5 emission scenario. The new strategy was based on combining physical understandings with the limited information available, and it utilised the covariance between the mean seasonal variations in precipitation intensity and the North Atlantic saturation vapour pressure. Rather than estimating the expected values and interannual variability, we tried to estimate an "upper bound" for the response in the precipitation intensity based on the assumption that the seasonal variations in the precipitation intensity are caused by the seasonal variations in temperature. Return values were subsequently derived from the estimated precipitation intensity through a simple and approximate scheme that combined the 1-year 24 h precipitation return values and downscaled annual wet-day mean precipitation for a 20-year event. The latter was based on the 95th percentile of a multi-model ensemble spread of downscaled climate model results. We found geographical variations in the shape of the seasonal cycle of the wet-day mean precipitation which suggest that different rain-producing mechanisms dominate in different regions. These differences indicate that the simple method used here to estimate the response of precipitation intensity to temperature was more appropriate for convective precipitation than for orographic rainfall.

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Communication of the role of natural variability in future North American climate

Cited by 737 publications

References 20 publications

Recent global-warming hiatus tied to equatorial Pacific surface cooling

Recent global-warming hiatus tied to equatorial Pacific surface cooling

Uncertainties in the timing of unprecedented climates

Simple and approximate estimations of future precipitation return values

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