CMIP5 Climate Model Analyses: Climate Extremes in the United States

Wuebbles, Donald J.; Meehl, Gerald A.; Hayhoe, Katharine; Karl, Thomas R.; Kunkel, Kenneth E.; Santer, Benjamin D.; Wehner, Michael; Colle, Brian A.; Fischer, Erich M.; Fu, Rong; Goodman, A.; Janssen, Emily; Kharin, Viatcheslav; Lee, Huikyo; Li, Wenhong; Long, Lindsey N.; Olsen, S. C.; Pan, Zaitao; Seth, Anji; Sheffield, Justin; Sun, Liqiang

doi:10.1175/bams-d-12-00172.1

Cited by 300 publications

(220 citation statements)

References 42 publications

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“…The ability of global models to reproduce the observed temperature extreme statistics has been assessed by Sillmann et al (2013a, b), Westby et al (2013) and Wuebbles et al (2014) using CMIP5 multi-model ensembles. Figure 8 shows a performance portrait of the normalized root mean square errors over the North American land area from the 28 available CMIP5 "historical" models for eight temperature based ETCCDI indices over 1979-2005 compared to the ERA Interim reanalysis (Dee et al 2011).…”

Section: Global and Regional Climate Model Skill In Simulating Tempermentioning

confidence: 99%

“…The CMIP3 and CMIP5 multimodel ensembles have been used to investigate projected changes of temperature extremes for the next couple of decades (e.g., mid-twentyfirst century) (e.g., Tebaldi et al 2006;Orlowsky and Seneviratne 2012;Sillmann et al 2013b;Wuebbles et al 2014). Climate change simulations in the CMIP5 multimodel ensembles showed greater changes in ETCCDI based on daily minimum temperatures than in ETCCDI based on daily maximum temperatures.…”

Section: Projected Trendsmentioning

confidence: 99%

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North American extreme temperature events and related large scale meteorological patterns: a review of statistical methods, dynamics, modeling, and trends

et al. 2015

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of: the physics of LSMP life cycles, comprehensive model assessment of LSMP-extreme temperature event linkages, and LSMP properties are needed. Generally, climate models capture observed properties of heat waves and cold air outbreaks with some fidelity. However they overestimate warm wave frequency and underestimate cold air outbreak frequency, and underestimate the collective influence of low-frequency modes on temperature extremes. Modeling studies have identified the impact of large-scale circulation anomalies and land-atmosphere interactions on changes in extreme temperatures. However, few studies have examined changes in LSMPs to more specifically understand the role of LSMPs on past and future extreme temperature changes. Even though LSMPs are resolvable by global and regional climate models, they are not necessarily well simulated. The paper concludes with unresolved issues and research questions.Abstract The objective of this paper is to review statistical methods, dynamics, modeling efforts, and trends related to temperature extremes, with a focus upon extreme events of short duration that affect parts of North America. These events are associated with large scale meteorological patterns (LSMPs). The statistics, dynamics, and modeling sections of this paper are written to be autonomous and so can be read separately. Methods to define extreme events statistics and to identify and connect LSMPs to extreme temperature events are presented. Recent advances in statistical techniques connect LSMPs to extreme temperatures through appropriately defined covariates that supplement more straightforward analyses. Various LSMPs, ranging from synoptic to planetary scale structures, are associated with extreme temperature events. Current knowledge about the synoptics and the dynamical mechanisms leading to the associated LSMPs is incomplete. Systematic studies

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Section: Global and Regional Climate Model Skill In Simulating Tempermentioning

confidence: 99%

Section: Projected Trendsmentioning

confidence: 99%

North American extreme temperature events and related large scale meteorological patterns: a review of statistical methods, dynamics, modeling, and trends

et al. 2015

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“…The intensity signal (see also Wuebbles et al, 2014) presents a similar pattern of stronger positive signal in the north and along the coasts, but in this case the region of consistency across models is more expansive across the US, with only the Colorado Plateau and the southern Great Plains not showing a robust increase in intensity (anomalies are nonetheless positive). The pattern can be interpreted as the superposition of the general (thermodynamic) tendency for more intense rainfall in a moister atmosphere (O'Gorman and Schneider, 2009), dampened in the middle of the continent by circulation-driven dry anomalies in summertime (Maloney et al, 2014).…”

Section: Rainfallmentioning

confidence: 77%

Projected changes in US rainfall erosivity

Biasutti

Seager

2015

Hydrol. Earth Syst. Sci.

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Abstract. Downscaled rainfall projections from 21 climate models from the CMIP5 (Coupled Model Intercomparison Project Phase 5) archive are used to estimate future changes in rainfall erosivity in the continental Unites States. To estimate erosivity from rainfall in the absence of sub-hourly data, we have used both daily rainfall values and the modified Fournier index -which is based on monthly rainfall accumulation -and derived the scaling relationship between rainfall and erosivity from observational estimates of both.The expectation of overall increase in erosivity is confirmed by these calculations, but a quantitative assessment is marred by large uncertainties. Specifically, the uncertainty in the method of estimation of erosivity is more consequential than that deriving from the spread in climate simulations and leads to changes of uncertain sign in parts of the southwest and Texas.We suggest that progress can be made by establishing a more reliable functional relationship between daily rainfall and erosivity.

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“…There are two major limitations for studying convective storms. First, historical data are problematic, which leads to low confidence in measured historical trends (Kunkel et al, 2013;. As mentioned in the following section, this can lead to underestimates of the risk of tornadoes in the data we are using, but these underestimates are likely to be concentrated in less-populated regions without much infrastructure where tornadoes go undetected.…”

Section: Tornadoesmentioning

confidence: 99%

“…Historically in the United States, there has been a trend for more extreme snowstorms-even in warm years-but no trends in ice storms have been observed over the past century (Kunkel et al, 2013). Our inclusion of ice storms, but not snowstorms, reflects the fact that ice storms can damage infrastructure, whereas snowstorms are more likely to merely disrupt infrastructure.…”

Section: Ice Stormsmentioning

confidence: 99%

Characterizing National Exposures to Infrastructure from Natural Disasters: Data and Methods Documentation

Narayanan¹,

Willis²,

Fischbach³

et al. 2016

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CMIP5 Climate Model Analyses: Climate Extremes in the United States

Cited by 300 publications

References 42 publications

North American extreme temperature events and related large scale meteorological patterns: a review of statistical methods, dynamics, modeling, and trends

North American extreme temperature events and related large scale meteorological patterns: a review of statistical methods, dynamics, modeling, and trends

Projected changes in US rainfall erosivity

Characterizing National Exposures to Infrastructure from Natural Disasters: Data and Methods Documentation

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