Weather and Climate Extremes最新文献

Extreme rainfall events (ERE) during the summer monsoon season have been occurring over most parts of India resulting in flooding and immense socio-economic loss. These extremes are becoming a frequent norm in the hilly and mountainous regions of the country such as Assam. Assam received one of the most historical EREs from 14–June 17, 2022. The present study analyses the performance of a suite of high-resolution ensemble model forecasts for this extreme event in terms of its intensity, and distribution with a lead time of up to 96 h. Furthermore, the 36 numerical experiments are carried out using two different land use and land cover (LULC) data sets (i.e. ISRO and USGS) and three different sets of parameterization schemes (i.e. planetary boundary layer, cumulus, and microphysics).

Rainfall distributions in the case of USGS LULC are relatively less coherent and underestimated (60–260 mm/day) against IMD (80–300 mm/day) including the rainfall categories heavy (HR), very heavy (VHR), and extremely heavy (EHR) rainfall throughout the day-1 to day-4. Among all the ensembles (E1-E10), USGS (E6 - E10) has underestimated rainfall (140–260 mm/day) compared to ISRO (150–280 mm/day), specifically in MR and HR categories over the upper Assam (UAD) and lower Assam (LAD) divisions. Further, the Bias Correction Ensemble (BCE) technique is applied to minimize the forecast errors. A rigorous statistical analysis in terms of frequency distribution, Taylor diagram, and benchmark skill scores is carried out to elucidate the model biases. The set of the model ensembles using ISRO (E1- E5) and USGS (E6- E10) reasonably captured the HR, VHR, and EHR. In addition, throughout the forecast hour, BCE E5 (E10) is noted with the distinct realistic (underestimated) representation of model bias (5–20 %) (10–30 %) over all the subdivisions of Assam. Our results suggest that the combined efforts of ensembles of physical parameterization schemes, along with proper LULC, and the BCE approach are required to overcome challenges to improve the skills of rainfall events, particularly over complex terrains such as Assam.

We analyzed the possible effects of recent sea surface temperature (SST) warming on the extraordinary East Asian summer monsoon (EASM) precipitation in 2020 summer. The dynamic and thermodynamic impacts of SST are examined by conducting regional climate model experiments with observed SST and cold SST where the 22-year SST trend is removed. In the presence of warm SST, precipitation increases in low latitudes but decreases in the EASM region. This dipolar precipitation change pattern opposes the precipitation anomalies in 2020 summer, indicating that the extraordinary 2020 EASM precipitation is not likely driven by recent SST warming. The warm SST suppresses the western North Pacific subtropical high expansion and weakens the southwesterly from the South China Sea toward the EASM region. In terms of large-scale atmospheric circulations, SST-induced wind changes strengthen the local Walker circulation in the South China Sea and the Philippines and the local Hadley circulation across the EASM region. These support the reduced EASM rainfall in the control experiment compared to the cold SST experiment and imply that the precipitation reduction by dynamical effects could exceed the precipitation increase by thermodynamic effects in the EASM region under warm SST.

Reanalysis-driven regional climate simulations using the Weather Research and Forecasting (WRF) model in New South Wales (NSW) and Australian Regional Climate Modelling (NARCliM) Version 2.0 are assessed for capturing precipitation extreme indices. Seven configurations of the WRF model driven by ECMWF (European Centre for Medium-Range Weather Forecasts) Reanalysis v5 (ERA5) for Australia from 1979 to 2020 at 20 km resolution are evaluated. We assess the spatiotemporal patterns of six selected Expert Team on Sector-Specific Climate Indices (ET-SCI) precipitation extremes by comparing regional climate model (RCM) simulations against gridded observations. The RCMs evaluated have varying levels of accuracy in simulating precipitation extremes. While they capture climatology and coefficient of variation of precipitation extremes relatively well, temporal correlation and trend reproduction present challenges. Some RCMs perform more effectively for specific extreme indices, while others encounter challenges in accurately replicating them. No single RCM excels in all aspects, highlighting the need to consider specific strengths when selecting RCMs for global climate model (GCM) driven simulations.

Tornadoes are a co-occurring extreme that can be produced by landfalling tropical cyclones (TCs). These tornadoes can exacerbate the loss of life and property damage caused by the TC from which they were spawned. It is uncertain how the severe weather environments of landfalling TCs may change in a future climate and how this could impact tornado activity from TCs. In this study, we investigated four TCs that made landfall in the U.S. and produced large tornado outbreaks. We performed four-member ensembles of convective-allowing (4-km resolution) regional climate model simulations representing each TC in the historical climate and a mid-twenty-first century future climate. To identify potentially tornadic storms, or TC-tornado (TCT) surrogates, we used thresholds for three-hourly maximum updraft helicity and radar reflectivity, as tornadoes are not resolved in the model. We found that the ensemble-mean number of TCT-surrogates increased substantially (56–299%) in the future, supported by increases in most-unstable convective available potential energy, surface-to-700-hPa bulk wind shear, and 0–1-km storm-relative helicity in the tornado-producing region of the TCs. On the other hand, future changes in most-unstable convective inhibition had minimal influence on future TCT-surrogates. This provides robust evidence that tornado activity from TCs may increase in the future. Furthermore, TCT-surrogate frequency between 00Z and 09Z increased for three of the four cases, suggesting enhanced tornado activity at night, when people are asleep and more likely to miss warnings. All of these factors indicate that TC-tornadoes may become more frequent and a greater hazard in the future, compounding impacts from future increases in TC winds and precipitation.

The study employed high-resolution atmospheric general circulation models (AGCM) to simulate tropical cyclones (TCs) and evaluated two TC genesis potential indices in reflecting projected TC changes in the western North Pacific (WNP) under a warming scenario. Both indices accurately represented the seasonal variation of TC genesis frequency (TCGF) and its spatial distribution in historical simulations and observation data. The widely-used TC genesis potential index (χGPI) projected a significant increase in TCGF in response to a warmer ocean surface. However, this projection conflicted with the significant reduction in the model projection due to the dominant control of SST on the χGPI. Higher SST in remote ocean basins often over dominated the destabilization effect of in-situ warmer SST and caused more stable atmospheric conditions in the WNP, resulting in fewer TC occurrences. By contrast, the revised index (χMqGPI), which considers gross moisture condensation, projected a TCGF decrease that more accurately reflected the decreasing trend of TCGF in the warming simulations by AGCM, although the degree of reduction was smaller than that derived directly from TC detection scheme. The results suggest the plausibility of using χMqGPI, based on the results of multimodel coarse-resolution CMIP6 climate models, to project future changes in TCGF in the WNP.

Marine heatwaves (MHWs) are extreme sea surface temperature (SST) events in all ocean basins, with far-reaching impacts on marine ecosystems and socio-economy. The leading patterns, trend, and interannual variability of summer MHWs in the tropical Indian Ocean (TIO) are investigated in this study. The first empirical orthogonal function (EOF) mode of frequency of MHWs exhibits a monopole pattern over the entire basin. This mode is highly associated with the concurrent Indian Ocean Basin warming, indicating a remarkable trend over the past four decades. The linear trend in MHW properties largely relates to increased summer Indian Ocean mean SST. The second EOF mode exhibits a zonal dipole with the MHW numbers increasing in the west and decreasing in the east. On the interannual timescale, the first two EOF modes are remotely affected by antecedent and concurrent El Niño events, respectively. The ocean mixed layer budget is utilized for examining the formation of different summer MHW patterns. During the preceding spring, the surface heat flux is important for the development of MHWs, while the ocean advections play a secondary role in the South Indian Ocean for the MHW monopole. Once the SST anomaly rises in summer, the ocean advections play a dominant role in maintaining the SST. Last, we assess the prediction skill of summer TIO MHWs by performing a bilinear seasonal statistical prediction model. Our results suggest the frequency of summer MHWs in the TIO could be predicted one season in advance. This study has great implications for understanding and predicting ocean extreme events in the TIO.

On 8–11 August 2022, South Korea experienced a catastrophic heavy rainfall event (HRE) with 14 fatalities. To elucidate its driving mechanisms, the present study performs a multiscale analysis by hierarchically delineating the synoptic and large-scale characteristics of the HRE. Its synoptic condition was featured by the confrontation of the western North Pacific subtropical high and the continental cyclone in the north of the Korean Peninsula. At their interface, a tremendous amount of moisture was transported in an elongated shape (i.e., atmospheric river) along with strong frontogenetic activity. This provided a favorable environment for potential instability. The continental cyclone was maintained throughout the HRE period, while a transient cyclone was superposed contributing to more intense rainfall in the early stage of the HRE. This persistent cyclone in the north of the Korean Peninsula originated from a far-upstream-originated cutoff low that became a part of the quasi-stationary wave train along the Asian subtropical jet. A linear model experiment suggests that the quasi-stationary wave train was excited by the enhanced tropical convection related to the boreal summer intraseasonal oscillation. The anomalously strong subtropical jet also acted as an effective waveguide. These results suggest that the integration of synoptic and large-scale processes is essential to understand this unprecedented HRE.

Due to the scarcity of observational data in the early 20th century, very limited research has explored the impact of human activities on temperature extremes at the regional scale. Here we used a newly developed homogenized near-surface air temperature dataset from the beginning of the 20th century to estimate the frequency and intensity of extreme temperatures in eastern China and evaluate their anthropogenic influence based on models from the Coupled Model Intercomparison Project Phase 6 (CMIP6). We found clear increases in warm extremes and decreases in cold extremes since 1901 for both annual and seasonal mean temperatures, with more pronounced changes in recent decades. The most significant warming occurred in spring and winter, approximately double the smallest warming observed in autumn. The CMIP6 models generally replicated the century-scale warming in annual and seasonal temperature extremes, showing increases in the frequency and intensity of warm extremes and corresponding decreases in cold extremes. The optimal fingerprinting detections suggest that the century-scale warming can be clearly attributed to anthropogenic forcing, including changes in seasonal extreme temperatures. Most observed changes in extreme temperatures were attributable to anthropogenic greenhouse gas emissions, partially offset by a smaller negative impact from anthropogenic aerosol forcing, whereas natural forcing has played a minor role. These results provide important information for accurately projecting future changes in temperature extremes.

During 2020, the Arctic is marked by extremely low sea ice coverage and hot climate. September Sea Ice Extent (SIE) was about 2.3 million km² below the 1979–2014 mean and the 2nd lowest on the 1979–2020 record, while regional summer (June–August, JJA) mean 2 m air temperature (TAS) was about 1.3 °C above the 1979–2014 mean and was the hottest on record at the time. Locally, September Sea Ice Concentration (SIC) was approximately 70% lower and JJA TAS can be as much as 6.0 °C higher than the 1979–2014 mean. Although the proximate cause for the extreme event was the continuously favorable atmospheric circulation patterns, wind conditions and ice-albedo feedback, the main objective of this paper is probabilistic extreme event attribution studies to assess the anthropogenic influence. Based on the CMIP6 multi-model ensemble products, modeled long-term trends of Arctic sea ice and TAS are consistent with observed trends when including anthropogenic forcing or greenhouse gas (GHG) forcing, while cannot exhibit observed trends with only aerosol or natural forcing. Further analysis reveals that human influence including GHG forcing has substantially increased the probability of occurrence of the 2020-like extreme events, which are rare in aerosol-only or natural-only forcing. The frequencies of 2020-like low SIC increase by 19 times with all forcing and 16 times with GHG forcing than with natural forcing. Future climate simulations under different Shared Socioeconomic Pathway (SSP) scenarios of SSP126, SSP245 and SSP585 show that the 2020-like extreme event that is currently considered rare is projected to become the norm and almost occur 1-in-1 year beyond 2041–2060. The probabilities will be approximately in the range of 0.85–1.00 for SIC and 0.76–0.99 for TAS from low emission of SSP126 to high emission of SSP585.