Weather and Climate Extremes最新文献

Accurate nowcasting is critical for preemptive action in response to heavy rainfall events (HREs). However, operational numerical weather prediction models have difficulty predicting HREs in the short term, especially for rapidly and sporadically developing cases. Here, we present multi-year evaluation statistics showing that deep-learning-based HRE nowcasting, trained with radar images and ground measurements, outperforms short-term numerical weather prediction at lead times of up to 6 h. The deep learning nowcasting shows an improved accuracy of 162%–31% over numerical prediction, at the 1-h to 6-h lead times, for predicting HREs in South Korea during the Asian summer monsoon. The spatial distribution and diurnal cycle of HREs are also well predicted. Isolated HRE predictions in the late afternoon to early evening which mostly result from convective processes associated with surface heating are particularly useful. This result suggests that the deep learning algorithm may be available for HRE nowcasting, potentially serving as an alternative to the operational numerical weather prediction model.

During the summer of 2021, the North American Pacific Northwest was affected by an extreme heatwave that broke previous temperature records by several degrees. The event caused severe impacts on human life and ecosystems, and was associated with the superposition of concurrent drivers, whose effects were amplified by climate change. We evaluate whether this record-breaking heatwave could have been foreseen prior to its observation, and how climate change affects North American Pacific Northwest worst-case heatwave scenarios. To this purpose, we use a stochastic weather generator with empirical importance sampling. The generator simulates extreme temperature sequences using circulation analogues, chosen with an importance sampling based on the daily maximum temperature over the region that recorded the most extreme impacts. We show how some of the large-scale drivers of the event can be obtained form the circulation analogues, even if such information is not directly given to the stochastic weather generator.

The Arctic is experiencing amplified climate warming, decreasing sea ice extent, increasingly earlier springtime snowmelt, and a related increase in fire activity. The transition from cold to warm season in the Arctic strongly varies between years, but our understanding of temperature and surface energy budget changes over the springtime is limited. Here we investigate intraseasonal variability of Arctic springtime temperature and surface energy budget components and their interannual trends over 40 years (1981–2020) across the terrestrial Arctic (above 60° N) using ERA5-Land reanalysis data. We found the central and western Siberian regions to have the highest interannual variability in spring temperature anomaly among all Arctic regions during the 40-year period. Also in this region, we discovered the strength increased for heat extremes and decreased for cold extremes when comparing the first and the last 20 years of our study. Peaks in composited extreme temperature and surface energy budget anomalies were observed to occur concurrently, indicating temperature extremes are not driven by surface energy budget components. Lastly, by utilizing power spectrum analyses, we identified the primary driver of temperature anomaly interannual variability to be operating at a 1- to 2-week frequency. Based on our findings and observations in the recent literature, we hypothesize that the observed interannual variability in springtime temperature can be attributed to increased Arctic sea ice decline and an increase in the frequency and strength of associated atmospheric blocking events.

Heatwaves (HWs) are extreme events magnified under climate change with critical implications for the human and environmental systems they impact. These phenomena are generally investigated as a large-scale effect over extensive regions. However, their regional-to-local characteristics and trends are responsible for the specific effects on local communities. This study presents a comprehensive analysis of the characteristics and evolution of regional HWs covering the 1950 to 2021 period across different European climates, central Europe (CE), France (FR), and the Iberian Peninsula (IP), including an analysis of the local and remote relationship between summer heat periods and winter-spring precipitation conditions. Our results confirm the general increase in frequency, intensity, duration, and spatial extent of the HW phenomena over the three domains but point out their uneven evolution under climate change. While a larger frequency increase in the number of heat periods affects IP and FR, it is over CE, where the largest frequency change is observed in the most recent decades. Over north-western FR and CE the most intense HW events have recently registered, further over CE HWs’ long-lasting durations between five to six days have tripled from the sixties to recent decades. It is indeed over the latter that a substantial increase in human exposure to HW phenomena is observed. Probably, the unalike progressions are related to the proven differential rate of warming between the mean and hottest days at northern and southern European domains and the influence of soil conditions over IP on the development of summer heat periods over FR and CE.

Tropical cyclone (TC) projections with atmosphere-only models are associated with uncertainties due to their inability to represent TC-ocean interactions. However, global coupled models, which represent TC-ocean interactions, can produce basin-scale sea surface temperature biases in seasonal to centennial simulations that lead to challenges in representing TC activity. Therefore, focusing on recent individual major hurricane events, we investigated the influence of TC-ocean coupling on the response of TCs to anthropogenic change using atmosphere-only and coupled atmosphere-ocean regional model simulations. Under an extremely warm scenario, coupling does not influence the signs of projected TC rainfall and intensity responses. Coupling, however, does influence the magnitude of projected intensity and especially rainfall. Within a 500 km radius region of the TCs, the projected rainfall increases in coupled simulations are 3–47 % less than in the atmosphere-only simulations, driven by enhanced TC-induced sea surface temperature cooling in the former. However, the influence of coupling on the magnitude of projected rainfall could vary considerably over the regions of highest rainfall generated by TCs.

In September 2020, Western North America was impacted by a highly anomalous meteorological event. Over the Pacific Northwest, strong and dry easterly winds exceeded historically observed values for the time of year and contributed to the rapid spread of several large wildfires. Nine lives were lost and over 5000 homes and businesses were destroyed in Oregon. The smoke from the fires enveloped the region for nearly two weeks after the event. Concurrently, the same weather system brought record-breaking cold, dramatic 24-h temperature falls, and early-season snowfall to parts of the Rocky Mountains. Here we use synoptic analysis and air parcel backward trajectories to build a process-based understanding of this extreme event and to put it in a climatological context. The primary atmospheric driver was the rapid development of a highly amplified 500 hPa tropospheric wave pattern that persisted for several days. A record-breaking ridge of high pressure characterized the western side of the wave pattern with a record-breaking trough of low pressure to the east. A notable anticyclonic Rossby wave breaking event occurred as the wave train amplified. Air parcel backward trajectories show that dry air over the Pacific Northwest, which exacerbated the fire danger, originated in the mid-troposphere and descended through subsidence to the surface. At the same time, dramatic temperature falls were recorded along the east side of the Rocky Mountains, driven by strong transport of high-latitude air near the surface.

Given the significant damage that hurricanes can cause every year, accurate forecasts of these extreme weather events are crucial. Ocean warming can substantially impact the intensity and track of hurricanes in the future. Forecasting the track of hurricanes is typically more challenging than intensity predictions since tracks are influenced not only by hurricane vortex dynamics but also by global and synoptic weather systems (i.e., environmental flow). The dynamical mechanisms that modulate hurricane trajectories under changes in the surface temperature and friction are not comprehensively established yet. The primary objective of this paper is to address this knowledge gap by conducting six real hurricanes and some non-hurricane simulations using the Weather Research and Forecasting (WRF) model. In total, 90 WRF simulations are carried out to characterize the impacts of varying the surface temperature and drag on hurricane tracks and their relationship with environmental flow patterns. It is found that ocean warming tends to intensify hurricanes by ∼20 % and decrease their azimuthal translational velocity, and vice versa when the surface is cooled. Hurricanes move more towards the west over the Atlantic Ocean when the surface temperature is decreased and vice versa. This was shown to be due to the changes in the average azimuthal speed of environmental flows. Increasing the surface temperature, destabilizes the atmosphere, and increases the surface friction velocity. Hence, increased surface friction appears to slow down the environmental flow and consequently hurricane track azimuthal translational speed. This finding was confirmed by another suite of simulations in which only the surface roughness length of the low-wind environmental flow regime was altered. It was shown that surface drag changes have a similar impact on hurricane tracks as surface temperature variations. Decreasing the default surface drag for low-wind regimes tends to further move the hurricanes toward the west and vice versa. This paper provides notable insights into future hurricane track trends and the role of ocean temperature and momentum exchange coefficients in hurricane track and environmental flow patterns. Moreover, the results of this study can be useful for advancing surface layer parameterizations and their impacts on hurricane track forecasts in weather/climate models.

Satellite products, such as the Integrated Multi-Satellite Retrievals of IMERG from the Global Precipitation Measurement (GPM) mission, have emerged as promising tools to analyze precipitation distribution and extremes, particularly in regions with low rain gauge density and sparse distribution, such as Brazil. However, regional validation of satellite data is crucial. In this context, the validation of GPM (IMERG) for the Parnaíba River Basin in northeastern Brazil is important due to its high hydrological potential and the presence of one of the largest expanding agricultural frontiers in the world. This study evaluates the estimation capacity of IMERG version 6 satellite data, including IMERG Early, Late, and Final products, for extreme precipitation in the Parnaíba River Basin from 2001 to 2020. A pixel point-to-point approach is used to compare the satellite estimates with observed precipitation data measured by rain gauges. Eight indices of extreme precipitation are analyzed, along with statistical measures such as bias, mean square error, root mean square error, probability of detection (POD), false alarm ratio (FAR), and the Kling-Gupta efficiency (KGE) index and its components. The results show that the IMERG Final estimates exhibit better agreement with in situ data at the daily scale compared to the IMERG Early and Late estimates. The lower Parnaíba region shows higher POD values, while the middle Parnaíba region exhibits higher KGE values, particularly in tropical climate areas. The IMERG products demonstrate different capabilities in observing extreme rainfall in the basin, with IMERG Final showing satisfactory results for 50 % of the analyzed indices, performing more robustly in capturing the PRCPTOT index and reasonably for CDD, RX5day, and R95p. We conclude that the IMERG Final product can be used as a data source for analyzing precipitation extremes in the Parnaíba River Basin, with bias adjustment recommended for better performance at the daily scale.

Two historic Meiyu events in 1998 and 2020 hit the Yangtze River Valley (YRV), causing catastrophic damage to the socio-economy. By tracking moisture supplies to the extreme precipitation events using Water Accounting Model-2Layers and ERA5 reanalysis, the moisture origins and their differences in feeding the YRV precipitation were revealed. Climatologically, the southwest monsoon channel is the most important moisture channel with the Indian Ocean contributing ∼45% and the Indo-China Peninsula contributing ∼16% of the YRV precipitation. During the two super Meiyu events, the Indian Ocean and the Indo-China Peninsula dominated the excessive moisture supply, which together contributed more than 65% of the extra precipitation. Moisture supply anomalies in 1998 and 2020 showed a robust spatial pattern of “west increase-east decrease”. When the YRV precipitation is higher than the normal, moisture mainly comes from the southwest sources, and moisture contribution from the northwestern Pacific is relatively small. We also found that the intensity of the western Pacific subtropical high is a major influencing factor that explained ∼47% of the YRV precipitation variation during 1991–2020. When it intensifies, an anomalous anticyclone is formed in the mid-lower troposphere around the tropical Northwest Pacific. In its northwestern flank, a strong southwesterly in the upwind of the YRV helps bring in more moisture through the southwest monsoon. In the downwind, it inhibits moisture supply from the northwestern Pacific Ocean. Compared with 2020, a drier condition over Indo-China Peninsula and YRV in 1998 led to a substantially less (∼29%) moisture supply to the YRV precipitation, resulting in a less strong Meiyu event in 1998.