Changes in the Probability of Heavy Precipitation: Important Indicators of Climatic Change

Groisman, P. Y.; Karl, Thomas R.; Easterling, David R.; Knight, Richard W.; Jamason, P.; Hennessy, Kevin; Suppiah, Ramasamy; Page, Cher M.; Wibig, Joanna; Fortuniak, Krzysztof; Razuvaev, V. N.; Douglas, Arthur V.; Førland, Eirik J.; Zhai, Panmao

doi:10.1007/978-94-015-9265-9_15

Cited by 342 publications

(388 citation statements)

References 31 publications

Supporting

Mentioning

349

Contrasting

Unclassified

Order By: Relevance

“…This may be a consequence of relatively infrequent precipitation in HIRHAM's southwestern United States in the warm season: this case has the smallest percentage of days with more than 2.5 mm of precipitation. Furthermore, its low seasonal average precipitation (Ͻ1 mm day Ϫ1 ) suggests a temporally varying shape parameter ␣ during the season (Groisman et al 1999), undermining the assumption that ␣ is unchanging, or the model may simply have behavior that is contrary to the assumptions here, such as adherence to a gamma distribution: this case has the smallest R 2 (Table 1) of the fits to the distribution in (1).…”

Section: ͑10͒mentioning

confidence: 82%

“…Equally important, the analysis does not apply to extreme precipitation, such as precipitation above the 99.9th percentile (e.g., Groisman et al 2005), for which the theory of statistical extremes more likely provides the appropriate description (e.g., Leadbetter et al 1983;Meehl et al 2000;Wilson and Toumi 2005).…”

Section: Discussionmentioning

confidence: 99%

“…We use data extracted from the National Climatic Data Center archives by Eischeid et al (2000) and further evaluated by Clark and Hay (2004), who summarize NCDC quality control procedures. We used observations as given and made no adjustment for gauge undercatch, which can produce a negative bias in observations of 3%-10% (e.g., Groisman and Legates 1994).…”

Section: A Observationsmentioning

confidence: 99%

See 2 more Smart Citations

A Possible Constraint on Regional Precipitation Intensity Changes under Global Warming

Gutowski

Takle

Kozak

et al. 2007

Journal of Hydrometeorology

View full text Add to dashboard Cite

Changes in daily precipitation versus intensity under a global warming scenario in two regional climate simulations of the United States show a well-recognized feature of more intense precipitation. More important, by resolving the precipitation intensity spectrum, the changes show a relatively simple pattern for nearly all regions and seasons examined whereby nearly all high-intensity daily precipitation contributes a larger fraction of the total precipitation, and nearly all low-intensity precipitation contributes a reduced fraction. The percentile separating relative decrease from relative increase occurs around the 70th percentile of cumulative precipitation, irrespective of the governing precipitation processes or which model produced the simulation. Changes in normalized distributions display these features much more consistently than distribution changes without normalization.Further analysis suggests that this consistent response in precipitation intensity may be a consequence of the intensity spectrum's adherence to a gamma distribution. Under the gamma distribution, when the total precipitation or number of precipitation days changes, there is a single transition between precipitation rates that contribute relatively more to the total and rates that contribute relatively less. The behavior is roughly the same as the results of the numerical models and is insensitive to characteristics of the baseline climate, such as average precipitation, frequency of rain days, and the shape parameter of the precipitation's gamma distribution. Changes in the normalized precipitation distribution give a more consistent constraint on how precipitation intensity may change when climate changes than do changes in the nonnormalized distribution. The analysis does not apply to extreme precipitation for which the theory of statistical extremes more likely provides the appropriate description. ABSTRACT Changes in daily precipitation versus intensity under a global warming scenario in two regional climate simulations of the United States show a well-recognized feature of more intense precipitation. More important, by resolving the precipitation intensity spectrum, the changes show a relatively simple pattern for nearly all regions and seasons examined whereby nearly all high-intensity daily precipitation contributes a larger fraction of the total precipitation, and nearly all low-intensity precipitation contributes a reduced fraction. The percentile separating relative decrease from relative increase occurs around the 70th percentile of cumulative precipitation, irrespective of the governing precipitation processes or which model produced the simulation. Changes in normalized distributions display these features much more consistently than distribution changes without normalization.Further analysis suggests that this consistent response in precipitation intensity may be a consequence of the intensity spectrum's adherence to a gamma distribution. Under the gamma distribution, when the total precipitation or number of p...

show abstract

Section: ͑10͒mentioning

confidence: 82%

Section: Discussionmentioning

confidence: 99%

Section: A Observationsmentioning

confidence: 99%

See 1 more Smart Citation

A Possible Constraint on Regional Precipitation Intensity Changes under Global Warming

Gutowski

Takle

Kozak

et al. 2007

Journal of Hydrometeorology

View full text Add to dashboard Cite

show abstract

“…In general, extreme precipitation is defined as daily precipitation exceeds a certain intensity level (such as 50 mm/d) or exceeds a certain quantile (such as >90th) or reaches a certain return period (Su et al, 2008;Groisman et al, 1999). In this study, two kinds of extreme series (annual maximum series, AM; peak over threshold series, POT) are considered.…”

Section: Methodsmentioning

confidence: 99%

Spatio-temporal trend and statistical distribution of extreme precipitation events in Huaihe River Basin during 1960–2009

et al. 2012

View full text Add to dashboard Cite

Based on the daily precipitation data of 27 meteorological stations from 1960 to 2009 in the Huaihe River Basin, spatio-temporal trend and statistical distribution of extreme precipitation events in this area are analyzed. Annual maximum series (AM) and peak over threshold series (POT) are selected to simulate the probability distribution of extreme precipitation. The results show that positive trend of annual maximum precipitation is detected at most of used stations, only a small number of stations are found to depict a negative trend during the past five decades, and none of the positive or negative trend is significant. The maximum precipitation event almost occurred in the flooding period during the 1960s and 1970s. By the L-moments method, the parameters of three extreme distributions, i.e., Generalized extreme value distribution (GEV), Generalized Pareto distribution (GP) and Gamma distribution are estimated. From the results of goodness of fit test and Kolmogorov-Smirnov (K-S) test, AM series can be better fitted by GEV model and POT series can be better fitted by GP model. By the comparison of the precipitation amounts under different return levels, it can be found that the values obtained from POT series are a little larger than the values from AM series, and they can better simulate the observed values in the Huaihe River Basin.

show abstract

“…This exponential dependence on temperature is motivated by the thermodynamic scaling of precipitation extremes under climate change 12 and the observed covariability of daily precipitation extremes with surface temperature 32 . The normalized precipitation ratep is assumed to follow a gamma distribution on wet days 33 , such that its probability density function, P , is given by…”

Section: Derivation Of Theory For Snowfall Extremesmentioning

confidence: 99%

Contrasting responses of mean and extreme snowfall to climate change

O’Gorman

2014

Nature

194

142

View full text Add to dashboard Cite

Snowfall is an important element of the climate system, and one that is expected to change in a warming climate [1][2][3][4] . Both mean snowfall and the intensity distribution of snowfall are important, with heavy snowfall events having particularly large economic and human impacts [5][6][7] .Simulations with climate models indicate that annual-mean snowfall declines with warming in most regions but increases in regions with very low surface temperatures 3,4 . The response of heavy snowfall events to a changing climate, however, is unclear. Here I show that in simulations with climate models under a high-emissions scenario, by the late twenty-first century there are smaller fractional changes in daily snowfall extremes than in mean snowfall over many Northern Hemisphere land regions. For example, for monthly climatological temperatures just below freezing and surface elevations below 1000m, the 99.99th percentile of daily snowfall decreases by 8% in the multimodel median as compared to a 65% reduction in mean snowfall. Both mean and extreme snowfall must decrease for a sufficiently large warming, but the climatological temperature above which snowfall extremes decrease with warming in the simulations is as high as -9 • C as compared to -14 • C for mean snowfall. These results are supported by a physically based theory that is consistent with the observed rain-snow transition. According to the theory, snowfall extremes occur near an optimal temperature that 1 is insensitive to climate warming, and this results in smaller fractional changes for higher percentiles of daily snowfall. The simulated changes in snowfall that I find would influence surface snow and its hazards; these changes also suggest that it may be difficult to detect a regional climate-change signal in snowfall extremes.Extremes of daily precipitation (including liquid and solid precipitation) are found to increase in intensity with climate warming in observations and simulations [8][9][10] , and this is physically consistent with greater saturation specific humidities in a warmer atmosphere [11][12][13] . However, little is known about the physical basis for changes in daily snowfall extremes, their past changes on a global or hemispheric scale, or how they change in global climate-model simulations. Regional observational studies show large interdecadal variations in measures of snowfall extremes 14 , but not necessarily significant long-term trends 15 . Extremes of seasonal-mean snowfall have been studied previously 16, 17 , but extremes on shorter timescales may respond differently 14 . Physically we expect heavy snowfall events to occur in a relatively narrow range of temperatures below the rain-snow transition; at much lower temperatures it is not "too cold to snow", but low saturation specific humidities make heavy snowfall unlikely. However, it is not clear what this means for the response to climate change, and previous studies have differed in their findings as to whether heavy snowfall events are predominantly associated with anomalou...

show abstract

Changes in the Probability of Heavy Precipitation: Important Indicators of Climatic Change

Cited by 342 publications

References 31 publications

A Possible Constraint on Regional Precipitation Intensity Changes under Global Warming

A Possible Constraint on Regional Precipitation Intensity Changes under Global Warming

Spatio-temporal trend and statistical distribution of extreme precipitation events in Huaihe River Basin during 1960–2009

Contrasting responses of mean and extreme snowfall to climate change

Contact Info

Product

Resources

About