Weather and Climate Extremes最新文献

This study investigates the predictability of the 2018 Northern Europe heatwave using the GloSea5 forecast model from the perspective of land-atmosphere interactions. We focus on an inverse relationship wherein soil drying leads to increased temperatures and the model's ability to simulate this hypersensitivity in the soil moisture-temperature coupling on the dry side of a breakpoint defined as the soil moisture threshold below which land feedbacks nonlinearly amplify extreme heat. When evaluating forecast model performance in predicting this heatwave, we compare deterministic forecast scores (Hit Rate (HR) and True Skill Score (TSS)) for whether model Surface Soil Moisture (SSM) falls within the hypersensitive regime. GloSea5 exhibits enhanced prediction skill for the extreme heat event when the modelled soil moisture is within the hypersensitive regime. To understand the skill of the heatwave forecast for hit and missed cases of capturing SSM below the breakpoint, we first evaluate the climatological model performance for the water- and energy-limited processes, and then perform a comparison classified by whether SSM verifies on the dry side of the wilting point. The composite analysis demonstrates that the reproducibility of the breakpoint is tied to an improvement in climatological land coupling processes, mainly for classification in the water-limited coupling regime. Therefore, the results suggest that the process-based connection between soil moisture and temperature is a potential source for improving heatwave forecasts on subseasonal to seasonal (S2S) time scales.

Climate extremes have grown increasingly severe and frequent, posing significant threats to both economies and ecosystems. Prior research largely focused on individual hazard occurrences, often overlooking the compounded effects of multiple extreme events. With the escalating anthropogenic activities and increasing temperatures in Asia, there is an imperative need to investigate the occurrence of compound dry hazards (CDHs). This study aims to conduct a comprehensive assessment of CDHs in Asia, with a specific focus on examining the co-occurrence of heatwaves, droughts, fire dangers, and extreme winds over a 42-year period from 1980 to 2021. To be specific, our research focuses on evaluating interannual variability, identifying geographical hotspots, analyzing temporal shifts in cascading compound events, and exploring the dependence structure within CDHs. Our results indicate a significant increase in the spatial extent of CDHs in recent decades, with varying patterns in annual average frequencies across Asian regions. Particularly significant is the concentration of CDH hotspots within developing countries situated in East Asia, South Asia, and Southeast Asia. Moreover, our analysis highlights substantial increases in both the frequency and duration of cascading events (CEs), particularly in densely populated areas across North, Central, East, and West Asia. Conversely, South Asia experiences conspicuous declines in CEs. Lastly, our investigation into the dependence structure among CDHs illustrates varying degrees of interdependence among dry hazards and diverse spatial relationships across different Asian regions. We believe that these findings are highly valuable for enhancing natural risk management, improving climate model accuracy, and fortifying strategies to address the evolving risks associated with compound climate extremes under climate change.

Attribution of extreme climate events to global climate change as a result of anthropogenic greenhouse gas emissions has become increasingly important. Extreme climate events arise at the intersection of natural climate variability and a forced response of the Earth system to anthropogenic greenhouse gas emissions, which may alter the frequency and severity of such events. Accounting for the effects of both natural climate variability and the forced response to anthropogenic climate change is thus central for the attribution. Here, we investigate the reproducibility of probabilistic extreme event attribution results under more explicit representations of natural climate variability. We employ well-established methodologies deployed in statistical Earth System Model emulators to represent natural climate variability as informed from its spatio-temporal covariance structures. Two approaches towards representing natural climate variability are investigated: (1) where natural climate variability is treated as a single component; and (2) where natural climate variability is disentangled into its annual and seasonal components. We showcase our approaches by attributing the 2022 Indo-Pakistani heatwave to human-induced climate change. We find that explicit representation of annual and seasonal natural climate variability increases the overall uncertainty in attribution results considerably compared to established approaches such as the World Weather Attribution Initiative. The increase in likelihood of such an event occurring as a result of global warming differs slightly between the approaches, mainly due to different assessments of the pre-industrial return periods. Our approach that explicitly resolves annual and seasonal natural climate variability indicates a median increase in likelihood by a factor of 41 (95% range: 6-603). We find a robust signal of increased likelihood and intensification of the event with increasing global warming levels across all approaches. Compared to its present likelihood, under 1.5 °C (2 °C) of global near-surface air temperature increase relative to pre-industrial temperatures, the likelihood of the event would be between 2.2 to 2.5 times (8 to 9 times) higher. We note that regardless of the different statistical approaches to represent natural variability, the outcomes on the conducted event attribution are similar, with minor differences mainly in the uncertainty ranges. Possible reasons for differences are evaluated, including limitations of the proposed approach for this type of application, as well as the specific aspects in which it can provide complementary information to established approaches.

Storm surges are the most important driver of flooding in many coastal areas. Understanding the spatial extent of storm surge events has important financial and practical implications for flood risk management, reinsurance, infrastructure reliability and emergency response. In this paper, we apply a new tracking algorithm to a high-resolution surge hindcast (CODEC, 1980–2017) to characterize the spatial dependence and temporal evolution of extreme surge events along the coastline of the UK and Ireland. We quantify the severity of each spatial event based on its footprint extremity to select and rank the collection of events. Several surge footprint types are obtained based on the most impacted coastal stretch from each particular event, and these are linked to the driving storm tracks. Using the collection of the extreme surge events, we assess the spatial distribution and interannual variability of the duration, size, severity, and type. We find that the northeast coastline is most impacted by the longest and largest storm surge events, while the English Channel experiences the shortest and smallest storm surge events. The interannual variability indicates that the winter seasons of 1989-90 and 2013–14 were the most serious in terms of the number of events and their severity, based on the return period along the affected coastlines. The most extreme surge event and the highest number of events occurred in the winter season 1989–90, while the proportion of events with larger severities was higher during the winter season 2013–14. This new spatial analysis approach of surge extremes allows us to distinguish several categories of spatial footprints of events around the UK/Ireland coast and link these to distinct storm tracks. The spatial dependence structures detected can improve multivariate statistical methods which are crucial inputs to coastal flooding assessments.

Daily precipitation of different intensities is expected to change differently in response to global warming. Based on station observations and simulations from the latest climate models, we investigated the non-uniform features of changes in daily precipitation frequency, intensity and amount over China. Results show that western China experiences an overall wetting trend across the spectrum of precipitation intensity, while eastern China exhibits negative trends in light-to-moderate precipitation and positive trends in heavy-to-extreme precipitation with respect to precipitation frequency and amount. Changes in precipitation intensity do not show a spatially consistent pattern of intensification in most intensity spectra, but exhibit the most pronounced intensification in heavy-to-extreme precipitation. Interestingly, changes in precipitation frequency dominate changes in the amount of precipitation for each intensity level, particularly for the spatial patterns. Although climate models show limited skills in reproducing the magnitudes of these observed changes, they show skills in simulating the sign of the changes. Also, they reasonably reproduce the observed non-uniform patterns of daily precipitation changes, especially for changes in the contributions from different intensity levels to annual total precipitation on average over the whole country. The evaluation of current climate models in simulating daily precipitation changes as a function of precipitation intensity suggests that improvement in the detection and attribution of precipitation changes in China can be gained by dividing daily precipitation into different categories.

Flash droughts threaten ecosystems substantially because of the fast onset and low predictability. Soil and atmospheric water stress are two main factors reducing ecosystem productivity during flash droughts. However, the long-term trends in the soil and atmospheric water stress on vegetation during flash droughts are unclear. By conducting long-term land surface model simulations, this study investigated the impact of atmospheric and soil water stress on gross primary productivity (GPP) during flash droughts and hot periods of flash droughts, as well as the long-term changes in water stress from 1961 to 2022 over China. The areas dominated by soil and atmospheric stress were 65.2% and 19.9% during flash droughts, respectively. During the hot periods of flash droughts, the areas dominated by atmospheric water stress were raised to 39.4%, and the areas dominated by soil water stress were reduced to 48.7%. During 1961–2022, the frequency, intensity, and duration of flash droughts all showed significant upward trends (p < 0.05) over China. Meanwhile, soil water stress on GPP decreased significantly (p < 0.05), but the atmospheric water stress increased significantly (p < 0.05). Correspondingly, the areas dominated by soil water stress decreased at 0.8%/decade, while the areas dominated by atmospheric water stress rose at 1.6%/decade during hot periods of flash droughts. With sensitivity simulations, we found that the water stress was weakened in the North China plain under irrigated conditions, but the trend was consistent with non-irrigated conditions over China. Our study indicated the importance of atmospheric moisture stress on vegetation productivity during flash droughts under climate warming.

Increases in air temperature lead to increased dryness of the air and potentially develops increased dryness in the soil. Extreme dryness (in the soil and/or in the atmosphere) affects the capacity of ecosystems for functioning and for modulating the climate. Here, we used long-term high temporal resolution (daily) soil moisture (SM) and vapor pressure deficit (VPD) data of high spatial resolution (∼0.1° × 0.1°) to show that compared to the reference period (1950–1990), the overall frequency of extreme soil dryness, extreme air dryness, and extreme compound dryness (i.e., co-occurrence of extreme soil dryness and air dryness) has increased by 1.2-fold [0.8,1.6] (median [10^th,90^th percentile], 1.6-fold [1,2.3], and 1.7-fold [0.9,2.5], respectively, over the last 31 years (1991–2021) across Europe. Our results also indicate that this increase in frequency of extreme compound dryness (between reference and 1991–2021 period) is largely due to increased SM-VPD coupling across Northern Europe, and due to decreasing SM and/or increasing VPD trend across Central and Mediterranean Europe. Furthermore, under the RCP8.5 (Representative Concentration Pathways 8.5) emission scenario, this increase in frequency of extreme compound dryness would be 3.3-fold [2.0,5.8], and 4.6-fold [2.3,11.9] by mid-21^st century (2031–2065) and late-21^st century (2066–2100), respectively. Additionally, we segregated the changes in frequency of extreme dryness across the most recent (year 2021) land cover types in Europe to show that croplands, broadleaved forest, and urban areas have experienced more than twice as much extreme dryness during 1990–2021 compared to the reference period of 1990–2021, which based on the future projection data will increase to more than three-fold by mid 21^st century. Such future climate-change induced increase in extreme dryness could have negative implications for functioning of ecosystems and compromise their capacity to adapt to rapidly rising dryness levels.

This research compares two different methods of tracing cyclones in the North Indian Ocean (NIO)- (i) Commonwealth Scientific and Industrial Research Organisation (CSIRO) Direct Detection (CDD) and Okubo-Weiss-Zeta parameter (OWZ) in the Coupled Model Intercomparison Project Phase 5 (CMIP5) model data. Many CMIP5 models are evaluated against TC observations from the International Best Track Archive for Climate Stewardship (IBTrACS) and a statistical Generalised Additive Model for climate change projections in the past (1970–2000). Estimates of TCs' potential future occurrence in the NIO are evaluated using CMIP5 models (2070–2 100). When compared to historical tracks, the geographic distribution of TCs generated by both detection techniques is consistent with what would be expected, and the frequency of TCs in the models is, with a few exceptions, consistent with observations. Generally, the OWZ plan results in more TCs per unit time than the CDD scheme. Though there are significant differences between the two tracking techniques, a small number of models have TC counts that are virtually similar. Compared to the CDD plan, the OWZ scheme generally has higher performance in the NIO area.

Windstorms are among the most impacting natural hazards affecting Western and Central Europe. Information on the associated impacts and losses are essential for risk assessment and the development of adaptation and mitigation strategies. In this study, we compare reported and estimated windstorm losses from five datasets belonging to three categories: Indices combining meteorological and insurance aspects, natural hazard databases, and loss reports from insurance companies. We analyse the similarities and differences between the datasets in terms of reported events, the number of storms per dataset and the ranking of specific storm events for the period October 1999 to March 2022 across 21 European countries. A total of 94 individual windstorms were documented. Only 11 of them were reported in all five datasets, while the large majority (roughly 60%) was solely recorded in single datasets. Results show that the total number of storms is different in the various datasets, although for the meteorological indices such number is fixed a priori. Additionally, the datasets often disagree on the storm frequency per winter season. Moreover, the ranking of storms based on reported/estimated losses varies in the datasets. However, these differences are reduced when the ranking is calculated relative to storm events that are common in the various datasets. The results generally hold for losses aggregated at European and at country level. Overall, the datasets provide different views on windstorm impacts. Thus, to avoid misleading conclusions, we use no dataset as “ground truth” but treat all of them as equal. We suggest that these different views can be used to test which features are relevant for calibrating windstorm models in specific regions. Furthermore, it could enable users to assign an uncertainty range to windstorm losses. We conclude that a combination of different datasets is crucial to obtain a representative picture of windstorm associated impacts.

Understanding the predictability of marine heatwaves (MHWs) and identifying the sources of their forecast errors are essential for enhancing their forecast accuracy. In the summer of 2018, a powerful MHW struck the Yellow Sea, resulting in significant economic losses for the sea cucumber culture industry in China's coastal areas. However, the ability to predict the evolution of this MHW remains uncertain. In this study, several forecast experiments were conducted based on a deterministic ocean forecast model to address this issue. The results demonstrate that this MHW can be effectively predicted with a lead time of less than 3 days. Specifically, the mean MHW forecast accuracy is 0.66 and the mean absence/presence accuracy is 0.79 at a 3-day lead time. Beyond a 3-day lead time, the MHW forecast accuracy steadily decreases, which is primarily due to the overpredicted “False Alarms” during its growth and decay phases. The overpredicted “False Alarms” are largely attributed to uncertainties in predicting wind and air temperature related to two typhoons passing through the Yellow Sea. Additionally, anomalous ocean circulation induced by atmospheric forcing uncertainties may also trigger MHW forecast errors through advection. Future efforts involving parameter optimization, air-sea coupling, ensemble forecasts and integration with artificial intelligence-based weather forecasts are suggested to improve the prediction of MHWs. Our findings may provide implications for stakeholders in preparation for any future occurrences of MHWs in the Yellow Sea.