scholarly journals Assessment of extreme wind speeds from Regional Climate Models – Part 1: Estimation of return values and their evaluation

2010 ◽  
Vol 10 (4) ◽  
pp. 907-922 ◽  
Author(s):  
M. Kunz ◽  
S. Mohr ◽  
M. Rauthe ◽  
R. Lux ◽  
Ch. Kottmeier
Keyword(s):  
Distribution Function ◽  
Climate Models ◽  
Regional Climate ◽  
Generalized Pareto Distribution ◽  
Regional Climate Models ◽  
Extreme Value Statistics ◽  
Threshold Method ◽  
Wind Speeds ◽  
Extreme Wind ◽  
Extreme Wind Speeds

Abstract. Frequency and intensity of gust wind speeds associated with severe mid-latitude winter storms are estimated by applying extreme value statistics to data sets from regional climate models (RCM). Maximum wind speeds related to probability are calculated with the classical peaks over threshold method, where a statistical distribution function is fitted to the reduced sample describing the tail of the distribution function. From different sensitivity studies it is found that the Generalized Pareto Distribution in combination with a Maximum-Likelihood estimator provide the most reliable and robust results. For a reference period from 1971 to 2000, the ability of the RCMs to realistically simulate extreme wind speeds is investigated. For this purpose, data from three RCM scenarios, including the REMO-UBA simulations at 10 km resolution and the so-called consortial runs performed with the CCLM at 18 km resolution (two runs), are evaluated with observations and a pre-existing storm hazard map for Germany. It is found that all RCMs tend to underestimate the magnitude of the gusts in a range between 10 and 30% for a 10-year return period. Averaged over the investigation area, the underestimation is higher for CCLM compared to REMO. The spatial distribution of the gusts, on the other hand, is well reproduced, in particular by REMO.

scholarly journals Determination of Precipitation Return Values in Complex Terrain and Their Evaluation

2010 ◽  
Vol 23 (9) ◽  
pp. 2257-2274 ◽  
Author(s):  
Barbara Früh ◽  
Hendrik Feldmann ◽  
Hans-Jürgen Panitz ◽  
Gerd Schädler ◽  
Daniela Jacob ◽  
...  
Keyword(s):  
Distribution Function ◽  
Climate Models ◽  
Daily Precipitation ◽  
Regional Climate ◽  
Generalized Pareto Distribution ◽  
Regional Climate Models ◽  
Threshold Value ◽  
Kappa Distribution ◽  
Peak Over Threshold ◽  
Precipitation Events

Abstract To determine return values at various return periods for extreme daily precipitation events over complex orography, an appropriate threshold value and distribution function are required. The return values are calculated using the peak-over-threshold approach in which only a reduced sample of precipitation events exceeding a predefined threshold is analyzed. To fit the distribution function to the sample, the L-moment method is used. It is found that the deviation between the fitted return values and the plotting positions of the ranked precipitation events is smaller for the kappa distribution than for the generalized Pareto distribution. As a second focus, the ability of regional climate models to realistically simulate extreme daily precipitation events is assessed. For this purpose the return values are derived using precipitation events exceeding the 90th percentile of the precipitation time series and a fit of a kappa distribution. The results of climate simulations with two different regional climate models are analyzed for the 30-yr period 1971–2000: the so-called consortium runs performed with the climate version of the Lokal Modell (referred to as the CLM-CR) at 18-km resolution and the Regional Model (REMO)–Umweltbundesamt (UBA) simulations at 10-km resolution. It was found that generally the return values are overestimated by both models. Averaged across the region the overestimation is higher for REMO–UBA compared to CLM-CR.

scholarly journals Extreme winds over Europe in the ENSEMBLES regional climate models

2013 ◽  
Vol 13 (1) ◽  
pp. 1179-1199
Author(s):  
S. D. Outten ◽  
I. Esau
Keyword(s):  
Climate Models ◽  
Pareto Distribution ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Decision Makers ◽  
Threshold Method ◽  
Statistical Estimates ◽  
Wind Speeds ◽  
Extreme Winds

Abstract. Extreme winds cause vast amounts of damage every year and represent a major concern for numerous industries including construction, afforestation, wind energy and many others. Under a changing climate, the intensity and frequency of extreme events are expected to change, and accurate predictions of these changes will be invaluable to decision makers and society as a whole. This work examines four regional climate model downscalings over Europe from the "ENSEMBLE-based Predictions of Climate Changes and their Impacts" project (ENSEMBLES), and investigates the predicted changes in the 50 yr return wind speeds and the associated uncertainties. This is accomplished by employing the peaks-over-threshold method with the use of the Generalised Pareto Distribution. The models show that for much of Europe the 50 yr return wind is projected to change by less than 2 m s−1, while the uncertainties associated with the statistical estimates are larger than this. In keeping with previous works in this field, the largest source of uncertainty is found to be the inter-model spread, with some locations showing differences in the 50 yr return wind of over 20 m s−1 between two different downscalings.

scholarly journals Extreme winds over Europe in the ENSEMBLES regional climate models

2013 ◽  
Vol 13 (10) ◽  
pp. 5163-5172 ◽  
Author(s):  
S. D. Outten ◽  
I. Esau
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Decision Makers ◽  
Threshold Method ◽  
Statistical Estimates ◽  
Wind Speeds ◽  
Projected Changes ◽  
Extreme Winds

Abstract. Extreme winds cause vast amounts of damage every year and represent a major concern for numerous industries including construction, afforestation, wind energy and many others. Under a changing climate, the intensity and frequency of extreme events are expected to change, and accurate projections of these changes will be invaluable to decision makers and society as a whole. This work examines four regional climate model downscalings over Europe following the SRES A1B scenario from the "ENSEMBLE-based Predictions of Climate Changes and their Impacts" project (ENSEMBLES). It investigates the projected changes in the 50 yr return wind speeds and the associated uncertainties. This is accomplished by employing the peaks-over-threshold method with the use of the generalised Pareto distribution. The models show that, for much of Europe, the 50 yr return wind is projected to change by less than 2 m s−1, while the uncertainties associated with the statistical estimates are larger than this. In keeping with previous works in this field, the largest source of uncertainty is found to be the inter-model spread, with some locations showing differences in the 50 yr return wind of over 20 m s−1 between two different downscalings.

scholarly journals Influence of Bias Correction Methods on Simulated Köppen−Geiger Climate Zones in Europe

Climate ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 18 ◽  
Author(s):  
Beáta Szabó-Takács ◽  
Aleš Farda ◽  
Petr Skalák ◽  
Jan Meitner
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Regional Climate ◽  
Generalized Pareto Distribution ◽  
Regional Climate Models ◽  
Climate Classification ◽  
Quantile Mapping ◽  
Temperature Bias ◽  
Simulated Precipitation ◽  
Precipitation And Temperature

Our goal was to investigate the influence of bias correction methods on climate simulations over the European domain. We calculated the Köppen−Geiger climate classification using five individual regional climate models (RCM) of the ENSEMBLES project in the European domain during the period 1961−1990. The simulated precipitation and temperature data were corrected using the European daily high-resolution gridded dataset (E-OBS) observed data by five methods: (i) the empirical quantile mapping of precipitation and temperature, (ii) the quantile mapping of precipitation and temperature based on gamma and Generalized Pareto Distribution of precipitation, (iii) local intensity scaling, (iv) the power transformation of precipitation and (v) the variance scaling of temperature bias corrections. The individual bias correction methods had a significant effect on the climate classification, but the degree of this effect varied among the RCMs. Our results on the performance of bias correction differ from previous results described in the literature where these corrections were implemented over river catchments. We conclude that the effect of bias correction may depend on the region of model domain. These results suggest that distribution free bias correction approaches are the most suitable for large domain sizes such as the pan-European domain.

scholarly journals Towards a definitive historical high-resolution climate dataset for Ireland – promoting climate research in Ireland

2019 ◽  
Vol 15 ◽  
pp. 263-276
Author(s):  
Jason Flanagan ◽  
Paul Nolan ◽  
Ray McGrath ◽  
Christopher Werner
Keyword(s):  
High Resolution ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Absolute Error ◽  
Sea Level Pressure ◽  
Wind Speeds ◽  
Uncertainty Estimates ◽  
Soil Temperatures ◽  
Error Standard Deviation

Abstract. There is strong and constant demand from various sectors (research, industry and government) for long-term, high-resolution (both temporal and spatial), gridded climate datasets. To address this demand, the Irish Centre for High-End Computing (ICHEC) has recently performed two high-resolution simulations of the Irish climate, utilising the Regional Climate Models (RCMs) COSMO-CLM5 and WRF v3.7.1. The datasets produced contain hourly outputs for an array of sub-surface, surface and atmospheric fields for the entire 36-year period 1981–2016. In this work, we list the climate variables that have been archived at ICHEC. We present preliminary uncertainty estimates (error, standard deviation, mean absolute error) based on Met Éireann station observations, for several of the more commonly used variables: 2 m temperature, 10 m wind speeds and mean sea level pressure at the hourly time scale; and precipitation at hourly and daily time scales. Additionally, analyses of 10 cm soil temperatures, CAPE 3 km, Showalter index and surface lifted index are presented.

scholarly journals Projected changes to wind loads coinciding with rainfall for building design in Canada based on an ensemble of Canadian regional climate model simulations

2020 ◽  
Vol 162 (2) ◽  
pp. 821-835 ◽  
Author(s):  
Dae Il Jeong ◽  
Alex J. Cannon ◽  
Robert J. Morris
Keyword(s):  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Building Design ◽  
Occurrence Frequency ◽  
Future Design ◽  
Wind Speeds ◽  
Rainfall Occurrence ◽  
Extreme Wind ◽  
Extreme Wind Speeds

Abstract Strong wind coinciding with rainfall is an important weather phenomenon in many science and engineering fields. This study investigates changes in hourly extreme driving rain wind pressure (DRWP)—a climatic variable used in building design in Canada—for future periods of specified global mean temperature change using an ensemble of a Canadian regional climate model (CanRCM4) driven by the Canadian Earth system model (CanESM2) under the Representative Concentration Pathway 8.5 scenario. Evaluation of the model shows that the CanRCM4 ensemble reproduces hourly extreme wind speeds and rainfall (> 1.8 mm/h) occurrence frequency and the associated design (5-year return level) DRWP across Canada well when compared with 130 meteorological stations. Significant increases in future design DRWP are projected over western, eastern, and northern Canada, with the areal extent and relative magnitude of the increases scaling approximately linearly with the amount of global warming. Increases in future rainfall occurrence frequency are driven by the combined effect of increases in precipitation amount and changes in precipitation type from solid to liquid due to increases in air temperature; these are identified as the main factors leading to increases in future design DRWP. Future risk ratios of the design DRWP are highly dependent on those of the rainfall occurrence, which shows large increases over the three regions, while they are partly affected by the increases in future extreme wind speeds over western and northeastern Canada. Increases in DRWP can be an emerging risk for existing buildings, particularly in western, eastern, and northern Canada, and a consideration for managing and designing buildings across Canada.

scholarly journals Aircraft-based observations and high-resolution simulations of an Icelandic dust storm

2012 ◽  
Vol 12 (3) ◽  
pp. 7949-7984 ◽  
Author(s):  
A.-M. Blechschmidt ◽  
J. E. Kristjánsson ◽  
H. Ólafsson ◽  
J. F. Burkhart ◽  
Ø. Hodnebrog
Keyword(s):  
Dust Storm ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
High Mass ◽  
Wind Speeds ◽  
Mixing Ratios ◽  
High Particle ◽  
Dust Sources ◽  
The Impact

Abstract. The first aircraft-based observations of an Icelandic dust storm are presented. The measurements were carried out over the ocean near Iceland's south coast in February 2007. This dust event occurred in conjunction with an easterly barrier jet of more than 30 m s−1. The aircraft measurements show high particle mass mixing ratios in an area of low wind speeds in the wake of Iceland near the coast, decreasing abruptly towards the jet. Simulations from the Weather Research and Forecasting Model coupled with Chemistry (WRF/Chem) indicate that the measured high mass mixing ratios and observed low visibility inside the wake are due to dust transported from Icelandic sand fields towards the ocean. This is confirmed by meteorological station data. Primary dust sources are glacial outwash terrains located near the Mýrdalsjökull glacier. Sea salt aerosols produced by the impact of strong winds on the ocean surface started to dominate as the aircraft flew away from Iceland into the jet. The present results support recent studies which suggest that Icelandic deserts should be considered as important dust sources in global and regional climate models.

scholarly journals Aircraft-based observations and high-resolution simulations of an Icelandic dust storm

2012 ◽  
Vol 12 (22) ◽  
pp. 10649-10666 ◽  
Author(s):  
A.-M. Blechschmidt ◽  
J. E. Kristjánsson ◽  
H. Ólafsson ◽  
J. F. Burkhart ◽  
Ø. Hodnebrog ◽  
...  
Keyword(s):  
Dust Storm ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
High Mass ◽  
Wind Speeds ◽  
Mixing Ratios ◽  
High Particle ◽  
Dust Sources ◽  
The Impact

Abstract. The first aircraft-based observations of an Icelandic dust storm are presented. The measurements were carried out over the ocean near Iceland's south coast in February 2007. This dust event occurred in conjunction with an easterly barrier jet of more than 30 m s−1. The aircraft measurements show high particle mass mixing ratios in an area of low wind speeds in the wake of Iceland near the coast, decreasing abruptly towards the jet. Simulations from the Weather Research and Forecasting Model coupled with Chemistry (WRF/Chem) indicate that the measured high mass mixing ratios and observed low visibility inside the wake are due to dust transported from Icelandic sand fields towards the ocean. This is confirmed by meteorological station data. Glacial outwash terrains located near the Mýrdalsjökull glacier are among simulated dust sources. Sea salt aerosols produced by the impact of strong winds on the ocean surface started to dominate as the aircraft flew away from Iceland into the jet. The present results support recent studies which suggest that Icelandic deserts should be considered as important dust sources in global and regional climate models.

scholarly journals Future changes in European winter storm losses and extreme wind speeds inferred from GCM and RCM multi-model simulations

2011 ◽  
Vol 11 (5) ◽  
pp. 1351-1370 ◽  
Author(s):  
M. G. Donat ◽  
G. C. Leckebusch ◽  
S. Wild ◽  
U. Ulbrich
Keyword(s):  
Climate Models ◽  
Model Simulations ◽  
Wind Speeds ◽  
Climate Simulations ◽  
Extreme Wind ◽  
Wide Range ◽  
Ensemble Mean ◽  
Extreme Wind Speeds ◽  
Projected Changes ◽  
Ipcc Sres

Abstract. Extreme wind speeds and related storm loss potential in Europe have been investigated using multi-model simulations from global (GCM) and regional (RCM) climate models. Potential future changes due to anthropogenic climate change have been analysed from these simulations following the IPCC SRES A1B scenario. The large number of available simulations allows an estimation of the robustness of detected future changes. All the climate models reproduced the observed spatial patterns of wind speeds, although some models displayed systematic biases. A storm loss model was applied to the GCM and RCM simulated wind speeds, resulting in realistic mean loss amounts calculated from 20th century climate simulations, although the inter-annual variability of losses is generally underestimated. In future climate simulations, enhanced extreme wind speeds were found over northern parts of Central and Western Europe in most simulations and in the ensemble mean (up to 5%). As a consequence, the loss potential is also higher in these regions, particularly in Central Europe. Conversely, a decrease in extreme wind speeds was found in Southern Europe, as was an associated reduction in loss potential. There was considerable spread in the projected changes of individual ensemble members, with some indicating an opposite signature to the ensemble mean. Downscaling of the large-scale simulations with RCMs has been shown to be an important source of uncertainty. Even RCMs with identical boundary forcings can show a wide range of potential changes. The robustness of the projected changes was estimated using two different measures. First, the inter-model standard deviation was calculated; however, it is sensitive to outliers and thus displayed large uncertainty ranges. Second, a multi-model combinatorics approach considered all possible sub-ensembles from GCMs and RCMs, hence taking into account the arbitrariness of model selection for multi-model studies. Based on all available GCM and RCM simulations, for example, a 25% mean increase in risk of loss for Germany has been estimated for the end of the 21st century, with a 90% confidence range of +15 to +35%.

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