Local Extreme Events Research Articles

Evaluating Magnitude Agreement and Occurrence Consistency of CHIRPS Product with Ground-Based Observations over Medium-Sized River Basins in Nepal

High spatio-temporal resolution and accurate long-term rainfall estimates are critical in sustainable water resource planning and management, assessment of climate variability and extremes, and hydro-meteorology-related water system decisions. The recent advent of improved higher-resolution open-access satellite-based rainfall products has emerged as a viable complementary to ground-based observations that can often not capture the rainfall variability on a spatial scale. In a developing country such as Nepal, where the rain-gauge monitoring network is sparse and unevenly distributed, satellite rainfall estimates are crucial. However, substantial errors associated with such satellite rainfall estimates pose a challenge to their application, particularly in complex orographic regions such as Nepal. Therefore, these precipitation products must be validated before practical usage to check their accuracy and occurrence consistency. This study aims to assess the reliability of the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) product against ground-based observations from 1986 to 2015 in five medium-sized river basins in Nepal, namely, Babai, Bagmati, Kamala, Kankai, and the West Rapti river basin. A set of continuous evaluation metrics (correlation coefficient, root mean square error, relative bias, and Kling-Gupta efficiency) were used in analyzing the accuracy of CHIRPS and categorical metrics (probability of detection, critical success index, false alarm ratio, and frequency bias index). The Probability of Detection and Critical Success Index values were found to be considerably low (<0.4 on average), while the false alarm ratio was significant (>0.4 on average). It was found that CHIRPS showed better performance in seasonal and monthly time scales with high correlation and indicated greater consistency in non-monsoon seasons. Rainfall amount (less than 10 mm and greater than 150 mm) and rainfall frequency was underestimated by CHIRPS in all basins, while the overestimated rainfall was between 10 and 100 mm in all basins except Kamala. Additionally, CHIRPS overestimated dry days and maximum consecutive dry days in the study area. Our study suggests that CHIRPS rainfall products cannot supplant the ground-based observations but complement rain-gauge networks. However, the reliability of this product in capturing local extreme events (such as floods and droughts) seems less prominent. A high-quality rain gauge network is essential to enhance the accuracy of satellite estimations.

Revising green roof design methods with downscaling model of rainfall time series.

Historically, green infrastructure for stormwater management has been event-based designed. This study aims to realign the green infrastructure design strategies with principles for robust decision making, through the example of green roofs design with the variational method and exemplified using the Norwegian context of the 3-step approach (3SA) for stormwater management. The 3SA consists of planning solutions to handle day-to-day rain at site scale through infiltration (step 1) and detention (step 2), and extreme events with safe floodways (step 3). An innovative framework based on downscaling of rainfall timeseries is suggested as follows: (i) long duration continuous simulation for retention variation and day-to-day discharge, corresponding to step 1 in the 3SA; (ii) intensive sampling of local extreme events to estimate reliability and robustness of solutions, corresponding to steps 2 and 3 in the 3SA. Comparing the traditional variational method to Highly-Informed-Design-Evaluation-Strategy (HIDES), it was found that the variational method possibly leads to incorrect decisions while the suggested novel approach was found to give more informed and reliable results by suggesting a design based on both operating mode and failure mode. It allows to embed solutions within the urban water system by facilitating the link between the steps of the 3SA. Such a framework was found to be data-wise applicable in the Norwegian context.

FYRE Climate: a high-resolution reanalysis of daily precipitation and temperature in France from 1871 to 2012

Abstract. Surface observations are usually too few and far between to properly assess multidecadal variations at the local scale and characterize historical local extreme events at the same time. A data assimilation scheme has been recently presented to assimilate daily observations of temperature and precipitation into downscaled reconstructions from a global extended reanalysis through an Ensemble Kalman fitting approach and to derive high-resolution fields. Recent studies also showed that assimilating observations at high temporal resolution does not guarantee correct multidecadal variations. The current paper thus proposes (1) to apply the data assimilation scheme over France and over the 1871–2012 period based on the SCOPE Climate reconstructions background dataset and all available daily historical surface observations of temperature and precipitation, (2) to develop an assimilation scheme at the yearly timescale and to apply it over the same period and lastly, (3) to derive the FYRE Climate reanalysis, a 25-member ensemble hybrid dataset resulting from the daily and yearly assimilation schemes, spanning the whole 1871–2012 period at a daily and 8 km resolution over France. Assimilating daily observations only allows reconstructing accurately daily characteristics, but fails in reproducing robust multidecadal variations when compared to independent datasets. Combining the daily and yearly assimilation schemes, FYRE Climate clearly performs better than the SCOPE Climate background in terms of bias, error, and correlation, but also better than the Safran reference surface reanalysis over France available from 1958 onward only. FYRE Climate also succeeds in reconstructing both local extreme events and multidecadal variability. It is freely available at https://doi.org/10.5281/zenodo.4005573 (precipitation, Devers et al., 2020b) and https://doi.org/10.5281/zenodo.4006472 (temperature, Devers et al., 2020c).

Past and future changes of the Austrian climate – Importance for tourism

The paper summarizes changes of the climate conditions in Austria impacting tourism as part of an extended literature review. The holistic approach of the study enabled to establish the physical links between emissions created by touristic activities and resultant climate responses. Since the end of the 19 th century, air temperature in Austria increased in all elevations by 1.8 °C (+20% compared to the global land areas). Depending on the scenario, a further increase of less than 2 °C (“Paris target”) to 4 °C (“worst-case scenario”; RCP 8.5) is expected until the year 2100, which means that the number of hot days either will stabilize at the current level (“Paris Target”) or current extreme years will become the new normal at the end of the century (“worst-case scenario”). Regarding snow, as the basic requirement for winter tourism, the natural snow cover duration and snow depth decreased especially in western and southern Austria. Depending on the elevation, a further decline of 10% to 40% (“Paris target”) and 50% to 90% (“worst-case scenario”), respectively, is projected until the year 2100. Sustained warming also reduces the time slots for technical snow production in all elevations. More frequent severe thunderstorms and related small-scale phenomena such as heavy precipitation, hail, squalls, small-scale floods and mudflow avalanches are to be expected to increase, especially in the worst-case scenario. Climate change effects emerge progressively in observation-based tourism-climate indicators and more sustained impacts are projected for the future. The magnitude of climate risks in the second half of the 21 st century can be substantially reduced by rapid, global climate mitigation and adaptation measures. Climatological boundary conditions in Austria have already changed and more profound, human induced changes are expected in future, further increasing impacts relevant for tourism in all seasons of the year. The following implications for tourism management can be deduced from this study: • Sector-specific, immediate and effective climate mitigation and adaptation measures are required to substantially reduce the magnitude of the major tourism relevant climate risks (recession of snow, ice and permafrost; heat stress; thunderstorms and related local extreme events; drought events) in the second half of the 21 st century. • Site- and sector-specific, tailored studies based on downscaled and bias-corrected regional climate model output should be performed to help analyze current and future business-related climate risks together with national climate service providers. • Systematic, coordinated and sector-specific awareness raising for climate change and its causes and impacts is needed to ensure acceptance and increase effectivity of mitigation and adaptation measures both for people working in tourism and the guests. • Targeted operational meteorological early warning systems need to be designed and locally implemented (heat stress; thunderstorms and related local extreme events).

Diffusion entropy analysis and random matrix analysis of the Indian stock market

We analyze daily prices of 42 stocks listed in the Nifty50 index of National Stock Exchange of India from 2006 to 2019. The Nifty50 is one of the leading indexes of Indian the stock market which comprises 50 dominant companies. We use well established techniques such as random matrix theory, complex network analysis, hierarchical clustering, diffusion entropy, and conditional entropy to study the correlation among stocks and topological properties of the Indian stock market. We perform the static and dynamic analysis of the Nifty50 stocks to extract useful information from them. In the static period analysis, we consider three sub periods i.e. before, during and the subprime crisis of 2008. In dynamic period analysis, we consider overlapped and non-overlapped time windows of one year with varying shifts. We study the effect of ‘global events’ (such as the sub-prime crisis of 2008 and the Chinese crisis) and ‘local events’ (such as General elections 2009, Assembly elections in 7 states, General elections 2014 and Demonetization in India). We find that global and local extreme events affect the correlations among Nifty50 stocks of the Indian stock market. The analysis performed in this paper may be helpful in the development of different indicators of systemic risks in India.

Criticality analysis of 3-year-long VLF subionospheric propagation data possibly related to significant earthquake events in Japan

Ionospheric anomalies possibly related to significant earthquakes (EQs) have been extensively investigated in recent years and could be considered promising for short-term EQ prediction. In this work, we study the subionospheric propagation data recorded during a time period of 3 years (1/1/2014–28/02/2017) by a network of 8 VLF/LF receivers deployed throughout Japan, to identify possible signatures of pre-EQ processes. Specifically, we analyzed the nighttime receiver amplitude data possibly related to any main EQ event with magnitude MW ≥ 5.5 that occurred in a wider area of Japan during the above-mentioned time period (16 main EQ events) by applying the method of critical fluctuations which is able to identify critical dynamics. Our results identified intermittency-induced criticality in the lower ionosphere before all the main seismic events that occurred in Japan during the time period of interest. Criticality was also identified prior to other global or local extreme events such as magnetic storms, solar flares and volcano eruptions, that happened during the analyzed time period, indicating that more research efforts combining the results of more analysis methods, should be devoted in the future in order to discriminate between the subionospheric perturbations related to EQs and the ones related to other extreme geophysical events.

Using Self-Organizing Maps to Identify Coherent CONUS Precipitation Regions

Abstract Extreme precipitation events have major societal impacts. These events are rare and can have small spatial scale, making statistical analysis difficult; both factors are mitigated by combining events over a region. A methodology is presented to objectively define “coherent” regions wherein data points have matching annual cycles. Regions are found by training self-organizing maps (SOMs) on the annual cycle of precipitation for each grid point across the contiguous United States (CONUS). Using the annual cycle for our intended application minimizes problems caused by consecutive dry periods and localized extreme events. Multiple criteria are applied to identify useful numbers of regions for our future application. Criteria assess these properties for each region: having many more events than experienced by a single grid point, good connectedness and compactness, and robustness to changing the number of regions. Our methodology is applicable across datasets and is tested here on both reanalysis and gridded observational data. Precipitation regions obtained align with large-scale geographical features and are readily interpretable. Useful numbers of regions balance two conflicting preferences: larger regions contain more events and thereby have more robust statistics, but more compact regions allow weather patterns associated with extreme events to be aggregated with confidence. For 6-h precipitation, 12–15 regions over the CONUS optimize our metrics. The regions obtained are compared against two existing region archetypes. For example, a popular set of regions, based on nine groups of states, has less coherent regions than defining the same number of regions with our SOM methodology.

Framing professional climate risk knowledge: Extreme weather events as drivers of adaptation innovation in Copenhagen, Denmark

While existing research suggests that extreme weather events influence perceptions of climate risk, the mechanisms shaping innovation in urban climate adaptation work remain understudied. In this article, we inquire into how recent localized extreme weather events in Copenhagen, Denmark, have spurred processes of innovation in adaptation pathways. We suggest that risk-framing work by professionals plays an important part in understanding urban innovation processes, since changes in public risk perceptions become part of collaborative learning processes amongst communities of professionals. Such learning processes can result in new technological pathways of innovation, as part of wider urban knowledge systems. We analyze these processes through 32 semi-structured interviews with professionals involved in creating knowledge about climate adaptation in urban Copenhagen. Specifically, we compare two knowledge and innovation pathways, for pluvial and coastal flooding, representing, respectively, an actualized extreme event and a near miss. Overall, we find that the presence (pluvial) or absence (coastal) of damage from localized extreme events matters to the knowledge and innovation pathways. Innovation in dominant technological solution spans, as well as related institutional decision-making routines, is more pervasive in pluvial flooding, whereas coastal flooding is widely considered as temporally distant. Analytically, we suggest that a distinction between regulative and expanded institutional framings of risk matters to how professionals understand their own room for manoeuvre. Recent pluvial flood events have not changed the official technical risk assessments. Yet when professionals consider such events in an expanded institutional frame, they incorporate increased public and political risk perceptions as they construct pathways for further innovation.

Two decades of climate driving the dynamics of functional and taxonomic diversity of a tropical small mammal community in western Mexico.

Understanding the effects of global climate disruption on biodiversity is important to future conservation efforts. While taxonomic diversity is widely studied, functional diversity of plants, and recently animals, is receiving increasing attention. Most studies of mammals are short-term, focus on temperate habitats, and rely on traits described in the literature rather than generating traits from observations. Unlike previous studies, this long-term field study assessed the factors driving the functional and taxonomic diversity of small-mammal assemblages in dry tropical forests using both traits recorded from literature and a demographic database. We assessed the drivers (abundance and biomass, temperature and rainfall) of taxonomic richness and functional diversity for two rain-driven seasons in two adjacent but distinct forests—upland and lowland (arroyo or riparian) forests. Our analysis found that rainfall, both seasonal and atypical, was the primary factor driving functional and taxonomic diversity of small-mammal assemblages. Functional responses differed between the two types of forests, however, with effects being stronger in the harsher conditions of the upland forests than in the less severe conditions prevailing in the arroyo (riparian) forest. The latter also supports a richer, more diverse, and more stable small-mammal assemblage. These findings highlight the importance of climate to tropical biological diversity, as extreme climate events (hurricanes, droughts and floods) and disruption of rainfall patterns were shown to decrease biodiversity. They also support the need to preserve these habitats, as their high taxonomic diversity and functional redundancy makes them resilient against global climate disruption and local extreme events. Tropical dry forests constitute a potential reservoir for biodiversity and the ecosystem services they provide. Unfortunately, these forests are among the most endangered terrestrial ecosystems because of deforestation and the likely impacts of global climate disruption.

Nonconservative higher-order hydrodynamic modulation instability.

The modulation instability (MI) is a universal mechanism that is responsible for the disintegration of weakly nonlinear narrow-banded wave fields and the emergence of localized extreme events in dispersive media. The instability dynamics is naturally triggered, when unstable energy sidebands located around the main energy peak are excited and then follow an exponential growth law. As a consequence of four wave mixing effect, these primary sidebands generate an infinite number of additional sidebands, forming a triangular sideband cascade. After saturation, it is expected that the system experiences a return to initial conditions followed by a spectral recurrence dynamics. Much complex nonlinear wave field motion is expected, when the secondary or successive sideband pair that is created is also located in the finite instability gain range around the main carrier frequency peak. This latter process is referred to as higher-order MI. We report a numerical and experimental study that confirms observation of higher-order MI dynamics in water waves. Furthermore, we show that the presence of weak dissipation may counterintuitively enhance wave focusing in the second recurrent cycle of wave amplification. The interdisciplinary weakly nonlinear approach in addressing the evolution of unstable nonlinear waves dynamics may find significant resonance in other nonlinear dispersive media in physics, such as optics, solids, superfluids, and plasma.

Over 5,000 Years of Ensemble Future Climate Simulations by 60-km Global and 20-km Regional Atmospheric Models

Abstract An unprecedentedly large ensemble of climate simulations with a 60-km atmospheric general circulation model and dynamical downscaling with a 20-km regional climate model has been performed to obtain probabilistic future projections of low-frequency local-scale events. The climate of the latter half of the twentieth century, the climate 4 K warmer than the preindustrial climate, and the climate of the latter half of the twentieth century without historical trends associated with the anthropogenic effect are each simulated for more than 5,000 years. From large ensemble simulations, probabilistic future changes in extreme events are available directly without using any statistical models. The atmospheric models are highly skillful in representing localized extreme events, such as heavy precipitation and tropical cyclones. Moreover, mean climate changes in the models are consistent with those in phase 5 of the Coupled Model Intercomparison Project (CMIP5) ensembles. Therefore, the results enable the assessment of probabilistic change in localized severe events that have large uncertainty from internal variability. The simulation outputs are open to the public as a database called “Database for Policy Decision Making for Future Climate Change” (d4PDF), which is intended to be utilized for impact assessment studies and adaptation planning for global warming.

Extreme Precipitation in an Atmosphere General Circulation Model: Impact of Horizontal and Vertical Model Resolutions

Abstract To investigate the influence of atmospheric model resolution on the representation of daily precipitation extremes, ensemble simulations with the atmospheric general circulation model ECHAM5 at different horizontal (from T213 to T31 spectral truncation) and vertical (from L31 to L19) resolutions and forced with observed sea surface temperatures and sea ice concentrations have been carried out for January 1982–September 2010. All results have been compared with the highest resolution, which has been validated against observations. Resolution affects both the representation of physical processes and the averaging of precipitation across grid boxes. The latter, in particular, smooths out localized extreme events. These effects have been disentangled by averaging precipitation simulated at the highest resolution to the corresponding coarser grid. Extremes are represented by seasonal maxima, modeled by the generalized extreme value distribution. Effects of averaging and representation of physical processes vary with region and season. In the tropical summer hemisphere, extreme precipitation is reduced by up to 30% due to the averaging effect, and a further 65% owing to a coarser representation of physical processes. Toward middle to high latitudes, the latter effect reduces to 20%; in the winter hemisphere it vanishes toward the poles. A strong drop is found between T106 and T63 in the convection-dominated tropics. At the lowest resolution, Northern Hemisphere winter precipitation extremes, mainly caused by large-scale weather systems, are in general represented reasonably well. Coarser vertical resolution causes an equatorward shift of maximum extreme precipitation in the tropics. The impact of vertical resolution on mean precipitation is less pronounced; for horizontal resolution it is negligible.

Towards a high‐resolution regional reanalysis for the European CORDEX domain

Atmospheric reanalyses covering the European region are mainly available as part of relatively coarse global reanalyses. The aim of this article is to present the development and evaluation of a next generation regional reanalysis for the European CORDEX EUR‐11 domain with a horizontal grid spacing of approximately 6 km. In this context, a reanalysis is understood to be an assimilation of heterogeneous observations with a physical model such as a numerical weather prediction (NWP) model. The reanalysis system presented here is based on the NWP model COSMO by the German Meteorological Service (Deutscher Wetterdienst) using a continuous nudging scheme. In order to assess the added value of data assimilation, a dynamical downscaling experiment has been conducted, i.e. an identical model set‐up but without data assimilation. Both systems have been evaluated for a 1 year test period, employing standard measures such as analysis increments, biases, or log‐odds ratios, as well as tests for distributional characteristics. An important aspect is the evaluation from different perspectives and with independent measurements such as satellite infrared brightness temperatures using forward operators, integrated water vapour from GPS stations, and ceilometer cloud cover. It can be shown that the reanalysis better resolves local extreme events; this is basically an effect of the higher spatio‐temporal resolution, as known from dynamical downscaling approaches. However, an important criterion for regional reanalyses is the coherence with independent observations of high temporal and spatial resolution, resulting in significant improvement over dynamical downscaling. The system is intended to become operational within a year, continuously reprocessing and evaluating longer time periods. The reanalysis data are planned to become available to the research community within a year.

Coupled Atmosphere‐Wildland Fire Modelling

Simulating the interaction between fire and atmosphere is critical to the estimation of the rate of spread of the fire. Wildfire's convection (i.e., entire plume) can modify the local meteorology throughout the atmospheric boundary layer and consequently affect the fire propagation speed and behaviour. In this study, we use for the first time the Méso‐NH meso‐scale numerical model coupled to the point functional ForeFire simplified physical front‐tracking wildfire model to investigate the differences introduced by the atmospheric feedback in propagation speed and behaviour. Both numerical models have been developed as research tools for operational models and are currently used to forecast localized extreme events. These models have been selected because they can be run coupled and support decisions in wildfire management in France and Europe. The main originalities of this combination reside in the fact that Méso‐NH is run in a Large Eddy Simulation (LES) configuration and that the rate of spread model used in ForeFire provides a physical formulation to take into account the effect of wind and slope. Simulations of typical experimental configurations show that the numerical atmospheric model is able to reproduce plausible convective effects of the heat produced by the fire. Numerical results are comparable to estimated values for fire‐induced winds and present behaviour similar to other existing numerical approaches.

Classification of synoptic patterns in the western Arctic associated with extreme events at Barrow, Alaska, USA

The coastal geography of Barrow, Alaska, makes the city vulnerable to weather events that cause flooding and erosion. This study uses the self-organizing map (SOM) algorithm, an unsu- pervised learning process that codifies large, multivariate datasets onto a 2-dimensional array, or map, to study large-scale circulation patterns associated with temperature and high wind extremes at Barrow. The analysis first uses the SOM algorithm to produce an automated 55 yr synoptic climatology of daily sea level pressure patterns for the western Arctic for August to November, when the area is potentially ice free. The results are in agreement with previous Arctic climatologies, showing the Aleutian Low to be dominant in southern Alaska, and high pressure prevalent over the Beaufort and Chukchi Seas. The SOM algorithm is then used to study circulation patterns associated with temperature and high wind extremes at Barrow. These results show that high winds are associ- ated with patterns containing a strong pressure gradient between the Aleutian Low and the Beaufort and Chukchi Seas and also with patterns that contain a low pressure system to the north of Barrow. High (low) air temperature extreme anomalies are associated with patterns that produce strong, southerly (northerly) air flow at Barrow. This study demonstrates the utility of using SOMs to investi- gate the relationship between local weather conditions and large-scale patterns. This approach can be applied to future global climate model (GCM) simulations to investigate the impact of changes in large-scale circulation patterns to local extreme events.