Extreme Precipitation Intensity Research Articles

Extreme Precipitation in China in Response to Emission Reductions under the Paris Agreement

To avoid more severe impacts from climate change, countries worldwide pledged to implement intended nationally determined contributions (INDCs) for emission reductions (as part of the Paris Agreement). However, it remains unclear what the resulting precipitation change in terms of regional extremes would be in response to the INDC scenarios. Here, we analyzed China’s extreme precipitation response of the next few decades to the updated INDC scenarios within the framework of the Paris Agreement. Our results indicate increases in the intensity and frequency of extreme precipitation (compared with the current level) in most regions in China. The maximum consecutive five-day precipitation over China is projected to increase ~16%, and the number of heavy precipitation days will increase as much as ~20% in some areas. The probability distributions of extreme precipitation events become wider, resulting in the occurrence of more record-breaking heavy precipitation in the future. We further considered the impacts of precipitation-related extremes and found that the projected population exposure to heavy precipitation events will significantly increase in almost all Chinese regions. For example, for heavy precipitation events that exceed the 20 year baseline return value, the population exposure over China increases from 5.7% (5.1–6.0%) to 15.9% (14.2–16.4%) in the INDC-pledge scenario compared with the present-day level. Limiting the warming to lower levels (e.g., 1.5 °C or 2.0 °C) would reduce the population exposure to heavy precipitation, thereby avoiding impacts associated with more intense precipitation events. These results contribute to an improved understanding of the future risk of climate extremes, which is paramount for the design of mitigation and adaptation policies in China.

Intensification of precipitation extremes in the world’s humid and water-limited regions

Changes in precipitation totals and extremes are among the most relevant consequences of climate change, but in particular regional changes remain uncertain. While aggregating over larger regions reduces the noise in time series and typically shows increases in the intensity of precipitation extremes, it has been argued that this may not be the case in water-limited regions. Here we investigate long-term changes in annual precipitation totals and extremes aggregated over the world’s humid, transitional, and dry regions as defined by their climatological water availability. We use the globally most complete observational datasets suitable for the analysis of daily precipitation extremes, and data from global climate model simulations. We show that precipitation totals and extremes have increased in the humid regions since the mid-20th century. Conversely, despite showing tendencies to increase, no robust changes can be detected in the drier regions, in part due to the large variability of precipitation and sparse observational coverage particularly in the driest regions. Future climate simulations under increased radiative forcing indicate total precipitation increases in more humid regions but no clear changes in the more arid regions, while precipitation extremes are more likely to increase than to decrease on average over both the humid and arid regions of the world. These results highlight the increasing risk of heavy precipitation in most regions of the world, including water-limited regions, with implications for related impacts through flooding risk or soil erosion.

Machine Learning for Projecting Extreme Precipitation Intensity for Short Durations in a Changing Climate

Climate change is one of the prominent factors that causes an increased severity of extreme precipitation which, in turn, has a huge impact on drainage systems by means of flooding. Intensity–duration–frequency (IDF) curves play an essential role in designing robust drainage systems against extreme precipitation. It is important to incorporate the potential threat from climate change into the computation of IDF curves. Most existing works that have achieved this goal were based on Generalized Extreme Value (GEV) analysis combined with various circulation model simulations. Inspired by recent works that used machine learning algorithms for spatial downscaling, this paper proposes an alternative method to perform projections of precipitation intensity over short durations using machine learning. The method is based on temporal downscaling, a downscaling procedure performed over the time scale instead of the spatial scale. The method is trained and validated using data from around two thousand stations in the US. Future projection of IDF curves is calculated and discussed.

Sensitivity of summer precipitation in regional spectral model simulations over eastern China to physical schemes: Daily, extreme and diurnal cycle

In this study, the capability of the National Centers for Environmental Prediction (NCEP) Regional Spectral Model (RSM) in reproducing the precipitation in eastern China is evaluated with the simulation of 25‐year (1983–2007) summertime climate driven by the NCEP‐Department of Energy reanalysis. The impacts of different physical schemes on daily precipitation, diurnal cycle and precipitation extremes are studied through detailed analyses. The results show that the RSM model can reproduce the precipitation characteristics over eastern China, and the results are sensitive to the internal model physics and spectral nudging method. The cloud water prediction scheme of CLD3 (CLDn, the cloud schemes, classified according to the complexity microphysical processes) and the modified version of the Kain‐Fritsch scheme (KF2) tend to overestimate the summer precipitation by reproducing higher humidity, while the CLD1 and the Relaxed Arakawa Schubert scheme (RAS) always underestimate it. It is also demonstrated that RSM has the capability to reproduce the spatial distributions of the extreme precipitation intensity and duration. The physical schemes have great impact on the simulations of extreme precipitation over eastern China, especially on regional scales. For the diurnal cycle of precipitation, the responses of the simulated amplitude to the physical schemes differ with regions. The RAS scheme underestimates the amplitudes over southeastern China, while the KF2 generates positive biases. The involving of the scale selective bias correction (SSBC) method can properly improve the simulated mean precipitation and diurnal cycle of summer precipitation. However, it shows lower skill in improving extreme precipitation indices over eastern China, especially in the experiments with CLD3 scheme. In summary, the selection of CLD1 and KF2 scheme, involving SSBC method performs better than the other experiments.

A comprehensive analysis of spatial and temporal variability of extreme precipitation in the Nenjiang River Basin, Northeast China

Better investigation of extreme precipitation in large river basins is important for hydro-meteorological research and water resources management. Based on daily precipitation data from 17 national meteorological stations in Nenjiang River Basin (NRB) during 1959–2011, spatial-temporal characteristics and trends of seven extreme precipitation indices were analyzed in this study using Mann-Kendall non-parametric test and the ensemble empirical mode decomposition (EEMD) method. We found that almost all selected extreme precipitation indices declined in regionally averaged values, except maximum 5-day precipitation amount (Rx5day). Both extreme precipitation indices and their trends demonstrated spatial varieties. Generally, lower basin obtained less extreme precipitation and tended to be drier. But the amount and intensity of extreme precipitation in upper basin, where are more humid, are rising. Extreme precipitation indices presented apparent non-linear process with periodic oscillations of 1.63–1.94a for IMF1 to 25–31a for IMF4. The findings of this study are useful for the management of water resources in NRB, as well as provide reference information for precipitation evolution under climate change in other regions.

Mean versus Extreme Precipitation Trends in Iran over the Period 1960–2017

Iran is located in one of the Earth’s dry belts. Subtropical high-pressure and inland deserts have created arid and semiarid conditions for about 75% of the country’s area. In addition to this natural dryness, recent global climate change has also affected precipitation in different parts of Iran in terms of both mean and extreme amounts. Precipitation extremes can have many negative effects on different parts of the Earth’s ecosystems. The possible pattern of precipitation trends during recent years merits further study given the potentially important impacts on agriculture and water resources in Iran. Globally, observations show significant changes in the characteristics of extreme precipitation. To study recent trends in mean and extreme precipitation events in Iran, we examined a number of mean and extreme precipitation indices for a 58-year period (1960–2017) for 33 synoptic stations throughout the country. Daily precipitation data were collected from the Iranian Meteorological Organization for this period for all stations. Nine precipitation-based indices (one mean and eight extreme) were employed to study precipitation trends. The results showed that most areas have undergone a significant increasing trend in extreme precipitation values, precipitation intensity, and precipitation frequency. Significant positive trends were seen in the southwestern regions of Iran and the coasts of the Persian Gulf for the same precipitation indices. The results of this study could be used by managers and experts for long-term planning.

Assessing urban flood and drought risks under climate change, China

AbstractRisk analysis of urban flood and drought can provide useful guidance for urban rainwater management. Based on an analysis of urban climate characteristics in 2,264 Chinese cities from 1958 to 2017, this study evaluated urban flood and drought risks. The results demonstrated that the annual average values of precipitation, aridity index, frequency and intensity of extreme precipitation and extreme drought events differed significantly in these cities. The values of the above six climatic indicators in the cities ranged from 9.29–2639.30 mm, 0.47–54.73, 1.08–8.79 time, 7.82–107.25 mm, 0.76–2.99 time, and 10.30–131.19 days, respectively. The geographical patterns of urban precipitation, aridity index, intensity and frequency of extreme precipitation and drought events in China fit well to the Hu‐Huanyong Line that was created in 1940s to identify the pattern of population distribution. Extreme precipitation in most cities has upward trends, except for those around the Hu‐Huanyong Line. The extreme drought events had upward trends in the cities east of the Hu‐Huanyong Line, but there were downward trends in the cities west of the line. The risk assessment indicated that 3.80% cities were facing serious flood and 6.01% cities were facing serious drought risks, which are located in the coast of southern China and northwestern China, respectively, and other 90.19% cities were facing different types of drought and flood risks in terms of their intensity and frequency.

Variation patterns of extreme precipitation and relation to ocean-atmospheric climate in Sichuan province China from 1961 to 2017

Based on daily precipitation records in the Sichuan province, spatiotemporal changes in extreme precipitation from 1961 to 2017 and the relation to ocean-atmospheric climate were investigated. The trends and their statistical significance were computed with the nonparametric Sen’s and Mann–Kendall tests. The characteristics of mutation and period were investigated with heuristic segmentation and continuous wavelet transform. The relations between extreme precipitation and ocean-atmospheric climate were diagnosed by cross-wavelet analysis. The results comprised three aspects. (1) The intensity, frequency, and duration of extreme precipitation increased in the Sichuan plateau, while the intensity and frequency of extreme precipitation decreased, but the duration of extreme precipitation did not change in the Sichuan basin. The contrary trends of extreme precipitation indices may have been influenced by the complex local geography, dramatically increased human activity, and source transportation of water vapor. (2) Temporally, the trends in extreme precipitation indices constituted slight changes in the Sichuan province. The Sichuan province experienced notable climate change because abrupt change points were observed for most of the extreme precipitation indices. Extreme precipitation was a fluctuation process from 1961 to 2017. (3) Because there was a decrease in precipitation during the warm phase periods of El Nino events and an increase during the cool phase periods of La Nina events in the Sichuan province, we show that the El Nino-Southern Oscillation (ENSO) has longer and stronger relations with extreme precipitation than the South Asian Summer Monsoon (SASM) or East Asian Summer Monsoon (EASM). The results of the present study will facilitate better decisions concerning preparedness for extreme precipitation events and management of water hazards in the Sichuan province.

Distinctive Evolutions of Eurasian Warming and Extreme Events Before and After Global Warming Would Stabilize at 1.5 °C

AbstractUtilizing the specially designed low‐warming experiments of the fully coupled Community Earth System Model, we investigate the evolutions of Eurasian climate and extreme events if global mean temperature would stabilize at 1.5 °C and 2 °C above preindustrial levels or overshoot the 1.5 °C (1.5 °C OS) threshold. We find that distinctive evolutions of surface temperature (TS) and extreme events appear in different regions of Eurasia, particularly over East China and Northern Eurasia. In the course of global warming, before global mean temperature stabilizes, the two regions display similar changes of TS and extreme events. However, after global warming halts, the two regions experience distinctive evolutions of local warming and extreme events. Over East China, where the adjacent ocean exhibits stronger‐than‐global mean warming, the local TS and both the strength and frequency of heat extremes keep increasing continuously. The extreme precipitation intensity increases even more rapidly compared to the change of heat extremes. In stark contrast, TS and heat extremes intensity over northern Eurasia decrease in both the 1.5 °C and 1.5 °C OS cases, except that the extreme precipitation experiences little change. We find that the most vulnerable period over northern Eurasia is the transition period, in which TS increases till its maximum and then decreases. The heat extremes during the transition period are also stronger and more frequent compared to the end of the 21st century over northern Eurasia.

Influence of convectively coupled equatorial waves and intra‐seasonal oscillations on rainfall extremes over India

Convectively coupled equatorial waves and intra‐seasonal oscillations are known to modulate rainfall over the Tropics significantly. The effect of these oscillations on the frequency distribution of extreme precipitation events (defined based on percentile threshold) over Indian region is investigated for the monsoon months of June–September. Based on the variance and phase of the wave, any given day is classified into wet phase and dry phase. Modulation of extreme event probability by the presence of these phases over a given region is quantified for different waves, namely Madden–Julian Oscillation (MJO), equatorial Rossby wave (ER), equatorial Kelvin wave and mixed Rossby gravity and tropical depressions (MT). It is shown that the presence of wet phase of the waves increases the probability of extreme precipitation occurrence by as much as 150% of the baseline probability while the presence of dry phase can decrease the probability by half over certain regions depending on the wave. Averaged over the whole country, wet phase increases the probability by 30–60% while dry phase decreases by 20–40% except for MT waves where there is increased extreme activity during dry phase. However, the intensity of extreme precipitation does not show significant sensitivity to the changes in phase of the waves.

편의보정된 다중지역기후모델 산출물의 불확실성 범위 추정 및 미래 전망

In this study, uncertainty ranges for bias-corrected temperature and precipitation in seven metro-cities were estimated using nine GCM-RCM Matrix, and climate changes were predicted based on the corrected temperature and precipitation. During the present climate (1981-2005), both uncertainties for annual temperature and precipitation and differences in regional uncertainties were reduced by bias correction methods. Model’s systematic errors such as cold bias of surface air temperature and underestimated precipitation during the second-Changma period were improved by a bias correction method. Uncertainties of annual variations for bias corrected temperature and precipitation were also decrease. Furthermore, not only mean values but also extreme values were improved by bias correction methods. During the future climate (2021-2050), differences in temperature and precipitation between two RCP scenarios (RCP4.5/8.5) were not quite large. Temperature had an obvious increasing tendency, while future precipitation did not change significantly compared to present one in terms of mean values. Uncertainties for future biascorrected temperature and precipitation were also reduced. In mid-21st centuries, models prospected that mean temperature increased thus lower extremes associated with cold wave decreased and upper extremes associated with heat wave increased. Models also predicted that variations of future precipitation increased thus the frequency and intensity of extreme precipitation increased.

Climate change or climate regimes? Examining multi-annual variations in the frequency of precipitation extremes over the Argentine Pampas

A recent period of increased precipitation over the Argentinian Pampas expanded the boundary of rain-fed agriculture. However, such changes may not be sustainable if they arose from transient climate regime shifts. Considerable research exists on trends and cycles in sub-daily to annual precipitation metrics including the frequency and intensity of extreme precipitation. However, efforts to identify wetter and drier phases (or regimes) in this region are scant. This article aims to bridge that gap and advance our understanding of the multi-annual behavior of regional precipitation extremes, which can have the greatest impacts. It is unlikely that all extreme events are drawn from a single probability distribution or generated by the same physical processes. Hence, hidden mixtures of Poisson distributions are fitted to several precipitation frequency metrics to explore whether the annual to decadal variations in extreme precipitation frequency are greater than anticipated from a single system, and representative of regime shifts. Statistically significant improvements in the fit over single distributions were found for statistical mixture models of the frequency of very wet days, and the frequency of wet spells. This supports the hypothesis that multiple weather regimes exist giving rise to wetter or drier epochs. Posterior probabilities of hidden states from the fitted mixture distributions were used to identify wetter and drier years for comparison with sea surface temperature anomalies. This confirmed the presence of two distinct regimes, supporting other research, into the dynamical influences of precipitation behavior in the Argentine Pampas.

Projection of frequency and intensity of extreme precipitation in Zambia: a CMIP5 study

Projection of frequency and intensity of extreme precipitation in Zambia: a CMIP5 study

Strong Dependence of Extreme Convective Precipitation Intensities on Gauge Network Density

AbstractExtreme convective precipitation on subhourly scales is notoriously misrepresented in rain gauge‐based observations, but uncertainties are weakly quantified at the 1 to 30 km scale. We employ a unique observing network, the high‐density WegenerNet and surrounding operational rain gauge network in southeastern Austria, to sample convective precipitation extremes at these scales. By systematically constructing lower‐density networks, we explore how estimated maximum area precipitation depends on observing station density. Using subhourly to hourly temporal resolution, we find a d−0.5(±0.1) power law decay of the event maximum area precipitation over distances d from 1 to 30 km, showing that operational gauge networks underrate extreme convective precipitation falling over small areas. Furthermore, extremes at point scale are found underestimated by operational networks by about 20%. We consider the dependencies representative for short‐duration convective events over similar regions at midlatitudes and the results valuable for high‐resolution climate model evaluation.

Reply to comments on “Temperature‐extreme precipitation scaling: a two‐way causality?”

In recent years, numerous studies have investigated the relationship of extreme precipitation intensities with temperature derived from a binning method using present‐day weather data. The aim of this approach is that these dependencies, or so‐called apparent scaling rates, provide insights on how precipitation extremes could evolve in the future climate. In a recent paper, Bao et al. showed that there is a large discrepancy between the present‐day apparent scaling rates—showing a negative dependency of daily rainfall extremes on temperature for Darwin—and the climate change response showing a substantial increase in daily rainfall extremes. Their results provide compelling evidence that it may not be straightforward to predict the climate change response from the temperature scaling. We agree with this statement, but disagree with the physical explanation put forward in that study and think that moisture limitations are the most likely cause. Therefore, we advocate the use of dew point temperatures in scaling studies as a much more direct proxy of the absolute humidity of the air in which precipitation extremes develop, which we think is physically more consistent with the underlying mechanism causing the increase in precipitation extremes with global warming.

Spatiotemporal Variability in Extreme Precipitation in China from Observations and Projections

Extreme precipitation events, which have intensified with global warming over the past several decades, will become more intense in the future according to model projections. Although many studies have been performed, the occurrence patterns for extreme precipitation events in past and future periods in China remain unresolved. Additionally, few studies have explained how extreme precipitation events developed over the past 58 years and how they will evolve in the next 90 years as global warming becomes much more serious. In this paper, we evaluated the spatiotemporal characteristics of extreme precipitation events using indices for the frequency, quantity, intensity, and proportion of extreme precipitation, which were proposed by the World Meteorological Organization. We simultaneously analyzed the spatiotemporal characteristics of extreme precipitation in China from 2011 to 2100 using data obtained from the Coupled Model Intercomparison Project Phase 5 (CMIP5) models. Despite the fixed threshold, 95th percentile precipitation values were also used as the extreme precipitation threshold to reduce the influence of various rainfall events caused by different geographic locations; then, eight extreme precipitation indices (EPIs) were calculated to evaluate extreme precipitation in China. We found that the spatial characteristics of the eight EPIs exhibited downward trends from south to north. In the periods 1960–2017 and 2011–2100, trends in the EPIs were positive, but there were differences between different regions. In the past 58 years, the extreme precipitation increased in the northwest, southeast, and the Tibet Plateau of China, while decreased in northern China. Almost all the trends of EPIs are positive in the next two periods (2011–2055 and 2056–2100) except for some EPIs, such as intensity of extreme precipitation, which decrease in southeastern China in the second period (2056–2100). This study suggests that the frequency of extreme precipitation events in China will progressively increase, which implies that a substantial burden will be placed on social economies and terrestrial ecological processes.

Future changes in annual precipitation extremes over Southeast Asia under global warming of 2°C

THIS ARTICLE PROVIDES detailed information on projected changes in annual precipitation extremes over Southeast Asia under global warming of 2°C based on the multi-model simulations of the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment Southeast Asia (SEACLID/CORDEX-SEA). Four indices of extreme precipitation are considered: annual total precipitation (PRCPTOT), consecutive dry days (CDD), frequency of rainfall exceeding 50 mm/day (R50mm), and intensity of extreme precipitation (RX1day). The ensemble mean of 10 simulations showed reasonable performance in simulating observed characteristics of extreme precipitation during the historical period of 1986–2005. The year 2041 was taken as the year when global mean temperature reaches 2°C above pre-industrial levels under unmitigated climate change scenario based on Karmalkar and Bradley (2017). Results indicate that the most prominent changes during the period of 2031–2051 were largely significant. Robust increases in CDD imply impending drier conditions over Indonesia, while increases in RX1day suggest more intense rainfall events over most of Indochina under 2°C global warming scenario. Furthermore, northern Myanmar is projected to experience increases in CDD, R50mm and RX1day, suggesting that the area may face more serious repercussions than other areas in Southeast Asia.

Do Extreme Precipitation Intensities Linked to Temperature Over India Follow the Clausius–Clapeyron Relationship?

Recent Intergovernmental Panel on Climate Change (IPCC) reports suggest that the atmosphere is getting warmer in almost every region across the globe 1,2 . As a consequence, extreme precipitation intensities (EPIs) are increasing in many regions according to the principle of Clausius–Clapeyron (CC) relationship, which states that atmospheric moisture storage capacity increases by ~7% per degree rise in temperature 3 . Therefore, the frequency of extreme precipitation events may increase with societal impacts on agriculture, economy, human health and animal habitats 4 .

Assessing the Robustness of Future Extreme Precipitation Intensification in the CMIP5 Ensemble

Abstract A warming climate is expected to intensify extreme precipitation, and climate models project a general intensification of annual extreme precipitation in most regions of the globe throughout the twenty-first century. We investigate the robustness of this future intensification over land across different models, regions, and seasons and evaluate the role of model interdependencies in the CMIP5 ensemble. Strong similarities in extreme precipitation changes are found between models that share atmospheric physics, turning an ensemble of 27 models into around 14 projections. We find that future annual extreme precipitation intensity increases in the majority of models and in the majority of land grid cells, from the driest to the wettest regions, as defined by each model’s precipitation climatology. The intermodel spread is generally larger over wet than over dry regions, smaller in the dry season compared to the wet season and at the annual scale, and largely reduced in extratropical compared to tropical regions and at the global scale. For each model, the future increase in annual and seasonal maximum daily precipitation amounts exceeds the range of simulated internal variability in the majority of land grid cells. At both annual and seasonal scales, however, there are a few regions where the change is still within the background climate noise, but their size and location differ between models. In extratropical regions, the signal-to-noise ratio of projected changes in extreme precipitation is particularly robust across models because of a similar change and background climate noise, whereas projected changes are less robust in the tropics.

Future extreme precipitation intensities based on a historic event

Abstract. In a warmer climate, it is expected that precipitation intensities will increase, and form a considerable risk of high-impact precipitation extremes. This study applies three methods to transform a historic extreme precipitation event in the Netherlands to a similar event in a future warmer climate, thus compiling a “future weather” scenario. The first method uses an observation-based non-linear relation between the hourly-observed summer precipitation and the antecedent dew-point temperature (the Pi–Td relation). The second method simulates the same event by using the convective-permitting numerical weather model (NWP) model HARMONIE, for both present-day and future warmer conditions. The third method is similar to the first method, but applies a simple linear delta transformation to the historic data by using indicators from The Royal Netherlands Meteorological Institute (KNMI)'14 climate scenarios. A comparison of the three methods shows comparable intensity changes, ranging from below the Clausius–Clapeyron (CC) scaling to a 3 times CC increase per degree of warming. In the NWP model, the position of the events is somewhat different; due to small wind and convection changes, the intensity changes somewhat differ with time, but the total spatial area covered by heavy precipitation does not change with the temperature increase. The Pi–Td method is simple and time efficient compared to numerical models. The outcome can be used directly for hydrological and climatological studies and for impact analysis, such as flood-risk assessments.