An ‘Observational Large Ensemble’ to compare observed and modeled temperature trend uncertainty due to internal variability

An ‘Observational Large Ensemble’ to compare observed and modeled temperature trend... AbstractEstimates of the climate response to anthropogenic forcing contain irreducible uncertainty due to the presence of internal variability. Accurate quantification of this uncertainty is critical for both contextualizing historical trends and determining the spread of climate projections. The contribution of internal variability to uncertainty in trends can be estimated in models as the spread across an initial condition ensemble. However, internal variability simulated by a model may be inconsistent with observations due to model biases. Here, statistical resampling methods are applied to observations in order to quantify uncertainty in historical 50-year (1966-2015) winter near-surface air temperature trends over North America related to incomplete sampling of internal variability. This estimate is compared with the simulated trend uncertainty in the NCAR CESM1 Large Ensemble (LENS). The comparison suggests that uncertainty in trends due to internal variability is largely over-estimated in LENS, which has an average amplification of variability of 32% across North America. The amplification of variability is greatest in the western United States and Alaska. The observationally-derived estimate of trend uncertainty is combined with the forced signal from LENS to produce an ‘Observational Large Ensemble’ (OLENS). The members of OLENS indicate the range of observationally-constrained, spatially-consistent temperature trends that could have been observed over the past 50 years if a different sequence of internal variability had unfolded. The smaller trend uncertainty in OLENS suggests that is easier to detect the historical climate change signal in observations than in any given member of LENS. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Climate American Meteorological Society

An ‘Observational Large Ensemble’ to compare observed and modeled temperature trend uncertainty due to internal variability

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1520-0442
D.O.I.
10.1175/JCLI-D-16-0905.1
Publisher site
See Article on Publisher Site

Abstract

AbstractEstimates of the climate response to anthropogenic forcing contain irreducible uncertainty due to the presence of internal variability. Accurate quantification of this uncertainty is critical for both contextualizing historical trends and determining the spread of climate projections. The contribution of internal variability to uncertainty in trends can be estimated in models as the spread across an initial condition ensemble. However, internal variability simulated by a model may be inconsistent with observations due to model biases. Here, statistical resampling methods are applied to observations in order to quantify uncertainty in historical 50-year (1966-2015) winter near-surface air temperature trends over North America related to incomplete sampling of internal variability. This estimate is compared with the simulated trend uncertainty in the NCAR CESM1 Large Ensemble (LENS). The comparison suggests that uncertainty in trends due to internal variability is largely over-estimated in LENS, which has an average amplification of variability of 32% across North America. The amplification of variability is greatest in the western United States and Alaska. The observationally-derived estimate of trend uncertainty is combined with the forced signal from LENS to produce an ‘Observational Large Ensemble’ (OLENS). The members of OLENS indicate the range of observationally-constrained, spatially-consistent temperature trends that could have been observed over the past 50 years if a different sequence of internal variability had unfolded. The smaller trend uncertainty in OLENS suggests that is easier to detect the historical climate change signal in observations than in any given member of LENS.

Journal

Journal of ClimateAmerican Meteorological Society

Published: Jun 20, 2017

References

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