Comparison of observed and multimodeled trends in annual extremes of temperature and precipitation

Kiktev, D. B.; Caesar, John; Alexander, Lisa V.; Shiogama, Hideo; Collier, Mark

doi:10.1029/2007gl029539

Cited by 50 publications

(42 citation statements)

References 10 publications

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“…First, detectability is assessed using transient climate simulations of the entire twentieth century rather than of the latter half of the twentieth century (Kiktev et al 2003(Kiktev et al , 2007 or future simulations (Hegerl et al 2004). Second, we consider temporal variations of precipitation extremes rather than just the long-term trends (Kiktev et al 2003(Kiktev et al , 2007Hegerl et al 2004). This has the potential to improve the detectability of external signals, especially if decadal variation is substantial.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…Overall, the large inter-model differences suggest that single-model results may not be robust over smaller spatial domains and that one needs to consider large scale patterns so as to detect ANT signals in extreme precipitation changes (Hegerl et al 2004;Tebaldi et al 2006;Kharin et al 2007;Kiktev et al 2007). …”

Section: Fingerprint From Another Modelmentioning

confidence: 99%

“…Kiktev et al (2003Kiktev et al ( , 2007 found that anthropogenic forcing contributed little to the simulation of trends in precipitation extremes, unlike in temperature extremes. Hegerl et al (2004) compared the observed and model simulated trends of precipitation extremes represented by annual maximum daily or 5-day precipitation amount over land and found that changes in heavy precipitation might be more detectable than changes in annual mean precipitation.…”

Section: Introductionmentioning

confidence: 99%

“…Using an atmospheric general circulation model forced by observed sea surface temperatures (SST), Kiktev et al (2003) found that the inclusion of anthropogenic forcing significantly improved the model performance in simulating observed trends in temperature extremes for 1950-1995. Kiktev et al (2007 provided an updated analysis using five coupled climate models that included anthropogenic forcing.…”

Section: Introductionmentioning

confidence: 99%

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Signal detectability in extreme precipitation changes assessed from twentieth century climate simulations

et al. 2008

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This study assesses the detectability of external influences in changes of precipitation extremes in the twentieth century, which is explored through a perfect model analysis with an ensemble of coupled global climate model (GCM) simulations. Three indices of precipitation extremes are defined from the generalized extreme value (GEV) distribution: the 20-year return value (P 20 ), the median (P m ), and the cumulative probability density as a probability-based index (PI). Time variations of areaaverages of these three extreme indices are analyzed over different spatial domains from the globe to continental regions. Treating all forcing simulations (ALL; natural plus anthropogenic) of the twentieth century as observations and using a preindustrial control run (CTL) to estimate the internal variability, the amplitudes of response patterns to anthropogenic (ANT), natural (NAT), greenhouse-gases (GHG), and sulfate aerosols (SUL) forcings are estimated using a Bayesian decision method. Results show that there are decisively detectable ANT signals in global, hemispheric, and zonal band areas. When only land is considered, the global and hemispheric detection results are unchanged, but detectable ANT signals in the zonal bands are limited to low latitudes. The ANT signals are also detectable in the P m and PI but not in P 20 at continental scales over Asia, South America, Africa, and Australia. This indicates that indices located near the center of the GEV distribution (P m and PI) may give better signal-tonoise ratio than indices representing the tail of the distribution (P 20 ). GHG and NAT signals are also detectable, but less robustly for more limited extreme indices and regions. These results are largely insensitive when model data are masked to mimic the availability of the observed data. An imperfect model analysis in which fingerprints are obtained from simulations with a different GCM suggests that ANT is robustly detectable only at global and hemispheric scales, with high uncertainty in the zonal and continental results.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Section: Fingerprint From Another Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Signal detectability in extreme precipitation changes assessed from twentieth century climate simulations

et al. 2008

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“…Vary high seasonal temperature anomalies have increased in frequency (Hansen et al 2012), and have been attributed in part to human influence in many sub-continental regions including western North America Christidis et al 2012a;Bindoff et al 2013). Previous studies have also suggested human influences on changes in some extreme daily temperature indices (decreases of cold extremes and increases of warm extremes) at global and continental scales (e.g., Christidis et al 2005;Shiogama et al 2006;Kiktev et al 2007;Min et al 2013;Morak et al 2013). Long-term changes in temperature extremes in the United States (US) appear to be in part connected to human influences (Meehl et al 2007(Meehl et al , 2009, in addition to substantial contributions from natural variability (Meehl et al 2012).…”

Section: Introductionmentioning

confidence: 99%

Attribution of the June-July 2013 Heat Wave in the Southwestern United States

Shiogama

Watanabe

Imada³

et al. 2014

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A severe heat wave occurred in the southwestern United States (US) during June and July 2013. To investigate the effects of natural variability and anthropogenic climate change on this event, we generated large ensemble simulations of possible weather using the MIROC5A climate model forced by "historical external forcing agents, sea surface temperature (SST) observations and sea ice (SIC) observations" both with and without human influence. It was suggested that both the anthropogenic warming and an atmospheric circulation regime related to the natural variability of SST and SIC made the heat wave event more likely. On the other hand, no significant human influence was found in atmospheric circulation patterns. These results were robust for two different estimates of anthropogenic signals on SST and SIC.

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Detectability of historical trends in station‐based precipitation characteristics over the continental United States

2015

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The goal of this paper is to detect secular trends in observed, station-based precipitation variations and extreme event occurrences over the United States. By definition, detectable trends are those that are unlikely to have arisen from internal variability alone. To represent this internal variability, we use station-specific, seasonally varying, daily time scale stationary stochastic weather models-models in which the simulated interannual-to-multidecadal precipitation variance is purely the result of the random evolution of daily precipitation events within a given time period-to first estimate the trends in various means and extremes that can occur even with fixed, climatological daily precipitation characteristics. Detection of secular trends in the observed climate-whether naturally or anthropogenically induced-can then be defined relative to this stochastic variability, i.e., as trends in the means and/or extremes that are likely to have occurred only through a change in the underlying precipitation characteristics. The derived results have two important ramifications. First, they identify "hot spot" regions in which trends in precipitation characteristics are already emerging from within the envelope of stochastic variability, including (but not limited to) positive trends in annual occurrence across most of the U.S. and positive trends in annual intensity and heavy-event accumulations across the Interior Plains and around the Great Lakes. Further, they identify "sentinel" metrics which show the greatest detectability-e.g., annual precipitation occurrence and intensity-as well as those which show the least detectability and hence are unlikely to produce any statistically meaningful signals-e.g., seasonal total precipitation and extremes.

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Comparison of observed and multimodeled trends in annual extremes of temperature and precipitation

Cited by 50 publications

References 10 publications

Signal detectability in extreme precipitation changes assessed from twentieth century climate simulations

Signal detectability in extreme precipitation changes assessed from twentieth century climate simulations

Attribution of the June-July 2013 Heat Wave in the Southwestern United States

Detectability of historical trends in station‐based precipitation characteristics over the continental United States

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