Estimating extremes in climate change simulations using the peaks-over-threshold method with a non-stationary threshold

Kyselý, Jan; Picek, Jan; Beranová, Romana

doi:10.1016/j.gloplacha.2010.03.006

Cited by 83 publications

(61 citation statements)

References 44 publications

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“…In addition, two subsequent POT events are considered independent when the minimum separation time between the events is four days, in accordance with other studies (e.g. Brabson and Palutikof, 2002;Kyselý et al, 2010;Van de Vyver, 2011), which corresponds with the duration of mesoscale heat waves. After declustering, we calibrate the univariate GPD Equation (2) to the selected extremes of the observations and the model's predictions ALD40, ALD10, and ALR04.…”

Section: Calibration Of the Classical Pot-modelsmentioning

confidence: 88%

“…Here we use the definition of heat waves proposed by Huth et al (2000) and employed in recent European studies (Hutter et al, 2007;Kyselý, 2010). Two thresholds, T 1 and T 2 are applied: a heat wave is defined as a continuous period of at least 5 days during which (1) T MAX is higher than T 1 in at least 3 days, (2) mean T MAX over the whole period is higher than T 1 , and (3) T MAX does not drop below T 2 .…”

Section: Heat Wave Eventmentioning

confidence: 99%

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New cloud and microphysics parameterisation for use in high‐resolution dynamical downscaling: application for summer extreme temperature over Belgium

Hamdi¹,

Vyver²,

Termonia³

2011

Intl Journal of Climatology

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ABSTRACT:We explore the use of high-resolution dynamical downscaling as a means to simulate extreme values of summer maximum surface air temperature over Belgium (T MAX ). We use the limited area version of the ARPEGE-IFS model, ALADIN. Our approach involves a sequence of daily integrations driven by perfect boundary conditions at the lateral boundaries provided by the ERA40 reanalysis. In this study, three different recent past simulations are evaluated against different station datasets: (1) 40 km spatial resolution (ALD40), (2) 10 km spatial resolution (ALD10), and (3) 4 km spatial resolution (ALR04) using a new parameterisation of deep convection, and microphysics allowing the use of ALADIN at resolutions ranging from a few tens of kilometers down to less than 4 km. The validation of ALD40 reveals a positive summer bias (2.2°C), even though the considerable spatial resolution enhancement by a factor of 4, ALD10 reduces slightly the warm biases (1.7°C). This warm bias on T MAX is strongly correlated with cloud cover representation. Result shows an overestimate of clear-sky occurrences by ALD10 and a developing solar radiation overestimate through the diurnal cycle, with 116 W m −2 maximum overestimate at noon. ALR04 reduces considerably the warm biases (−0.2°C) which suggests of its ability to simulate weakly forced convective cloud in the summer over Belgium. In addition, the Generalized Pareto Distribution (GPD) of the extreme high temperatures produced by the different simulations has been compared to observations of the same period. ALD40 and ALD10 gave a GPD distribution that did not replicate the observed distribution well and, thus, overestimated the extremes. This study shows that the consistent treatment of deep convection and cloud-radiation interaction when increasing the horizontal resolution is very important when studying extremely high temperatures events.

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Section: Calibration Of the Classical Pot-modelsmentioning

confidence: 88%

Section: Heat Wave Eventmentioning

confidence: 99%

New cloud and microphysics parameterisation for use in high‐resolution dynamical downscaling: application for summer extreme temperature over Belgium

Hamdi¹,

Vyver²,

Termonia³

2011

Intl Journal of Climatology

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“…This selection is even more complicated when it is unsure whether the underlying series is nonstationary: the nonstationarity in the flow series could be reflected in the use of a threshold changing with the covariates influencing the original flow series, as discussed in Kysely et al [2010]. In order to facilitate the comparison of results across the two different catchments and across the annual or seasonal divisions the threshold u was selected to be the value for which an average of four events per year (annual series) or two events per season (winter and summer series) are recorded.…”

Section: Methodsmentioning

confidence: 99%

Detection and attribution of urbanization effect on flood extremes using nonstationary flood‐frequency models

Prosdocimi

Kjeldsen

Miller

2015

Water Resources Research

177

111

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This study investigates whether long-term changes in observed series of high flows can be attributed to changes in land use via nonstationary flood-frequency analyses. A point process characterization of threshold exceedances is used, which allows for direct inclusion of covariates in the model; as well as a nonstationary model for block maxima series. In particular, changes in annual, winter, and summer block maxima and peaks over threshold extracted from gauged instantaneous flows records in two hydrologically similar catchments located in proximity to one another in northern England are investigated. The study catchment is characterized by large increases in urbanization levels in recent decades, while the paired control catchment has remained undeveloped during the study period . To avoid the potential confounding effect of natural variability, a covariate which summarizes key climatological properties is included in the flood-frequency model. A significant effect of the increasing urbanization levels on high flows is detected, in particular in the summer season. Point process models appear to be superior to block maxima models in their ability to detect the effect of the increase in urbanization levels on high flows.

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“…Besides time as a unique predictor, problems interrelating different geoscientific variables with each other have been extensively discussed, including the effect of meteorological variables on ozone concentration (Baur et al, 2004), the modelling of tropical cyclone intensity based on an additive QR model with different climatic covariates (Elsner et al, 2008;Jagger and Elsner, 2009), or the soil-moisture impact on hot extremes in southeastern Europe (Hirschi et al, 2011). Kyselý et al (2010) used QR for obtaining threshold values for timedependent extreme value analysis of climate simulations. In the context of sea-level research, Barbosa (2008) studied linear QR models for selected tide gauge records from the Baltic Sea.…”

Section: Quantile Regression (Qr) Analysismentioning

confidence: 99%

Spatial patterns of linear and nonparametric long-term trends in Baltic sea-level variability

Donner

Ehrcke

Barbosa

et al. 2012

Nonlin. Processes Geophys.

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Abstract. The study of long-term trends in tide gauge data is important for understanding the present and future risk of changes in sea-level variability for coastal zones, particularly with respect to the ongoing debate on climate change impacts. Traditionally, most corresponding analyses have exclusively focused on trends in mean sea-level. However, such studies are not able to provide sufficient information about changes in the full probability distribution (especially in the more extreme quantiles). As an alternative, in this paper we apply quantile regression (QR) for studying changes in arbitrary quantiles of sea-level variability. For this purpose, we chose two different QR approaches and discuss the advantages and disadvantages of different settings. In particular, traditional linear QR poses very restrictive assumptions that are often not met in reality. For monthly data from 47 tide gauges from along the Baltic Sea coast, the spatial patterns of quantile trends obtained in linear and nonparametric (splinebased) frameworks display marked differences, which need to be understood in order to fully assess the impact of future changes in sea-level variability on coastal areas. In general, QR demonstrates that the general variability of Baltic sea-level has increased over the last decades. Linear quantile trends estimated for sliding windows in time reveal a wide-spread acceleration of trends in the median, but only localised changes in the rates of changes in the lower and upper quantiles.

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Estimating extremes in climate change simulations using the peaks-over-threshold method with a non-stationary threshold

Cited by 83 publications

References 44 publications

New cloud and microphysics parameterisation for use in high‐resolution dynamical downscaling: application for summer extreme temperature over Belgium

New cloud and microphysics parameterisation for use in high‐resolution dynamical downscaling: application for summer extreme temperature over Belgium

Detection and attribution of urbanization effect on flood extremes using nonstationary flood‐frequency models

Spatial patterns of linear and nonparametric long-term trends in Baltic sea-level variability

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