Variability Of Extreme Events Research Articles

Spatio-temporal trends in the frost regime reveal late frost exposure to white spruce (Picea glauca [Moench] Voss) persists in northeastern America

Abstract The characteristics of the frost regime (intensity, frequency, and timing) contribute to shaping tree species adaptations and distribution as well as ecosystem productivity and functions. However, climate change increases the variability in extreme events; therefore, the different characteristics of the frost regime may diverge under climate change. Using the BioSim 11 software, we simulated daily air temperature at 512 locations over northeastern North America between 1901–2021 to determine how the spatio-temporal trends in the frost regime varied over this complex landscape and if spatio-temporal trends in extreme climatic events such as frosts are stronger compared to changes in aggregated climate variables such as mean annual air temperature and growing degree-days. We also used an eco-physiological model to conduct a case study focussing on white spruce to determine if trees are currently more exposed to growing season frosts than they were in the past by modelling the timings of budburst using the thermal time model. Our results showed that, at 67% of locations (343 locations), the day of the year of the last frost in spring (minimum daily air temperature<0 °C) occurred, on average, earlier by seven days during 2001–2021 compared to 1901–1920, whereas it occurred, on average, later by four days at 33% of locations (169 locations). The average temporal trends in frost occurrence were similar in magnitude to the average trends in aggregated climate variables; however, their variances were larger compared to the aggregated climate metrics, showing that the frost regime does not change uniformly throughout our study area. Our case study also revealed that white spruce remains exposed to late frosts of low and intermediate intensities (minimum daily air temperature <0; <−2 °C) compared to the past but exposure to high-intensity frosts (minimum daily air temperature <−4 °C) is rare. Since extreme events such as late frosts diverge in their response to climate change compared to aggregated climate variables, the mean annual temperature is not sufficient to predict how climate change will impact ecosystems through frost regimes.

Variability of Extreme Events in Coastal and Inland Areas of South Korea during 1961–2020

The increased concentrations of greenhouse gases have led to global warming and an increased frequency and intensity of extreme weather events. Such changes in weather patterns may have unexpected implications for everyday life and water resource management in coastal and inland areas; thus, it is critical to understand the pattern of the changes. This study investigated how extreme weather events have changed in inland and coastal South Korea in the past 60 years (1961–2020) at different temporal scales, from monthly to yearly. This study quantified extreme weather events using multiple meteorological indices such as consecutive dry days (CDD), consecutive wet days (CWD), tropical nights, and icy and frosty days. The trends in the extreme weather indices were statistically tested using a non-parametric test. The results showed increases in the minimum and maximum air temperature and the frequency of warm and cold nights and days. The number of CDD and maximum five-day precipitation (RX5day) at the coastal and inland stations increased in the extreme precipitation-related index. The number of warm days and warm nights increased significantly at the majority of weather stations over the 60 year study period. The number of CWD increased during the selected period, but this was not statistically significant. In addition, we found that the temporal variations in the indices became greater over time, which implies the frequency and severity of extreme events such as drought and storm events may increase in the future. This study could help researchers determine the climatic areas at the selected stations that are critical for optimal water resource management planning and/or modeling.

Extreme rainfall events over the Okavango river basin, southern Africa

Characteristics of extreme rainfall events in the highly biodiverse Okavango River Basin (ORB) in west central southern Africa are not well understood. Hence, this study analyses the frequencies, intensities, spatial distributions, variability and trends of extreme rainfall events for the main rainy season, January–April (JFMA). The events are accumulated over 1-day (DP1) and 3-days (DP3). To identify these events, a method that accounts for both event intensity and spatial extent is used. On average, DP1 (DP3) events contribute ∼10% (∼17%) rainfall totals but in some years their contributions exceed 30%. Tropical-extratropical cloud bands are responsible for most of the events with tropical lows also important. The considerable interannual variability in extreme events appears related to El Niño-Southern Oscillation (ENSO) and the Botswana High.Although ENSO influences the extreme events as well as rainfall totals more generally over southern Africa, by far the wettest season over the iconic Okavango Delta region, in Ngamiland, Botswana, occurred during neutral JFMA 2017. These heavy rains resulted from a deeper Angola Low, weaker mid-level Botswana High which favoured convection in the region together with anomalous westerly moisture fluxes from the tropical South East Atlantic and enhanced low level moisture convergence over most of the region during January-early March. A dry period from mid-March was broken by the second most intense rainfall event on April 22nd, resulting from a cut-off low.Significant increasing trends were found in DP1 frequencies, as well as in rain-days and rain totals over many areas. These trends have important implications for water and agricultural management, and wildlife conservation in the highly biodiverse ORB.

Global assessment of spatiotemporal variability of wet, normal and dry conditions using multiscale entropy-based approach

In recent decades, human-induced climate change has caused a worldwide increase in the frequency/intensity/duration of extreme events, resulting in enormous disruptions to life and property. Hence, a comprehensive understanding of global-scale spatiotemporal trends and variability of extreme events at different intensity levels (e.g., moderate/severe/extreme) and durations (e.g., short-term/long-term) of normal, dry and wet conditions is essential in predicting/forecasting/mitigating future extreme events. This article analyses these aspects using estimates of a non-stationary standardized precipitation evapotranspiration index corresponding to different accumulation periods for 0.5° resolution CRU grids at globe-scale. Results are analyzed with respect to changes in land-use/landcover and geographic/location indicators (latitude, longitude, elevation) at different time scales (decadal/annual/seasonal/monthly) for each continent. The analysis showed an (i) increasing trend in the frequency/count of both dry and wet conditions and variability of dry conditions, and (ii) contrasting (decreasing) trend in the variability of wet conditions, possibly due to climate change-induced variations in atmospheric circulations. Globally, the highest variability in the wet and dry conditions is found during the Northern hemisphere's winter season. The decadal-scale analysis showed that change in variability in dry and wet conditions has been predominant since the 1930s and 1950s, respectively and is found to be increasing in recent decades.

ТЕМПЕРАТУРНЫЙ РЕЖИМ Г. МУРМАНСК ЗА ПОСЛЕДНИЕ 70 ЛЕТ: ИЗМЕНЧИВОСТЬ И ЭКСТРЕМАЛЬНЫЕ СОБЫТИЯ

Extreme temperature events and their long-term variability are investigated using daily air temperature data from Murmansk weather station for the period of 1942-2018. It was found that the variability of extremely warm and cold events in Murmansk in winter is mainly explained rather by the dynamics of storm tracks than by the local processes of air mass transformation in the region. It is shown that the main characteristics of extreme events demonstrate a clearly pronounced seasonality. It was revealed that the positive trend in seasonal mean temperatures is mainly due to the reduced number of extremely cold events in Murmansk. The study shows that the leading factor determining the variability of extreme events is the Arctic Oscillation (AO). In turn, the AO anomalies are formed as a result of the dynamic stratosphere-troposphere coupling, which is expressed in the response of storm tracks to sudden stratospheric warmings.

Fine scale surface climate in complex terrain using machine learning

AbstractAccurate and high spatial resolution (<100 m) surface climate information is crucial for process‐based modelling in hydrology, ecology, agriculture, urban studies etc, especially in complex terrain landscapes where coarse grid resolution information (∼10 km) is inadequate to represent pronounced local variability. We used a machine learning‐based workflow to predict high resolution (30 m) and sub‐daily atmospheric variables fields of near‐surface air temperature and humidity, and wind speed. The method used the Principal Component Analysis (PCA) decomposition applied on ground stations observations or Global Climate Model (GCM) residual error, in a sequence with bias correction and statistical models (Linear Regression‐LR, Artificial Neural Network model‐ANN and Empirical Quantile Mapping‐EQM) to provide downscaling from large scale atmospheric conditions to complex terrain variability. The predictions described relationships of Principal Component (PC) scores dependent on GCM temporal variability on 6‐hourly basis (with LR or ANN or EQM), and PC loadings dependent on topographic indexes to help providing horizontal sub‐grid extrapolation. The methods were validated with a 1‐year dataset from a dense weather stations network deployed in a complex terrain basin in tropical climate of Southeast Brazil. We present an exhaustive description of the PC modes daily/seasonal variability for each variable, and their spatial variability associated to the topography and thermal driven circulations. The predictions in general substantially improved accuracy when compared to GCM outputs, especially near the valley and in sheltered area where local effects are mandatories. Specially, ANN and EQM significantly improved the predictions at the variability of extreme events, such as the formation of strong cold air pooling or wetting in the valley.

Meteorological observations for Eversleigh Station, near Armidale, New South Wales, Australia: 1877–1922

AbstractHistorical weather observations on the daily scale are vital for the improvement of reanalysis products and the analysis of long‐term variability of extreme events. While daily datasets extend for several centuries in parts of the Northern Hemisphere, the majority of historical data for the Southern Hemisphere are monthly averages or totals. In this paper, we describe a newly recovered dataset of ten daily meteorological variables for 1877 to 1922 from Eversleigh, a property in the New England region of New South Wales in Australia. Here, we present the full process of data rescue, from digitization to quality control and an assessment of homogeneity. We show that the majority of variables were recorded to a high standard and that the data are of general use for climate analysis. Forty years of daily temperature, cloud cover, wind, and pressure observations are now available for the New England Plateau, offering data that are more complete than any other records for the region in the Australian Bureau of Meteorology dataset. The Eversleigh dataset now provides an opportunity to gain more insight into the 19th century weather and climate of eastern Australia during a time of large interannual climate variability before the dominant impact of an anthropogenic warming signal.

Contribution of large‐scale circulation anomalies to variability of summer precipitation extremes in northeast China

We propose a short‐lived extreme precipitation event, which is identified by the definition of 75th and 95th percentile using gauge observations across northeast China (NEC). Atmospheric variables from reanalysis dataset are then composited to examine the contribution of synoptic‐scale circulations to variations in extreme events. Results show that the divergence aloft combined with lower pressures at surface lead to upward vertical motion of circulations, along with flow around low‐pressure area and high pressure to east NEC derived from associated wavetrain conveying efficient moisture, providing favorable environment for occurrence of precipitation extremes. The northwestward shift of western Pacific subtropical high, together with northward shifted position of upper‐level westerlies over NEC, results in the strengthened southwesterly, which transports more moisture into NEC and produces extreme rainfall. Moreover, the stronger anomalies of dynamical quantities present for 95th percentile cases. The roughly same percentage of summertime short‐lived extreme events related to cut‐off lows (COLs) is found for both percentile composites, 21.2% (79 out of 373) and 21.1% (77 out of 365) for 75th and 95th percentile events, respectively. Overall, the contribution of COLs to short‐lived extreme precipitation is insignificant during 1960–2015 in NEC, although COLs have been documented to be more active in summer over NEC. The time series of COL‐induced events exhibits inter‐annual variability with not statistically significant linear trend, suggesting that COLs play a tiny role to the slightly decreasing trend of short‐lived precipitation events over NEC.

Is 'Resilience' Maladaptive? Towards an Accurate Lexicon for Climate Change Adaptation.

Climate change adaptation is a rapidly evolving field in conservation biology and includes a range of strategies from resisting to actively directing change on the landscape. The term ‘climate change resilience,’ frequently used to characterize adaptation strategies, deserves closer scrutiny because it is ambiguous, often misunderstood, and difficult to apply consistently across disciplines and spatial and temporal scales to support conservation efforts. Current definitions of resilience encompass all aspects of adaptation from resisting and absorbing change to reorganizing and transforming in response to climate change. However, many stakeholders are unfamiliar with this spectrum of definitions and assume the more common meaning of returning to a previous state after a disturbance. Climate change, however, is unrelenting and intensifying, characterized by both directional shifts in baseline conditions and increasing variability in extreme events. This ongoing change means that scientific understanding and management responses must develop concurrently, iteratively, and collaboratively, in a science-management partnership. Divergent concepts of climate change resilience impede cross-jurisdictional adaptation efforts and complicate use of adaptive management frameworks. Climate change adaptation practitioners require clear terminology to articulate management strategies and the inherent tradeoffs involved in adaptation. Language that distinguishes among strategies that seek to resist change, accommodate change, and direct change (i.e., persistence, autonomous change, and directed change) is prerequisite to clear communication about climate change adaptation goals and management intentions in conservation areas.

Trends in Daily Temperature and Precipitation Extremes for the Southeastern United States: 1948–2012

Abstract Spatial and temporal trends in temperature and precipitation extremes were investigated for the period 1948–2012 across the southeastern United States using 27 previously defined indices. Results show that regionwide warming in extreme minimum temperatures and cooling in extreme maximum temperatures occurred. The disproportionate changes in extreme daytime and nighttime temperatures are narrowing diurnal temperature ranges for most locations. The intensity and magnitude of extreme precipitation events increased overall, except for more easterly locations, particularly in South Carolina. These indices further show that warming in minimum temperatures has been pronounced most in summer and least in winter. Fall has become significantly wetter, while spring and summer have become drier, on average. Principal component analysis (PCA) was used to characterize a “geography of extremes” based on temperature and precipitation extreme indices. The PCA based on temperature indices revealed two coherent western and eastern subregions that share common modes of variability in extremes. Precipitation indices resulted in a greater number of smaller, spatially coherent groups exhibiting similar modes of variability. This classification regime illustrates important variations in extremes that exist on subregional scales. These findings have relevance for established climate research institutes, local governments, resource managers, and community planners interested in the variability of extreme events throughout the region.

Driving a conceptual model climate by different processes: Snapshot attractors and extreme events

In a low-order chaotic model of global atmospheric circulation the effects of driving, i.e., time-dependent (periodic, chaotic, and noisy) forcing, are investigated, with particular interest in extremal behavior. An approach based on snapshot attractors formed by a trajectory ensemble is applied to represent the time-dependent likelihood of extreme events in terms of a physical observable. A single trajectory-based framework, on the other hand, is used to determine the maximal value and the kurtosis of the distribution of the same observable. We find the most significant effect of the driving on the magnitude, relative frequency, and variability of extreme events when its characteristic time scale becomes comparable to that of the model climate. Extreme value statistics is pursued by the method of block maxima, and found to follow Weibull distributions. Deterministic drivings result in shape parameters larger in modulus than stochastic drivings, but otherwise strongly dependent on the particular type of driving. The maximal effects of deterministic drivings are found to be more pronounced, both in magnitude and variability of the extremes, than white noise, and the latter has a stronger effect than red noise.

Variability of extreme events in the Colombian Pacific and Caribbean catchment basins

This paper analyses the behavior of extreme events of surface precipitation and temperature inside the Pacific and Caribbean Catchment Basins in Colombia using several datasets such as observations, reconstructed data, NCEP-NCAR and ERA-40 reanalyses and data from the regional model REMO. We use an extreme value method that selects the time series excesses over a nonstationary threshold and adjusts them to a generalized Pareto distribution. The goodness of fit is evaluated through a test that includes the Cramer–von Mises, Kolmogorov–Smirnov and Anderson–Darling statistics and the p values generated by parametric bootstrap resampling. The test not only evaluates the goodness of fit but also the threshold choice. The parameters are presented in maps that allow recognition of the features of the extreme behaviour inside the catchment basins, and differences and similarities between them. Maps of return periods for the maximum extreme events are also presented. A strong influence of the El Nino–Southern oscillation on the extreme events of both temperature and precipitation is found in the two catchment basins.

Global change and agricultural management options for groundwater sustainability

According to the general circulation models (CGMs) for future climate projections, a temperature increase, precipitation decrease, and an increase in the variability of extreme events may be expected in the future, likely reducing available water resources. For the western Mediterranean, future climate change projections indicate that temperature increase may range from 1.5°C to 3.6°C, and the precipitation decline will reach between 10% and 20%, which may result in a significant reduction of natural groundwater recharge. With the use of modelling tools, the amount of groundwater recharge under different climate change scenarios and varying agricultural management practices can be predicted, and water budget attributes can be estimated, which may allow for quantifying impacts, and assist in defining adaptation strategies. For the Inca–Sa Pobla basin (Balearic Islands, Spain), under future climate change projections, agricultural management alternatives of crop type distribution and irrigation demands are required for planned adaptation strategies. In the area, where irrigation water for agricultural practices originates from groundwater resources, adaptation measures based on a change from mixed crops to potatoes and a 20% decrease of agricultural land cultivation have proven to be efficient for the hydrologic system and associated wetland sustainability.

A Homogeneous Record (1896–2006) of Daily Weather and Climate at Mohonk Lake, New York*

Abstract Reliable, long-term records of daily weather and climate are relatively rare but are crucial for understanding long-term trends and variability in extreme events and other climate metrics that are not resolvable at the monthly time scale. Here, the distinct features of a continuous, long-term (1896–2006) daily weather record from Mohonk Lake, New York, are highlighted. The site is optimal for daily climate analyses, since it has experienced negligible land-use change, no station moves, and has maintained methodological and instrumental consistency over the entire period of record. Unlike many sites, the site has always used maximum/minimum thermometers rather than shifting to the automated Maximum/Minimum Temperature Sensor. Notable results from the analysis of this record include 1) a warming trend driven largely by trends in maximum temperatures, especially during summer, 2) increasing diurnal temperature range during summer, and 3) a reduction in the number of freeze-days per year with little change in the length of the freeze-free season. These findings deviate from some regional level trends, suggesting there may be value in revisiting selected, consistently monitored, and maintained stations similar to Mohonk for focused analyses of regional climate change.

Regional Characteristics of Scale-based Precipitation Systems from Ten Years of TRMM PR Data

The regional characteristics of scale-based precipitation systems on the basis of the size of the systems were investigated using a 10-year dataset from the Precipitation Radar aboard the Tropical Rainfall Measuring Mission satellite. The size of a precipitation system is defined as the number of contiguous rain pixels. The first result is on the diurnal features of precipitation systems stratified by size as small ( 104 km2) rain area and stratiform/convective types, respectively. It was found that large overland precipitation systems developed mostly in the evening, following the early-afternoon rainfall maxima of small systems. While large systems showed clear migration properties, small systems had rain peaks at nearly the same local time, that is, without propagation. The diurnal features of small systems were almost uniform over the land or oceans. However, the vertical profiles of rainfall rates obtained for small systems exhibited regional variations. The second result is on the spatial and interannual variations in extreme events. The extreme events are defined as 1000 systems with the highest volumetric rainfall. These events were unevenly distributed and appeared particularly around the mouth of the La Plata basin and the northwestern and western Pacific. The year-to-year variations in these extreme events over the global tropics were consistent with the occurrence of tropical cyclones over the western Pacific.

Detecting changes in extreme precipitation and extreme streamflow in the Dongjiang River Basin in southern China

Abstract. Extreme hydro-meteorological events have become the focus of more and more studies in the last decade. Due to the complexity of the spatial pattern of changes in precipitation processes, it is still hard to establish a clear view of how precipitation has changed and how it will change in the future. In the present study, changes in extreme precipitation and streamflow processes in the Dongjiang River Basin in southern China are investigated with several nonparametric methods, including one method (Mann-Kendall test) for detecting trend, and three methods (Kolmogorov–Smirnov test, Levene's test and quantile test) for detecting changes in probability distribution. It was shown that little change is observed in annual extreme precipitation in terms of various indices, but some significant changes are found in the precipitation processes on a monthly basis, which indicates that when detecting climate changes, besides annual indices, seasonal variations in extreme events should be considered as well. Despite of little change in annual extreme precipitation series, significant changes are detected in several annual extreme flood flow and low-flow series, mainly at the stations along the main channel of Dongjiang River, which are affected significantly by the operation of several major reservoirs. To assess the reliability of the results, the power of three non-parametric methods are assessed by Monte Carlo simulation. The simulation results show that, while all three methods work well for detecting changes in two groups of data with large sample size (e.g., over 200 points in each group) and large differences in distribution parameters (e.g., over 100% increase of scale parameter in Gamma distribution), none of them are powerful enough for small data sets (e.g., less than 100 points) and small distribution parameter difference (e.g., 50% increase of scale parameter in Gamma distribution). The result of the present study raises the concern of the robustness of statistical change-detection methods, shows the necessity of combined use of different methods including both exploratory and quantitative statistical methods, and emphasizes the need of physically sound explanation when applying statistical test methods for detecting changes.