Weather and Climate Extremes最新文献

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.

With global warming accelerating, the heavily populated and rapidly urbanized coastal regions of the Pearl River Delta (PRD) and the Yangtze River Delta (YRD) stand as representative areas with mounting concerns about extreme heat stress. This study analyzes differentiated effects of temperature (TAS) and relative humidity (RH) on human heat stress measured by wet-bulb globe temperature (WBGT) in those urban regions based on machine learning and mathematical derivation, while also examining the impacts of global warming and urbanization on prospective heat risks. To generate fine-scale climate projections targeted at the PRD and YRD, two global projections forced by Representative Concentration Pathway (RCP) 8.5 scenario are dynamically downscaled using non-hydrostatic Regional Climate Model version 4.7 (RegCM4), with the urban density and extent updated every year based on Shared Socioeconomic Pathways 5-8.5 (SSP5) scenario, thereby incorporating the transient urban growth into future projections. The bias-corrected downscaled simulations effectively capture the distinct interdependencies between TAS and RH on WBGT across different regions, similar to the observed patterns during the historical period. While the absolute contribution of TAS to WBGT is larger than RH regardless of warming levels and regions, the relative increase in RH becomes more pronounced with warming. Under RCP8.5 scenario, unprecedentedly extreme WBGT is projected to emerge in the far future (2080–2099). In contrast, the effect of urbanization appears to be more dominant in the near future (2030–2049) as urban density under SSP5 scenario is projected to peak around the 2040s and gradually decrease afterwards. The reduction of RH is found in the intensely urbanized areas locally, but it does not significantly lower WBGT because the positive contribution of increased TAS is more dominant. As a result, highly urbanized regions still exhibit higher WBGT compared to other areas. In addition, urban heat island effect is more pronounced for compact areas with high urban density (i.e., PRD) and at night. Despite the smaller temperature increase from urban heat island effect compared to global warming, it can play a critical role in exacerbating heat stress, adding to the already dangerous humid and hot conditions.

Summer extreme heat events happen frequently in northern China during recent decades, which have serious impacts on the society and ecosystem. The present study reveals that there is a synergistic effect of the preceding winter positive mid-latitude North Atlantic SST anomaly (pMNA SSTA) and summer negative tropical eastern Indian Ocean SST anomaly (nTEI SSTA) on strengthening the summer extreme heat events in northern China. The extreme heat events are stronger and more frequent when the two factors cooccur, and the probability of a strengthened extreme heat events is higher, which indicates a synergistic effect of the two factors. The preceding winter pMNA SSTA and summer nTEI SSTA exert their synergistic effect through a series of coupled oceanic-land-atmospheric bridges. The preceding winter pMNA SSTA could lead to an anomalous anticyclone over central Asia via the eastward propagating Rossby wave, which decreases snowfall and the subsequent snow cover there. The negative snow cover anomaly may persist into spring and induce a local anomalous anticyclone in spring via the snow-hydrological effect, which decreases the precipitation over the southern flank of the anomalous anticyclone. The decreased soil moisture persists into summer and induces the eastward propagating Rossby wave, and favors the increase of atmosphere thickness over northern China. The summer nTEI SSTA can also induce the anomalous anticyclone over northern China via the northeastward Rossby wave propagation. Thus, the two factors exhibit evident synergistic effect on the atmospheric circulation anomaly over northern China. The anomalous anticyclone corresponds to the increased atmosphere thickness, which favors the increase of air temperature in northern China and strengthening of extreme heat events. Therefore, the preceding winter pMNA SSTA and summer nTEI SSTA have significant synergistic effect on strengthening the summer extreme heat events in northern China.