Trends In Precipitation Extremes Research Articles

Response to comments by Yuan Wang on “Trends of extreme precipitation in Eastern China and their possible causes”

In his comments, Wang cites a number of works to dispute the conclusion in our previous work, which attributes the observed decreases/increases in light/heavy precipitation in eastern China primarily to global warming rather than the regional aerosol effect. However, most of the cited works (admittedly, including our previous work), employ correlation analysis, which has little bearing on the cause–effect relationship. Theoretical analyses and/or modeling studies are needed to ascertain the cause–effect relationship. We argue that theoretical analyses and modeling results show that global warming is the primary cause of the widely observed phenomena of suppression of light precipitation and enhancement of heavy precipitation across the globe, including in eastern China.

Recent changes in precipitation extremes in the Heihe River basin, Northwest China

Changes in rainfall extremes pose a serious and additional threat to water resources planning and management, natural and artificial oasis stability, and sustainable development in the fragile ecosystems of arid inland river basins. In this study, the trend and temporal variation of extreme precipitation are analyzed using daily precipitation datasets at 11 stations over the arid inland Heihe River basin in Northwest China from 1960 to 2011. Eight indices of extreme precipitation are studied. The results show statistically significant and large-magnitude increasing and decreasing trends for most indices, primarily in the Qilian Mountains and eastern Hexi Corridor. More frequent and intense rainfall extremes have occurred in the southern part of the desert area than in the northern portion. In general, the temporal variation in precipitation extremes has changed throughout the basin. Wet day precipitation and heavy precipitation days show statistically significant linear increasing trends and step changes in the Qilian Mountains and Hexi Corridor. Consecutive dry days have decreased obviously in the region in most years after approximately the late 1980s, but meanwhile very long dry spells have increased, especially in the Hexi Corridor. The probability density function indicates that very long wet spells have increased in the QilianMountains. The East Asian summer monsoon index and western Pacific subtropical high intensity index possess strong and significant negative and positive correlations with rainfall extremes, respectively. Changes in land surface characteristics and the increase in water vapor in the wet season have also contributed to the changes in precipitation extremes over the river basin.

Climatological features and trends of extreme precipitation during 1979–2012 in Beijing, China

Abstract. In this study, three kinds of hourly precipitation series with the spatial resolution of 0.1° are used to analyze the climatological features and trends of extreme precipitation during the period of 1979–2012 in Beijing, China. The results show that: (1) the spatial distribution of median annual precipitation, with a range from 500 to 825 mm, is similar to that of local topography, which increases from the northwest to the southeast. Taking the urban area as a centre, the inter-annual precipitation in the Beijing area displays an outward decreasing tendency at the maximum rate of 125 mm per decade (125 mm × 10 a−1); (2) extreme precipitation amount, which accounts for 40–48% of total precipitation amount, has a similar spatial distribution to average annual precipitation; (3) the spatial distribution of extreme precipitation days and threshold estimated as the upper 95 percentile are significantly different from that of extreme precipitation, with maximum values concentrated on the urban area and the eastern mountain area, and minimum values in northwest; (4) extreme precipitation days (Ex_pd95) show an opposite distribution to extreme precipitation threshold (Ex_pv95), indicating that areas with greater precipitation threshold may has less precipitation days, and vice versa; (5) an apparent spatiotemporal decreasing tendency is detected in extreme precipitation amount. The downward tendencies are also found in extreme precipitation threshold. Unlike Ex_pv95, in most of the study area, Ex_pd95 is virtually unchanged; (6) downward trends of extreme precipitation is slightly smaller than that of annual precipitation, and the reducing amplitude of north-eastern areas are much higher than the areas in the southwest.

Trends of extreme precipitation in eastern China and their possible causes

Significant increases of heavy precipitation and decreases of light precipitation have been reported over widespread regions of the globe. Global warming and effects of anthropogenic aerosols have both been proposed as possible causes of these changes. We examine data from urban and rural meteorological stations in eastern China (1955–2011) and compare them with Global Precipitation Climatology Project (GPCP) data (1979–2007) and reanalysis data in various latitude zones to study changes in precipitation extremes. Significant decreases in light precipitation and increases in heavy precipitation are found at both rural and urban stations, as well as low latitudes over the ocean, while total precipitation shows little change. Characteristics of these changes and changes in the equatorial zone and other latitudes suggest that global warming rather than aerosol effects is the primary cause of the changes. In eastern China, increases of annual total dry days (28 days) and ≥10 consecutive dry days (36%) are due to the decrease in light precipitation days, thereby establishing a causal link among global warming, changes in precipitation extremes, and higher meteorological risk of floods and droughts. Further, results derived from the GPCP data and reanalysis data suggest that the causal link exists over widespread regions of the globe.

Investigation of changes in the annual maximum flood in the Yellow River basin, China

Flooding in the Yellow River basin (YRB) has changed significantly over the last few decades because of climate change and human activities. Determining how the flooding changed and identifying the main driving factors of flood change is crucial to flood risk assessment and water resources planning. However, few studies have been conducted, especially in the whole YRB. To fill this gap, we investigate the spatial and temporal change of the annual maximum flood (AMAXF) in the YRB using observed data from 32 key hydrological stations (including 15 mainstream stations and 17 tributary stations). The Mann–Kendall test combined with trend index method is used to evaluate the trend in precipitation and AMAXF. The trend results indicate the AMAXF over the whole basin is dominated by decreasing trends: 72% of the stations exhibit significant decreasing trends (at 0.1 significance level) and 22% of the stations show no significant decreasing trends in the AMAXF. Both flood trends and abrupt change time exhibit obvious regional differences: the flood decreases is more pronounced in the midstream basins than in the headwater of the basin; the abrupt changes mainly occurred in the early 1990s for the upper reaches, and in the late 1990s for the middle reaches. To investigate the causes of flood change, the trends of precipitation extremes are analyzed in relation to the trends of peak floods. The analysis reveals that the decreasing precipitation extremes only results in the AMAXF reduction in the upstream basin. The decreasing AMAXF in the midstream and downstream of the basin are mainly attributable to the impacts of human activities (mainly including dam construction and soil conservation practices). In general, anthropogenic impacts play an increasingly important role in the AMAXF changes in the YRB. According to the possible changes in forcing factors, the AMAXF over the whole basin is expected to further decrease in the near future. However, there are still large flood risks in the tributary basins due to the collapse of check dams caused by extreme storms.

Spatiotemporal Analysis of Extreme Hourly Precipitation Patterns in Hainan Island, South China

To analyze extreme precipitation patterns in Hainan Island, hourly precipitation datasets from 18 stations, for the period from 1967 to 2012, were investigated. Two precipitation concentration indices (PCI) and 11 extreme precipitation indices (EPI) were chosen. PCI1 indicated a moderate seasonality in yearly precipitation and PCI2 showed that at least 80% of the total precipitation fell in 20% of the rainiest hours. Furthermore, the spatial variations of PCI1 and PCI2 differed. Linear regression indicated increasing trends in 11 of the calculated EPI. Principal component analysis found that the first recalculated principal component represented the 11 EPI. The recalculated principal component revealed an increasing trend in precipitation extremes for the whole island (except the interior section). Trend stability analysis of several of EPI suggested that the southern parts of Hainan Island, and especially the city of Sanya, should receive more attention to establish the drainage facilities necessary to prevent waterlogging.

Global trends in extreme precipitation: climate models versus observations

Abstract. Precipitation events are expected to become substantially more intense under global warming, but few global comparisons of observations and climate model simulations are available to constrain predictions of future changes in precipitation extremes. We present a systematic global-scale comparison of changes in historical (1901–2010) annual-maximum daily precipitation between station observations (compiled in HadEX2) and the suite of global climate models contributing to the fifth phase of the Coupled Model Intercomparison Project (CMIP5). We use both parametric and non-parametric methods to quantify the strength of trends in extreme precipitation in observations and models, taking care to sample them spatially and temporally in comparable ways. We find that both observations and models show generally increasing trends in extreme precipitation since 1901, with the largest changes in the deep tropics. Annual-maximum daily precipitation (Rx1day) has increased faster in the observations than in most of the CMIP5 models. On a global scale, the observational annual-maximum daily precipitation has increased by an average of 5.73 mm over the last 110 years, or 8.5% in relative terms. This corresponds to an increase of 10% K−1 in global warming since 1901, which is larger than the average of climate models, with 8.3% K−1. The average rate of increase in extreme precipitation per K of warming in both models and observations is higher than the rate of increase in atmospheric water vapor content per K of warming expected from the Clausius–Clapeyron equation. We expect our findings to help inform assessments of precipitation-related hazards such as flooding, droughts and storms.

Monthly Maximum Accumulated Precipitation Forecasting Using Local Precipitation Data and Global Climate Modes

Successive days of precipitation are known to cause flooding in monsoon-susceptible countries. Forecasting of daily precipitation facilitates the prediction of the occurrences of rainfall and number of wet days. Using the maximum five-day accumulated precipitation (MX5d), we can predict the magnitude of precipitation in a specific period as it may indicate the extreme precipitation. In this study, a method to forecast monthly extreme precipitation using artificial neural networks (ANNs) is assessed using past MX5d data and global climate indices such as Southern Oscillation Index (SOI), Madden Julian Oscillation (MJO), and Dipole Mode Index (DMI) in Kuantan and Kota Bharu, Malaysia. The use of combined inputs (MX5d with SOI, MJO, and DMI) is proposed to investigate the concurrent effect of lagged values of local precipitation data and global climate indices on seasonal extreme precipitation. Four cases of data are sampled representing two major seasonal variations in Malaysia. The analysis of extreme precipitation trends is important for the prediction of high precipitation events. ANNs are widely applied in the hydrology field because of their nonlinear ability in predicting nonstationary and seasonal data. In this paper, we have compared ANNs with seasonal autoregressive integrated moving average (ARIMA) and regression analysis using out-of-sample test data. The results for Kuantan indicate that seasonal ARIMA is the best method to forecast extreme precipitation when MX5d lags are used as input. For Kota Bharu, ANN exhibits better generalization ability than ARIMA and regression analysis when dual inputs (lagged MX5d and lagged global climate indices) are utilized in the model.

Multi‐dataset comparison of gridded observed temperature and precipitation extremes over China

ABSTRACTFive gridded datasets containing measures of temperature and precipitation extremes over the past five decades over China are compared. Time series, spatial averages and trends in China's coldest and warmest days and nights and wettest days are estimated from these datasets that vary in their station network density, interpolation procedures and the order in which extremes are calculated. We find that country‐wide trends in temperature extremes are coherent irrespective of dataset choice although actual values are generally smaller in the dataset in which indices are calculated from daily interpolated grids rather than from station points. There are also some regional differences in trends especially over regions with sparse data networks, e.g. the Tibetan Plateau. Averaged across China, trends calculated over the period 1961–2009 vary from a minimum of 0.13 °C decade−1(hottest day) to 0.65 °C decade−1(coldest night). The coldest day and night have tended to warm faster than the warmest day and night. Trends in precipitation extremes are much less coherent spatially and can be positive or negative depending on the choice of dataset. The length of station record chosen is also vital to eliminate spurious trends. The results have important implications for detection and attribution and model evaluation studies.

A perturbation approach for assessing trends in precipitation extremes across Iran

Summary Extreme precipitation events have attracted a great deal of attention among the scientific community because of their devastating consequences on human livelihood and socio-economic development. To assess changes in precipitation extremes in a given region, it is essential to analyze decadal oscillations in precipitation extremes. This study examines temporal oscillations in precipitation data in several sub-regions of Iran using a novel quantile perturbation method during 1980–2010. Precipitation data from NASA’s Modern-Era Retrospective Analysis for Research and Applications-Land (MERRA-Land) are used in this study. The results indicate significant anomalies in precipitation extremes in the northwest and southeast regions of Iran. Analysis of extreme precipitation perturbations reveals that perturbations for the monthly aggregation level are generally lower than the annual perturbations. Furthermore, high-oscillation and low-oscillation periods are found in extreme precipitation quantiles across different seasons. In all selected regions, a significant anomaly (i.e., extreme wet/dry conditions) in precipitation extremes is observed during spring.

Trends in extreme temperature and precipitation in Muscat, Oman

Abstract. Changes in frequency and intensity of weather events often result in more frequent and intensive disasters such as flash floods and persistent droughts. In Oman, changes in precipitation and temperature have already been detected, although a comprehensive analysis to determine long-term trends is yet to be conducted. We analysed daily precipitation and temperature records in Muscat, the capital city of Oman, mainly focusing on extremes. A set of climate indices, defined in the RClimDex software package, were derived from the longest available daily series (precipitation over the period 1977–2011 and temperature over the period 1986–2011). Results showed significant changes in temperature extremes associated with cooling. Annual maximum value of daily maximum temperature (TX), on average, decreased by 1°C (0.42°C/10 year). Similarly, the annual minimum value of daily minimum temperature (TN) decreased by 1.5°C (0.61°C/10 year), which, on average, cooled at a faster rate than the maximum temperature. Consequently, the annual count of days when TX > 45°C (98th percentile) decreased from 8 to 3, by 5 days. Similarly, the annual count of days when TN < 15°C (2nd percentile) increased from 5 to 15, by 10 days. Annual total precipitation averaged over the period 1977–2011 is 81 mm, which shows a tendency toward wetter conditions with a 6 mm/10 year rate. There is also a significant tendency for stronger precipitation extremes according to many indices. The contribution from very wet days to the annual precipitation totals steadily increases with significance at 75% level. When The General Extreme Value (GEV) probability distribution is fitted to annual maximum 1-day precipitation, the return level of a 10-year return period in 1995–2011 was estimated to be 95 mm. This return level in the recent decade is about 70% higher than the return level for the period of 1977–1994. These results indicate that the long-term wetting signal apparent in total precipitation can be attributed largely to the increases in extreme precipitation in recent decades.

Uncertainty in the future change of extreme precipitation over the Rhine basin: the role of internal climate variability

Future changes in extreme multi-day precipitation will influence the probability of floods in the river Rhine basin. In this paper the spread of the changes projected by climate models at the end of this century (2081–2100) is studied for a 17-member ensemble of a single Global Climate Model (GCM) and results from the Coupled Model Intercomparison Project Phase 3 (CMIP3) ensemble. All climate models were driven by the IPCC SRES A1B emission scenario. An analysis of variance model is formulated to disentangle the contributions from systematic differences between GCMs and internal climate variability. Both the changes in the mean and characteristics of extremes are considered. To estimate variances due to internal climate variability a bootstrap method was used. The changes from the GCM simulations were linked to the local scale using an advanced non-linear delta change approach. This approach uses climate responses of the GCM to transform the daily precipitation of 134 sub-basins of the river Rhine. The transformed precipitation series was used as input for the hydrological Hydrologiska Byrans Vattenbalansavdelning model to simulate future river discharges. Internal climate variability accounts for about 30 % of the total variance in the projected climate trends of average winter precipitation in the CMIP3 ensemble and explains a larger fraction of the total variance in the projected climate trends of extreme precipitation in the winter half-year. There is a good correspondence between the direction and spread of the changes in the return levels of extreme river discharges and extreme 10-day precipitation over the Rhine basin. This suggests that also for extreme discharges a large fraction of the total variance can be attributed to internal climate variability.

The role of the export of tropical moisture into midlatitudes for extreme precipitation events in the Mediterranean region

The aims of the study are twofold: firstly, to investigate the role of the export of humid tropical air in the formation of cool season heavy precipitating events (HPEs) in the Mediterranean region (MR); and secondly, to examine the possible linkage between the export of humid tropical air and the multiyear trend in extreme precipitation in the region. For this purpose, we analyze the spatial distributions of a number of key atmospheric variables with a reanalysis data for more than 50 intense HPEs for the MR. The results of this evaluation for both individual and composite events suggest that the HPEs are associated with atmospheric rivers (ARs). The MR HPEs are being characterized by the poleward export of humid air of tropical origin into the midlatitude MR from the Atlantic Ocean and Arabian Sea. These export events appear to be associated with the effects of hurricanes or intense cyclones in the North Atlantic. It was also found that the linear trend (for 1979–2013) of the frequency of humid days (days with precipitable water greater than 20 kg m−2) is consistent with recent changes in the character of precipitation over the MR and southern Europe.

Paradox of Peak Flows in a Changing Climate

Paradox of Peak Flows in a Changing Climate

Spatial distribution and temporal trends in precipitation extremes over the Hengduan Mountains region, China, from 1961 to 2012

Extreme precipitation events will cause serious disasters, and their changing trends require thorough evaluation. Spatial distribution and temporal trends of extreme precipitation events were analyzed based on the daily precipitation data of 27 meteorological stations in the Hengduan Mountains region from 1961 to 2012. Twelve indices of precipitation extreme were studied. The results were as follows: except for consecutive dry days, consecutive wet days and maximum 5-day precipitation, other indices of precipitation extreme demonstrated non- significant increasing trends, and most indices fluctuated from 1961 to 2012. Increasing trends in precipitation indices were greater than those in precipitation days. The spatial distribution for precipitation extremes exhibited a declining trend from southwest to northeast, which reflected regional differences and the influence of topography in the Hengduan Mountains region. Furthermore, the relationship between precipitation extremes and elevation indicated that precipitation extreme events decreased with altitude. The extreme precipitation indexes had positive correlations with the annual total precipitation, and their correlation coefficients were statistically significant at the 1% significance level, except for consecutive dry days. Continuous wavelet transform analysis presented significant periodic variations with periods of 2–4 year, 5-year, and 10-year in the extreme precipitation, and there was a 2–4 year resonance cycle with the South/East Asian summer monsoon index. The South/East Asian Summer Monsoon was an important influence on precipitation extremes in the Hengduan Mountains region.

Trends in precipitation extremes and return levels in the Hawaiian Islands under a changing climate

Trends of annual maximum 1-day precipitation in three major Hawaiian Islands are investigated using a nonparametric Mann-Kendall method and Sen's test (MKS). The records are from 24 stations on Oahu, Maui, and Hawaii, and the period of analysis ranges from 1960 to 2009. To complement the MKS method, a non-stationary three- parameter generalized extreme value (GEV) distribution is also used to detect trends in precipitation extremes. Both methods demonstrate that negative trends prevail for Oahu and Maui but positive trends dominate the Island of Hawaii. The influence of the location and the scale parameter in the GEV model on different return levels (2-year, 20-year, and 100-year) are explicitly described. The return-level threshold values are found to change with time considerably. As a result, a rare storm with daily precipitation of 300 mm (20-year return period) in 1960 has become a rather common storm event (3-5-year return period) in 2009 on the Island of Hawaii. The opposite trend behavior in extreme events is observed on Oahu and Maui, where rainfall extremes have become less frequent in the last five decades. A positive relationship is found between the precipitation extremes and Southern Oscillation Index (SOI), implying greater extreme events during La Ni˜ na years and the opposite for El Niyears.

Projections of precipitation extremes based on a regional, non-stationary peaks-over-threshold approach: A case study for the Netherlands and north-western Germany

Projections of extreme precipitation are of great importance, considering the potential severe impacts on society. In this study a recently developed regional, non-stationary peaks-over-threshold approach is applied to two transient simulations of the RACMO2 regional climate model for the period 1950–2100, driven by two different general circulation models. The regional approach reduces the estimation uncertainty compared to at-site approaches. The selection of a threshold for the peaks-over-threshold model is tackled from a new perspective, taking advantage of the regional setting. Further, a regional quantile regression model using a common relative trend in the threshold is introduced. A considerable bias in the extreme return levels is found with respect to gridded observations. This bias is corrected for by adjusting the parameters in the peaks-over-threshold model.In summer a significant increase in extreme precipitation over the study area is found for both RACMO2 simulations, mainly as a result of an increase of the variability of the excesses over the threshold. However, the magnitude of this trend in extreme summer precipitation depends on the driving general circulation model. In winter an increase in extreme precipitation corresponding with an increase in mean precipitation is found for both simulations. This trend is due to an increase of the threshold.

Trends in Extreme Precipitation Events in the Indus River Basin and Flooding in Pakistan

In the absence of a sufficiently dense network of climate stations covering all topographic regions of the Indus River basin and delivering high quality data over the last 30 years or more, daily precipitation data were obtained from the National Centers for Environmental Prediction-Department of the Enviornment (NCEP-DOE) Reanalysis 2 dataset for the period 1979 to 2011. The daily precipitation data were transformed into time series of frequency of extreme precipitation events of 1-day and 10-day durations defined in terms of 90th and 99th percentile threshold exceedances. The non-parametric Mann-Kendall trend test was applied to determine whether statistically significant changes in precipitation extremes occurred over time, in due consideration of autocorrelation in the data.Extreme precipitation showed a high spatial variability, with the highest daily and 10-day precipitation totals, and thus highest 90th and 99th percentiles, in the southeastern lowlands at the foot of the Himalayas and the lowest in the Karakorum. Significantly decreasing trends in extreme precipitation were observed in the western part of the Indus River basin; significantly increasing trends were mainly detected in the very high mountainous regions in the east (Transhimalaya and Himalayas) and in the north (Hindu Kush and Karakorum) of the Indus basin. High precipitation rates are not common in the arid climate of these high mountainous regions. Future flood management plans need to consider the increasing trends in extreme precipitation events in these areas.

Evaluation of global climate models in simulating extreme precipitation in China

Variations in extreme precipitation can be described by various indices. In order to evaluate a climate model's ability to simulate extreme precipitation, gridded extreme precipitation indices from observations are needed. There are two ways to obtain gridded extreme precipitation indices from station-based observations: either through interpolation of station-based extreme indices (EISTA) or estimated from gridded precipitation datasets (EIGRID). In this work, we evaluated these two methods and compared observational extreme precipitation indices in China to those obtained from a set of widely used global climate models. Results show that the difference between the two methods is quite large; and in some cases it is even larger than the difference between model simulations and observed gridded EISTA. Based on the sensitivity of the indices to horizontal resolution, it was suggested that EIGRID is more appropriate for evaluating extreme indices simulated by models. Subsequently, historic simulations of extreme precipitation from 21 CMIP5 (Coupled Model Intercomparison Project Phase 5) global climate models were evaluated against two reanalysis datasets during 1961–2000. It was found that most models overestimate extreme precipitation in the mountain regions in western China and northern China and underestimate extreme precipitation in southern China. In eastern China, these models simulate mean extreme precipitation fairly well. Despite this bias, the temporal trend in extreme precipitation for western China is well captured by most models. However, in eastern China, the trend of extreme precipitation is poorly captured by most models, especially for the so-called southern flood and northern drought pattern. Overall, our results suggest that the dynamics of inter-decadal summer monsoon variability should be improved for better prediction of extreme precipitation by the global climate models.

Geographically heterogeneous temporal trends of extreme precipitation in Wisconsin, USA during 1950-2006

Large flood events in recent years in the state of Wisconsin, USA raised a question of whether high precipitation events are on the rise. The objective of the research was to examine temporal and spatial patterns of extreme precipitation in Wisconsin during 1950–2006. The daily precipitation data used in the study were created by researchers at the University of Wisconsin-Madison using spatial interpolation of weather stations data across the state to a grid mesh with a spatial resolution of 8 km. For extreme precipitation indices, we calculated 99th, 95th, 90th, 85th, and 80th percentiles of daily total precipitation (>1 mm) in a year and the number of days per year with daily precipitation exceeding 10 mm, 20 mm, and 50 mm. We also conducted the Mann–Kendall test for trend, examined how geographical heterogeneity varied over time, and built quantile regression models for annual summer precipitation. Main findings include the following: the temporal trend of extreme precipitation varied widely across the state; the highest percentile index showed an increasing trend over the largest area, whereas indices of less extreme precipitation tended to generally decrease; extreme precipitation tended to show more dispersed and skewed spatial patterns than annual total precipitation. Overall, indices related to frequency showed more similar spatial and temporal trends to total precipitation than magnitude indices.