Weather and Climate Extremes最新文献

The 2021 Pacific Northwest heatwave resulted in record temperatures observed across the Canadian provinces of British Columbia, Alberta and Saskatchewan as well as the US states of Washington and Oregon. Previous studies of extreme temperatures over arbitrarily-defined rectangular regions covering parts of Oregon, Washington and British Columbia have estimated return periods of 200–100 000 years, generally based on data since 1950, with some analyses suggesting that the event would have been considered impossible based on statistical fits to pre-2021 data, or based on climate models failing to simulate such events. We estimate a return period of 1152 (126-$\infty$) years for the 2021 event averaged over British Columbia, based on a generalized extreme value distribution (GEV) with a location parameter a function of global mean surface temperature fitted to 1950–2021 ERA5 data. British Columbia was the province where the highest absolute temperature of 49.6 °C was measured, and where the largest impacts on human mortality and ecosystems were reported. However, we show that this return period is reduced to 236 (52-$\infty$) years when the analysis period is extended back to 1940, using newly-available ERA5 data, owing to an extreme heatwave observed in 1941. While the 1941 event was 1.7 °C cooler than the 2021 event in British Columbia, it was a rarer event relative to the cooler climatology of the time, with an estimated return period of 735 (135-$\infty$) years. Over this longer period we also find that almost all CMIP6 models underestimate variability in annual maximum temperatures over British Columbia. The return period of the 1941 heatwave was comparable to that of the 2021 event in Alberta and Saskatchewan, though not in Washington or Oregon. While the 2021 event was an unprecedented and extremely intense heatwave whose likelihood was much increased by human-induced climate change, our results indicate that this event was not as rare as previously thought in Western Canada.

With increased global warming, heatwaves are expected to become more intense, frequent, and persistent. Although the spatiotemporal characteristics of heatwaves have been extensively studied, the vast majority of these studies have solely used near-surface air temperatures, particularly daily maximum temperatures (T_max), to identify heatwaves. Given that air temperature alone proves inadequate as a metric for human heat stress. Here, using the relative threshold in conjunction with the absolute threshold and basing it on wet bulb globe temperature (WBGT), we develop a novel definition of human-perceived heatwaves. The combined effect of temperature and humidity is considered in this definition. On this basis, we quantify the climatology of and long-term changes in heatwaves in China based on homogenized in situ observations and outputs from climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). The results show that the distribution of human-perceived heatwaves coincides with densely populated areas in the southeastern part of China, despite their limited spatial extent. The observed trends in human-perceived heatwaves have accelerated since the 1960s. It is now anticipated that moderate or worse human-perceived heatwaves will affect more than half of China's population. Moreover, CMIP6 climate projections suggest that the percentage of China's population exposed to historically unprecedented human-perceived heatwaves would increase rapidly in a warmer future, except for the sustainability scenario. It is noted that the increase in severe human-perceived heatwaves is more rapid than that in severe traditional T_max-based heatwaves, suggesting that the hazard of heatwaves to humans may have been underestimated by previous T_max-based studies. Our findings demonstrate the urgent need for additional planning and adaptation actions beyond the framework for short-term disaster reduction frameworks currently in place. Although we concentrated on China in this article, our method for evaluating human-perceived heatwaves is easily extended to handle comparable issues everywhere.

A long-lasting, wide-ranging, and record-breaking extreme high-temperature (EHT) event hit China in the summer of 2022, causing adverse impacts on electricity supply, agriculture, and people's livelihoods. The abnormal persistence of the eastward-shifted South Asian high (SAH) in the upper troposphere was the dominant driver of the durative enhancement of EHT and can explain approximately 55.7% of the event's occurrence, compared to the 14.5% contribution of western Pacific Subtropical high (WPSH) to this event. As the SAH extends eastward, the East Asian westerly jet tends to shift northward, the combination of which could have caused persistent descending motion over East China and thus evoked the EHT. The eastward shift of the SAH in summer 2022 was jointly affected by the preceding-spring record-low snow cover over the Tibetan Plateau, the contemporaneous record-low aerosol levels in East China, the record-high precipitation of Indian subcontinent and the record-high sea surface temperature in the North Atlantic since 1990. Notably, during 1990–2022, for the 2022-like EHT, only 38.43% of that is related to the couple of westward-shifting WPSH and eastward-extending SAH. Approximately 47.6% of 2022-like EHT is just corresponding to an abnormally eastward-extending SAH, suggesting the non-negligible role of SAH in the China's EHT prediction.

The cold air outbreaks in the northern parts of India exert significant impacts on human health, energy, agriculture and transportation. In this study, we investigate the synoptic dynamics of cold waves and their linkages to large scale circulations for the winter period from 1982 to2020. Cold waves are classified into normal and intense (NCWs and ICWs) based on intensity and we examine the underlying mechanisms of formation and their atmospheric drivers. Notably, the spatial extent of the ICWs is almost double than that of normal ones thereby having the potential to affect a wider population. The NCWs are often influenced by western disturbances, leading to the inflow of cold air from Siberia (a region of shallow high). In contrast, ICWs are mostly linked to the presence of an omega block over the Ural-Siberian region. The downstream portion of the Ural block favours the inflow of cold northerlies into north India, leading to cold air advection and extreme cold wave conditions. The influence of Arctic warming for ICWs is further confirmed through a prominent Quasi-Resonant Amplification (QRA) fingerprint. Furthermore, La Niña condition seems to play a crucial role in triggering ICWs over north India. During La Niña, the prominent low level cyclonic anomaly helps in advecting the cold air from the higher latitudes into the country. The frequency as well as the duration of cold wave events are also found to be higher in La Niña years compared to El Niño and neutral years. The trend analysis of cold wave events over north India reveals a significant decrease in the frequency, duration and intensity during the analysis period due to a combination of various factors such as rising winter minimum temperatures (due to global warming), decreasing number of synoptic winter weather systems and Arctic amplification.

This study evaluates statistical downscaling techniques using different metrics and compares climate change signals and extreme precipitation and temperature changes under future climate change scenarios in the Bosque watershed, North-Central Texas. The study utilizes observed gridded Daymet data to assess the effectiveness of statistical downscaling techniques. It involves comparing the mean, the 90th percentile, 10th percentile, wet day frequency, and Cumulative Distribution Function (CDF) of climate model simulations before and after downscaling and the Daymet data during the historical period (1981–2005). Furthermore, the study analyzes changes in climate change signals, extreme precipitation, and temperature values under both near-future (2031–2060) and far-future (2070–2099) climate scenarios. The Ratio Delta method (DeltaSD) and Equi-Distant Quantile Mapping (EDQM) statistical downscaling techniques adjust the mean annual, the wet days frequency, the 90th and 10th percentiles, and the CDF of Global Climate Models (GCMs) simulations of historical precipitation and temperature. The downscaling techniques influenced the climate change signal and changes in extreme values in the future climate. When examining future climate projections produced using the DeltaSD method, we observe a more pronounced reduction in precipitation, while simulations generated through EDQM exhibit a higher frequency of heavy precipitation events (R10mm, R20mm) and consecutive dry days (CDD). It's worth noting that the uncertainties associated with the statistical downscaling techniques are relatively small and not statistically significant (≤0.05). In contrast, substantial and significant uncertainties arise from the choice of emission scenarios and the selection of driving GCMs. Across most climate change scenarios, there is a consistent trend towards increased temperatures and extreme temperature indices. The trend of extreme temperature indices shows variation following the choice of emission scenarios where a significant change in temperature extremes was detected under the RCP8.5 emission scenario.

A material consequence of climate change is the intensification of extreme precipitation in most regions across the globe. The respective trend signal is already detectable at global to regional scales, but long-term variability still dominates local observational records, which are the basis for extreme precipitation risk assessment. Whether the frequency of extreme events is purely random or subject to a low-frequency internal variability forcing is therefore highly relevant for modelling the expected number of extreme events in a typical observational record. Based on millennial climate simulations, we show that long-term variability is largely random, with no clear indication of low-frequency decadal to multidecadal variability. Nevertheless, extreme precipitation events occur highly irregularly, with potential clustering (11% probability of five or more 100-year events in 250 years) or long disaster gaps with no events (8% probability for no 100-year events in 250 years). Even for decadal precipitation records, a complete absence of any tail events is not unlikely, as, for example, in typical 70-year observational or reanalysis data, the probability is almost 50%. This generally causes return levels – a key metric for infrastructure codes or insurance pricing – to be underestimated. We also evaluate the potential of employing information across neighbouring locations, which substantially improves the estimation of return levels by increasing the robustness against potential adverse effects of long-term internal variability. The irregular occurrence of events makes it challenging to estimate return periods for planning and for extreme event attribution.

The Sahara is the largest and driest of the hot deserts on Earth, with regions where rainfall reaches the surface on average less than once a year. Water resources are scarce, and rainfall tends to occur sporadically in space and time. While rain is a precious resource in the Sahara, heavy precipitation events (HPEs) in the desert have the potential to trigger flash floods on the barren soil. Because of the sparse rainfall monitoring network and the relatively poor performance of global models in representing rainfall over the Sahara, the analysis of Saharan HPEs has primarily relied on case studies. Therefore, general rainfall characteristics of Saharan HPEs are unexplored, and the prevailing weather conditions enabling such rainfall are unknown. To tackle this problem, we utilised satellite-derived precipitation estimations (IMERG) spanning 21 years (2000–2021) to identify $\sim 42\cdot 10^3$ small ($>10^3{\mathrm{km}}^2$) to large ($<10^6{\mathrm{km}}^2$) HPEs in the Sahara and to extract their rainfall properties, and atmospheric reanalyses (ERA5) to examine the corresponding meteorological conditions in which they develop. Three case studies illustrate the relevance of cyclones for exceptionally large HPEs, including one in the driest region of the Sahara. Saharan HPEs occur, on average, every second day. They are more common in summer than in the other seasons, occur most frequently in the southern Sahara, and exhibit a clear convectively-driven diurnal cycle. Winter events have, on average, larger spatial extent, longer duration, and are characterised by larger areas exhibiting more extreme rainfall in terms of return periods. Autumn HPEs are concentrated in the western Sahara, while events in the north of the desert and in its driest core in the northeast occur mainly in winter and spring. In these regions, north of the Tropic of Cancer, events are highly associated with surface cyclones. HPEs that were associated with cyclones are characterised by larger spatial extent and rainfall volume. Considering that weather and climate models often depict synoptic-scale weather systems more accurately than rainfall patterns, the association of Saharan HPEs with surface cyclones and other synoptic-scale systems can aid in comprehending the effects of climate change in the desert. Furthermore, it underscores the potential for higher predictability of these events.

Rainfall frequency analysis, an essential work for water resources management, is often conducted by using the annual maximum rainfall series. For rainfall stations with short record lengths and outliers presence, the use of annual maximum series for rainfall frequency analysis may yield design rainfall estimates of higher uncertainties. Moreover, for regions with cyclostationary climate patterns, the annual maximum rainfalls may be caused by different prevalent storm types, which differ in terms of their occurrence frequency and storm rainfall characteristics. In this study, we propose a novel event-maximum-rainfall-based mixture distribution modeling approach for rainfall frequency analysis. By considering the event-maximum rainfalls of individual storm events, the sample size for parameter estimation increases, and the uncertainty of design rainfall estimates reduces. Mixture distribution modeling enables a thorough investigation of the contributing probabilities of different storm types to the annual maximum rainfall. Through rigorous stochastic simulation, we demonstrated the superiority of the proposed approach over the conventional annual maximum rainfall approach. The proposed approach was applied to four representative rainfall stations in Taiwan, and the results revealed that the proposed approach is more robust than the conventional annual maximum rainfall approach. The results provide insights into the contributions of individual storm types to the annual maximum rainfall.

Estimating the storm surge magnitude and annual exceedance probability is a key element in the siting and design of coastal nuclear power plants in both the U.S. and France. However, differences in storm climatology, specifically the relative importance of tropical cyclones (TCs) versus extratropical storms (XTCs), have driven differences in estimation method development. This work compares purely statistical modeling with combined statistical and numerical simulation modeling approaches for extreme storm surge applied to the U.S. North Atlantic coast which is subject to both tropical and extratropical storms. Two frequency analysis methods are applied to observed water levels and compared to a copula-based joint probability analysis of TCs and automated frequency analysis of XTCs that is enriched with numerically simulated storms. One frequency analysis method is applied using (1) hourly at-site data and (2) hourly at-site data enriched with additional data from a homogeneous region. The other frequency analysis method is applied using (1) hourly at-site data and (2) hourly at-site data enriched with monthly water level maxima. Variables of interest used in the comparison are skew storm surge, maximum instantaneous storm surge, non-tidal residual and maximum seal level. The performance of the methods (mean surge and water level estimates and confidence intervals) depend on the variable of interest and, to some extent, on return period. Inclusion of additional information (e.g., regional water levels, and monthly maxima) in the frequency analysis methods does not have a large impact on estimated mean surge and water levels, but significantly reduces resulting confidence intervals (over 40% reduction in some cases). However, the confidence intervals still grow with increasing return period. Inclusion of simulated storms in the joint probability analysis results in significantly different mean surge and water level estimates (up to 25% higher than the frequency analysis in some cases). The joint probability analysis confidence intervals are wider than those for the frequency analysis methods lower return periods (e.g., 60%–80% wider at 100 years), but they grow much more slowly and are significantly narrower for higher return periods (e.g., 40%–60% narrower at 1 000 years). Although there are appreciable differences between the results documented in this paper, these are reasonable due to differences in the data and methods used in this comparison.

Severe weather often occurs in the areas where different cold-warm and dry-wet air masses converge orthogonally. The orthogonal convergence of two-adjacent air parcels can be observed from planetary-scale to synoptic-scale and meso-scale circulations. This study pointed out that an orthogonal convergence of two-adjacent horizontal air parcels can produce a significant non-zero shear stress with its modulus as anomalous energy density and form vertical motions of new airflows, while tailgating and head-on horizontal convergences cannot. Therefore, the orthogonal convergence can induce strong vertical motions and result in severe weather. Continuous convergence of airflows can lead to unusual climatic events. Storm cases of single tornado, tornado swarms, a strong tropical cyclone, and extratropical cyclones associated with heavy rainfall or heavy airborne dust show that the shear stress modulus and total kinetic energy of anomalous winds can better explain extreme weather events than other commonly used dynamical parameters such as divergence and vorticity. Therefore, the shear stress modulus can be used in the forecasting of extreme weather events in operation.