Comparison of climate change scenarios for Sweden based on statistical and dynamical downscaling of monthly precipitation

Hellström, Cecilia; Chen, Deliang; Achberger, Christine; Räisänen, Jouni

doi:10.3354/cr019045

Cited by 111 publications

(77 citation statements)

References 24 publications

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“…Methods based on non-linear ANNs were found to be the best at modelling the inter-annual variability but these had a strong negative bias in the estimation of extremes; circumvented by the development of a novel re-sampling method. Similar results to Murphy (2000) and Hellström et al (2001) were obtained when applying six of the methods to the HadAM3P model forced by two different emissions scenarios. The inter-method differences in the future change estimates for precipitation indices were at least as large as the differences between the emissions scenarios for a single method.…”

Section: Relative Performance Of Dynamical and Statistical Methodssupporting

confidence: 68%

“…Climate change beyond the range of the data used to condition the model was hypothesized as a possible reason for this difference. Hellström et al (2001) compared dynamical outputs from the Rossby Centre RCM (RCA1) driven by two different GCMs (HadCM2 and ECHAM4/OPYC3) with regression models based on large-scale circulation indices and including a humidity measure. All downscaling methods improved the simulation of the seasonal cycle and statistical and dynamical methods driven by ECHAM4 showed higher simulation skill.…”

Section: Relative Performance Of Dynamical and Statistical Methodsmentioning

confidence: 99%

“…In the simplest form, multiple regression models are built using grid cell values of atmospheric variables as predictors for surface temperature and precipitation (e.g. Hanssen-Bauer and Førland, 1998;Hellström et al, 2001). Other more complex techniques include using the principal components of pressure fields or geopotential heights (e.g.…”

Section: Statistical Downscalingmentioning

confidence: 99%

See 2 more Smart Citations

Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling

Fowler

Blenkinsop

Tebaldi

2007

Intl Journal of Climatology

1,925

1,491

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Abstract:There is now a large published literature on the strengths and weaknesses of downscaling methods for different climatic variables, in different regions and seasons. However, little attention is given to the choice of downscaling method when examining the impacts of climate change on hydrological systems. This review paper assesses the current downscaling literature, examining new developments in the downscaling field specifically for hydrological impacts. Sections focus on the downscaling concept; new methods; comparative methodological studies; the modelling of extremes; and the application to hydrological impacts.Consideration is then given to new developments in climate scenario construction which may offer the most potential for advancement within the 'downscaling for hydrological impacts' community, such as probabilistic modelling, pattern scaling and downscaling of multiple variables and suggests ways that they can be merged with downscaling techniques in a probabilistic climate change scenario framework to assess the uncertainties associated with future projections. Within hydrological impact studies there is still little consideration given to applied research; how the results can be best used to enable stakeholders and managers to make informed, robust decisions on adaptation and mitigation strategies in the face of many uncertainties about the future. It is suggested that there is a need for a move away from comparison studies into the provision of decision-making tools for planning and management that are robust to future uncertainties; with examination and understanding of uncertainties within the modelling system.

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Section: Relative Performance Of Dynamical and Statistical Methodssupporting

confidence: 68%

Section: Relative Performance Of Dynamical and Statistical Methodsmentioning

confidence: 99%

Section: Statistical Downscalingmentioning

confidence: 99%

See 1 more Smart Citation

Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling

Fowler

Blenkinsop

Tebaldi

2007

Intl Journal of Climatology

1,925

1,491

View full text Add to dashboard Cite

show abstract

“…This family of downscaling approaches is not typically capable of resolving the complex underlying dynamics of precipitation processes and is thus unable to produce realistic and sufficiently accurate precipitation at high spatiotemporal resolutions (Gutmann et al 2012). On the other hand, dynamical downscaling approaches are computationally more demanding than their statistical counterparts (Hellstrom et al 2001) but are able to resolve the inherent precipitation dynamics (Schmidli et al 2007). In addition, the family of dynamical downscaling methods is also able to provide hydrometeorological variables (e.g., downward radiation, surface temperature, and surface wind speed) that are physically consistent with the downscaled precipitation and required by many hydrological models.…”

Section: Introductionmentioning

confidence: 99%

Dynamical Precipitation Downscaling for Hydrologic Applications Using WRF 4D-Var Data Assimilation: Implications for GPM Era

Lin

Ebtehaj

Bras

et al. 2015

Journal of Hydrometeorology

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The objective of this study is to develop a framework for dynamically downscaling spaceborne precipitation products using the Weather Research and Forecasting (WRF) Model with four-dimensional variational data assimilation (4D-Var). Numerical experiments have been conducted to 1) understand the sensitivity of precipitation downscaling through point-scale precipitation data assimilation and 2) investigate the impact of seasonality and associated changes in precipitation-generating mechanisms on the quality of spatiotemporal downscaling of precipitation. The point-scale experiment suggests that assimilating precipitation can significantly affect the precipitation analysis, forecast, and downscaling. Because of occasional overestimation or underestimation of small-scale summertime precipitation extremes, the numerical experiments presented here demonstrate that the wintertime assimilation produces downscaled precipitation estimates that are in closer agreement with the reference National Centers for Environmental Prediction stage IV dataset than similar summertime experiments. This study concludes that the WRF 4D-Var system is able to effectively downscale a 6-h precipitation product with a spatial resolution of 20 km to hourly precipitation with a spatial resolution of less than 10 km in grid spacingrelevant to finescale hydrologic applications for the era of the Global Precipitation Measurement mission.

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“…* Correspondence to: R. E. Benestad, The Norwegian Meteorological Institute, 0313 Oslo, Norway. E-mail: rasmus.benestad@met.no A variety of empirical downscaling studies were reported by the Intergovernmental Panel on Climate Change (IPCC) (2001), and several studies including empirical downscaling of precipitation have been performed for Fennoscandia during the later years (Busuioc et al, 2001;Hellström et al, 2001;Benestad, 2002;Hanssen-Bauer et al, 2003;Hellström, 2003;Linderson et al, 2004;;Chen et al, 2005;Imbert and Benestad, 2005). Comparisons of results from different downscaling techniques HanssenBauer et al, 2003), different choices of domain size (Benestad, 2001) and different choices of predictors (Benestad, 2004b;Linderson et al, 2004) indicate that the results may be sensitive to choices made in downscaling.…”

Section: Introductionmentioning

confidence: 99%

An evaluation of statistical models for downscaling precipitation and their ability to capture long‐term trends

Benestad

Hanssen-Bauer

Førland

2006

Intl Journal of Climatology

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Abstract:Large-scale changes in the sea-level pressure do not necessary reflect changes in the atmospheric moisture budget, and hence may not give a good representation of changes in precipitation as a result of a global warming. Statistical models that use both sea-level pressure and large-scale precipitation as predictors are evaluated for a number of locations in Fennoscandia. The statistical models in most cases were capable of capturing 60-80% of the year-to-year seasonal variations in precipitation, and a correlation analysis over independent data indicated predictive correlation scores in the range 0.2-0.5. A comparison between statistical models based on large-scale precipitation, sea-level pressure, and a mixture of these, indicated similar skills in terms of variance and predictive skill of inter-annual variations. Analyses of their ability to capture recent precipitation trends reveal potential problems regarding reconstructing long-term changes in the past. One explanation for the statistical models not giving similar past trend values as given by the station observations may be partly because the precipitation trends during the most recent 50 years are not well defined since the interval is not sufficiently long. This is supported by the fact that trend analysis for station observations based on two different data products, and different trend analysis strategies, do not correspond well with each other. An analysis for possible non-stationarities between large and local spatial scales does not indicate any significant presence of non-stationarities.

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Comparison of climate change scenarios for Sweden based on statistical and dynamical downscaling of monthly precipitation

Cited by 111 publications

References 24 publications

Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling

Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling

Dynamical Precipitation Downscaling for Hydrologic Applications Using WRF 4D-Var Data Assimilation: Implications for GPM Era

An evaluation of statistical models for downscaling precipitation and their ability to capture long‐term trends

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