Distinguishing the Roles of Natural and Anthropogenically Forced Decadal Climate Variability

Solomon, Amy; Goddard, Lisa; Kumar, Arun; Carton, James A.; Deser, Clara; Fukumori, Ichiro; Greene, Arthur M.; Hegerl, Gabriele C.; Kirtman, Ben P.; Kushnir, Yochanan; Newman, Matthew; Smith, Doug; Vimont, Daniel J.; Delworth, Thomas L.; Meehl, Gerald A.; Stockdale, Tim

doi:10.1175/2010bams2962.1

Cited by 137 publications

(116 citation statements)

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“…Optimal detection techniques that seek to rotate the fingerprint of climate change so as to maximize signalto-noise ratios in model simulations are likely even more susceptible to bias (46). Underestimation of internal variability can also be expected to lead to predicted ranges that are too narrow, possibly also because of the projection of anthropogenic forcing onto natural modes of variability (1).…”

Section: Discussionmentioning

confidence: 99%

“…sea surface temperature | climate variability | multiproxy synthesis | proxy data reconstruction V ariations in sea surface temperature (SSTs) have widespread implications for society and are the basis of most regional decadal prediction efforts (1). Magnitudes of variability in regional SSTs are inferred either using observations or simulations from general circulation models (GCMs).…”

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confidence: 99%

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Ocean surface temperature variability: Large model–data differences at decadal and longer periods

Laepple

Huybers

2014

Proc. Natl. Acad. Sci. U.S.A.

149

141

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The variability of sea surface temperatures (SSTs) at multidecadal and longer timescales is poorly constrained, primarily because instrumental records are short and proxy records are noisy. Through applying a new noise filtering technique to a global network of late Holocene SST proxies, we estimate SST variability between annual and millennial timescales. Filtered estimates of SST variability obtained from coral, foraminifer, and alkenone records are shown to be consistent with one another and with instrumental records in the frequency bands at which they overlap. General circulation models, however, simulate SST variability that is systematically smaller than instrumental and proxy-based estimates. Discrepancies in variability are largest at low latitudes and increase with timescale, reaching two orders of magnitude for tropical variability at millennial timescales. This result implies major deficiencies in observational estimates or model simulations, or both, and has implications for the attribution of past variations and prediction of future change.sea surface temperature | climate variability | multiproxy synthesis | proxy data reconstruction V ariations in sea surface temperature (SSTs) have widespread implications for society and are the basis of most regional decadal prediction efforts (1). Magnitudes of variability in regional SSTs are inferred either using observations or simulations from general circulation models (GCMs). At synoptic and interannual timescales, there is overall agreement between observational and GCM estimates of SST variability (2-4). At decadal timescales, however, instrumental records generally show greater regional SST variability than found in the Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble of GCM simulations (4) and in earlier simulations (5-7). Discrepancies are greatest at low latitudes where model−data mismatches in variance reach a factor of 2 (Fig. 1).Estimating regional SST variability at multidecadal and longer timescales presents a quandary. Discrepancies with instrumentally observed SST variability at decadal timescales calls into question the credibility of GCM estimates at longer timescales. At the same time, instrumental observations covering more than 100 y of SST variability are sparse. For example, whereas 68% of ocean grid boxes in the Climate Research Unit's instrumental compilation of SSTs have a 30-y interval with an observational density of at least 100 observations per year, only 19% of grid boxes have such coverage over a 100-y interval (8). Furthermore, SST variability observed during the last century represents contributions from natural and anthropogenic sources that are difficult to disentangle and are not necessarily representative of any other interval (9).One is inevitably led to using paleoclimate proxies to constrain multidecadal and longer-term variability. Numerous paleoclimate reconstructions document low-frequency SST variability (10-15), but the degree to which these reconstructions afford quantitative constraints of annual...

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Ocean surface temperature variability: Large model–data differences at decadal and longer periods

Laepple

Huybers

2014

Proc. Natl. Acad. Sci. U.S.A.

149

141

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“…This is also true for the studies on extremes, which point out that signal-tonoise ratios for these extremes over specific time scales and areas might be more favourable to predictability than those of mean quantities. An important point, highlighted by Solomon et al (2011) and illustrated impressively by van Oldenborgh et al (2012), is the fact that a large part of predictive skill on multiannual to decadal time scales is associated with external forcing, that is, a consequence of the long-term climate change signals. Thus, an initialisation with the actual state of the climate system is not necessary to exploit this skill.…”

Section: P U B L I S H E D B Y T H E I N T E R N a T I O N A L M E T mentioning

confidence: 99%

“…This is due to the combination of increased awareness of potential implications of any climate change signal (natural or anthropogenic) on the one hand and typical time spans of economic or societal planning on the other hand. As anthropogenically forced climate change signals and natural decadal variations may be of similar magnitude for the next decades, initialised decadal predictions could be of great socio-economic value (Solomon et al, 2011). For this reason, the Coupled Model Intercomparison Project in its fifth phase (CMIP5) introduced a framework for initialised decadal predictions, in order to explore the ability of state-of-the-art earth system models and initialisation procedures to yield additional value to the long-term projections and to foster the scientific understanding of predictability on these time scales (see Taylor et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Evaluating decadal predictions of northern hemispheric cyclone frequencies

Kruschke¹,

Rust²,

Kadow³

et al. 2014

Tellus A: Dynamic Meteorology and Oceanography

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A B S T R A C T Mid-latitudinal cyclones are a key factor for understanding regional anomalies in primary meteorological parameters such as temperature or precipitation. Extreme cyclones can produce notable impacts on human society and economy, for example, by causing enormous economic losses through wind damage. Based on 41 annually initialised (1961Á2001) hindcast ensembles, this study evaluates the ability of a single-model decadal forecast system (MPI-ESM-LR) to provide skilful probabilistic three-category forecasts (enhanced, normal or decreased) of winter (ONDJFM) extra-tropical cyclone frequency over the Northern Hemisphere with lead times from 1 yr up to a decade. It is shown that these predictions exhibit some significant skill, mainly for lead times of 2Á5 yr, especially over the North Atlantic and Pacific. Skill for intense cyclones is generally higher than for all detected systems. A comparison of decadal hindcasts from two different initialisation techniques indicates that initialising from reanalysis fields yields slightly better results for the first forecast winter (month 10Á15), while initialisation based on an assimilation experiment provides better skill for lead times between 2 and 5 yr. The reasons and mechanisms behind this predictive skill are subject to future work. Preliminary analyses suggest a strong relationship of the model's skill over the North Atlantic with the ability to predict upper ocean temperatures modulating lower troposphere baroclinicity for the respective area and time scales.

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“…For near-term climate predictions, these long-term evolving modes need to be correctly phased with observations, implying a good initialization. The ocean, characterized by strong inertia and, more particularly, oceanic variability modes such as the Atlantic Meridional Overturning Circulation (AMOC), have been shown to be the major source of predictability in the climate system (Griffies and Bryan 1997;Boer 2004;Pohlmann et al 2004;Knight et al 2006;Solomon et al 2011;Doblas-Reyes et al 2011). Accordingly, initialization of the ocean state improves some aspects of climate forecast (Smith et al 2007;Pohlmann et al 2009;Mochizuki et al 2010), especially in the North Atlantic sector (Keenlyside et al 2008;Garcia-Serrano et al 2012;Yeager et al 2012;Doblas-Reyes et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Interannual predictability of Arctic sea ice in a global climate model: regional contrasts and temporal evolution

et al. 2014

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The predictability of the Arctic sea ice is investigated at the interannual time scale using decadal experiments performed within the framework of the fifth phase of the Coupled Model Intercomparison Project with the CNRM-CM5.1 coupled atmosphere-ocean global climate model. The predictability of summer Arctic sea ice extent is found to be weak and not to exceed 2 years. In contrast, robust prognostic potential predictability (PPP) up to several years is found for winter sea ice extent and volume. This predictability is regionally contrasted. The marginal seas in the Atlantic sector and the central Arctic show the highest potential predictability, while the marginal seas in the Pacific sector are barely predictable. The PPP is shown to decrease drastically in the more recent period. Regarding sea ice extent, this decrease is explained by a strong reduction of its natural variability in the Greenland-Iceland-Norwegian Seas due to the quasi-disappearance of the marginal ice zone in the center of the Greenland Sea. In contrast, the decrease of predictability of sea ice volume arises from the combined effect of a reduction of its natural variability and an increase in its chaotic nature. The latter is attributed to a thinning of sea ice cover over the whole Arctic, making it more sensitive to atmospheric fluctuations. In contrast to the PPP assessment, the prediction skill as measured by the anomaly correlation coefficient is found to be mostly due to external forcing. Yet, in agreement with the PPP assessment, a weak added value of the initialization is found in the Atlantic sector. Nevertheless, the trend-independent component of this skill is not statistically significant beyond the forecast range of 3 months. These contrasted findings regarding potential predictability and prediction skill arising from the initialization suggest that substantial improvements can be made in order to enhance the prediction skill.

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Distinguishing the Roles of Natural and Anthropogenically Forced Decadal Climate Variability

Cited by 137 publications

References 113 publications

Ocean surface temperature variability: Large model–data differences at decadal and longer periods

Ocean surface temperature variability: Large model–data differences at decadal and longer periods

Evaluating decadal predictions of northern hemispheric cyclone frequencies

Interannual predictability of Arctic sea ice in a global climate model: regional contrasts and temporal evolution

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