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Do seasonal-to-decadal climate predictions underestimate the predictability of the real world?

ABSTRACT: Seasonal-to-decadal predictions are inevitably uncertain, depending on the size of the predictable signal relative to unpredictable chaos. Uncertainties can be accounted for using ensemble techniques, permitting quantitative probabilistic forecasts. In a perfect system, each ensemble member would represent a potential realization of the true evolution of the climate system, and the predictable components in models and reality would be equal. However, we show that the predictable component is sometimes lower in models than observations, especially for seasonal forecasts of the North Atlantic Oscillation and multiyear forecasts of North Atlantic temperature and pressure. In these cases the forecasts are underconfident, with each ensemble member containing too much noise. Consequently, most deterministic and probabilistic measures underestimate potential skill and idealized model experiments underestimate predictability. However, skilful and reliable predictions may be achieved using a large ensemble to reduce noise and adjusting the forecast variance through a postprocessing technique proposed here.

SUBMITTER: Eade R

PROVIDER: S-EPMC4373130 | biostudies-literature | 2014 Aug

REPOSITORIES: biostudies-literature

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Do seasonal-to-decadal climate predictions underestimate the predictability of the real world?

Eade Rosie R Smith Doug D Scaife Adam A Wallace Emily E Dunstone Nick N Hermanson Leon L Robinson Niall N

Geophysical research letters 20140808 15

Seasonal-to-decadal predictions are inevitably uncertain, depending on the size of the predictable signal relative to unpredictable chaos. Uncertainties can be accounted for using ensemble techniques, permitting quantitative probabilistic forecasts. In a perfect system, each ensemble member would represent a potential realization of the true evolution of the climate system, and the predictable components in models and reality would be equal. However, we show that the predictable component is som ...[more]

PMID: 25821271

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Project description:Species' response to rapid climate change can be measured through shifts in timing of recurring biological events, known as phenology. The Gulf of Maine is one of the most rapidly warming regions of the ocean, and thus an ideal system to study phenological and biological responses to climate change. A better understanding of climate-induced changes in phenology is needed to effectively and adaptively manage human-wildlife conflicts. Using data from a 20+ year marine mammal observation program, we tested the hypothesis that the phenology of large whale habitat use in Cape Cod Bay has changed and is related to regional-scale shifts in the thermal onset of spring. We used a multi-season occupancy model to measure phenological shifts and evaluate trends in the date of peak habitat use for North Atlantic right (Eubalaena glacialis), humpback (Megaptera novaeangliae), and fin (Balaenoptera physalus) whales. The date of peak habitat use shifted by +18.1 days (0.90 days/year) for right whales and +19.1 days (0.96 days/year) for humpback whales. We then evaluated interannual variability in peak habitat use relative to thermal spring transition dates (STD), and hypothesized that right whales, as planktivorous specialist feeders, would exhibit a stronger response to thermal phenology than fin and humpback whales, which are more generalist piscivorous feeders. There was a significant negative effect of western region STD on right whale habitat use, and a significant positive effect of eastern region STD on fin whale habitat use indicating differential responses to spatial seasonal conditions. Protections for threatened and endangered whales have been designed to align with expected phenology of habitat use. Our results show that whales are becoming mismatched with static seasonal management measures through shifts in their timing of habitat use, and they suggest that effective management strategies may need to alter protections as species adapt to climate change.

Dataset Information

Do seasonal-to-decadal climate predictions underestimate the predictability of the real world?

Publications

Do seasonal-to-decadal climate predictions underestimate the predictability of the real world?

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