Evaluation of regional, very heavy precipitation events in the upper Mississippi region using climate model ensembles

Abstract Heavy precipitation is a major hazard over Europe. It is well established that climate model projections indicate a tendency toward more extreme daily rainfall events. It is still uncertain, however, how this changing intensity translates at the subdaily time scales. The main goal of the present study is to examine possible differences in projected changes in intense precipitation events over Europe at the daily and subdaily (3-hourly) time scales using a state-of-the-science climate model. The focus will be on one representative concentration pathway (RCP8.5), considered as illustrative of a high rate of increase in greenhouse gas concentrations over this century. There are statistically significant differences in intense precipitation projections (up to 40%) when comparing the results at the daily and subdaily time scales. Over northeastern Europe, projected precipitation intensification at the 3-hourly scale is lower than at the daily scale. On the other hand, Spain and the western seaboard exhibit an opposite behavior, with stronger intensification at the 3-hourly scale rather than the daily scale. While the mean properties of the precipitation distributions are independent of the analyzed frequency, projected precipitation intensification exhibits regional differences. This finding has implications for the extrapolation of impacts of intense precipitation events, given the daily time scale at which the analyses are usually performed.

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Reliability and importance of structural diversity of climate model ensembles

Climate Dynamics ◽

10.1007/s00382-013-1733-9 ◽

2013 ◽

Vol 41 (9-10) ◽

pp. 2745-2763 ◽

Cited By ~ 18

Author(s):

Tokuta Yokohata ◽

James D. Annan ◽

Matthew Collins ◽

Charles S. Jackson ◽

Hideo Shiogama ◽

...

Keyword(s):

Climate Model ◽

Structural Diversity ◽

Climate Model Ensembles ◽

Model Ensembles

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Correlations between patterns of 19th and 20th century surface temperature change and HadCM2 Climate Model ensembles

Geophysical Research Letters ◽

10.1029/2000gl011861 ◽

2001 ◽

Vol 28 (6) ◽

pp. 1007-1010 ◽

Cited By ~ 3

Author(s):

T. C. Johns ◽

J. M. Gregory ◽

P. A. Stott ◽

J. F. B. Mitchell

Keyword(s):

Surface Temperature ◽

Temperature Change ◽

20Th Century ◽

Climate Model ◽

Surface Temperature Change ◽

Climate Model Ensembles ◽

Model Ensembles ◽

19Th And 20Th Century

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A Comparison of CMIP3 Simulations of Precipitation over North America with Observations: Daily Statistics and Circulation Features Accompanying Extreme Events

Journal of Climate ◽

10.1175/jcli-d-12-00374.1 ◽

2013 ◽

Vol 26 (10) ◽

pp. 3209-3230 ◽

Cited By ~ 26

Author(s):

Anthony M. DeAngelis ◽

Anthony J. Broccoli ◽

Steven G. Decker

Keyword(s):

North America ◽

Extreme Events ◽

Extreme Precipitation ◽

Large Scale ◽

Climate Model ◽

Heavy Precipitation ◽

Convective Precipitation ◽

Southern Mexico ◽

Extreme Precipitation Events ◽

Precipitation Events

Abstract Climate model simulations of daily precipitation statistics from the third phase of the Coupled Model Intercomparison Project (CMIP3) were evaluated against precipitation observations from North America over the period 1979–99. The evaluation revealed that the models underestimate the intensity of heavy and extreme precipitation along the Pacific coast, southeastern United States, and southern Mexico, and these biases are robust among the models. The models also overestimate the intensity of light precipitation events over much of North America, resulting in fairly realistic mean precipitation in many places. In contrast, heavy precipitation is simulated realistically over northern and eastern Canada, as is the seasonal cycle of heavy precipitation over a majority of North America. An evaluation of the simulated atmospheric dynamics and thermodynamics associated with extreme precipitation events was also conducted using the North American Regional Reanalysis (NARR). The models were found to capture the large-scale physical mechanisms that generate extreme precipitation realistically, although they tend to overestimate the strength of the associated atmospheric circulation features. This suggests that climate model deficiencies such as insufficient spatial resolution, inadequate representation of convective precipitation, and overly smoothed topography may be more important for biases in simulated heavy precipitation than errors in the large-scale circulation during extreme events.

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Linkage between Projected Precipitation and Atmospheric Thermodynamic Changes

Journal of Climate ◽

10.1175/jcli-d-19-0785.1 ◽

2020 ◽

Vol 33 (16) ◽

pp. 7155-7178

Author(s):

Jiao Chen ◽

Aiguo Dai ◽

Yaocun Zhang

Keyword(s):

Climate Model ◽

Convective Available Potential Energy ◽

Heavy Precipitation ◽

Percentage Increase ◽

Tropical Oceans ◽

Hourly Data ◽

Frequency Changes ◽

Precipitation Changes ◽

Underlying Processes ◽

Precipitation Events

AbstractLight–moderate precipitation is projected to decrease whereas heavy precipitation may increase under greenhouse gas (GHG)-induced global warming, while atmospheric convective available potential energy (CAPE) over most of the globe and convective inhibition (CIN) over land are projected to increase. The underlying processes for these precipitation changes are not fully understood. Here, projected precipitation changes are analyzed using 3-hourly data from simulations by a fully coupled climate model, and their link to the CAPE and CIN changes is examined. The model approximately captures the spatial patterns in the mean precipitation frequencies and the significant correlation between the precipitation frequencies or intensity and CAPE over most of the globe or CIN over tropical oceans seen in reanalysis, and it projects decreased light–moderate precipitation (0.01 1 mm h−1) in a warmer climate. Results show that most of the light–moderate precipitation events occur under low-CAPE and/or low-CIN conditions, which are projected to decrease greatly in a warmer climate as increased temperature and humidity shift many of such cases into moderate–high CAPE or CIN cases. This results in large decreases in the light–moderate precipitation events. In contrast, increases in heavy precipitation result primarily from its increased probability under given CAPE and CIN, with a secondary contribution from the CAPE/CIN frequency changes. The increased probability for heavy precipitation partly results from a shift of the precipitation histogram toward higher intensity that could result from a uniform percentage increase in precipitation intensity due to increased water vapor in a warmer climate.

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On the role of CO2 in enhancing the temporal clustering of heavy precipitation across Europe

10.5194/egusphere-egu2020-2764 ◽

2020 ◽

Author(s):

Zhiqi Yang ◽

Gabriele Villarini

Keyword(s):

Climate Model ◽

Cox Regression ◽

Heavy Precipitation ◽

The Arctic ◽

Cmip5 Models ◽

Economic Losses ◽

Climate Modes ◽

Temporal Clustering ◽

Simulated Precipitation ◽

Precipitation Events

Heavy precipitation has increased across many areas of the world, not only in terms of amounts but also of intensity and frequency, causing billions of dollars in economic losses and numerous fatalities. Our ability to prepare for and adapt to these events is tied to our understanding of the physical processes responsible for these events, and how they may respond to changes in anthropogenic forcings. Here we focus on the temporal clustering of heavy precipitation across Europe, highlight what the major climate drivers responsible for it are, and how it may change in response to changes in the concentration of greenhouse gasses. More specifically, we use a peak over threshold approach to identify heavy precipitation events, and Cox regression to relate the occurrence of these events to four climate modes that have been connected with the occurrence of heavy precipitation across Europe: the Arctic Oscillation (AO), the North Atlantic Oscillation (NAO), the East Atlantic (EA) pattern, and the Scandinavia pattern (SCAND). We use outputs from the Coupled Model Intercomparison Project Phase 5 (CMIP5), and experiments that allow us to focus on the response to CO2 (pre-industrial, 1pctCO2, abrupt4&#215;CO2). To further detect the effects of downscaling on model-simulated precipitation, we also considered the accuracy of the EURO-CORDEX regional climate model (RCM) on capturing the temporal clustering in heavy precipitation across Europe. We find that: 1) the CMIP5 models can capture the temporal clustering in heavy precipitation across Europe as a function of these four climate modes; 2) the increases in CO2 are expected to lead to a strengthening of the relationship between the climate modes and the occurrence of heavy precipitation events; 3) the response to an abrupt increase in CO2 is generally stronger compared to a more gradual one.

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The implications of soil erosion model conceptualization, bias-correction methods and climate model ensembles on soil erosion projections under climate change

10.5194/egusphere-egu2020-8158 ◽

2020 ◽

Author(s):

Joris de Vente ◽

Joris Eekhout

Keyword(s):

Climate Change ◽

Soil Erosion ◽

Extreme Precipitation ◽

Climate Models ◽

Climate Model ◽

Erosion Model ◽

Distribution Mapping ◽

Climate Model Ensembles ◽

Model Ensembles ◽

The Impact

Climate models project increased extreme precipitation for the coming decades, which may lead to higher soil erosion in many locations worldwide. The impact of climate change on soil erosion is most often assessed by applying a soil erosion model forced by bias-corrected climate model output. A literature review among more than 100 papers showed that many studies use different soil erosion models, bias-correction methods and climate model ensembles. In this study, we assessed how these differences affect the outcome of climate change impact assessments on soil erosion. The study was performed in two contrasting Mediterranean catchments (SE Spain), where climate change is projected to lead to a decrease in annual precipitation sum and an increase in extreme precipitation, based on the RCP8.5 emission scenario. First, we assessed the impact of soil erosion model selection using the three most widely used model concepts, i.e. a model forced by precipitation (RUSLE), a model forced by runoff (MUSLE), and a model forced by precipitation and runoff (MMF). Depending on the model, soil erosion in the study area is projected to decrease (RUSLE) or increase (MUSLE and MMF). The differences between the model projections are inherently a result of their model conceptualization, such as a decrease of soil loss due to decreased annual precipitation sum (RUSLE) and an increase of soil loss due to increased extreme precipitation and, consequently, increased runoff (MUSLE). An intermediate result is obtained with MMF, where a projected decrease in detachment by raindrop impact is counteracted by a projected increase in detachment by runoff. Second, we evaluated the implications of three bias&#8208;correction methods, i.e. delta change, quantile mapping and scaled distribution mapping. Scaled distribution mapping best reproduces the raw climate change signal, in particular for extreme precipitation. Depending on the bias&#8208;correction method, soil erosion is projected to decrease (delta change) or increase (quantile mapping and scaled distribution mapping). Finally, we assessed the effect of climate model ensembles on soil erosion projections. We showed that individual climate models may project opposite changes with respect to the ensemble average, hence, climate model ensembles are essential in soil erosion impact assessments to account for climate model uncertainty. We conclude that in climate change impact assessments it is important to select a soil erosion model that is forced by both precipitation and runoff, which under climate change may have a contrasting effect on soil erosion. Furthermore, the impact of climate change on soil erosion can only accurately be assessed with a bias&#8208;correction method that best reproduces the projected climate change signal, in combination with a representative ensemble of climate models.

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Heat Waves in Florida: Climatology, Trends, and Related Precipitation Events

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-18-0165.1 ◽

2019 ◽

Vol 58 (3) ◽

pp. 447-466 ◽

Cited By ~ 5

Author(s):

Shealynn R. Cloutier-Bisbee ◽

Ajay Raghavendra ◽

Shawn M. Milrad

Keyword(s):

Heat Wave ◽

Large Scale ◽

Climate Model ◽

Heat Waves ◽

Heavy Precipitation ◽

Daily Maximum ◽

Recent Climate ◽

Climate Model Simulations ◽

Southern Florida ◽

Precipitation Events

AbstractHeat waves are increasing in frequency, duration, and intensity and are strongly linked to anthropogenic climate change. However, few studies have examined heat waves in Florida, despite an older population and increasingly urbanized land areas that make it particularly susceptible to heat impacts. Heavy precipitation events are also becoming more frequent and intense; recent climate model simulations showed that heavy precipitation in the three days after a Florida heat wave follow these trends, yet the underlying dynamic and thermodynamic mechanisms have not been investigated. In this study, a heat wave climatology and trend analysis are developed from 1950 to 2016 for seven major airports in Florida. Heat waves are defined based on the 95th percentile of daily maximum, minimum, and mean temperatures. Results show that heat waves exhibit statistically significant increases in frequency and duration at most stations, especially for mean and minimum temperature events. Frequency and duration increases are most prominent at Tallahassee, Tampa, Miami, and Key West. Heat waves in northern Florida are characterized by large-scale continental ridging, while heat waves in central and southern Florida are associated with a combination of a continental ridge and a westward extension of the Bermuda–Azores high. Heavy precipitation events that follow a heat wave are characterized by anomalously large ascent and moisture, as well as strong instability. Light precipitation events in northern Florida are characterized by advection of drier air from the continent, while over central and southern Florida, prolonged subsidence is the most important difference between heavy and light events.

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Representation of U.S. Warm Temperature Extremes in Global Climate Model Ensembles

Journal of Climate ◽

10.1175/jcli-d-18-0075.1 ◽

2019 ◽

Vol 32 (9) ◽

pp. 2591-2603 ◽

Cited By ~ 1

Author(s):

Emily Hogan ◽

Robert E. Nicholas ◽

Klaus Keller ◽

Stephanie Eilts ◽

Ryan L. Sriver

Keyword(s):

United States ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Extreme Temperature ◽

The United States ◽

Temperature Extremes ◽

Warm Temperature ◽

Climate Model Ensembles ◽

Model Ensembles

Abstract Extreme temperature events can have considerable negative impacts on sectors such as health, agriculture, and transportation. Observational evidence indicates the severity and frequency of warm extremes are increasing over much of the United States, but there are sizeable challenges both in estimating extreme temperature changes and in quantifying the relevant associated uncertainties. This study provides a simple statistical framework using a block maxima approach to analyze the representation of warm temperature extremes in several recent global climate model ensembles. Uncertainties due to structural model differences, grid resolution, and internal variability are characterized and discussed. Results show that models and ensembles differ greatly in the representation of extreme temperature over the United States, and variability in tail events is dependent on time and anthropogenic warming, which can influence estimates of return periods and distribution parameter estimates using generalized extreme value (GEV) distributions. These effects can considerably influence the uncertainty of model hindcasts and projections of extremes. Several idealized regional applications are highlighted for evaluating ensemble skill and trends, based on quantile analysis and root-mean-square errors in the overall sample and the upper tail. The results are relevant to regional climate assessments that use global model outputs and that are sensitive to extreme warm temperature. Accompanying this manuscript is a simple toolkit using the R statistical programming language for characterizing extreme events in gridded datasets.

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