Reassessing Statistical Downscaling Techniques for Their Robust Application under Climate Change Conditions

Gutiérrez, José Manuel; San-Martín, Daniel; Brands, Swen; Manzanas, Rodrigo; Herrera, Sixto

doi:10.1175/jcli-d-11-00687.1

Cited by 170 publications

(213 citation statements)

References 49 publications

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“…For this study, we chose to select the predictors out of the nearest grid point of the atmospheric reanalysis data set, which is a common approach in downscaling studies (e.g. Gutiérrez et al, 2013;Hofer et al, 2012Hofer et al, , 2015. It prevents dubious correlations with remote indices and ensures that the local glacier features are indeed related to the local atmospheric state (from the coarse data set perspective).…”

Section: Atmospheric Predictorsmentioning

confidence: 99%

ENSO influence on surface energy and mass balance at Shallap Glacier, Cordillera Blanca, Peru

et al. 2015

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Abstract. The El Niño/Southern Oscillation (ENSO) is a major driver of climate variability in the tropical Andes, where recent Niño and Niña events left an observable footprint on glacier mass balance. The nature and strength of the relationship between ENSO and glacier mass balance, however, varies between regions and time periods, leaving several unanswered questions about its exact mechanisms. The starting point of this study is a 4-year long time series of distributed surface energy and mass balance (SEB/SMB) calculated using a process-based model driven by observations at Shallap Glacier (Cordillera Blanca, Peru). These data are used to calibrate a regression-based downscaling model that links the local SEB/SMB fluxes to atmospheric reanalysis variables on a monthly basis, allowing an unprecedented quantification of the ENSO influence on the SEB/SMB at climatological time scales (1980-2013. We find a stronger and steadier anti-correlation between Pacific sea-surface temperature (SST) and glacier mass balance than previously reported. This relationship is most pronounced during the wet season (December-May) and at low altitudes where Niño (Niña) events are accompanied with a snowfall deficit (excess) and a higher (lower) radiation energy input. We detect a weaker but significant ENSO anticorrelation with total precipitation (Niño dry signal) and positive correlation with the sensible heat flux, but find no ENSO influence on sublimation. Sensitivity analyses comparing several downscaling methods and reanalysis data sets resulted in stable mass balance correlations with Pacific SST but also revealed large uncertainties in computing the mass balance trend of the last decades. The newly introduced open-source downscaling tool can be applied easily to other glaciers in the tropics, opening new research possibilities on even longer time scales.

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Section: Atmospheric Predictorsmentioning

confidence: 99%

ENSO influence on surface energy and mass balance at Shallap Glacier, Cordillera Blanca, Peru

et al. 2015

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“…In most downscaling studies, no optimisation of the predictor domains has been performed, and only a few of them have tested even a handful of different domains (Timbal and McAvaney, 2001;Timbal et al, 2003;Gutiérrez et al, 2013). Timbal and McAvaney (2001) especially found that choosing an informative predictor domain is an important issue for the analogue selection.…”

Section: Predictor Domains: Optimisationmentioning

confidence: 99%

“…This algorithm is potentially applicable in other contexts. This study has already shown that it could be applied at different target locations, but one may also think of considering another predictand, such as minimum or maximum temperature (Gutiérrez et al, 2013), or optimising the spatial domain of other predictors. As a result, this algorithm could be perfectly applied to another type of statistical downscaling method.…”

Section: An Algorithm To Provide Near-optimum Predictor Domainsmentioning

confidence: 99%

Optimising predictor domains for spatially coherent precipitation downscaling

Radanovics¹,

Vidal²,

Sauquet³

et al. 2013

Hydrol. Earth Syst. Sci.

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Abstract. Statistical downscaling is widely used to overcome the scale gap between predictors from numerical weather prediction models or global circulation models and predictands like local precipitation, required for example for medium-term operational forecasts or climate change impact studies. The predictors are considered over a given spatial domain which is rarely optimised with respect to the target predictand location. In this study, an extended version of the growing rectangular domain algorithm is proposed to provide an ensemble of near-optimum predictor domains for a statistical downscaling method. This algorithm is applied to find five-member ensembles of near-optimum geopotential predictor domains for an analogue downscaling method for 608 individual target zones covering France. Results first show that very similar downscaling performances based on the continuous ranked probability score (CRPS) can be achieved by different predictor domains for any specific target zone, demonstrating the need for considering alternative domains in this context of high equifinality. A second result is the large diversity of optimised predictor domains over the country that questions the commonly made hypothesis of a common predictor domain for large areas. The domain centres are mainly distributed following the geographical location of the target location, but there are apparent differences between the windward and the lee side of mountain ridges. Moreover, domains for target zones located in southeastern France are centred more east and south than the ones for target locations on the same longitude. The size of the optimised domains tends to be larger in the southeastern part of the country, while domains with a very small meridional extent can be found in an east-west band around 47 • N. Sensitivity experiments finally show that results are rather insensitive to the starting point of the optimisation algorithm except for zones located in the transition area north of this east-west band. Results also appear generally robust with respect to the archive length considered for the analogue method, except for zones with high interannual variability like in the Cévennes area. This study paves the way for defining regions with homogeneous geopotential predictor domains for precipitation downscaling over France, and therefore de facto ensuring the spatial coherence required for hydrological applications.

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“…Hewitson and Crane 2006;Jeong et al 2012) and the associated issues. Reviews of the methods outlined in Table 1 may be found in, for example, Bürger et al 2012;Gutiérrez et al 2013;Christensen et al 2007).…”

Section: Downscaling Approachesmentioning

confidence: 99%

Interrogating empirical-statistical downscaling

et al. 2013

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The delivery of downscaled climate information is increasingly seen as a vehicle of climate services, a driver for impacts studies and adaptation decisions, and for informing policy development. Empirical-statistical downscaling (ESD) is widely used; however, the accompanying responsibility is significant, and predicated on effective understanding of the limitations and capabilities of ESD methods. There remain substantial contradictions, uncertainties, and sensitivity to assumptions between the different methods commonly used. Yet providing decision-relevant downscaled climate projections to help support national and local adaptation is core to the growing global momentum seeking to operationalize what is, in effect, still foundational research. We argue that any downscaled climate information must address the criteria of being plausible, defensible and actionable. Climate scientists cannot absolve themselves of their ethical responsibility when informing adaptation and must, therefore, be diligent in ensuring any information provided adequately addresses these three criteria. Frameworks for supporting such assessment are not well developed. We interrogate the conceptual foundations of statistical downscaling methodologies and their assumptions, and articulate a framework for evaluating and integrating downscaling output into the wider landscape of climate information. For ESD there are key criteria that need to be satisfied to underpin the credibility of the derived product. Assessing these criteria requires the use of appropriate metrics to test the comprehensive treatment of local climate response to large-scale forcing, and to compare across methods. We illustrate the potential consequences of methodological choices on the interpretation of downscaling results and explore the purposes, benefits and limitations of using statistical downscaling.

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Reassessing Statistical Downscaling Techniques for Their Robust Application under Climate Change Conditions

Cited by 170 publications

References 49 publications

ENSO influence on surface energy and mass balance at Shallap Glacier, Cordillera Blanca, Peru

ENSO influence on surface energy and mass balance at Shallap Glacier, Cordillera Blanca, Peru

Optimising predictor domains for spatially coherent precipitation downscaling

Interrogating empirical-statistical downscaling

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