Non-stationary return levels of CMIP5 multi-model temperature extremes

Cheng, Linyin; Phillips, T. J.; AghaKouchak, Amir

doi:10.1007/s00382-015-2625-y

Cited by 21 publications

(15 citation statements)

References 69 publications

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“…Grotjahn () noted the CCSM4 distribution being narrower than the reanalysis. Cheng et al () find FGOALS‐g2 colder, in climatology and return values near the CCV, than the other models common to our study and theirs.…”

Section: Resultssupporting

confidence: 54%

“…Grotjahn (2016) noted the CCSM4 distribution being narrower than the reanalysis. Cheng et al (2015) find FGOALS-g2 colder, in climatology and return values near the CCV, than the other models common to our study and theirs. Figure 5 shows future scenarios using both historical (Fh) and future (Ff) climatologies to define anomalies.…”

Section: Past and Future Lsmp Index Distributionssupporting

confidence: 50%

“…Sillmann et al () assess CMIP5 model performance on ETCCDI temperature extremes (http://etccdi.pacificclimate.org/list_27_indices.shtml) and single out the MIROC models for criticism. Cheng et al () find FGOALs‐g2 and these MIROC models to have a larger mean error than other models we examine.…”

Section: Resultsmentioning

confidence: 51%

“…In general, most models tend to have similar distributions in the Hh and Ff panels. But, within the Ff panels, the number of events per decade increases toward the end of the period for most models, especially for RCP8.5.The main exceptions are the MIROC models; their disparity from other models has been noted before (Cheng et al, 2015;Sillmann et al, 2013a).…”

Section: 1029/2018jd029000mentioning

confidence: 94%

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Future Projections of the Large‐Scale Meteorology Associated with California Heat Waves in CMIP5 Models

Palipane

Grotjahn

2018

JGR Atmospheres

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Previous work showed that climate models capture historical large‐scale meteorological patterns (LSMPs) associated with California Central Valley heat waves including both ways these heat waves form. This work examines what models predict under the Representative Concentration Pathway (RCP) 4.5 and RCP8.5 scenarios. Model performance varies, so a multimodel average weights each model based on its historical performance in four parameters. An LSMP index (LSMPi) defined using upper atmosphere variables captures dates of past extreme surface temperature maxima. LSMPi correlates well with all values of California Central Valley‐average surface maximum temperature. LSMPi distributions in future simulations shift ~0.6 standard deviations higher between 1961–2000 and 2061–2100 for RCP8.5 data. Based on the historical climatology, future scenarios show a large increase in the frequency and duration of heat waves in every model. Four times as many heat waves occur and their median duration doubles, using historical thresholds. Of the two ways heat waves form, Type 1 has similar frequency in the future. But, Type 2 becomes much more common because Type 2 has a preexisting hot anomaly in Southwestern Canada, much like the historical to future climatological change in that region (a “global warming” signal). The 20‐year return value anomaly increases by 30–40%. The average of the 50 hottest temperatures increases 3.5–6 K depending on the scenario. When extreme values are defined using the future climatology, the models and their average have no consistent increase or decrease of distribution properties such as shape, scale, and return values of the extremes compared to historical values.

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Section: Resultssupporting

confidence: 54%

Section: Past and Future Lsmp Index Distributionssupporting

confidence: 50%

Section: Resultsmentioning

confidence: 51%

Section: 1029/2018jd029000mentioning

confidence: 94%

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Future Projections of the Large‐Scale Meteorology Associated with California Heat Waves in CMIP5 Models

Palipane

Grotjahn

2018

JGR Atmospheres

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“…The two NS FFA methods described in this review include (1) the use of precipitation projections from future climate scenarios in rainfall‐runoff models and (2) the use of a safety margin to adjust the design flood estimates derived from ST extreme value analysis (SEVA). While countries such as Norway, United Kingdom, Belgium, and Germany have adopted the safety margin approach in engineering guidelines [ Madsen et al ., ], the underlying ST assumption is often challenged and relaxed in the scientific literature to allow application of NS extreme value analysis (NEVA) [ AghaKouchak et al ., ; Begueria and Vicente‐Serrano , ; Begueria et al ., ; Cheng et al ., ; Cooley , ; Gilleland and Katz , ; Griffis and Stedinger , ; Katz , ; Lopez and Frances , ; Salas and Obeysekera , ; Silva et al ., ; Stedinger and Griffis , ; Tramblay et al ., ; Cheng et al ., ; Villarini et al ., ; Jakob , ; Steinschneider and Lall , ]. Indeed, the review by Madsen et al .…”

Section: Introductionmentioning

confidence: 99%

Predicting nonstationary flood frequencies: Evidence supports an updated stationarity thesis in the United States

Luke

Vrugt

AghaKouchak

et al. 2017

Water Resources Research

Self Cite

109

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Nonstationary extreme value analysis (NEVA) can improve the statistical representation of observed flood peak distributions compared to stationary (ST) analysis, but management of flood risk relies on predictions of out‐of‐sample distributions for which NEVA has not been comprehensively evaluated. In this study, we apply split‐sample testing to 1250 annual maximum discharge records in the United States and compare the predictive capabilities of NEVA relative to ST extreme value analysis using a log‐Pearson Type III (LPIII) distribution. The parameters of the LPIII distribution in the ST and nonstationary (NS) models are estimated from the first half of each record using Bayesian inference. The second half of each record is reserved to evaluate the predictions under the ST and NS models. The NS model is applied for prediction by (1) extrapolating the trend of the NS model parameters throughout the evaluation period and (2) using the NS model parameter values at the end of the fitting period to predict with an updated ST model (uST). Our analysis shows that the ST predictions are preferred, overall. NS model parameter extrapolation is rarely preferred. However, if fitting period discharges are influenced by physical changes in the watershed, for example from anthropogenic activity, the uST model is strongly preferred relative to ST and NS predictions. The uST model is therefore recommended for evaluation of current flood risk in watersheds that have undergone physical changes. Supporting information includes a MATLAB® program that estimates the (ST/NS/uST) LPIII parameters from annual peak discharge data through Bayesian inference.

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Climate‐adaptive design approach for embedded footing under extreme climate event

Mahmoudabadi

Ravichandran

2021

Num Anal Meth Geomechanics

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Recent records indicate that extreme climate events adversely affect the performance of earthen structures and superstructures supported on them by varying the strength and deformation properties of subsurface soil. To better design such structures, the effects of climate events must be well understood, and the conventional design procedures must be improved by incorporating the knowledge of climatology in geotechnical engineering. In this study, a new climate‐adaptive design method is developed to investigate the impact of extreme climate events on the safety and serviceability performances of embedded footing through incorporating the site‐specific hydrological loads such as precipitation, evapotranspiration, and water table depth to geotechnical parameters. The proposed method was applied to two arid climate sites in the United States, Austin, TX, and Albuquerque, NM. The site‐specific extreme hydrological cycle was determined based on historical records. The mathematical model was solved for temporal and spatial variations of the degree of saturation and matric suction considering the hydrological loads as the upper and lower boundary conditions, respectively. The results showed that the worst performance (higher elastic settlement and lower ultimate bearing capacity) was observed during the period when the degree of saturation in the influence zone was the highest. The critical design parameters including ultimate bearing capacity and settlement obtained from the proposed method increased by 28% and 35%, respectively, in Austin, compared to those calculated from conventional approaches where the soil is assumed to be fully saturated. In Albuquerque, the increase in ultimate bearing capacity and settlement were 61% and 45%, respectively.

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Non-stationary return levels of CMIP5 multi-model temperature extremes

Cited by 21 publications

References 69 publications

Future Projections of the Large‐Scale Meteorology Associated with California Heat Waves in CMIP5 Models

Future Projections of the Large‐Scale Meteorology Associated with California Heat Waves in CMIP5 Models

Predicting nonstationary flood frequencies: Evidence supports an updated stationarity thesis in the United States

Climate‐adaptive design approach for embedded footing under extreme climate event

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