Summarising changes in air temperature over Central Europe by quantile regression and clustering

Barbosa, Susana; Scotto, Manuel G.; Alonso, Ana

doi:10.5194/nhess-11-3227-2011

Cited by 52 publications

(43 citation statements)

References 32 publications

(25 reference statements)

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“…The scale and shape parameters are assumed to be constant since a large number of parameters hinder the model estimation and is not easily physically interpretable, particularly in the case of the shape parameter. Furthermore, Scotto et al (2011) assumed for the same dataset of daily tide gauge records a GEV model with time-varying scale parameter but found the corresponding changes in time insignificant, therefore favouring a simpler, constant-scale, GEV model. The occurrence of extreme sea-levels can be assessed in terms of return levels and return periods.…”

Section: Extreme Value Theorymentioning

confidence: 99%

Changes in extreme sea-levels in the Baltic Sea

Ribeiro

Barbosa

Scotto

et al. 2014

Tellus A: Dynamic Meteorology and Oceanography

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A B S T R A C TIn a climate change context, changes in extreme sea-levels rather than changes in the mean are of particular interest from the coastal protection point of view. In this work, extreme sea-levels in the Baltic Sea are investigated based on daily tide gauge records for the period 1916Á2005 using the annual block maxima approach. Extreme events are analysed based on the generalised extreme value distribution considering both stationary and time-varying models. The likelihood ratio test is applied to select between stationary and nonstationary models for the maxima and return values are estimated from the final model. As an independent and complementary approach, quantile regression is applied for comparison with the results from the extreme value approach. The rates of change in the uppermost quantiles are in general consistent and most pronounced for the northernmost stations.

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Section: Extreme Value Theorymentioning

confidence: 99%

Changes in extreme sea-levels in the Baltic Sea

Ribeiro

Barbosa

Scotto

et al. 2014

Tellus A: Dynamic Meteorology and Oceanography

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“…Other studies may suggest more complex changes. Barbosa et al (2011) use quantile regression and clustering to show that the changes in the 5th percentiles and the 95th percentiles of daily air temperature over central Europe are not the same as changes in medians, although they do not explicitly examine to what extent these differences can be explained by changing standard deviations. Ballester et al (2010) find evidence that accounting for changes in skewness in marginal distributions allows for a more accurate representation of changes in cold extremes than can be obtained from just considering changes in mean and standard deviation.…”

Section: W K Huang Et Al: Temperature Extremes In Ccsm3mentioning

confidence: 99%

Estimating changes in temperature extremes from millennial-scale climate simulations using generalized extreme value (GEV) distributions

Huang

Stein

McInerney

et al. 2016

Adv. Stat. Clim. Meteorol. Oceanogr.

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Abstract. Changes in extreme weather may produce some of the largest societal impacts of anthropogenic climate change. However, it is intrinsically difficult to estimate changes in extreme events from the short observational record. In this work we use millennial runs from the Community Climate System Model version 3 (CCSM3) in equilibrated pre-industrial and possible future (700 and 1400 ppm CO 2 ) conditions to examine both how extremes change in this model and how well these changes can be estimated as a function of run length. We estimate changes to distributions of future temperature extremes (annual minima and annual maxima) in the contiguous United States by fitting generalized extreme value (GEV) distributions. Using 1000-year pre-industrial and future time series, we show that warm extremes largely change in accordance with mean shifts in the distribution of summertime temperatures. Cold extremes warm more than mean shifts in the distribution of wintertime temperatures, but changes in GEV location parameters are generally well explained by the combination of mean shifts and reduced wintertime temperature variability. For cold extremes at inland locations, return levels at long recurrence intervals show additional effects related to changes in the spread and shape of GEV distributions. We then examine uncertainties that result from using shorter model runs. In theory, the GEV distribution can allow prediction of infrequent events using time series shorter than the recurrence interval of those events. To investigate how well this approach works in practice, we estimate 20-, 50-, and 100-year extreme events using segments of varying lengths. We find that even using GEV distributions, time series of comparable or shorter length than the return period of interest can lead to very poor estimates. These results suggest caution when attempting to use short observational time series or model runs to infer infrequent extremes.

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“…In a time series analysis context, variations in the distribution of temperature and precipitation records have been studied by various authors (Koenker and Schorfheide, 1994;Draghicescu, 2002;Zhou and Wu, 2009;Timofeev and Sterin, 2010;Cannon, 2011;Barbosa et al, 2011). 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).…”

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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Summarising changes in air temperature over Central Europe by quantile regression and clustering

Cited by 52 publications

References 32 publications

Changes in extreme sea-levels in the Baltic Sea

Changes in extreme sea-levels in the Baltic Sea

Estimating changes in temperature extremes from millennial-scale climate simulations using generalized extreme value (GEV) distributions

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

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