100-year Return Level Research Articles

Hurricane risk variability along the Gulf of Mexico coastline.

Hurricane risk characteristics are examined across the U. S. Gulf of Mexico coastline using a hexagonal tessellation. Using an extreme value model, parameters are collected representing the rate or λ (frequency), the scale or σ (range), and the shape or ξ (intensity) of the extreme wind distribution. These latent parameters and the 30-year return level are visualized across the grid. The greatest 30-year return levels are located toward the center of the Gulf of Mexico, and for inland locations, along the borders of Louisiana, Mississippi, and Alabama. Using a geographically weighted regression model, the relationship of these parameters to sea surface temperature (SST) is found to assess sensitivity to change. It is shown that as SSTs increase near the coast, the frequency of hurricanes in these grids decrease significantly. This reinforces the importance of SST in areas of likely tropical cyclogenesis in determining the number of hurricanes near the coast, along with SSTs along the lifespan of the storm, rather than simply local SST. The range of hurricane wind speeds experienced near Florida is shown to increase with increasing SSTs (insignificant), suggesting that increased temperatures may allow hurricanes to maintain their strength as they pass over the Florida peninsula. The modifiable areal unit problem is assessed using multiple grid sizes. Moran’s I and the local statistic G are calculated to examine spatial autocorrelation in the parameters. This research opens up future questions regarding rapid intensification and decay close to the coast and the relationship to changing SSTs.

Hazard analysis of typhoon-related external events using extreme value theory

Abstract Background After the Fukushima accident, the importance of hazard analysis for extreme external events was raised. Methods To analyze typhoon-induced hazards, which are one of the significant disasters of East Asian countries, a statistical analysis using the extreme value theory, which is a method for estimating the annual exceedance frequency of a rare event, was conducted for an estimation of the occurrence intervals or hazard levels. For the four meteorological variables, maximum wind speed, instantaneous wind speed, hourly precipitation, and daily precipitation, the parameters of the predictive extreme value theory models were estimated. Results The 100-year return levels for each variable were predicted using the developed models and compared with previously reported values. It was also found that there exist significant long-term climate changes of wind speed and precipitation. Conclusion A fragility analysis should be conducted to ensure the safety levels of a nuclear power plant for high levels of wind speed and precipitation, which exceed the results of a previous analysis.

Spatiotemporal patterns of extreme hurricanes impacting US coastal cities

US coastal cities are regularly subjected to destruction by tropical cyclones. The risk of tropical cyclone winds varies along the length of the coastline. We analyze landfalling North Atlantic basin tropical cyclones whose intensities are considered extreme relative to their landfall location. To be considered extreme, a tropical cyclone’s wind speed must exceed the 50-year return level for a given city. Of interest is the spatial and temporal patterns of these extreme hurricane wind events for fifteen coastal cities, which are organized into four coastal regions: Northeast Atlantic, Southeast Atlantic, Florida, and Gulf. Findings suggest that extreme hurricanes along the Florida and Atlantic coasts cluster in time, specifically decades, while there is no temporal clustering detected along the Gulf. Atlantic coast hurricane clusters are in part due to the likelihood of one intense hurricane impacting multiple coastal cities, which is unlikely to happen along the Gulf due to the alignment of the coast. It is also unlikely for an intense hurricane to impact multiple Florida cities as an extreme hurricane, suggesting a physical mechanism enables the temporal clustering seen here. The results of this work advocate for annual and decadal hurricane risk to include: (1) the likelihood of temporal clusters of extreme hurricanes along the Atlantic and Florida coasts and (2) extreme hurricanes impacting multiple cities along the Atlantic coast.

A Bayesian analysis of nonstationary generalized extreme value distribution of annual spring discharge minima

In many areas throughout the world, extensive groundwater pumping has facilitated significant social development and economic growth, but has typically resulted in a decrease in groundwater level and a decline and change in spring discharge. The declining trend and changing seasonality of spring discharge lead to nonstationarity in hydrological processes. When we apply the generalized extreme value distribution to karst spring discharge, several assumptions including independence, identical distribution, and stationarity must be met. To investigate the response of spring discharge to extensive groundwater development and extreme climate change, a nonstationary generalized extreme value (NSGEV) model is proposed by assuming the location parameter to be the sum of a linear and a periodic temporal function to describe the declining trend and seasonality of spring discharge. Bayes’ theorem treats parameters as random variables and provides ways to convert the prior distribution of parameters into a posterior distribution. Statistical inferences based on posterior distribution can provide a more comprehensive representation of the parameters. In this paper we use Markov Chain Monte Carlo method, which can solve high-dimensional integral computation in the Bayes equation, to estimate the parameters of NSGEV model. Then the NSGEV model was used to calculate the distribution of minimum discharge values of Niangziguan Springs in North China. The results show that NSGEV model is able to represent the distribution of minimum values and to predict the cessation time of Niangziguan Springs discharge with two controllable variables: time and return period. With a 100-year return level, flow cessation of Niangziguan Springs would occur in April 2022. Moreover, the probability of Niangziguan Springs discharge cessation is 1/27 in 2025, and 1/19 in 2030. This implies that the probability of Niangziguan Springs cessation will increase dramatically with time.

Seasonal and extreme precipitation characteristics for the watersheds of the Canadian Prairie Provinces as simulated by the NARCCAP multi-RCM ensemble

This study evaluates projected changes to characteristics of winter, spring, summer and fall seasonal precipitation and rain and snow dominated daily precipitation extremes for 47 watersheds, located mainly in the Alberta, Saskatchewan and Manitoba provinces of Canada, using a multi-Regional Climate Model (RCM) ensemble available through the North American Regional Climate Change Assessment Program. The set of simulations considered includes those performed with the six participating RCMs for the 1980–2004 period driven by National Centre for Environmental Prediction reanalysis II and those driven by four Atmosphere–Ocean General Circulation Models (AOGCMs) for the 1971–2000 and 2041–2070 periods. For precipitation extremes, regional frequency analysis is used to develop projected changes to selected 10-, 30- and 50-year return levels of rain and snow dominated extremes (i.e. RDEs and SDEs) separately. The uncertainties due to internal dynamics and physics of the RCMs and those due to the lateral boundary data from driving AOGCMs are studied at the watershed level and evaluated in terms of coefficient of variation on the basis of a multi-RCM ensemble. In general, the structural uncertainty appears to be larger than that associated with the choice of the driving AOGCM for most of the precipitation characteristics considered. Analyses of multi-RCM ensemble-averaged projected changes to mean seasonal precipitation and various return levels of RDEs and SDEs show an increase over nearly all the study domain. The changes to seasonal precipitation are not generally found statistically significant at 5 % significance level but those for RDEs and SDEs are found significant more often for return levels of smaller return period (10-year) compared to those of larger return period (50-year). It is expected that changes in seasonal and extreme precipitation characteristics will have important implications for managing regional water security-related issues in the Canadian Prairie Provinces.

Sources of 21st century regional sea-level rise along the coast of northwest Europe

Abstract. Changes in both global and regional mean sea level, and changes in the magnitude of extreme flood heights, are the result of a combination of several distinct contributions most, but not all, of which are associated with climate change. These contributions include effects in the solid earth, gravity field, changes in ocean mass due to ice loss from ice sheets and glaciers, thermal expansion, alterations in ocean circulation driven by climate change and changing freshwater fluxes, and the intensity of storm surges. Due to the diverse range of models required to simulate these systems, the contributions to sea-level change have usually been discussed in isolation rather than in one self-consistent assessment. Focusing on the coastline of northwest Europe, we consider all the processes mentioned above and their relative impact on 21st century regional mean sea levels and the 50-year return flood height. As far as possible our projections of change are derived from process-based models forced by the A1B emissions scenario to provide a self-consistent comparison of the contributions. We address uncertainty by considering both a mid-range and an illustrative high-end combination of the different components. For our mid-range ice loss scenario we find that thermal expansion of seawater is the dominant contributor to change in northwest European sea level by 2100. However, the projected contribution to extreme sea level, due to changes in storminess alone, is in some places significant and comparable to the global mean contribution of thermal expansion. For example, under the A1B emissions scenario, by 2100, change in storminess contributes around 15 cm to the increase in projected height of the 50-year storm surge on the west coast of the Jutland Peninsula, compared with a contribution of around 22 cm due to thermal expansion and a total of 58 cm from all of the contributions we consider. An illustrative combination of our high-end projections suggests increases in the 50-year return level of 86 cm at Sheerness, 95 cm at Roscoff, 106 cm at Esbjerg, and 67cm at Bergen. The notable regional differences between these locations arise primarily from differences in the rates of vertical land movement and changes in storminess.

Regional frequency analysis of extreme groundwater levels.

Flood risk is generally perceived as being a consequence of surface water inundation. However, large damage is also caused by high groundwater levels. In surface hydrology, statistical frequency analysis is a standard tool to estimate discharge with a given return period or exceedance probability. First, a suitable probability distribution is fit to a series of annual maximum peaks. Second, this distribution is used to determine the discharge corresponding to the desired return period. Where only short series of recorded data are available, the estimates can often be improved by regional frequency analysis (RFA). Unfortunately, there is little information in the literature on analogous approaches for the estimation of extreme groundwater levels. In this contribution, the applicability of l-moments-based RFA for the estimation of extreme groundwater levels is investigated. The main issues specific to groundwater levels are (1) appropriate transformation of the data, (2) criteria for identification of statistically homogeneous regions, (3) consideration of correlation between sites, and (4) choice of distribution function. This study is based on data from more than 1100 observation sites in four shallow Austrian Aquifers with a record length of 10 to 50 years. Results show that homogeneous regions for l-moments-based RFA can be identified covering about one half of the total area of the aquifers. The confidence intervals for the 30- and 100-year return levels can be significantly reduced by RFA. Out of the four investigated distribution functions, none is to be preferred generally.

A hierarchical Bayesian approach for the analysis of climate change impact on runoff extremes

In this study the impact of climate change on runoff extremes is investigated over the Pacific Northwest (PNW). This paper aims to address the question of how the runoff extremes change in the future compared to the historical time period, investigate the different behaviors of the regional climate models (RCMs) regarding the runoff extremes and assess the seasonal variations of runoff extremes. Hydrologic modeling is performed by the variable infiltration capacity (VIC) model at a 1/8° resolution and the model is driven by climate scenarios provided by the North American Regional Climate Change Assessment Program (NARCCAP) including nine regional climate model (RCM) simulations. Analysis is performed for both the historical (1971–2000) and future (2041–2070) time periods. Downscaling of the climate variables including precipitation, maximum and minimum temperature and wind speed is done using the quantile-mapping (QM) approach. A spatial hierarchical Bayesian model is then developed to analyse the annual maximum runoff in different seasons for both historical and future time periods. The estimated spatial changes in extreme runoffs over the future period vary depending on the RCM driving the hydrologic model. The hierarchical Bayesian model characterizes the spatial variations in the marginal distributions of the General Extreme Value (GEV) parameters and the corresponding 100-year return level runoffs. Results show an increase in the 100-year return level runoffs for most regions in particular over the high elevation areas during winter. The Canadian portions of the study region reflect higher increases during spring. However, reduction of extreme events in several regions is projected during summer. Copyright © 2013 John Wiley & Sons, Ltd.

A copula-based multivariate analysis of Canadian RCM projected changes to flood characteristics for northeastern Canada

In the present work, climate change impacts on three spring (March–June) flood characteristics, i.e. peak, volume and duration, for 21 northeast Canadian basins are evaluated, based on Canadian regional climate model (CRCM) simulations. Conventional univariate frequency analysis for each flood characteristic and copula based bivariate frequency analysis for mutually correlated pairs of flood characteristics (i.e. peak–volume, peak–duration and volume–duration) are carried out. While univariate analysis is focused on return levels of selected return periods (5-, 20- and 50-year), the bivariate analysis is focused on the joint occurrence probabilities P1 and P2 of the three pairs of flood characteristics, where P1 is the probability of any one characteristic in a pair exceeding its threshold and P2 is the probability of both characteristics in a pair exceeding their respective thresholds at the same time. The performance of CRCM is assessed by comparing ERA40 (the European Centre for Medium-Range Weather Forecasts 40-year reanalysis) driven CRCM simulated flood statistics and univariate and bivariate frequency analysis results for the current 1970–1999 period with those observed at selected 16 gauging stations for the same time period. The Generalized Extreme Value distribution is selected as the marginal distribution for flood characteristics and the Clayton copula for developing bivariate distribution functions. The CRCM performs well in simulating mean, standard deviation, and 5-, 20- and 50-year return levels of flood characteristics. The joint occurrence probabilities are also simulated well by the CRCM. A five-member ensemble of the CRCM simulated streamflow for the current (1970–1999) and future (2041–2070) periods, driven by five different members of a Canadian Global Climate Model ensemble, are used in the assessment of projected changes, where future simulations correspond to A2 scenario. The results of projected changes, in general, indicate increases in the marginal values, i.e. return levels of flood characteristics, and the joint occurrence probabilities P1 and P2. It is found that the future marginal values of flood characteristics and P1 and P2 values corresponding to longer return periods will be affected more by anthropogenic climate change than those corresponding to shorter return periods but the former ones are subjected to higher uncertainties.

Analysis of streamflow characteristics over Northeastern Canada in a changing climate

An analysis of streamflow characteristics (i.e. mean annual and seasonal flows and extreme high and low flows) in current and future climates for 21 watersheds of north-east Canada covering mainly the province of Quebec is presented in this article. For the analysis, streamflows are derived from a 10-member ensemble of Canadian Regional Climate Model (CRCM) simulations, driven by the Canadian Global Climate Model simulations, of which five correspond to current 1970–1999 period, while the other five correspond to future 2041–2070 period. For developing projected changes of streamflow characteristics from current to future periods, two different approaches are used: one based on the concept of ensemble averaging while the other approach is based on merged samples of current and similarly future simulations following multiple comparison tests. Verification of the CRCM simulated streamflow characteristics for the 1970–1999 period suggests that the model simulated mean hydrographs and high flow characteristics compare well with those observed, while the model tends to underestimate low flow extremes. Results of projected changes to mean annual streamflow suggest statistically significant increases nearly all over the study domain, while those for seasonal streamflow show increases/decreases depending on the season. Two- and 5-year return levels of 15-day low flows are projected to increase significantly over most part of the study domain, though the changes are small in absolute terms. Based on the ensemble averaging approach, changes to 10- and 30-year return levels of high flows are not generally found significant. However, when a similar analysis is performed using longer samples, significant increases to high flow return levels are found mainly for northernmost watersheds. This study highlights the need for longer samples, particularly for extreme events in the development of robust projections.

Return period estimates of extreme sea level along the east coast of India from numerical simulations

Estimates of return periods of extreme sea level events along the coast are useful for impact assessment. In this study, a vertically integrated 2D model was developed for the simulation of storm surges in the Bay of Bengal. The bathymetry for the model was derived from an improved ETOPO-5 data set, which was prepared in our earlier work. The meteorological forcing for the model was obtained from the cyclone model of Holland using the data available for 136 low-pressure systems that occurred during 1974–2000 in the Bay of Bengal. The simulated total sea level and the surge component were obtained for each event. The simulated peak levels showed good agreement with the observations available at few stations. The annual maxima of sea levels, extracted from the simulations, were fitted with Gumbel distribution using r-largest annual maxima method to estimate the 5- and 50-year return periods of extreme events at 26 stations along the east coast of India. The return periods estimated from simulated sea levels showed good agreement with those obtained from observations. The 5- and 50-year return levels of total sea level along the east coast of India show a considerable increase from south to north, with the 50-year return total sea levels being as high as 6.9 and 8.7 m at stations along the north eastern coast such as Sagar Island and Chandipur, respectively. The high return levels are expected at these stations as the cyclones developed in the Bay of Bengal generally move north or north-west, producing extreme events in the northern part, and moreover, these stations are characterized by high tidal ranges. However, at some regions in the southern part such as Surya Lanka and Machilipatnam, though 50-year return levels of total sea level are not very high (2.98 and 2.97 m, respectively) because of the relatively lower tidal ranges, high return levels of surges (0.84 and 0.57 m, respectively) are found. In addition to the role of shallow depths (5.0 and 6.1 m, respectively) at the two stations, the high return levels of surges are attributed to the effect of geometrical configuration at Surya Lanka and width (100 km) and orientation of continental shelf at Machilipatnam.

기상인자와 비정상성 빈도해석 모형을 이용한 낙동강유역의 계절강수량 전망

본 연구에서는 Bayesian 통계기법을 이용한 비정상성 빈도해석모형을 토대로 외부 기상인자에 의한 변동성을 고려할 수 있는 계절강수량 예측모형을 구축하였으며, 낙동강유역내의 10개 관측소에서 관측된 37년간의 강수량 자료를 이용하여 연도별 여름강수량을 추출하고 이들 관측소의 여름강수량에 물리적인 영향을 미치는 기상인자로서 SST(sea surface temperature)와 OLR(outgoing longwave radiation)을 공간상관성을 검토하여 선정하였다. 모형의 적합성을 검토하기 위해 2010년 여름강수량 사후 확률분포의 중앙값과 관측치를 비교하였으며, 그 결과 각각 858.2mm와 888.1mm로, 이는 구축된 모형이 적절하게 여름강수량을 모의하고 있음을 보여준다. 2010년 겨울 SST 관측 값과, 예년 평균값으로 가정한 2011년 6월 OLR을 이용하여 2011년 여름강수량을 예측하였다. 예측된 2011년 여름강수량은 967.7mm로, 확률적으로 예년 여름강수량의 평균인 680mm를 상회할 확률이 92.9% 이상인 것으로 나타났으며, 또한 50년 빈도에 해당하는 여름강수량을 추정한 결과, 50년 빈도 여름강수량 1400mm를 상회할 확률도 약 73.7%인 것으로 분석되었다. This study developed a climate informed Bayesian nonstationary frequency model which allows us to forecast seasonal summer rainfall at Nakdong River. We constructed a 37-year summer rainfall data set from 10 weather stations within Nakdong river basin, and two climate indices from sea surface temperature (SST) and outgoing longwave radiation (OLR) were derived through correlation analysis. The selected SST and OLR have been widely acknowledged as a climate driver for summer rainfall. The developed model was applied first to the 2010-year summer rainfall (888.1 mm) in order to assure ourself. We demonstrated model performance by comparing posterior distributions. It was confirmed that the proposed model is able to produce a reasonable forecast. The forecasted value is about 858.2 mm, and the difference between forecast and observation is about 30 mm. As the second case study, 2011-year summer rainfall forecast was made using an observed winter SSTs and an assumed 50% value of OLRs. The forecasted value is 967.7 mm and associated exceedance probability over average summer rainfall 680 mm is 92.9%. In addition, 50-year return period for summer rainfall was projected through the nonstationary frequency model. An exceedance probability over 1,400 mm corresponding to the 50-year return level is about 73.7%.

Changes in storm surges in southern Europe from a regional model under climate change scenarios

Changes in storm surges in the Mediterranean Sea and the Atlantic Iberian coasts during the 21st century are explored based on the outputs of a numerical barotropic regional model. Three different climate scenarios (A2, A1B and B1) for the period 2000–2099 together with a control run for 1950–2000 have been simulated forced by greenhouse gases concentrations. A hindcast run for the 20th century (1958–2001) forced with a dynamical downscaling of ERA40 reanalysis has also been performed. Comparisons between hindcast and control run show consistency on their average statistics and their spatial distribution in terms of extreme events. Results for the 21st century reveal that storm surges frequency and magnitude decrease. Changes reach 50% in the number of events and up to 8 cm in the 50-year return levels. The analysis shows a progressive decrease in the return levels not fully explained by a negative trend in the mean atmospherically-induced sea level and a linear dependence with winter NAO. Likewise, negative events show the opposite behavior, with an increase in their frequency and magnitude although smaller than for positive surges.

Climate and solar signals in property damage losses from hurricanes affecting the United States

The authors show that historical property damage losses from US hurricanes contain climate signals. The methodology is based on a statistical model that combines a specification for the number of loss events with a specification for the amount of loss per event. Separate models are developed for annual and extreme losses. A Markov chain Monte Carlo procedure is used to generate posterior samples from the models. Results indicate the chance of at least one loss event increases when the springtime north–south surface pressure gradient over the North Atlantic is weaker than normal, the Atlantic ocean is warmer than normal, El Nino is absent, and sunspots are few. However, given at least one loss event, the magnitude of the loss per annum is related only to ocean temperature. The 50-year return level for a loss event is largest under a scenario featuring a warm Atlantic Ocean, a weak North Atlantic surface pressure gradient, El Nino, and few sunspots. The work provides a framework for anticipating hurricane losses on seasonal and multi-year time scales.

Dry spell characteristics over Canada in a changing climate as simulated by the Canadian RCM

Dry spells, defined as extended periods of dry days, can serve as indicators of drought conditions, and are often used in the management of water resource systems, particularly for agriculture. In this study, the Canadian RCM (CRCM) projected changes to dry spell characteristics over Canada, for the April–September period, and their validation in current climate, are presented. Two CRCM integrations are considered; one validation integration for the 1961–2000 period and a transient climate change integration for the 1961–2100 period, corresponding to the SRES A2 scenario. The ability of the model to simulate dry spell characteristics, i.e. mean number of dry days, mean number of dry spells and selected return levels of maximum dry spell durations, and associated errors are assessed through comparison of integrations for the current 1971–2000 April–September period with those observed, derived from the observed precipitation records. Results suggest an underestimation of the mean number of dry days and 10- and 30-year return levels, while the model slightly overestimates the mean number of dry spells, at the grid-cell scale. Analysis of projected changes to dry spell characteristics for the future 2041–2070 and 2071–2100 periods, with respect to 1971–2000 period, suggests significant changes, particularly for the southern Prairies, where both mean number of dry days and return levels of maximum dry spell durations are projected to increase. Furthermore, combined analysis of changes to the amount of precipitation and mean number of dry days also suggests potential increase in drought conditions in future climates in this already drought-prone region for the April–September period. In addition to southern Prairies, this study also suggests significant changes to dry spell characteristics for other regions of Canada.

Characterisation of extreme winter precipitation in Mediterranean coastal sites and associated anomalous atmospheric circulation patterns

Abstract. We present an analysis of daily extreme precipitation events for the extended winter season (October–March) at 20 Mediterranean coastal sites covering the period 1950–2006. The heavy tailed behaviour of precipitation extremes and estimated return levels, including associated uncertainties, are derived applying a procedure based on the Generalized Pareto Distribution, in combination with recently developed methods. Precipitation extremes have an important contribution to make seasonal totals (approximately 60% for all series). Three stations (one in the western Mediterranean and the others in the eastern basin) have a 5-year return level above 100 mm, while the lowest value (estimated for two Italian series) is equal to 58 mm. As for the 50-year return level, an Italian station (Genoa) has the highest value of 264 mm, while the other values range from 82 to 200 mm. Furthermore, six series (from stations located in France, Italy, Greece, and Cyprus) show a significant negative tendency in the probability of observing an extreme event. The relationship between extreme precipitation events and the large scale atmospheric circulation at the upper, mid and low troposphere is investigated by using NCEP/NCAR reanalysis data. A 2-step classification procedure identifies three significant anomaly patterns both for the western-central and eastern part of the Mediterranean basin. In the western Mediterranean, the anomalous southwesterly surface to mid-tropospheric flow is connected with enhanced moisture transport from the Atlantic. During ≥5-year return level events, the subtropical jet stream axis is aligned with the African coastline and interacts with the eddy-driven jet stream. This is connected with enhanced large scale ascending motions, instability and leads to the development of severe precipitation events. For the eastern Mediterranean extreme precipitation events, the identified anomaly patterns suggest warm air advection connected with anomalous ascent motions and an increase of the low- to mid-tropospheric moisture. Furthermore, the jet stream position (during ≥5-year return level events) supports the eastern basin being in a divergence area, where ascent motions are favoured. Our results contribute to an improved understanding of daily precipitation extremes in the cold season and associated large scale atmospheric features.

Spatial Hierarchical Modeling of Precipitation Extremes From a Regional Climate Model

The goal of this work is to characterize the extreme precipitation simulated by a regional climate model (RCM) over its spatial domain. For this purpose, we develop a Bayesian hierarchical model. Since extreme value analyses typically only use data considered to be extreme, the hierarchical approach is particularly useful as it sensibly pools the limited data from neighboring locations. We simultaneously model the data from both a control and future run of the RCM which allows for easy inference about projected change. Additionally, this hierarchical model is the first to spatially model the shape parameter which characterizes the nature of the distribution’s tail. Our hierarchical model shows that for the winter season, the RCM indicates a general increase in 100-year precipitation return levels for most of the study region. For the summer season, the RCM surprisingly indicates a significant decrease in the 100-year precipitation return level.

Assessment for the 30-Year Daily Precipitation Change due to Global Warming Using Regional Frequency Analysis

This study assessed the future change of the 30-year return level of daily precipitation due to global warming for Kagoshima Prefecture, (except for the Amami Islands) in southern Kyushu, Japan, using regional frequency analysis. The 20km-mesh regional climate model (MRI-RCM20) was used for this analysis. The present climate data was reproduced for the years 1981 through 2000, and the future climate data was projected for the years 2081 through 2100 under the greenhouse gas emission scenario SRES A2. Over Kagoshima Prefecture, the future change of the regional average of annual maximum daily precipitation was projected to increase by only 3.3%. However, the quantile that corresponds to the non-exceedance probability of the 30-year return level was projected to increase by 14.5%. As a result, in Kagoshima Prefecture, the 30-year return level of daily precipitation was projected as likely to increase by 18.3% in about one hundred years.

Quantifying the Uncertainty Range of 30-Year Daily Precipitation Change due to Global Warming Using Regional Frequency Analysis

This study quantified the uncertainty range of future change in the 30-year return level of daily precipitation due to global warming for Kagoshima Prefecture (except for the Amami Islands), Japan, based on regional frequency analysis, following the results of Ishihara (2010b). The uncertainty due to resampling variability was quantified with a Monte Carlo simulation based on the regional quantile function, using projection results from MRI-RCM20. The 5%–95% uncertainty ranges of the regional 30-year quantile for the present and future climates were 1.941 ± 0.117 and 2.217 ± 0.160, respectively. Moreover, the 5%–95% range of the simulated future change ratio of the regional 30-year quantile was 1.142 ± 0.107. Based on the previous result that the annual maximum daily precipitation averaged over the region is projected to increase by 3.3%, the 5%–95% uncertainty range of the future change ratio of the regional 30-year return level of daily precipitation in the region was projected to be 1.180 ± 0.111. This result indicates that the regional 30-year return level of daily precipitation in the region is likely to increase by 6.9%–29.1% in about 100 years.

A comparison study of extreme precipitation from six different regional climate models via spatial hierarchical modeling

We analyze output from six regional climate models (RCMs) via a spatial Bayesian hierarchical model. The primary advantage of this approach is that the statistical model naturally borrows strength across locations via a spatial model on the parameters of the generalized extreme value distribution. This is especially important in this application as the RCM output we analyze have extensive spatial coverage, but have a relatively short temporal record for characterizing extreme behavior. The hierarchical model we employ is also designed to be computationally efficient as we analyze RCM output for nearly 12000 locations. The aim of this analysis is to compare the extreme precipitation as generated by these RCMs. Our results show that, although the RCMs produce similar spatial patterns for the 100-year return level, their characterizations of extreme precipitation are quite different. Additionally, we examine the spatial behavior of the extreme value index and find differing spatial patterns for the point estimates for the RCMs. However, these differences may not be significant given the uncertainty associated with estimating this parameter.