Weather and Climate Extremes最新文献

The Canadian province of British Columbia (BC) is subjected to large-scale, destructive floods. The most dramatic was a mid-November 2021 event when atmospheric rivers (ARs) linked to high-intensity storms caused heavy rainfall in southwestern BC, triggering catastrophic flooding. This study examines 37 floods from 2000 to 2021 using information from over 250 climatological stations and compares events with the mid-November 2021 flood. The dates of the floods showed a bi-modal pattern: a primary season (spring to early summer, 16 floods) and a secondary season (fall to early winter, 21 floods). Five mechanisms controlled these floods: heavy rainfall, rapid snowmelt, severe ice jam, rain-on-snow, and a mixture of snowmelt and ice jam; the mid-November 2021 flood was mainly driven by heavy rainfall. Of the 37 floods, those affected by either heavy rainfall (18 floods) or rain-on-snow (10 floods) were used to derive a relationship between the average daily precipitation amount over the duration of an event and the associated integrated water vapour transport $\left(\overline{IVT}\right)$. Flood events showed a strong linear relationship between these variables with R² $\ge 0.85$, p < 0.05, and values of these parameters were significantly higher for the mid-November 2021 flood than for > 90% of the others, although they were not the highest. The mid-November 2021 flood was also one of the four rainfall-related floods that occurred in the secondary season with $\overline{IVT}$ > 400 kg m⁻¹ s⁻¹. The frequency of flood events over the last five years of the study period has slightly decreased when considering flood events with unknown insured cost. In contrast, insured costs of these events have increased, suggesting that present-day floods are becoming more impactful and may require changes to flood management strategies to reduce costs.

The occurrence of extreme storm surges and precipitation simultaneously or successively can lead to compound flooding. The interaction between extreme storm surges and precipitation holds significant implications for understanding the potential contributing to compound flood risk in coastal areas. This study examines the likelihood of joint occurrence for compound extreme storm surges and precipitation along the China's coastline using observations and model data sets based on tail dependence. We assess the complete characteristics of the tail dependence from observations at the spatio-temporal scale. Subsequently, we perform a principal component analysis to classify the compound flood into 6 synoptic patterns based on the mean sea level pressure data in two typical points (Xiamen and Shijiusuo). We analyze the structure dependence of both observed and simulated surge data and compare the dependence between the historical and the future tail dependence. The result shows that the Yellow Sea and East China Sea exhibit higher dependence compared to the Bohai Sea and South China Sea. The southeastern sea of China has more significant seasonal variation in dependence relative to the northern sea of China. The result indicates that the dominant weather type in Xiamen is associated with low sea pressure and high land pressure, while the type in Shijiusuo is located at the southern edge of a low-pressure center. Projected probabilities of future compound events (2015–2050) have shown substantial increases of 23.9%, 25.38%, 58.21%, and 119.47% over the current period (1979–2014), according to climate models CMCC-CM2-VHR4, GFDL-CMC192-SST, ECEarth3P-HR, and HadGEM3-GC31-HM, respectively. These findings emphasize the correlation between extreme precipitation and storm surges, contributing to a deeper understanding of the compound flood and promoting disaster prevention and control.

The Tinderbox Drought (2017–2019) was one of the most severe droughts recorded in Australia. The extreme summer air temperatures (>40 °C) combined with drought, contributed to the unprecedented Black Summer bushfires in 2019–20 over southeast Australia. Whilst the temperature extremes were largely driven by synoptic processes, it is important to understand to what extent interactions between land and atmosphere played a role. In this study, we use the WRF-LIS-CABLE land-atmosphere coupled model to examine the impacts of changes in leaf area index (LAI) and albedo by contrasting simulations with climatological and time-varying LAI and albedo. We analyse the impact of these biophysical feedbacks on temperature extremes and fire risk during the Tinderbox Drought and the Black Summer bushfires. Remote-sensing data showed a decrease in LAI (0.1–4.0 m² m⁻²) over the three years of the drought along the southeast coast of Australia relative to the long-term climatology, while albedo increased inland (0.02–0.14). These changes in LAI and albedo were accompanied by an overall decrease in daily maximum temperature (T_max) in the vast majority of interior regions (by ∼0.5 °C) and, in the 2019–20 summer, by a clear increase in T_max in the coastal regions of up to ∼1 °C. Increased albedo explained most of the decreases in T_max inland, whereas increases in T_max along the coasts were mostly associated with LAI declines. The magnitude of the impact of biophysical changes on temperature demonstrates the potential impact that would be missed in simulations that assumed fixed vegetation properties. Finally, we only found a small impact from LAI and albedo changes on the fire risk (as measured by the fuel moisture index) preceding the Black Summer bushfires, suggesting these biophysical feedbacks did not significantly modulate fire risk. Our results have implications for coupled simulations relying on climatological LAI and albedo, including operation weather and seasonal climate predictions.

The motivation of our study is to provide forecasters and users complementary guidance and tools to identify and predict atmospheric conditions that could lead to life-threatening flash floods. Using hourly and sub-hourly rainfall datasets, proximity radiosondes, ERA5 reanalysis of extreme rainfall events in the UK during 2000–2020, and case studies in 2021, we observe a three-layered atmospheric structure, consisting of Moist Absolute Unstable Layers (MAULs) embedded in a conditional unstable layer sandwiched between a stable upper layer and a near-stable low layer. Based on our analysis, we propose a conceptual model to describe the atmospheric properties of a ‘rainfall extreme’ environment, with a particular focus on the thermodynamics associated with sub-hourly rainfall production processes. We then set this model within a wider framework to describe the precursor synoptic and mesoscale environments necessary for sub-hourly rainfall extremes in the mid-latitudes. We show that evolution of the Omega block and Rex Vortex couplet provides the optimal environmental conditions for sub-hourly rainfall extremes. These results provide the potential to develop a ‘4-stage’ warning system to assist in the identification and forecasting of life threatening short-duration extreme rainfall intensities and flash floods.

Dry and humid-heat extremes are two types of heat extremes, each exhibiting unique climatological characteristics and impacts on different sectors of society. Using historical simulations and projections produced under two Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5) by models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6), we show a comparative assessment of the future changes in dry- and humid-heat extremes over global land. Relative to 1995–2014, projections for the mid-term future (2041–2060) and long-term future (2081–2100) periods suggest that most global regions will experience an increase in frequency and intensity of both dry- and humid-heat extremes, especially the tropical regions. In these future periods, the peak occurrences of dry- and humid-heat extremes in mid-to high-latitude regions often occur within the same month. However, there will be a one-to two-month gap between the peak occurrences of dry- and humid-heat extremes in tropical regions, primarily due to monsoonal circulations that introduce variability by causing dry-heat extremes before the onset of monsoons and humid-heat extremes as the monsoons commence. This suggests the need for sector-specific adaptation strategies during different periods of the year for tropical regions. Under both future scenarios, whether considering individual exposure or land area, the average level of exposure to extreme humid-heat days is projected to increase more significantly compared to dry-heat days. The above results highlight the risks associated with the intensification of humid heat in future climate scenarios and warrant the development of effective strategies to mitigate the adverse effects.

The extraction of individual events from continuous time series is a common challenge in many extreme value studies. In the field of environmental science, various methods and algorithms for event identification (de-clustering) have been applied in the past. The distinctive features of extreme events, such as their temporal evolutions, durations, and inter-arrival times, vary significantly from one location to another making it difficult to identify independent events in the series. In this study, we propose a new automated approach to detect independent events from time series, by identifying the standard event duration across locations using event correlations. To account for the inherent variability at a given site, we incorporate the standard deviation of the event duration through a soft-margin approach. We apply the method to 1 485 tide gauge records from across the global coast to gain new insights into the typical durations of independent storm surges along different coastline stretches. The results highlight the effects of both local characteristics at a given tide gauge and seasonality on the derived storm durations. Additionally, we compare the results obtained with other commonly used de-clustering techniques showing that these methods are more sensitive to the chosen threshold.

Northeast China, recognized as a global flash drought hotspot and a region of nationally important commercial grains and ecological fragility, is highly susceptible to the profound impacts of droughts on both food security and ecological safety. However, the regional-scale characteristics, possible causes and impacts of flash droughts across Northeast China are rarely investigated. Soil moisture data from 2000 to 2022 were utilized to identify the onset, frequency and duration of flash droughts using the quantile method. The spatial trajectories of flash droughts were determined based on the patch-scale centroid transfers. Further, the possible drivings and ecological impacts of flash droughts were analyzed using datasets of climatic variables and gross primary productivity. We found that flash drought coverage with short onset (1- pentad onset) was generally larger than that with long onset (2-, 3- and 4-pentad onset). The affected area of flash droughts generally decreased, while the onset speed increased over the past two decades. Flash droughts frequently occurred the intersection of western Jilin Province, southern Inner Mongolia Autonomous Region, and northern Liaoning Province but with short duration. Compared with 2001–2011, the start, middle and end centroids of flash droughts in 2012–2021 showed more spatial dynamic changes, mainly shifting towards the northeast-southwest and southeast-northwest directions. The onset of flash drought due to potential evapotranspiration, temperature, precipitation deficit, and vapor pressure anomalies account for an average of 33%, 28%, 22%, and 16%, respectively. However, the contributions of dominant meteorological factors and their combinations varied remarkably in different sub-regions. Long onset flash droughts exhibited larger impact on GPP than short onset flash droughts. This study highlights that due to climate change, the affected area of flash droughts in hotspot regions decreased, but the onset speed and spatial dynamics increased, and followed by a more severe ecological impact of short-onset flash drought. Therefore, it is imperative to incorporate the increasing impacts of flash droughts, which is critical to ensuring regional food security and ecological safety in the Northeast China.

Severe floods and landslides in Eastern Northeast Brazil in May 2022 led to severe impacts with human losses and material damage. These disasters were a direct consequence of extremely heavy rainfall days. A rapid attribution study was performed to assess the role of anthropogenic climate change in 7 and 15-day mean rainfall over this region. A dense network of 389 weather stations was analysed resulting in 79 selected stations containing consistent and spatially well-distributed data over the study region with records starting in the 1970s. Daily rainfall from a multi-model ensemble of climate simulations were also examined to investigate the role of climate change in modifying the likelihood of such extreme events over the studied region. However, such an analysis was hindered by the fact that most investigated models have deficiencies in representing convection associated with warm rains, which are key for the manifestation of such extreme events associated with Easterly Wave Disturbances. From the observational analysis, both 7 and 15-day mean events in 2022 were found to be exceptionally rare, with an approximately 1-in-500 and 1-in-1000 chance of happening in any year in today's climate, respectively. Even though both events were located far outside the previously observed records, because of the short observational record and associated uncertainties it was not possible to quantify how much climate change made these events more likely to happen. The performed analysis also revealed that global warming increased the intensity of such extreme rainfall: rainfall events as rare as those investigated here occurring in a 1.2 °C cooler climate would have been approximately a fifth less intense. Combining observations with the physical understanding of the climate system, this study showed that human-induced climate change is, at least in part, responsible for the increase in likelihood and intensity of heavy rainfall events as observed in May 2022. Besides, the extreme nature, as a result of such events, made it so extraordinary that population exposure and vulnerability was identified as the main driver for the observed impacts, although long-term impacts and recovery will likely be mediated by socio-economic, demographic and governance factors.

Drought is one of the key drivers of food insecurity in Afghanistan, which is among the most food insecure countries in the world. In this study, we build on previous research and seek to answer the central question: “What is the influence of El Niño-Southern Oscillation (ENSO) on drought outlooks and agricultural yield outcome in Afghanistan, and how do these influences vary spatially?” We do so by utilizing multiple indicators of droughts and available wheat yield reports. We find a clear distinction in the probability of drought (defined as being in the lower tercile) in Afghanistan during La Niña compared to El Niño events since 1981. The probability of drought in Afghanistan increased during La Niña, particularly in the North, Northeast, and West regions. La Niña events are related to an increase in the probability of snow drought, particularly in parts of the Amu Darya basin. It is found that relative to El Niño events, snow water equivalent [total runoff] during La Niña events January–March (March–July total runoff) decreases between 9% and 30% (28%–42%) for the five major basins in the country. The probability of agricultural drought during La Niña events is found to be higher than 70% in the rainfed and irrigated areas of the Northeast, North, and West regions. This result is at least partly supported by reported wheat yield composites related to La Niña events that tend to be lower than for El Niño events across all regions in the case of rainfed wheat (statistically significant in Northeast, West, and South regions) and in some cases for irrigated wheat. The results of this study have direct implications for improving early warning of worsening food insecurity in Afghanistan during La Niña events, given that we now have long-lead and skillful forecasts of ENSO up to 18–24 months in advance, which could potentially be used to provide earlier warning of worsening food insecurity in Afghanistan

Climate strongly influences fire activity, and the alignment of multiple weather and climate extremes, such as co-occurrence of hot and dry, leads to more severe fires. Recognizing the knowledge gap on the combination of compound drought–heatwave events and fire activities, we applied an event coincidence analysis to investigate their spatiotemporal dependence in China during 2003–2020 based on remote sensing active fire data and 2 206-site meteorological observations. It is found that the spatial extent of fire activities and their simultaneous occurrence with compound drought–heatwaves both expanded. The geographical hotspots of frequent fire activities following compound drought–heatwave events were predominantly situated in southwestern and southern China. There was a significant upward trend of fire activities following compound drought–heatwave events, with the ratio of compound drought–heatwave events being followed by fire activities reached 0.70 ± 0.06 in 2013–2020, indicating a growth of 19% compared to the period of 2003–2012. Our findings contribute to a better understanding of the spatiotemporal dependence of compound drought–heatwave and fire activity and provide insights into their mitigation strategies.