A Spatial representation of f an Added Valu lue In Index: Europe and Ext xtreme Events James Ciarlo` , Erika Coppola, Adriano Fantini, Cosimo Solidoro, & Filippo Giorgi 1
Added Value “Nested RCM simulations are indeed useful as climate downscaling tools to the extent that they add valuable information to that produced by GCMs” Torma et al. (2015) Topography (m) Torma et al. (2015) 2
Kolmogorov- Smirnov Distance maximum distance between two cumulative distribution functions Torma et al. (2015) 3
Where is the Added Value? 4
Proposed Method for EACH and EVERY 1. Interpolate to common grid [RCM] individual grid-point! 2. Calculate difference in PDFs Relative Probability Difference 3. … 𝐸 𝑁𝑃𝐸 = σ 𝑄 𝑁 ∆𝑤 − σ 𝑄 𝑃 ∆𝑤 σ 𝑄 𝑃 ∆𝑤 5
Probability Difference 6
Probability Difference OR: Relative Probability Difference [0 - 100%] 7
Added value 1. Interpolate to common grid [RCM] 2. Calculate difference in PDFs 3. Compare the differences 𝐵𝑊 = 𝐸 𝐻𝐷𝑁 − 𝐸 𝑆𝐷𝑁 Units depend on [Relative] Probability Difference 8
Observation Data Data-set Region Resolution Reference EURO4M Alps 5 km Isotta et al. (2014) Spain02 Spain 0.11° Herrera et al. (2015) SAFRAN France 8 km Vidal et al. (2010) ENG REGR UK 0.11° Perry et al. (2009) KLIMAGRID Norway 1 km Mohr (2009) PTHBV Sweden 4 km Johansson (2002) CARPATCLIM Carpathians 0.10° Szalai et al. (2013) REGNIE Germany 1 km Rauthe et al. (2013) GRIPHO Italy 12 km Fantini et al. (in progress) more data- sets may be included… 9
Probabil ilit ity Dif ifference: EURO-CORDEX 12km 10
Added Valu lue: EURO-CORDEX 12km 11
Extreme q95-100 fraction q95 12
full PDF q95-100 fraction 13
q0-25 q99.9-100 fraction fraction q0-5 fraction q0-50 q95-100 fraction fraction 14
Adapting for Downscaling Signal Difference between RCM and GCM anomalies, compared with the corresponding region-average change in each model. 𝐸𝑇 ∆𝑄 = ∆𝑄 𝑆𝐷𝑁 𝑗 − ∆ ത 𝑄 𝑆𝐷𝑁 𝑗 − (∆𝑄 𝐻𝐷𝑁 𝑘 − ∆ ത 𝑄 𝐻𝐷𝑁 𝑘 ) Giorgi et al. (2016) for EACH and EVERY Historical Data: Future Data: individual grid-point! 𝐸 𝑁𝑃𝐸 = σ 𝑄 𝑁 ∆𝑤 − σ 𝑄 𝑃 ∆𝑤 σ 𝑄 𝑔 ∆𝑤 − σ 𝑄 ℎ ∆𝑤 𝐸 𝑁𝑃𝐸 = σ 𝑄 𝑃 ∆𝑤 σ 𝑄 ℎ ∆𝑤 𝐵𝑊 = 𝐸 𝐻𝐷𝑁 − 𝐸 𝑆𝐷𝑁 15
Convective Permitting (3 km) • Russell Glazer, Emanuela Pichelli, Paolo Stocchi, Jose Abraham Torres Alavez Carpathians Alps SE Europe 16
CORDEX Domains • Moetasim Ashfaq, Melissa Bukovsky, Sushant Das, Russell Glazer, Francesca Raffaele, Taleena Sines, Jose Abraham Torres Alavez, (Xuejie Gao?) 17
Thank You for your attention! Email: jciarlo@ictp.it 18
References • Fantini A. et al. (2018). Assessment of multiple daily precipitation statistics in ERA-Interim driven Med-CORDEX and EURO-CORDEX experiments against high resolution observations. Climate Dynamics , 51 : 877-900. • Giorgi F. et al. (2016). Enhanced summer convective rainfall at Alpine high elevations in response to climate warming. Nature Geoscience , 9 : 584-589. • Herrera S. et al. (2015). Update of the Spain02 gridded observational dataset for Euro-CORDEX evaluation: assessing the effect of the interpolation methodology. International Journal of Climatology, doi:19.1002/joc.4391 • Isotta F.A. et al. (2014). The climate of daily precipitation in the Alps: development and analysis of a high-resolution grid dataset from pan-Alpine raingauge data. International Journal of Climatology , 34 : 1657-1675. • Johansson B. (2002). Estimation of areal precipitation for hydrological modelling. PhD Thesis . Earth Sciences Centre, Goteborg University, Report nr. A76. • Kanamitsu M. & DeHaan L. (2001). The Added Value Index: A new metric to quantify the added value of regional models. Journal of Geophysical Research , 116 (D11106): 1-10. • Kanamitsu, M. & Kanamaru H. (2007). Fifty‐seven‐year California Reanalysis Downscaling at 10 km (CaRD10). Part I: System detail and validation with observations. Journal of Climate , 20 : 5553 – 5571. • Mohr M. (2009). Comparisons of versions 1.1 and 1.0 of gridded temperature and precipitation data for Norway. Technical report Met. No note 19 . • Perry M. et al. (2009). The generation of daily gridded datasets of temperature and rainfall for the UK. Met Office Climate Memorandum No. 24 . • Rauthe M. et al. (2013). A Central European Precipitation Climatology – part I: generation and validation of a high resolution gridded daily data set (HYRAS). Meteorologische Zeitschrift , 22 : 235-256. • Rummukainen M. (2016). Added value in regional climate modelling. Climatic Change , 7 : 145-159. • Szalai S. et al. (2013). Climate of the greater Carpathian region. Final Technical Report . www.carpatclim-eu.org • Torma C. et al. (2015). Added value of regional climate modelling over areas characterized by complex terrain – Precipitation over the Alps. Journal of Geophysical Research: Atmospheres , 120 : 3957-3972. • Vidal J.P. et al. (2010). A 50-year high-resolution atmospheric reanalysis over France with the Safran system. International Journal of Climatology , 30 : 1627-1644. 19
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