On the use of IPCC-class models to assess the impact of climate on Living Marine Resources

Stock, Charles A.; Alexander, Michael A.; Bond, Nicholas A.; Brander, Keith; Cheung, William W. L.; Curchitser, Enrique N.; Delworth, Thomas L.; Dunne, John; Griffies, Stephen M.; Haltuch, Melissa A.; Hare, Jonathan A.; Hollowed, Anne B.; Lehodey, Patrick; Levin, Simon A.; Link, Jason S.; Rose, Kenneth A.; Rykaczewski, Ryan R.; Sarmiento, Jorge L.; Stouffer, Ronald J.; Schwing, Franklin B.; Vecchi, Gabriel A.; Werner, Francisco E.

doi:10.1016/j.pocean.2010.09.001

Cited by 286 publications

(265 citation statements)

References 163 publications

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“…To ensure robust skill score statistics, the continuity of satellite ocean color measurements over the next decades is an essential requirement. Finally, the integrated end-to-end evaluation of the food chain needs still to be carried out to bridge the gap between NPP and fish biomass (12).…”

Section: Discussionmentioning

confidence: 99%

“…However, although past studies have investigated the predictive skill of physical variables such as sea surface temperature (SST) (8) or precipitation (9) in the tropical Pacific, none has explored the potential predictability of natural variations of biogeochemical variables like NPP. This is all of the more surprising as the ability to predict natural variations of NPP at interannual-to-decadal timescales may be of key relevance to fisheries management (10)(11)(12).…”

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confidence: 99%

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Multiyear predictability of tropical marine productivity

Séférian

Bopp

Gehlen

et al. 2014

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

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With the emergence of decadal predictability simulations, research toward forecasting variations of the climate system now covers a large range of timescales. However, assessment of the capacity to predict natural variations of relevant biogeochemical variables like carbon fluxes, pH, or marine primary productivity remains unexplored. Among these, the net primary productivity (NPP) is of particular relevance in a forecasting perspective. Indeed, in regions like the tropical Pacific (30°N-30°S), NPP exhibits natural fluctuations at interannual to decadal timescales that have large impacts on marine ecosystems and fisheries. Here, we investigate predictions of NPP variations over the last decades (i.e., from 1997 to 2011) with an Earth system model within the tropical Pacific. Results suggest a predictive skill for NPP of 3 y, which is higher than that of sea surface temperature (1 y). We attribute the higher predictability of NPP to the poleward advection of nutrient anomalies (nitrate and iron), which sustain fluctuations in phytoplankton productivity over several years. These results open previously unidentified perspectives to the development of science-based management approaches to marine resources relying on integrated physical-biogeochemical forecasting systems.forecast | ecosystem management | marine biogeochemistry

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Multiyear predictability of tropical marine productivity

Séférian

Bopp

Gehlen

et al. 2014

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

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“…Considerations for applying global climate models (GCMs) to living marine resources have been detailed for both climate projections (Stock et al 2011) and seasonal forecasts (Hobday et al 2016). In general, the foundation of successful seasonal forecasting applications is a skillful forecast for some desired physical parameter, upon which biological responses can be added through statistical or dynamical models.…”

Section: Introductionmentioning

confidence: 99%

On the skill of seasonal sea surface temperature forecasts in the California Current System and its connection to ENSO variability

et al. 2017

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underlie SST predictability. A simple persistence forecast provides considerable skill for lead times up to ~4 months, while skill above persistence is mostly confined to forecasts of late winter/spring and derives primarily from predictable evolution of ENSO-related variability. Specifically, anomalously weak (strong) equatorward winds are skillfully forecast during El Niño (La Niña) events, and drive negative (positive) upwelling anomalies and consequently warm (cold) temperature anomalies. This mechanism prevails during moderate to strong ENSO events, while years of ENSO-neutral conditions are not associated with significant forecast skill in the wind or significant skill above persistence in SST. We find also a strong latitudinal gradient in predictability within the CCS; SST forecast skill is highest off the Washington/Oregon coast and lowest off southern California, consistent with variable wind forcing being the dominant driver of SST predictability. These findings have direct implications for regional downscaling of seasonal forecasts and for short-term management of living marine resources.

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“…These can be used to simulate historical climates and to project future climates under different emission scenarios-now representative concentration pathways (RCPs)-resulting from different assumptions about socio-economical trends (IPCC 2000). However, currently there is a necessity to generate finer-scale projections of climate variables at a higher spatio-temporal resolution, as users from diverse communities and disciplines require information at a spatial scale that is not provided by the current generation of GCMs (Franklin et al 2013;Semenov and Stratonovitch 2010;Stock et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Effects of variance adjustment techniques and time-invariant transfer functions on heat wave duration indices and other metrics derived from downscaled time-series. Study case: Montreal, Canada

Gaitán

2016

Nat Hazards

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Statistical downscaling techniques are often used to generate finer-scale projections of climate variables affected by local-scale processes not resolved by coarse resolution numerical models like global climate models (GCMs). Statistical downscaling models rely on several assumptions in order to produce finer-/local-scale projections of the variable of interest; one of these assumptions is the time-invariance of the relationships between predictors (e.g. coarse resolution GCM output) and the local-scale predictands (e.g. gridded observation-based time-series or weather station observations). However, in the absence of future observations, statistical downscaling studies use historical data to evaluate their models and assume that these historical simulation skills will be retained in the future. In addition, regression-based downscaling models fail to reproduce the observed variance, and hence their projections need to be adjusted accordingly. Two approaches are usually employed to perform this adjustment: randomization and variance inflation. Here, we study the effect of the stationarity assumption when downscaling daily maximum temperatures and using the downscaled information to estimate historical and future metrics like return periods and heat waves durations over Montreal, Canada; and the effect of the two variance adjustment techniques on the historical and future time-series. To do so, we used regional climate model (RCM) output from the Canadian RCM 4.2, as proxies of historical and future local climates, and daily maximum temperatures obtained from the Canadian GCM 3.1. The results show that the root-mean-squared errors between the pseudo-observations and the statistically downscaled time-series (historical and future) varied over time, with higher errors in the future period; and the effects of randomization and variance inflation on the tails of the statistically downscaled time-series.

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On the use of IPCC-class models to assess the impact of climate on Living Marine Resources

Cited by 286 publications

References 163 publications

Multiyear predictability of tropical marine productivity

Multiyear predictability of tropical marine productivity

On the skill of seasonal sea surface temperature forecasts in the California Current System and its connection to ENSO variability

Effects of variance adjustment techniques and time-invariant transfer functions on heat wave duration indices and other metrics derived from downscaled time-series. Study case: Montreal, Canada

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