Downscaling of Global Climate Change Estimates to Regional Scales: An Application to Iberian Rainfall in Wintertime

Storch, Hans von; Zorita, Eduardo; Cubasch, Ulrich

doi:10.1175/1520-0442(1993)006<1161:dogcce>2.0.co;2

Cited by 582 publications

(412 citation statements)

References 14 publications

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“…Improving their resolution may by hindered due to limitations in computing power and in the understanding of all the processes involved. Furthermore, it is estimated that the skillful scale of GCMs is about 8 times the grid scale; therefore GCM output should not be applied to smaller scales (von Storch et al 1993, Johannesson et al 1995. Present GCM climate projections must be converted or downscaled to higher regional or local resolutions.…”

Section: Resale or Republication Not Permitted Without Written Consenmentioning

confidence: 99%

“…In spite of their resolution being about 10 times higher than that of GCMs, in some experiments; they have failed to reproduce observed precipitation statistics at the spatial and temporal scales required for regional impact assessment (Bates et al 1998, Giorgi 1991. Furthermore, they are computationally expensive, and most impact assessment research groups do not have access to a LAM or its output.Another strategy to overcome the gap between large and local scales is the use of empirical downscaling techniques (von Storch et al 1993). It requires empirical linkages between large-scale circulation patterns or variables that are well resolved and projected by GCMs, and the local climate variables of interest.…”

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confidence: 99%

“…These relationships can be used to obtain small-scale realisations from large-scale climate-change scenarios provided by GCMs. We use canonical correlation analysis (CCA), which attempts to find optimally coupled anomaly patterns on both spatial scales (von Storch & Zwiers 1999). CCA has found wide application to precipitation modelling: von Storch et al (1993) applied CCA to winter Iberian rainfall; Gyalistras et al (1994) applied it not only to precipitation at some stations in Switzerland, but also to several other local meteorological elements; and Busuioc & von Storch (1996) applied it to monthly Romanian precipitation amounts.…”

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confidence: 99%

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Downscaling of GCM scenarios to assess precipitation changes in the little rainy season (March-June) in Cameroon

Penlap¹,

Matulla²,

Storch³

et al. 2004

Clim. Res.

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Large-scale climate forcings on local precipitation in Cameroon are analysed during the little rainy season (March-June). Variables found to have strong influence are used to downscale GCM projected rainfall for 2010-2049. In particular, 2 IPCC IS92a scenarios, simulated by the ECHAM4/OPYC3 climate model, are investigated. First, monthly precipitation data from at 33 meteorological stations are grouped into homogeneous rainfall regions using self-organising feature maps (SOFMs). SOFMs identified 3 groups of stations with related time-series variability. Then, an empirical orthogonal function procedure, followed by canonical correlation analysis (CCA), is used to derive statistical relationships between the homogeneous regions and large-scale variables from the NCEP/NCAR Reanalysis Project. A CCA model is established for every region. Numerous fields at different pressure levels are used as macro-scale predictors. All possible combinations of 2 predictors are systematically tested in 3 validation experiments. Those combinations that perform well in the experiments are used to derive local-scale precipitation scenarios from the general circulation model (GCM) climate projection experiments. Different combinations of large-scale variables enter the model depending on region. A composite analysis suggests that precipitation is related to an advective (convective) phenomenon in the northern (southern) part of the study domain. Moreover, precipitation changes based on 2 IS92a emission scenarios as simulated by ECHAM4/OPYC3 are calculated. The trace-gas-only and the trace-gas-plus-sulphate integrations induce changes ranging locally from + 44 to -10% and from + 36 to -9% respectively, relative to the control period. KEY WORDS: Cameroon · Precipitation · Regionalisation · Downscaling · Climate change Resale or republication not permitted without written consent of the publisherClim Res 26: [85][86][87][88][89][90][91][92][93][94][95][96] 2004 the tropics, they cannot be used for projecting localscale changes (Grotch & MacCracken 1991). This is particularly true for surface climate variables needed for impact studies (Kamga 2000). Improving their resolution may by hindered due to limitations in computing power and in the understanding of all the processes involved. Furthermore, it is estimated that the skillful scale of GCMs is about 8 times the grid scale; therefore GCM output should not be applied to smaller scales (von Storch et al. 1993, Johannesson et al. 1995. Present GCM climate projections must be converted or downscaled to higher regional or local resolutions. The leading techniques used are dynamical and statistical (empirical) downscaling (Hewitson & Crane 1996).In dynamical downscaling, a limited-area model (LAM) of the area of interest is nested in a GCM and evolves with it while using its output as boundary conditions (Giorgi 1990, Giorgi et al. 1994. Because of their process-based approach, LAMs are expected to generate reliable regional results, since topography, land-use patterns and other...

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Section: Resale or Republication Not Permitted Without Written Consenmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Downscaling of GCM scenarios to assess precipitation changes in the little rainy season (March-June) in Cameroon

Penlap¹,

Matulla²,

Storch³

et al. 2004

Clim. Res.

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“…In contrast, in the case of time series prediction, auto-correlation is an essential part of the contained information and must be preserved. The statistical downscaling technique, a multivariate method used here, has been developed by von Storch et al (1993). The idea is to correlate the potential climate predictor variables with the regional observations and to look for high correlations.…”

Section: Statistical Downscalingmentioning

confidence: 99%

“…In contrast to von Storch et al (1993) who split the records into a fit and a validation period, we use a cross-validation technique (Michaelsen 1987) and Monte Carlo simulations. In the cross-validation, if n time steps of data are available, n models are fitted by using n -1 different time steps each.…”

Section: Validationmentioning

confidence: 99%

A Multivariate Baltic Sea Environmental Index

Dippner

Kornilovs

Junker

2012

AMBIO

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Since 2001/2002, the correlation between North Atlantic Oscillation index and biological variables in the North Sea and Baltic Sea fails, which might be addressed to a global climate regime shift. To understand inter-annual and inter-decadal variability in environmental variables, a new multivariate index for the Baltic Sea is developed and presented here. The multivariate Baltic Sea Environmental (BSE) index is defined as the 1st principal component score of four z-transformed time series: the Arctic Oscillation index, the salinity between 120 and 200 m in the Gotland Sea, the integrated river runoff of all rivers draining into the Baltic Sea, and the relative vorticity of geostrophic wind over the Baltic Sea area. A statistical downscaling technique has been applied to project different climate indices to the sea surface temperature in the Gotland, to the Landsort gauge, and the sea ice extent. The new BSE index shows a better performance than all other climate indices and is equivalent to the Chen index for physical properties. An application of the new index to zooplankton time series from the central Baltic Sea (Latvian EEZ) shows an excellent skill in potential predictability of environmental time series.

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Downscaling large-scale circulation to local winter rainfall in north-eastern Mexico

Cavazos

1997

Int. J. Climatol.

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The large‐scale atmospheric controls on winter rainfall variability for north‒eastern Mexico and south‒eastern Texas are diagnosed based on 8 years (1985–1993) of daily data from the Goddard Space Flight Center four‒dimensional data assimilation scheme. Downscaling techniques based on artificial neural nets are used to derive transfer functions from the large‒scale circulation to local precipitation. Daily rainfall amounts are predicted from synoptic sea‒level pressure, 500 hPa heights, and the 1000–500 hPa thickness, and summed over the cool season (NDJF). The monthly rainfall totals are also predicted independently from other indices of the large‒scale circulation, such as 1000–500 hPa thickness, the Pacific/North American (PNA) pattern, and a standardized Southern Oscillation Index (SOI) derived from the Tahiti minus Darwin sea‒level pressure difference. Time‒lagged component scores from a rotated principal component analysis of sea‒level pressure, 500 hPa heights, and 1000–500 hPa thickness serve as input to a neural net that produces time‒series of daily rainfall amounts for 20 grid‒points in the study area. The correlation between the observed and predicted rainfall is greater than 0·7 in the coastal plains of the Gulf of Mexico and less than 0·7 over the Sierra Madre and the Gulf, suggesting an increase in the importance of local rainfall processes in the last two regions. The analysis shows a systematic relationship between the performance of the net and physiography, which is confirmed by the consistency of the patterns of spatial correlation, mean absolute error, and root‒mean‒square error at different time‒scales. The net captures the phase and amplitude of most of the rainfall events, reflecting the influence of the large‒scale circulation. However, interannual fluctuations in rainfall associated with persistent El Niño conditions are partly bypassed by the net, suggesting that more information is needed to predict extreme events. The PNA pattern does not seem to be associated with local rainfall in north‒eastern Mexico and south‒eastern Texas during the period analysed. © 1997 The Royal Meteorological Society.

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Downscaling of Global Climate Change Estimates to Regional Scales: An Application to Iberian Rainfall in Wintertime

Cited by 582 publications

References 14 publications

Downscaling of GCM scenarios to assess precipitation changes in the little rainy season (March-June) in Cameroon

Downscaling of GCM scenarios to assess precipitation changes in the little rainy season (March-June) in Cameroon

A Multivariate Baltic Sea Environmental Index

Downscaling large-scale circulation to local winter rainfall in north-eastern Mexico

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