A comparison study of extreme precipitation from six different regional climate models via spatial hierarchical modeling

Schliep, Erin M.; Cooley, Daniel; Sain, Stephan R.; Hoeting, Jennifer A.

doi:10.1007/s10687-009-0098-2

Cited by 61 publications

(54 citation statements)

References 21 publications

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“…MS50F, Berkeley, CA 94720, USA e-mail: mfwehner@lbl.gov Schliep et al 2010;Fowler et al 2010). A typical methodology to arrive at a GEV description of the tails is to first form a distribution of the ''block maxima'' extracted from all of the values.…”

Section: Introductionmentioning

confidence: 99%

Very extreme seasonal precipitation in the NARCCAP ensemble: model performance and projections

2012

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Seasonal extreme daily precipitation is analyzed in the ensemble of NARCAPP regional climate models. Significant variation in these models' abilities to reproduce observed precipitation extremes over the contiguous United States is found. Model performance metrics are introduced to characterize overall biases, seasonality, spatial extent and the shape of the precipitation distribution. Comparison of the models to gridded observations that include an elevation correction is found to be better than to gridded observations without this correction. A complicated model weighting scheme based on model performance in simulating observations is found to cause significant improvements in ensemble mean skill only if some of the models are poorly performing outliers. The effect of lateral boundary conditions are explored by comparing the integrations driven by reanalysis to those driven by global climate models. Projected mid-century future changes in seasonal precipitation means and extremes are presented and discussions of the sources of uncertainty and the mechanisms causing these changes are presented.

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Section: Introductionmentioning

confidence: 99%

Very extreme seasonal precipitation in the NARCCAP ensemble: model performance and projections

2012

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“…A large number of studies have focused on modelling precipitation extremes in relation to climate model output (e.g. Benestad, 2010;Burton et al, 2010;Cooley and Sain, 2010;Nguyen et al, 2010;Schliep et al, 2010;De Michele et al, 2011;Olsson et al, 2012;Gregersen et al, 2013). These studies used different indices to characterize the tail of the distribution of precipitation data.…”

Section: Precipitation Observationsmentioning

confidence: 99%

“…For example, in the STARDEX project (Haylock and Goodess, 2004) a set of six core precipitationrelated indices was defined, and the "Expert Team on Climate Change Detection Indices" (ETCCDI) (Peterson, 2005) defined a set of eleven precipitation indices, including those from STARDEX. In the literature, some of the more commonly used indices are: percentiles, often the 95th or 99th (Beldring et al, 2008;Hundecha and Bárdossy, 2008;Benestad, 2010;Cooley and Sain, 2010;Iizumi et al, 2011); the maximum precipitation in one day or a specific number of consecutive days (Segond et al, 2006;Beniston et al, 2007;Sang and Gelfand, 2009a, b;Burton et al, 2010;Schliep et al, 2010); precipitation amounts for T -year return periods (Frei et al, 2006;Fowler and Ekström, 2009;Kysley and Beranova, 2009); and the Intensity-Duration(-Area)-Frequency (ID(A)F) relationship (De Michele et al, 2001Nguyen et al, 2010;Olsson et al, 2012). …”

Section: Precipitation Observationsmentioning

confidence: 99%

On the importance of observational data properties when assessing regional climate model performance of extreme precipitation

Pinya

Sørup

Christensen

et al. 2013

Hydrol. Earth Syst. Sci.

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Abstract. In recent years, there has been an increase in the number of climate studies addressing changes in extreme precipitation. A common step in these studies involves the assessment of the climate model performance. This is often measured by comparing climate model output with observational data. In the majority of such studies the characteristics and uncertainties of the observational data are neglected.This study addresses the influence of using different observational data sets to assess the climate model performance. Four different data sets covering Denmark using different gauge systems and comprising both networks of point measurements and gridded data sets are considered. Additionally, the influence of using different performance indices and metrics is addressed. A set of indices ranging from mean to extreme precipitation properties is calculated for all the data sets. For each of the observational data sets, the regional climate models (RCMs) are ranked according to their performance using two different metrics. These are based on the error in representing the indices and the spatial pattern.In comparison to the mean, extreme precipitation indices are highly dependent on the spatial resolution of the observations. The spatial pattern also shows differences between the observational data sets. These differences have a clear impact on the ranking of the climate models, which is highly dependent on the observational data set, the index and the metric used. The results highlight the need to be aware of the properties of observational data chosen in order to avoid overconfident and misleading conclusions with respect to climate model performance.

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“…Extreme event theory (EVT) berkembang dengan pesat sejak diperkenalkan oleh Fisher dan Tippet [8] dan diformalkan oleh Gnedenko [9]. Saat ini EVT telah mencakup multivariate (Tawn [18]; Coles dan Tawn [1,2] [16]). Aplikasinya merambah berbagai macam disiplin seperti hidrologi, biologi, ekologi, klimatologi, telekomunikasi dan bahkan olah raga.…”

Section: Pendahuluanunclassified

Mengenal Data Ekstrim dan Distribusinya

Bisono¹

2012

jti

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People adopt many definitions for extremes. One such definition is that extremes are simply the upper tail data. In this paper we briefly review the types of upper tail data, the limiting distribution of extreme values, the analysis and the model diagnostics. We also provide a simple example of fitting generalized extreme values (GEV) for climate extremes.Keywords: Extreme value, generalized extreme value distribution, generalized pareto distribution, return level. PendahuluanEkstrim dapat diartikan sebagai 'besar', 'banyak', 'sering' atau sebaliknya 'amat jarang'. Kejadian yang dikategorikan sebagai ekstrim (extreme event) adalah kejadian yang jarang terjadi namun memberikan dampak yang besar dan tidak terduga datingnya (Embrechts et al. [7]). Bencana alam, misalnya gempa bumi, badai, dan banjir besar, adalah contoh kejadian ekstrim di bidang ekologi lingkungan yang bisa membawa dampak besar sosial dan ekonomi. Di negara-negara dimana asuransi sudah menjadi bagian yang tak terpisahkan, kejadian ekstrim seperti ini dapat mengakibatkan klaim asuransi yang sangat besar. Nilainya bisa mencapai 26% dari seluruh total klaim dalam setahun. Sehingga mau tak mau industri asuransi memberikan perhatian besar atas probabilitas munculnya kejadian ekstrim. Pengetahuan tentang kejadian ekstrim di bidang iklim sangat diperlukan oleh hidrologis, perencana tata kota atau desainer bangunan. Misal, saat akan membangun waduk yang dapat mengantisipasi perubahan iklim sampai 50 bahkan 100 tahun ke depan, diperlukan prediksi terjadinya kejadian ekstrim; seberapa besar atau seberapa sering banjir akan melanda daerah tersebut.Dalam Statistika, data ekstrim secara sederhana didefinisikan sebagai data yang terletak di ujung distribusi, bisa di ujung kanan atau kiri. Analisa data ekstrim pada dasarnya mengamati kelakuan data di ujung ini. Tujuan dari tulisan ini adalah memberikan gambaran selengkap nya tentang nilai ekstrim, distribusi dan pemodelannya, serta diagnosa model. Contoh sederhana pemodelannya diberikan di bagian akhir tulisan. Metode PenelitianBeberapa jenis nilai ekstrim, distribusinya, analisa dan diagnose model akan dibahas dalam bagian ini. Nilai EkstrimBeberapa jenis nilai ekstrim diantaranya adalah blok maksima (block maxima), nilai di atas ambang (values over threshold), dan r-nilai terbesar dari data terurut (r-largest ordered data). Blok maksima adalah nilai maksimum dari observasi dalam jangka waktu tertentu, misal dari data produk cacat perhari hanya diambil nilai maksimum data selama satu bulan. Dengan demikian, hanya 12 observasi dalam setahun yang dipergunakan, 353 (=365-12) observasi lainnya terbuang sia-sia yang merupakan pemborosan data yang cukup besar. Ini adalah salah satu kekurangan dari menggunakan blok maksima. Mungkin saja dua atau lebih data terbesar dalam setahun terjadi di bulan yang sama, padahal dalam blok maksima hanya diambil satu nilai terbesar, dengan kata lain, ada resiko kehilangan informasi penting. Oleh karena itu sebagian orang menggunakan r-nilai terbesar. Tipe data ekstrim yang ...

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A comparison study of extreme precipitation from six different regional climate models via spatial hierarchical modeling

Cited by 61 publications

References 21 publications

Very extreme seasonal precipitation in the NARCCAP ensemble: model performance and projections

Very extreme seasonal precipitation in the NARCCAP ensemble: model performance and projections

On the importance of observational data properties when assessing regional climate model performance of extreme precipitation

Mengenal Data Ekstrim dan Distribusinya

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