Seasonal Extreme Precipitation Research Articles

Testing tree-ring cellulose δ18O with water isotopes for Holocene lake δ18O interpretations in the central Rocky Mountains USA

Stable isotopes of water preserved in geologic archives, primarily as oxygen (δ18O), have proven critical for documenting Earth’s climatic and hydrologic systems past and present. However, timescale differences of water isotope inputs to proxy systems and the signal embedded in long paleorecords often confound translation to observed hydroclimatic metrics. Here, a unique 20-year dataset of meteorology, hydrology, and the isotopic composition of weekly meteoric and surface water samples (δ18O, δ2H) are combined with paleoclimate δ18O data from tree-ring cellulose and lake carbonate to better understand proxy signals of Upper Colorado river basin drought. Annual tree-ring cellulose δ18O from Picea engelmannii growing within a glacier-fed creek and a spring discharge area were used to derive annual source water δ18O using a cellulose source-water isotope model. Comparisons with the monitoring record indicates that tree-ring cellulose δ18O tracks variations in wet and dry hydroclimatic extremes. Source water isotopes are shown to reflect the hydroclimate of the current year and some number of previous years as an effective moisture-discharge proxy rather than a precipitation isotope proxy. Results contextualize Holocene lake carbonate δ18O data. The contemporary-to-paleo comparison identifies changes in seasonal precipitation extremes during recent millennia and several earlier arid and monsoon-dominated Holocene periods that exceed the arid maximum of the calibration period.

Trends in seasonal precipitation extremes and associated temperatures along continental Chile

Trends in seasonal precipitation extremes and associated temperatures along continental Chile

Role of mean and variability change in changes in European annual and seasonal extreme precipitation events

Abstract. The frequency of precipitation extremes is set to change in response to a warming climate. Thereby, the change in extreme precipitation event occurrence is influenced by both a shift in the mean and a change in variability. How large the individual contributions are from either of them (mean or variability) to the change in precipitation extremes is largely unknown. This is, however, relevant for a better understanding of how and why climate extremes change. For this study, two sets of forcing experiments from the regional CRCM5 initial-condition large ensemble are used: a set of 50 members with historical and RCP8.5 forcing and a 35-member (700-year) ensemble of pre-industrial natural forcing. The concept of the probability risk ratio is used to partition the change in extreme-event occurrence into contributions from a change in mean climate or a change in variability. The results show that the contributions from a change in variability are in parts equally important to changes in the mean and can even exceed them. The level of contributions shows high spatial variation, which underlines the importance of regional processes for changes in extremes. While over Scandinavia or central Europe the mean influences the increase in extremes more, the increase is driven by changes in variability over France, the Iberian Peninsula, and the Mediterranean. For annual extremes, the differences between the ratios of contribution of mean and variability are smaller, while on seasonal scales the difference in contributions becomes larger. In winter (DJF) the mean contributes more to an increase in extreme events, while in summer (JJA) the change in variability drives the change in extremes. The level of temporal aggregation (3, 24, 72 h) has only a small influence on annual and winterly extremes, while in summer the contribution from variability can increase with longer durations. The level of extremeness for the event definition generally increases the role of variability. These results highlight the need for a better understanding of changes in climate variability to better understand the mechanisms behind changes in climate extremes.

Comparison of multimodel ensembles of global and regional climate models projections for extreme precipitation over four major river basins in southern Africa— assessment of the historical simulations

This study assesses the performance of large ensembles of global (CMIP5, CMIP6) and regional (CORDEX, CORE) climate models in simulating extreme precipitation over four major river basins (Limpopo, Okavango, Orange, and Zambezi) in southern Africa during the period 1983–2005. The ability of the model ensembles to simulate seasonal extreme precipitation indices is assessed using three high-resolution satellite-based datasets. The results show that all ensembles overestimate the annual cycle of mean precipitation over all basins, although the intermodel spread is large, with CORDEX being the closest to the observed values. Generally, all ensembles overestimate the mean and interannual variability of rainy days (RR1), maximum consecutive wet days (CWD), and heavy and very heavy precipitation days (R10mm and R20mm, respectively) over all basins during all three seasons. Simple daily rainfall intensity (SDII) and the number of consecutive dry days (CDD) are generally underestimated. The lowest Taylor skill scores (TSS) and spatial correlation coefficients (SCC) are depicted for CDD over Limpopo compared with the other indices and basins, respectively. Additionally, the ensembles exhibit the highest normalized standard deviations (NSD) for CWD compared to other indices. The intermodel spread and performance of the RCM ensembles are lower and better, respectively, than those of GCM ensembles (except for the interannual variability of CDD). In particular, CORDEX performs better than CORE in simulating extreme precipitation over all basins. Although the ensemble biases are often within the range of observations, the statistically significant wet biases shown by all ensembles underline the need for bias correction when using these ensembles in impact assessments.

Arctic Seasonal Variability and Extremes, and the Role of Weather Systems in a Changing Climate

AbstractThe effects of global warming are strongly amplified in the Arctic, causing rapidly rising temperatures and ongoing dramatic loss of sea ice, which itself is subject to large interannual variability. We investigate changes in seasonal‐mean temperature and precipitation, its variability and extremes, using large‐ensemble climate model data with a representative concentration pathway 8.5 forcing scenario for historical (S2000) and end‐of‐century projections (S2100). Our results reveal regionally and seasonally dependent changes in Arctic interannual temperature and precipitation variability that are strongly linked to sea‐ice loss. We show a doubling in precipitation variability over the Arctic Ocean and a significant reduction in temperature variability in the Barents Sea. Extremely warm seasons in S2000 rank among the coldest seasons or become unrealistic in S2100. We further show the key role of large‐scale weather systems for shaping seasonal temperature and precipitation extremes in the Arctic which persists under climate warming.

Rare Event Simulation of Extreme European Winter Rainfall in an Intermediate Complexity Climate Model

AbstractWe test the application of a rare event simulation (RES) algorithm to accelerate the sampling of extreme winter rainfall over Europe in a climate model. The genealogical particle analysis algorithm, an ensemble method that interrupts the simulation at intermediate times to clone realizations in which an extreme event is developing, is applied to the intermediate complexity general circulation model PlaSim. We show that the algorithm strongly reduces the numerical effort required to estimate probabilities of extremes, demonstrating the potential of RES of seasonal precipitation extremes.

The Role of the Circulation Patterns in Projected Changes in Spring and Summer Precipitation Extremes in the U.S. Midwest

Abstract Recent studies suggest springtime wet extremes and summertime dry extremes will occur more frequently in the U.S. Midwest, potentially leading to devastating agricultural consequences. To understand the role of circulation patterns in the projected changes in seasonal precipitation extremes, the k-means clustering approach is applied to the large-ensemble experiments of Community Earth System Model, version 2 (CESM2-LE), and ensemble projections of CMIP6. We identify two key atmospheric circulation patterns that are associated with the extremely wet spring and extremely dry summer in the U.S. Midwest. The springtime wet extremes are typically linked to baroclinic waves with a northward shift of the North American westerly jet and positive anomalies in sea level pressure over the western Atlantic, which favor the development of the Great Plains low-level jet. The summertime dry extremes are associated with the development of an anomalous ridge with suppressed storm tracks over the central United States. The projected increase in springtime wet extremes and summertime dry extremes can be attributed to significantly more frequent occurrences of the associated atmospheric regimes. Particularly, the intensity of wet extremes is expected to increase mainly due to the enhanced moisture flux from the Gulf of Mexico. The moisture budget analysis suggests that the precipitation extremes are mainly associated with the dynamic component of atmospheric circulation. CESM2-LE and CMIP6 exhibit good agreement in the projected changes in circulation patterns and precipitation extremes. Our results explain the mechanism of the projected changes in the Midwest seasonal precipitation and highlight the contribution of circulation patterns to hydroclimatic extremes.

Phylogenomic insights into the origin and evolutionary history of evergreen broadleaved forests in East Asia under Cenozoic climate change.

The evergreen versus deciduous leaf habit is an important functional trait for adaptation of forest trees and has been hypothesized to be related to the evolutionary processes of the component species under paleoclimatic change, and potentially reflected in the dynamic history of evergreen broadleaved forests (EBLFs) in East Asia. However, knowledge about the shift of evergreen versus deciduous leaf with the impact of paleoclimatic change using genomic data remains rare. Here, we focus on the Litsea complex (Lauraceae), a key lineage with dominant species of EBLFs, to gain insights into how evergreen versus deciduous trait shifted, providing insights into the origin and historical dynamics of EBLFs in East Asia under Cenozoic climate change. We reconstructed a robust phylogeny of the Litsea complex using genome-wide single-nucleotide variants (SNVs) with eight clades resolved. Fossil-calibrated analyses, diversification rate shifts, ancestral habit, ecological niche modelling and climate niche reconstruction were employed to estimate its origin and diversification pattern. Taking into account studies on other plant lineages dominating EBLFs of East Asia, it was revealed that the prototype of EBLFs in East Asia probably emerged in the Early Eocene (55-50 million years ago [Ma]), facilitated by the greenhouse warming. As a response to the cooling and drying climate in the Middle to Late Eocene (48-38 Ma), deciduous habits were evolved in the dominant lineages of the EBLFs in East Asia. Up to the Early Miocene (23 Ma), the prevailing of East Asian monsoon increased the extreme seasonal precipitation and accelerated the emergence of evergreen habits of the dominant lineages, and ultimately shaped the vegetation resembling that of today.

Moisture sources of the first rainy season extreme precipitation events in the hotspots of Guangdong, South China

AbstractThe first rainy season (April–May–June) extreme precipitation events over South China tend to frequently occur in specific hotspots, resulting in a great threat to local society and economy. Abundant moisture supply is essential to precipitation formation. In this study, we employ the FLEXPART model to investigate the geographical moisture sources for extreme precipitation events in three hotspots of Guangdong from west to east (termed as Area‐A, B, and C). During both the periods before and after the onset of the South China Sea Summer Monsoon (hereafter SCSSM), the moisture contributions of ocean regions are larger than that of land regions for all the hotspots, but the main moisture source regions are different. Before the onset of SCSSM, the main source regions are South China Sea and Southeastern Asia, which contribute 36.50 and 31.79% to Area‐A, 35.85 and 27.83% to Area‐B, while 42.27 and 23.81% to Area‐C, respectively. After the onset of SCSSM, the contribution of moisture from Indian Ocean increases a lot. Thus, the main source regions are Indian Ocean, Southeastern Asia and South China Sea, which contribute 31.52, 24.58 and 18.13% to Area‐A, 29.21, 21.82 and 14.99% to Area‐B, 26.62, 20.70%, and 28.19% to Area‐C, respectively. Among the three hotspots, the contribution of ocean regions for Area‐C is the largest. Southeastern Asia is the main land contributor before and after the onset of SCSSM for all the hotspots. This study implies the important role of both ocean and land moisture sources on extreme precipitation variations over South China.

Effect difference of climate change and urbanization on extreme precipitation over the Guangdong-Hong Kong-Macao Greater Bay Area

Increasing extreme precipitation caused by climate change and human activities may have severe consequences on people's lives and property. In this study, the effect difference of urbanization, climate change and their compound effect on the annual and seasonal extreme precipitation under various Shared Socioeconomic Pathways are investigated using a convection-permitting (4 km) Weather Research and Forecasting model in the mega urban agglomeration. The enhancement effects of urbanization and climate change on extreme precipitation are also compared in the near future (2021–2050) and far future (2071–2100) periods of various Shared Socioeconomic Pathways-Representative Concentration Pathways (SSP-RCP) emission scenarios. The Guangdong-Hong Kong-Macao Greater Bay Area (GBA), a subtropical coastal world-class urban agglomeration in China, is taken as a representative in this study. The results indicate that, annual extreme and total precipitation will be more intensive and more frequent at regional scale along with rising long-lived greenhouse gas (LLGHG) concentration, while future urbanization exacerbates precipitation at local scale. Urban-induced total precipitation increases in the downwind areas and some urban areas of urban agglomeration, but still slightly decreases (all below 1.5%) over the entire study area. And more robust and stronger urban-induced signal in extreme precipitation characteristic is found in and around the urban areas where the total precipitation increases, as seen with an increase of >20% in amount and day number. In the compound scenarios, the interaction between urban and LLGHGs on precipitation was nonlinear and can be better explored by using convective permitting simulations. While urbanization can amplify or dampen local to regional precipitation, increased concentration of LLGHGs still make regional precipitation shift toward a general enhancement. Urbanization still has an impact on spatial distribution of precipitation despite a warming background climate. Additionally, seasonal heterogeneity of precipitation will be enhanced in all the future scenarios, especially in the future urbanization scenarios. The enhancement of urbanization (increase by about 42.1%, 38.0%, 14.0% in the precipitation amount, days and intensity respectively) on summer extreme precipitation change is greater than that of low-level LLGHG concentration (same as above, but increase by about 38.7%, 35.4%, 13.4%), despite their different mechanisms in altering extreme precipitation. The dominant factor of extreme precipitation varies with urbanization level and the LLGHG concentration. More attention should be paid to urban-induced precipitation changes under sustainable development scenarios with low-level LLGHG concentration.

Accounting for the spatial structure of weather systems in detected changes in precipitation extremes

The detection of changes over time in the distribution of precipitation extremes is complicated by noise at the spatial scale of weather systems. Traditional approaches for quantifying observed changes in extreme precipitation return values are often based on single-station analyses, which fail to account for the spatial coherence of individual storms and hence yield unrealistic and potentially misleading estimates of the true underlying changes in extremes. In this paper, we demonstrate how the use of a flexible statistical method that robustly accounts for the so-called “storm dependence” in measurements of daily precipitation removes a challenging source of noise and results in improved estimates of changes in the distribution of precipitation extremes. Furthermore, our analysis provides important insights into the spatial structure of seasonal extreme precipitation across increasing event rarity. Applying the methodology to long-term in situ records of daily precipitation from the central United States, we find that properly accounting for storm dependence leads to increased detection of statistically significant changes in return values as compared with existing approaches. We also find that simultaneous precipitation extremes in this region tend to organize on scales of 100–200 km for high quantile levels, which is consistent with observed spatial patterns in the NEXRAD Stage IV radar-based data set.

North Pacific and North Atlantic Jet Covariability and Its Relationship to Cool Season Temperature and Precipitation Extremes

Abstract Cool season (September–May) extreme temperature and precipitation events are frequently tied to surface cyclones and anticyclones, which are modulated on the synoptic scale by the state and evolution of the upper-tropospheric jet stream. This study adopts a jet-centered approach to classify the prevailing large-scale flow pattern into jet regimes based on the leading modes of variability of the North Pacific jet (NPJ) and the North Atlantic jet (NAJ), respectively. The characteristics of these joint NPJ–NAJ regimes are subsequently examined using composite analysis to identify large-scale flow environments conducive to the occurrence of anomalous temperatures and precipitation across North America. The analysis reveals that composite large-scale flow environments associated with each joint NPJ–NAJ regime can be approximated as a linear combination of the separate large-scale environments that characterize each NPJ regime and NAJ regime independently. Furthermore, knowledge of the joint NPJ–NAJ regime provides more precision regarding the relative likelihood and spatial coverage of anomalous temperatures and precipitation than would be obtained from consideration of the NPJ or NAJ regime in isolation. The frequencies of each joint NPJ–NAJ regime can also be modulated by the occurrence of sudden stratospheric warmings, with increased frequencies of an equatorward-shifted NAJ and a retracted NPJ during the 30-day period following a warming event. The results from the present study demonstrate that knowledge of the joint NPJ–NAJ regime exhibits the potential to inform forecasts of anomalous temperatures and precipitation at medium-range and subseasonal time scales. Significance Statement The development of cool season temperature and precipitation extremes are modulated by the state and evolution of the upper-tropospheric jet stream. Therefore, this study adopts a jet-centered approach to quantify the extent to which temperature and precipitation extremes over North America are related to the coevolution of the North Pacific and North Atlantic segments of the jet stream. The analysis demonstrates that the relative likelihood and spatial coverage of temperature and precipitation extremes varies significantly across North America based on the combined state of the North Pacific and North Atlantic jets. The jet-centered approach utilized in this study exhibits the potential to inform operational medium-range (6–10 day) and subseasonal forecasts of temperature and precipitation across North America.

The capability of CMIP6 models on seasonal precipitation extremes over Central Asia

The accurate extreme precipitation simulations of the GCMs during the historical period are more likely to offer reliable forecasts in the future. In this study, we evaluated the abilities of 22 GCMs of CMIP6 in simulating extreme precipitation over Central Asia in the early spring, summer, and late winter, when extreme precipitation can be very threatening. Based on seven extreme precipitation indices, robust skill score techniques such as Taylor diagrams and DISO (Distance between Indices of Simulation and Observation) are employed to rank the overall model performance. The results show that all models have difficulties in simulating the overall spatial patterns of all extreme precipitation in early spring and late winter. Differently, in summer, most of them can fairly well capture the spatial pattern of PRCPTOT and the CDD. Most models show consistent bias of extreme precipitation indices in early spring and late winter, with overall overestimated CDD and CWD and underestimated SDII, Rx5day, R95PTOT, and R10mm. However, there is high inter-model variability in the regional bias of summer, and the areas of inconsistency that emerge always contain most of Xinjiang or its surrounding mountain regions, possibly due to a lack of topographic correction in these areas with very complex topography. The model ensemble simulations generally outperform the individual models in terms of CC (Correlation coefficient) and DISO, while the improvement is limited regarding RMS (Centered mean square error) and RSD (Relative standard deviations). This study may serve as a reference to further investigate the sources of persistent systematic biases in current GCMs over Central Asia, which is expected to assist policymakers in adopting strategies reflecting the state of scientific understanding of the likelihood.

Testing some grouping methods to achieve a low error quantile estimate for high resolution (0.25° x 0.25°) precipitation data

The study focuses on the estimation of a technique, a method for developing a phenomenon, to obtain a quantile with minimal or low error (AR and R-RMSE bias). To arrive at such a solution, a case study of the Mahanadi River system (Mahanadi Basin) was conducted along with the integration of various techniques available in past and present literature, to come up with a novel solution. Which could answer practical questions in water resource planning and management for addressing a wide range of problems such as meteorological draught analysis, agricultural planning, precipitation forecasting and downscaling, design of water control and conveyance structures, and land-use planning and management. A gridded rainfall data set of resolution 0.25° x 0.25° (1901 – 2017) obtained from IMD Pune is used to calculate the statistics that will be used for the regionalization of precipitation. Other attributes or variables used for regionalization are seasonality measurements and location parameters (latitude, longitude, and elevation). The L-moment statistics are computed from the time series rainfall data and the ratios of the L-coefficient of variance and the L-coefficient of skewness, i.e., the L-moment ratio, are the main components in computing quantile estimates of selected regions for effective regional frequency analysis. To determine potential scenarios for homogeneous regions, the use of seasonal extreme precipitation will serve as a basis for regionalization.

Evaluation and multimodel projection of seasonal precipitation extremes over central Asia based on CMIP6 simulations

AbstractCentral Asia faces an increasing challenge related to water resources arising from severe droughts and floods that have impacted the region in the past decades. Using a comprehensive set of extreme precipitation indices, we assess the performance of CMIP6 models in representing observed precipitation extremes and investigate future responses of these extremes to greenhouse gas emissions under four shared socioeconomic pathways (SSP). Particularly, this study identifies robust signals of projected changes in spring and summer precipitation extremes over central Asia. Results show that the CMIP6 models reasonably reproduced the spatial distribution and variability of precipitation extremes over southern central Asia (SCA) and northern central Asia (NCA) during the spring and summer seasons, respectively. Across the SSPs, the CMIP6 models project a decrease in total spring wet‐day precipitation amount (PRCPTOT) over SCA and a significant increase in PRCPTOT over the NCA. The projected changes are characterized by a significant increase in maximum consecutive 5‐day precipitation (RX5day), wet‐day intensity (SDII), and the number of heavy precipitation days (R10mm); these suggest that the spring will be characterized by intense precipitation extremes. Moreover, no significant change is projected for consecutive wet days (CWD) and dry spells (CDD) over SCA. Nonetheless, the CMIP6 models project a significant increase in summer PRCPTOT over SCA and a decrease in summer PRCPTOT, RX5day, R10mm, SDII, and CWD over NCA while projecting a significant and robust increase in dry spells. It is also found that the severity of the projected dry spell deepens across the SSP spectra and gets more pronounced towards the end of the 21st century. Notably, the intermodel spread of the precipitation extremes is smaller during the spring over SCA, larger during the summer season over NCA, and more probable in the warmer future.

Combined Impacts of Climate Variability Modes on Seasonal Precipitation Extremes Over China

Combined Impacts of Climate Variability Modes on Seasonal Precipitation Extremes Over China

Phenological Water Balance Applications for Trend Analyses and Risk Management

The overarching goal of this work is to develop and demonstrate methods that support effective agro-pastoral risk management in a changing climate. Disaster mitigation strategies, such as the Sendai Framework for Disaster Risk Reduction (SFDRR), emphasize the need to address underlying causes of disaster risk and to prevent the emergence of new risks. Such assessments can be difficult, because they require transforming changes in meteorological outcomes into sector-specific impact. While it is common to examine trends in seasonal precipitation and precipitation extremes, it is much less common to study how these trends interact with crop and pasture water needs. Here, we show that the Water Requirement (WR) component of the widely used Water Requirement Satisfaction Index (WRSI) can be used to enhance the interpretation of precipitation changes. The WR helps answer a key question: was the amount of rainfall received in a given season enough to satisfy a crop or pasture's water needs? Our first results section focuses on analyzing spatial patterns of climate change. We show how WR values can be used to translate east African rainfall declines into estimates of crop and rangeland water deficits. We also show that increases in WR, during recent droughts, has intensified aridity in arid regions. In addition, using the PWB, we also show that precipitation increases in humid areas of western east Africa have been producing increasingly frequent excessive rainfall seasons. The second portion of our paper focuses on assessing temporal outcomes for a fixed location (Kenya) to support drought-management scenario development. Kenyan rainfall is decreasing and population is increasing. How can we translate this data into actionable information? The United Nations and World Meteorological Organization advise nations to proactively plan for agro-hydrologic shocks by setting aside sufficient grain and financial resources to help buffer inevitable low-crop production years. We show how precipitation, WR, crop statistics, and population data can be used to help guide 1-in-10 and 1-in-25-year low crop yield scenarios, which could be used to guide Kenya's drought management planning and development. The first and second research components share a common objective: using the PWB to translate rainfall data into more actionable information that can inform disaster risk management and development planning.

Trends in Arctic seasonal and extreme precipitation in recent decades

Daily precipitation data from the European Centre for Medium-Range Weather Forecasts (ERA-Interim) from 1979 to 2016 are analyzed to determine the trends in seasonal and extreme precipitation across the pan-Arctic and estimate the contributions to the trends from the dynamic (e.g., changes in circulation patterns) and thermodynamic processes (e.g., sea ice melt–water vapor feedback) and their interactions. The trends in the seasonal total precipitation are generally consistent with the trends in the occurrence of seasonal extreme precipitation. Although the trends vary considerably in direction and magnitude across the pan-Arctic and the seasons, more regions experience a statistically significant positive trend than negative trend, particularly in autumn and winter seasons and over areas of the Arctic Ocean and the northern North Atlantic. Statistically significant negative trends are mostly found in areas of northern Eurasian and North America. The thermodynamic processes account for more than 85% of the total trends, with the rest of the trends explained by the dynamic processes (e.g., changes in circulation patterns) and the interaction between dynamic and thermodynamic processes.

Contributions of natural climate variability on the trends of seasonal precipitation extremes over China

AbstractThe increased trends of extreme precipitation events over China have been partially attributed to global warming, while it is unclear how the natural climate variability affect these variations in extreme rainfall trends. Here we examine the contributions of large‐scale modes climate variability from the Pacific Ocean, which are represented by El Niño‐Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO), on the trends of seasonal extreme events across China. Results show that the effects of climate variability modes on precipitation extremes exhibit evidently spatiotemporal characteristics at the seasonal scale. A significant positive trend is found over southeastern and northwestern China in four seasons, and substantial increases in two types of extreme events are also seen over northwestern China in spring and winter. Most of the seasonal trends of extreme precipitation events can be explained by the trends of the principal components. El Niño (La Niña) and PDO–like sea surface temperature (SST) anomalies over the Pacific have a close linkage with the variations in precipitation extremes. By modulating the large‐scale atmospheric circulations, these Pacific SST anomalies associated with climatic oscillations, such as ENSO and PDO, provide the conducive conditions responsible for the occurrences of seasonal precipitation extremes across China. The seasonal trends of extreme events can be largely explained by the variability of the ENSO and PDO episodes, with a relatively larger modulation of PDO as compared to ENSO. These researches have important implications for improving seasonal flood disaster predictability and climate model performances.

Variability and Transitions in Precipitation Extremes in the Midwest United States

AbstractMonthly to seasonal precipitation extremes, both flood and drought, are important components of regional climates worldwide, and are the subjects of numerous investigations. However, variability in and transition between precipitation extremes, and associated impacts are the subject of far fewer studies. Recent such events in the Midwest region of the United States, such as the 2011–12 flood to drought transition in the upper Mississippi River basin and the flood to drought transition experienced in parts of Kentucky, Ohio, Indiana, and Illinois in 2019, have sparked concerns of increased variability and rapid transitions between precipitation extremes and compounded economic and environmental impacts. In response to these concerns, this study focuses on characterizing variability and change in Midwest precipitation extremes and transitions between extremes over the last 70 years. Overall we find that the Midwest as a region has gotten wetter over the last seven decades, and that in general the annual maximum and median wetness, defined using the standardized precipitation index (SPI), have increased at a larger magnitude than the annual minimum. We find large areas of the southern Midwest have experienced a significant increase in the annual SPI range and associated magnitude of transition between annual maximum and minimum SPI. We additionally find wet to dry transitions between extremes have largely increased in speed (i.e., less time between extremes), while long-term changes in transition frequency are more regional within the Midwest.