The Potential to Narrow Uncertainty in Regional Climate Predictions

By Ed Hawkins, R. Sutton. 2009

2,559 citations recorded by the source corpus.

Subfields: Atmospheric Science, Economics and Econometrics, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/964105343f08d1ca48f7b76bd7cb0cdcd24a4c8b

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Citations in the corpus

An update of IPCC climate reference regions for subcontinental analysis of climate model data: definition and aggregated datasets (2020, methodology): “This dataset can be directly used by researchers and stakeholders for a variety of purposes, including assessing the internal variability, model and scenario uncertainty components (Hawkins and Sutton, 2009) or assisting in the comparison and selection of representative sub-ensembles for impact stud”
Two Limits of Initial-Value Decadal Predictability in a CGCM (2010, background): “When considering the global mean temperature, Hawkins and Sutton (2009) suggest that the forced response gives more reliable information than do the initial conditions during the first predicted decade.”
Guidance for representing uncertainty on global temperature change maps (2016, background): “Although climate scientists generally consider model output in terms of its uncertainty (e.g. Hawkins and Sutton, 2009), we chose to use certainty rather than uncertainty to avoid double negatives (e.g. ‘low uncertainty’) that could confuse survey participants; this choice echoes the IPCC’s use of…”
Partitioning climate projection uncertainty with multiple large ensembles and CMIP5/6 (2020, background): “Uncertainties in climate change projections can be attributed to different sources – in context of CMIP to three specific ones (Hawkins and Sutton, 2009), described as follows.”
Robust adaptation to climate change (2010, background): “…(through international comparison studies involving large ensembles of climate models and downscaling methods such as PRUDENCE,1 ENSEMBLES,2 NARCCAP3), the prospect of reducing uncertainty depends on further progress being made in the underpinning climate science (Hawkins and Sutton, 2009).”
A climate model projection weighting scheme accounting for performance and interdependence (2017, background): “Our ability to predict climate change is riddled with uncertainties: different scenarios of societal and technological development, imperfect models, and natural variability are the main reasons that future projections cannot be certain and deterministic [Knutti, 2008; Hawkins and Sutton, 2009; Knut”
Reduced global warming from CMIP6 projections when weighting models by performance and independence (2020, background): “Model uncertainty (sometimes also referred to as structural uncertainty or response uncertainty) is used here to describe the differing responses of climate models to a given forcing due to their structural differences following the definition by Hawkins and Sutton (2009).”
High-resolution large-scale onshore wind energy assessments: A review of potential definitions, methodologies and future research needs (2021, neutral): “[139]).”
Decadal prediction skill in a multi-model ensemble (2012, methodology): “In model studies the effect of AMO fluctuations on global mean temperature is fairly small; van Oldenborgh et al (2009b) find a maximum correlation of 0.25 in the MPI ECHAM5/OM-1 model and in the CCSM 3 control run the correlations are even lower (Hofer et al 2011) 1 .”
Projecting future crop productivity for global economic modeling (2014, background): “To account for the degree of 33 uncertainty in climate projections (Randall, et al., 2007, Hawkins and Sutton, 2009, Hawkins and 34 Sutton, 2011) and in yield projections (Rötter, et al., 2011), we supply yield projections from 2 35 global crop growth models, DSSAT (Jones, et al., 2003, Hoogenboom,…”
Decadal climate prediction: an update from the trenches (2014, methodology): “Hawkins and Sutton (2009a) schematically suggested that as a prediction progresses, initial condition predictability would become relatively unimportant as the spread from past and projected changes in anthropogenic external forcing increased, with some crossover point reached at around a decade.”
The end of model democracy? (2010, background): “Climate projections are inherently uncertain, and part of the uncertainty related to variability is irreducible (Hawking and Sutton 2009).”
Poorest countries experience earlier anthropogenic emergence of daily temperature extremes (2016, background): “…concept has been considered in both observational records and climate model simulations, for a range of climate indices such as temperature (Hawkins and Sutton 2009, Diffenbaugh and Scherer 2011, Joshi et al 2011, Mahlstein et al 2011, Hawkins and Sutton 2012, Hawkins et al 2014),…”
The effects of 1.5 and 2 degrees of global warming on Africa in the CORDEX ensemble (2018, background): “Focusing on 1.5 ◦C is more challenging since the point in time when this GWL is reached is much closer than the commonly used end of the century and natural variability can strongly dominate on such near-term time periods, especially at the regional scale (e.g. Hawkins and Sutton 2009).”
A decadal prediction case study: Late twentieth-century North Atlantic Ocean heat content (2012, background): “The premise behind the experimental design is that there is potential to improve climate predictions, and in particular regional predictions, on time scales of up to a few decades by reducing the forecast uncertainty associated with intrinsic climate variability (Hawkins and Sutton 2009).”