How Well Can a Climate Model Simulate an Extreme Precipitation Event: A Case Study Using the Transpose-AMIP Experiment

2018 ◽  
Vol 31 (16) ◽  
pp. 6543-6556 ◽  
Author(s):  
Jian Li ◽  
Haoming Chen ◽  
Xinyao Rong ◽  
Jingzhi Su ◽  
Yufei Xin ◽  
...  
Keyword(s):  
Extreme Precipitation ◽  
Heavy Rainfall ◽  
Climate Model ◽  
Extreme Event ◽  
Spatial Scales ◽  
Precipitation Amount ◽  
Temporal Distribution ◽  
Extreme Precipitation Event ◽  
Precipitation Event ◽  
Climate System Model

A high-impact extreme precipitation event over the Yangtze River valley (YRV) in the midsummer of 2016 is simulated using the Climate System Model of Chinese Academy of Meteorological Sciences (CAMS-CSM). After validation of the model’s capability in reproducing the climatological features of precipitation over the YRV, the Transpose Atmospheric Model Intercomparison Project (T-AMIP)–type experiment, which runs the climate model in the weather forecast mode, is applied to investigate the performance of the climate model in simulating the spatial and temporal distribution of rainfall and the related synoptic circulation. Analyses of T-AMIP results indicate that the model realistically reproduces the heavy rainfall centers of accumulated precipitation amount along the YRV, indicating that the climate model has the ability to simulate the severity of the extreme event. However, the frequency–intensity structure shows similar biases as in the AMIP experiment, especially the underestimation of the maximum hourly intensity. The simulation of two typical heavy rainfall periods during the extreme event is further evaluated. The results illustrate that the model shows different performances during periods dominated by circulation systems of different spatial scales. The zonal propagation of heavy rainfall centers during the first two days, which is related to the eastward movement of the southwest vortex, is well reproduced. However, for another period with a smaller vortex, the model produces an artificial steady heavy rainfall center over the upwind slope of the mountains rather than the observed eastward movement of the precipitation centers.

Impact of Warmer Eastern Tropical Pacific SST on the March 2015 Atacama Floods

2016 ◽  
Vol 144 (11) ◽  
pp. 4441-4460 ◽  
Author(s):  
Deniz Bozkurt ◽  
Roberto Rondanelli ◽  
René Garreaud ◽  
Andrés Arriagada
Keyword(s):  
Extreme Precipitation ◽  
Climate Model ◽  
Atacama Desert ◽  
Tropical Pacific ◽  
Extreme Precipitation Event ◽  
Precipitation Event ◽  
Northern Chile ◽  
Eastern Tropical Pacific ◽  
Sst Anomaly ◽  
Sst Anomalies

Abstract Northern Chile hosts the driest place on Earth in the Atacama Desert. Nonetheless, an extreme precipitation event affected the region on 24–26 March 2015 with 1-day accumulated precipitation exceeding 40 mm in several locations and hourly mean rainfall rates higher than 10 mm h−1, producing floods and resulting in casualties and significant damage. The event is analyzed using ERA-Interim, surface station data, sounding observations, and satellite-based radar. Two main conditions favorable for precipitation were present at the time of the event: (i) a cutoff low (COL) off the coast of northern Chile and (ii) positive sea surface temperature (SST) anomalies over the eastern tropical Pacific. The circulation driven by the COL was strong but not extraordinary. Regional Climate Model, version 4 (RegCM4), is used to test the sensitivity of precipitation to SST anomalies by removing the warm SST anomaly in the eastern tropical Pacific. The cooler simulation produced very similar COL dry dynamics to that simulated in a control run (with observed SST), but suppressed the precipitation by 60%–80% over northern Chile and 100% in parts of the Atacama Desert due to the decreased availability of precipitable water. The results indicate that the warm SST anomaly over the eastern Pacific, favored by the onset of El Niño 2015/16, was instrumental to the extreme precipitation event by providing an anomalous source of water vapor transported to Atacama by the circulation ahead of the COL.

scholarly journals Rapid attribution of the August 2016 flood-inducing extreme precipitation in south Louisiana to climate change

2017 ◽  
Vol 21 (2) ◽  
pp. 897-921 ◽  
Author(s):  
Karin van der Wiel ◽  
Sarah B. Kapnick ◽  
Geert Jan van Oldenborgh ◽  
Kirien Whan ◽  
Sjoukje Philip ◽  
...  
Keyword(s):  
Climate Change ◽  
Extreme Precipitation ◽  
Climate Model ◽  
Gulf Coast ◽  
Global Climate ◽  
Return Time ◽  
Extreme Precipitation Event ◽  
Anthropogenic Climate Change ◽  
Precipitation Event ◽  
Flooding Event

Abstract. A stationary low pressure system and elevated levels of precipitable water provided a nearly continuous source of precipitation over Louisiana, United States (US), starting around 10 August 2016. Precipitation was heaviest in the region broadly encompassing the city of Baton Rouge, with a 3-day maximum found at a station in Livingston, LA (east of Baton Rouge), from 12 to 14 August 2016 (648.3 mm, 25.5 inches). The intense precipitation was followed by inland flash flooding and river flooding and in subsequent days produced additional backwater flooding. On 16 August, Louisiana officials reported that 30 000 people had been rescued, nearly 10 600 people had slept in shelters on the night of 14 August and at least 60 600 homes had been impacted to varying degrees. As of 17 August, the floods were reported to have killed at least 13 people. As the disaster was unfolding, the Red Cross called the flooding the worst natural disaster in the US since Super Storm Sandy made landfall in New Jersey on 24 October 2012. Before the floodwaters had receded, the media began questioning whether this extreme event was caused by anthropogenic climate change. To provide the necessary analysis to understand the potential role of anthropogenic climate change, a rapid attribution analysis was launched in real time using the best readily available observational data and high-resolution global climate model simulations. The objective of this study is to show the possibility of performing rapid attribution studies when both observational and model data and analysis methods are readily available upon the start. It is the authors' aspiration that the results be used to guide further studies of the devastating precipitation and flooding event. Here, we present a first estimate of how anthropogenic climate change has affected the likelihood of a comparable extreme precipitation event in the central US Gulf Coast. While the flooding event of interest triggering this study occurred in south Louisiana, for the purposes of our analysis, we have defined an extreme precipitation event by taking the spatial maximum of annual 3-day inland maximum precipitation over the region of 29–31° N, 85–95° W, which we refer to as the central US Gulf Coast. Using observational data, we find that the observed local return time of the 12–14 August precipitation event in 2016 is about 550 years (95 % confidence interval (CI): 450–1450). The probability for an event like this to happen anywhere in the region is presently 1 in 30 years (CI 11–110). We estimate that these probabilities and the intensity of extreme precipitation events of this return time have increased since 1900. A central US Gulf Coast extreme precipitation event has effectively become more likely in 2016 than it was in 1900. The global climate models tell a similar story; in the most accurate analyses, the regional probability of 3-day extreme precipitation increases by more than a factor of 1.4 due to anthropogenic climate change. The magnitude of the shift in probabilities is greater in the 25 km (higher-resolution) climate model than in the 50 km model. The evidence for a relation to El Niño half a year earlier is equivocal, with some analyses showing a positive connection and others none.

scholarly journals Rapid attribution of the August 2016 flood-inducing extreme precipitation in south Louisiana to climate change

2016 ◽  
Author(s):  
Karin van der Wiel ◽  
Sarah B. Kapnick ◽  
Geert Jan van Oldenborgh ◽  
Kirien Whan ◽  
Sjoukje Philip ◽  
...  
Keyword(s):  
Climate Change ◽  
Extreme Precipitation ◽  
Climate Model ◽  
Gulf Coast ◽  
Global Climate ◽  
Return Time ◽  
Extreme Precipitation Event ◽  
Anthropogenic Climate Change ◽  
Precipitation Event ◽  
Flooding Event

Abstract. A stationary low pressure system and elevated levels of precipitable water provided a nearly continuous source of precipitation over Louisiana, United States (U.S.) starting around 10 August, 2016. Precipitation was heaviest in the region broadly encompassing the city of Baton Rouge, with a three-day maximum found at a station in Livingston, LA (east of Baton Rouge) from 12–14 August, 2016 (648.3 mm, 25.5 inches). The intense precipitation was followed by inland flash flooding and river flooding and in subsequent days produced additional backwater flooding. On 16 August, Louisiana officials reported that 30,000 people had been rescued, nearly 10,600 people had slept in shelters on the night of 14 August, and at least 60,600 homes had been impacted to varying degrees. As of 17 August, the floods were reported to have killed at least thirteen people. As the disaster was unfolding, the Red Cross called the flooding the worst natural disaster in the U.S. since Super Storm Sandy made landfall in New Jersey on 24 October, 2012. Before the floodwaters had receded, the media began questioning whether this extreme event was caused by anthropogenic climate change. To provide the necessary analysis to understand the potential role of anthropogenic climate change, a rapid attribution analysis was launched in real-time using the best readily available observational data and high-resolution global climate model simulations. The objective of this study is to show the possibility of performing rapid attribution studies when both observational and model data, and analysis methods are readily available upon the start. It is the authors aspiration that the results be used to guide further studies of the devastating precipitation and flooding event. Here we present a first estimate of how anthropogenic climate change has affected the likelihood of a comparable extreme precipitation event in the Central U.S. Gulf Coast. While the flooding event of interest triggering this study occurred in south Louisiana, for the purposes of our analysis, we have defined an extreme precipitation event by taking the spatial maximum of annual 3-day inland maximum precipitation over the region: 29–31º N, 85–95º W, which we refer to as the Central U.S. Gulf Coast. Using observational data, we find that the observed local return time of the 12–14 August precipitation event in 2016 is about 550 years (95 % confidence interval (C.I.): 450–1450). The probability for an event like this to happen anywhere in the region is presently 1 in 30 years (C.I. 11–110). We estimate that these probabilities and the intensity of extreme precipitation events of this return time have increased since 1900. A Central U.S. Gulf Coast extreme precipitation event has effectively become more likely in 2016 than it was in 1900. The global climate models tell a similar story, with the regional probability of 3-day extreme precipitation increasing due to anthropogenic climate change by a factor of more than a factor 1.4 in the most accurate analyses. The magnitude of the shift in probabilities is greater in the 25 km (higher resolution) climate model than in the 50 km model. The evidence for a relation to El Niño half a year earlier is equivocal, with some analyses showing a positive connection and others none.

scholarly journals Hydro-meteorologic Assessment of October 2015 Extreme Precipitation Event on Santee Experimental Forest Watersheds, South Carolina

2016 ◽  
pp. 19-30 ◽  
Author(s):  
Devendra M. Amatya ◽  
Charles A. Harrison ◽  
Carl C. Trettin
Keyword(s):  
South Carolina ◽  
Extreme Precipitation ◽  
Land Surface ◽  
Extreme Event ◽  
Extreme Precipitation Event ◽  
Precipitation Event ◽  
Storm Events ◽  
Maximum Rainfall ◽  
Natural Resource Conservation Service ◽  
Peak Flood

The extreme precipitation event on October 3-4, 2015, likely resulting from the convergence of a persistent deep easterly flow, the continuous supply of moisture, the terrain, and the circulation associated with Hurricane Joaquin off the eastern Atlantic Coast (http://cms.met.psu. edu/sref/severe/2015/04Oct2015.pdf) resulted in extreme and prolonged flooding in many parts of South Carolina. We present the precipitation amounts and intensities observed at four gauges on the USDA Forest Service Santee Experimental Forest (SEF) watersheds during this extreme event in conjunction with the antecedent conditions for 5 days prior to the event. All four rain gauges recorded 24-hr maximum rainfall of 340 mm or more during October 3-4, exceeding the Natural Resource Conservation Service (NRCS) 100-yr 24-hr design rainfall data. The 5-day antecedent measured rainfall prior to October 3 already exceeded 170 mm in three of the four gauges resulting in weekly (September 28-October 4 totals exceeding 625 mm in all gauges. Local surface water ponding of as much as 0.46 m above land surface was observed on one of the groundwater wells at an elevation of 10.395 m. The recorded stage heights at one 1st order (WS 80) and one- 2nd order (WS79) watershed gauging stations over topped the compound weir (WS 80) and weir/culvert (WS 79) outlets, with the highest stages coming near the invert of the bridge above the weir gauges and inundating large riparian areas upstream of them. Preliminary calculations yielded peak flood discharges of at least 17.4 m3 s-1 (10.9 m3 s-1 km-2 or 996 cfs/mi2) and 33.9 m3 s-1 (6.8 m3 s-1 km-2 or 620 cfs/mi2) for a 1st and 2nd order watersheds, respectively. These values exceeded the previously measured peak discharges within a 25-year record of 3.8 m3 s-1 and 11.2 m3 s-1 for these two watersheds that were recorded on October 24, 2008. When compared with computed design discharges the estimated peak flood discharges on October 4, 2015 exceed the values for a 500-yr return period. These extreme peak flood discharge results may provide insights for a need to revisit existing approaches for hydrologic analyses and design of cross drainage and other water management structures as concerns about extreme storm events resulting from global warming continue.

scholarly journals Modelling the extreme precipitation event over Madeira Island on 20 February 2010

2011 ◽  
Vol 11 (9) ◽  
pp. 2437-2452 ◽  
Author(s):  
T. Luna ◽  
A. Rocha ◽  
A. C. Carvalho ◽  
J. A. Ferreira ◽  
J. Sousa
Keyword(s):  
Extreme Precipitation ◽  
Extreme Event ◽  
Wrf Model ◽  
Weather Conditions ◽  
Horizontal Resolution ◽  
Extreme Precipitation Event ◽  
Cumulus Parameterization ◽  
Precipitation Event ◽  
Madeira Island ◽  
Strong Winds

Abstract. In the morning of the 20 February of 2010 an extreme precipitation event occurred over Madeira Island. This event triggered several flash floods and mudslides in the southern parts of the island, resulting in 42 confirmed deaths, 100 injured, and at least 8 people still missing. These extreme weather conditions were associated to a weather frontal system moving northeastwards embedded in a low pressure area centered in the Azores archipelago. This storm was one in a series of such storms that affected Portugal, Spain, Morocco and the Canary islands causing flooding and strong winds. These storms were bolstered by an unusually strong sea surface temperature gradient across the Atlantic Ocean. In this study, the WRF model is used to evaluate the intensity and predictability of this precipitation extreme event over the island. The synoptic/orographic nature of the precipitation is also evaluated, as well as the sensitivity of the model to horizontal resolution and cumulus parameterization. Orography was found to be the main factor explaining the occurrence, amplitude and phase of precipitation over the Island.

scholarly journals Response of a mixed grass prairie to an extreme precipitation event

Ecosphere ◽  
2015 ◽  
Vol 6 (10) ◽  
pp. art172 ◽  
Author(s):  
Amy L. Concilio ◽  
Janet S. Prevéy ◽  
Peter Omasta ◽  
James O'Connor ◽  
Jesse B. Nippert ◽  
...  
Keyword(s):  
Extreme Precipitation ◽  
Extreme Precipitation Event ◽  
Precipitation Event ◽  
Mixed Grass Prairie ◽  
Mixed Grass

scholarly journals Polarimetric Radar Signatures and Performance of Various Radar Rainfall Estimators during an Extreme Precipitation Event over the Thousand-Island Lake Area in Eastern China

2019 ◽  
Vol 11 (20) ◽  
pp. 2335 ◽  
Author(s):  
Yabin Gou ◽  
Haonan Chen ◽  
Jiafeng Zheng
Keyword(s):  
Extreme Precipitation ◽  
Rapid Development ◽  
Eastern China ◽  
Rain Gauge ◽  
Extreme Precipitation Event ◽  
Precipitation Event ◽  
Distribution Data ◽  
Lake Area ◽  
Polarimetric Radar ◽  
Radar Rainfall

Polarimetric radar provides more choices and advantages for quantitative precipitation estimation (QPE) than single-polarization radar. Utilizing the C-band polarimetric radar in Hangzhou, China, six radar QPE estimators based on the horizontal reflectivity (ZH), specific attenuation (AH), specific differential phase (KDP), and double parameters that further integrate the differential reflectivity (ZDR), namely, R(ZH, ZDR), R(KDP, ZDR), and R(AH, ZDR), are investigated for an extreme precipitation event that occurred in Eastern China on 1 June 2016. These radar QPE estimators are respectively evaluated and compared with a local rain gauge network and drop size distribution data observed by two disdrometers. The results show that (i) although R(AH, ZDR) underestimates in the light rain scenario, it performs the best among all radar QPE estimators according to the normalized mean error; (ii) the optimal radar rainfall relationship and consistency between radar measurements aloft and their surface counterparts are both required to obtain accurate rainfall estimates close to the ground. The contamination from melting layer on AH and KDP can make R(AH), R(AH, ZDR), R(KDP), and R(KDP, ZDR) less effective than R(ZH) and R(ZH,ZDR). Instead, adjustments of the α coefficient can partly reduce such impact and hence render a superior AH–based rainfall estimator; (iii) each radar QPE estimator may outperform others during some time intervals featured by particular rainfall characteristics, but they all tend to underestimate rainfall if radar fails to capture the rapid development of rainstorms.

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