Assessing the Skill of Precipitation and Temperature Seasonal Forecasts in Spain: Windows of Opportunity Related to ENSO Events

Frías, M. D. Rodríguez; Herrera, Sixto; Cofiño, Antonio S.; Gutiérrez, José Manuel

doi:10.1175/2009jcli2824.1

Cited by 54 publications

(55 citation statements)

References 53 publications

(60 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using linear regression in a 1-yr-out cross-validation framework, and applying the index based on daily satellite retrievals as only predictor variable, local precipitation totals in the former mentioned two regions have been hindcast with highly significant correlations of up to 0.84 and 0.71, while the corresponding results for the spatially aggregated hindcast are 0.75 and 0.58, respectively. These results outperform the skill of general circulation models (Doblas-Reyes et al 2009;Frias et al 2010) and competing empirical indices (Folland et al 2012), and in case of the Iberian Peninsula, even exceed the predictability that can be potentially achieved by the latter (Folland et al 2012).…”

Section: Discussionmentioning

confidence: 81%

Seasonal Predictability of Wintertime Precipitation in Europe Using the Snow Advance Index

2012

Self Cite

View full text Add to dashboard Cite

This study tests the applicability of Eurasian snow cover increase in October, as described by the recently published snow advance index (SAI), for forecasting December-February precipitation totals in Europe. On the basis of a classical correlation analysis, global significance was obtained and locally significant correlation coefficients of up to 0.89 and 20.78 were found for the Iberian Peninsula and southern Norway, respectively. For a more robust assessment of these results, a linear regression approach is followed to hindcast the precipitation sums in a 1-yr-out cross-validation framework, using the SAI as the only predictor variable. With this simple empirical approach, local-scale precipitation could be reproduced with a correlation of up to 0.84 and 0.71 for the Iberian Peninsula and southern Norway, respectively, while catchment aggregations on the Iberian Peninsula could be hindcast with a correlation of up to 0.73. These findings are confirmed when repeating the hindcast approach to a degraded but much longer version of the SAI. With the recommendation to monitor the robustness of these results as the sample size of the SAI increases, the authors encourage its use for the purpose of seasonal forecasting in southern Norway and the Iberian Peninsula, where general circulation models are known to perform poorly for the variable in question.

show abstract

Section: Discussionmentioning

confidence: 81%

Seasonal Predictability of Wintertime Precipitation in Europe Using the Snow Advance Index

2012

Self Cite

View full text Add to dashboard Cite

show abstract

“…This explains the different sensitivity of temperature and precipitation to teleconnection indices over the area. However, recent studies have emphasised that a small fraction of the predictability of winter precipitation over the Cantabrian coast might depend in a non-linear way on the state of the El Niño Southern Oscillation (ENSO) events (Frías et al 2010). …”

Section: Introductionmentioning

confidence: 99%

Climate change impacts on coastal and pelagic environments in the southeastern Bay of Biscay

Chust¹,

Borja²,

Caballero³

et al. 2011

Clim. Res.

View full text Add to dashboard Cite

The impacts of global climate change on the Basque coast and the pelagic systems within the southeastern Bay of Biscay are reviewed. Climate projections under greenhouse gas emission scenarios indicate that this area will experience changes in climate throughout the 21st century, including warming of surface air (especially heat wave episodes), intensification of extreme daily rainfall (10%), warming of the upper 100 m of the ocean layer (1.5 to 2.05°C), and sea level rise (SLR; 29 to 49 cm). Observations made in the bay throughout the 20th century for air temperature and mean sea level are in agreement with these projections. Trends in ocean-climatic historical observations within the area, including sea temperature, precipitation, upwelling/downwelling, turbulence and wave climate, are also reviewed. The main impacts on the coast are expected to be from SLR, especially in low-lying areas (mostly urbanised) within estuaries. Sandy beaches are also expected to undergo significant mean shoreline retreats of between 25 and 40% of their width. As the sea level rises, the natural migration of saltmarshes and intertidal seagrasses landward will be constrained, in most cases, by existing anthropogenic fixed boundaries. Empirical relationships between the distribution and dynamics of the long-term biological measures (plankton, primary production, benthos, and fisheries) on the one hand, and ocean-climatic variability on the other, indicate that pelagic and coastal water ecosystems will be affected by ocean warming, increased stratification, shifts in anomaly patterns and streamflow regimes. The largest uncertainties are associated with the lack of downscaled projections within the bay on ocean circulation, ocean-meteorological indices, wave climate and ocean acidification.

show abstract

“…In the former case the downscaling method must be generic -applicable to a number of different variables-and must preserve the physical consistency among the variables; this poses a serious limitation on the number of feasible downscaling methods (Maraun et al 2010). The analogs method (Zorita and von Storch 1999;Frías et al 2010) is a popular and simple generic downscaling technique which meets this requirement. Other statistical downscaling methods attempt to model the relationships between relevant variables by regressing other variables on the generated values of key variables (e.g.…”

Section: Statistical Downscaling Methodsmentioning

confidence: 99%

Statistical downscaling of climate impact indices: testing the direct approach

et al. 2014

Self Cite

View full text Add to dashboard Cite

Climate Impact Indices (CIIs) are being increasingly used in different socioeconomic sectors to transfer information about climate change impacts to stakeholders. Typically, CIIs comprise into a single index several weather variables -such as temperature, wind speed, precipitation and humidity-which are relevant for a particular problem of interest. Moreover, most of the CIIs require daily (or monthly) physical coherence among these variables for their proper calculation. This constraints the number of statistical downscaling techniques suitable for a component-wise approach to this problem. We test the suitability of the alternative "direct" downscaling approach in which the downscaling method is applied directly to the CII, thus circumventing the multi-variable problem and allowing the use of a wider range of downscaling methods. For illustrative purposes, we consider two popular CIIs -the Fire Weather Index (FWI) and the Physiological Equivalent Temperature (PET), used in the wildfire and tourism sectors, respectively-and compare the performance of the two approaches using the analog method, a simple and popular method providing inter-variable dependence. The results obtained with 'perfect' reanalysis predictors are comparable for both approaches, although smaller accuracy is obtained in general with the direct approach. Moreover, similar climate change 'deltas' are obtained with both approaches when applied to an illustrative future global projection using the A. Casanueva ( ) · Climatic Change (2014) 127:547-560 ECHAM5 model. Overall, there is a trade-off between performance and simplicity which needs to be balanced for each particular application.

show abstract

Assessing the Skill of Precipitation and Temperature Seasonal Forecasts in Spain: Windows of Opportunity Related to ENSO Events

Cited by 54 publications

References 53 publications

Seasonal Predictability of Wintertime Precipitation in Europe Using the Snow Advance Index

Seasonal Predictability of Wintertime Precipitation in Europe Using the Snow Advance Index

Climate change impacts on coastal and pelagic environments in the southeastern Bay of Biscay

Statistical downscaling of climate impact indices: testing the direct approach

Contact Info

Product

Resources

About