Weather and Climate Extremes最新文献

Angola is exceptionally vulnerable to climate change, and sectors such as health, agricultural, water resources and ecosystems may endure severe impacts. Here, an extensive analysis of the signal of climate change on temperature, precipitation, extremes and compound events, for the end of the 21st century, is presented. The analysis is based on a CORDEX-Africa multi-model ensemble at 0.44° resolution built with 19 individual simulations, which allows a robust study of climate change future projections and depict model's uncertainty. For the RCP8.5, the end of the century future warming can reach maxima values $\sim$ 7 °C for maximum temperature in south-eastern Angola, and 6 °C for minimum temperature. The extreme temperatures (90th percentile) is projected to rise more than 7 °C in southern areas. In general, projections display a rainfall reduction in the drier seasons and a rise in the wet seasons, leading to sharper annual cycles; it is also projected a growth on extreme precipitation (95th percentile), as much as plus $\sim$ 50 % in some coastal regions. Angola is projected to endure in the future more frequent and longer heatwaves and droughts. In agreement with the RCP8.5, up to 10 heatwaves and more 4 moderate droughts will occur, respectively in coastal and interior areas. Finally, the number of days when a compound of heatwave and moderate drought occurs is projected to growth immensely, around +30 % for many regions, which corresponds to multiply by 10 these events in the future. For the RCP4.5, changes are projected to be smaller but significant in what regards especially extremes and compound events. The magnitude of the projected changes for vulnerable countries as Angola constitute an urgent call for global mitigation and national to regional adaptation strategies, and ultimately to a constant effort of updating and deepen the quality of climate information produced.

Dryland ecosystems are highly vulnerable to extreme droughts under climate change. Yet, response of vegetation productivity across global drylands to changes in drought stress in a warming climate remains obscure. Here, we investigated future changes in drought stress, characterized by low soil moisture (SM) and high vapor pressure deficit (VPD), under severe drought conditions and its impact on gross primary productivity (GPP) deviations in drylands, based on the Coupled Model Intercomparison Project Phase 6 (CMIP6) Earth system model (ESM) simulations. Under both intermediate (SSP2-4.5) and high (SSP5-8.5) emission scenarios, the dryland ecosystems are projected to experience more intense, extensive and frequent severe drought events owing to increasing VPD. The probabilities of high VPD-dominated drought stress in the end of the 21st century would be nearly double (2.1–2.4 times) of the present-day (39%). Excluding the carbon dioxide (CO₂) fertilization effect, the annual GPP loss caused by severe drought is projected to further deteriorate over more than half fraction (56.9–70.9%) of global vegetated dryland areas, reaching 2.0 (1.9–2.2) times of the present-day (with an area-weighted total of −21.5 KgC m⁻² yr⁻¹) by the end of the 21st century. Such aggravating reduction is predominantly induced by drought stress with higher-than-usual VPD anomaly. The high VPD-dominated drought stress would lead to approximately 100% (95–102%) of annual aggregated dryland GPP loss by the end of 21st century from the present-day 68%. Our results suggest an increasing risk of high atmospheric aridity-dominated drought stress on dryland ecosystems. It is of great urgency to make adaption and mitigation strategies for the natural and cultivated vegetation in drylands.

We document 1394 extreme precipitation events (EPEs) over Aotearoa New Zealand’s (ANZ) Regional Councils between March 1996 and December 2021. The characteristics of EPEs are documented using a novel spatio-temporal framework that diagnoses the peak intensity, duration, and accumulation of the EPE using the ERA-5 and MERRA-2 reanalysis products. Properties of EPEs were evaluated according to region across ANZ, and clear regional differences are highlighted. In particular, it is found that the duration of an EPE has a stronger influence than the peak intensity on the total accumulated precipitation across all regions and precipitation event types (large-scale or convective). Since larger precipitation accumulations have greater potential to cause extensive flooding over larger areas, an important implication is the need for numerical weather prediction in ANZ to forecast the duration of an intense precipitation event adequately in order to improve emergency preparedness.

Coastal flood damage is primarily the result of extreme sea levels. Climate change is expected to drive an increase in these extremes. While proper estimation of changes in storm surges is essential to estimate changes in extreme sea levels, there remains low confidence in future trends of surge contribution to extreme sea levels. Alerting local populations of imminent extreme sea levels is also critical to protecting coastal populations. Both predicting and projecting extreme sea levels require reliable numerical prediction systems. The SurgeMIP (surge model intercomparison) community has been established to tackle such challenges. Efforts to intercompare storm surge prediction systems and coordinate the community's prediction and projection efforts are introduced. An overview of past and recent advances in storm surge science such as physical processes to consider and the recent development of global forecasting systems are briefly introduced. Selected historical events and drivers behind fast increasing service and knowledge requirements for emergency response to adaptation considerations are also discussed. The community's initial plans and recent progress are introduced. These include the establishment of an intercomparison project, the identification of research and development gaps, and the introduction of efforts to coordinate projections that span multiple climate scenarios.

This study developed a novel approach that integrated climate model selection and multi-model ensemble (MME) construction to effectively represent model uncertainties and, consequently, improve consistency in the evaluation of changes to extreme rainfall in different scenarios. Our focus was to combine 10 regional climate model (RCM) simulations, forced by two global climate models (GCM), especially for estimation of design rainfall in a changing climate. We hypothesized that the natural variability and statistically higher moment attributes in the extreme rainfall simulations from RCMs were not fully preserved. Therefore, the MME approach could be more effective in climate change studies, largely due to the use of multiple climate models. First, an experimental study was proposed to validate the efficacy of the proposed modeling framework approach adopting L-moments to quantify relative importance among climate models and their use for representing natural variability in the MME construction. The proposed approach was then applied to climate change scenarios collected from multiple RCMs for the Han-River watershed during both historical (1981–2005) and future (2006–2 100) periods. The results showed that the climate model selection informed by natural variability demonstrated better performance, representing nearly identical distribution to the observed annual maximum rainfall (AMR) in the Han River watershed. The range of the selected scenario was relatively narrower than that of all the scenarios, and the change rate was more consistent with the limited zero crossing, reflecting improvement in both model performance and consistency over historical and future periods, respectively. The change rate of the MME under RCP8.5 appeared to be an approximately 20% increase for the near (2011–2040) and far (2071–2 100) future, and the degree of increase in the rate for the mid-future (2041–2070) was slightly lower than that for the other periods, with increase of approximately 10%.

Freezing rain is one of the most damaging weather phenomena in winter or early spring in many parts of the world, affecting traffic, power lines and agriculture. Thus, reliable and computationally efficient prediction of its occurrence is urgently needed in weather forecast operations. However, there are different thermodynamic processes that can lead to freezing rain, resulting in unsatisfactory forecasting performance of the state-of-the-art Numerical Weather Prediction (NWP) models. Here a data-driven forecasting method for freezing rain using machine learning technologies is proposed. Observations of weather phenomenon collected from 2 515 national weather stations of China for winter of 2016–2019 and the corresponding atmospheric predictors derived from ERA5 reanalysis are used. The prediction function is constructed based on the classification and regression tree, and the predicting variables include temporal and vertical profiles of fundamental thermodynamic and kinematic parameters from 500 hPa to 1000 hPa, with a total dimension of 2 304. The LightGBM (Light Gradient Boosting Machine) framework is adopted to train our prediction model and an algorithm-level approach of modifying the loss function is used to address the imbalance of classes to improve forecasting skill. Results show that the data-driven prediction model, namely DDFR (data driven forecast of freezing rain), out-performs the benchmark NWP, i.e., ECMWF IFS product. It's improvements in terms of TS score range from 120% to 258% depending on different forecast leading times, which range from 0 to 12 h. In addition, DDFR is applied in an operational NWP model of China. The problem of domain adaptation is tackled and transfer learning method is employed to adapt the original DDFR to this NWP model. The effectiveness of such adaptation has been demonstrated by its performance on both training and testing datasets.

Wave setup is a physical process that induces a temporal increase of the mean water level due to wave dissipation by bottom friction and breaking in the surf zone, extending over tens to hundreds of meters in the cross-shore direction. Wave setup contribution to coastal sea level solely induced by wind and atmospheric effects can increase by more than 100% under extreme events and conditions favoring its formation. It is therefore crucial to consider this phenomenon when assessing sea-level-related coastal hazards. Previous studies estimated the wave setup effect by means of numerical modeling and empirical formulations at regional and global scale. Such analyses require either high computational capacity to implement high-resolution numerical models over large domains, and/or accurate information on coastal morphological features from global or regional databases. Although the Mediterranean Sea is a fetch-limited environment, waves generated from extra-tropical cyclones are powerful enough for wave setup to develop, and subsequently for a potential significant wave setup contribution to extreme coastal sea level. Through the use of both numerical and empirical methods, we investigate the uncertainty associated to wave setup representation on the frequency and magnitude of coastal extreme sea levels occurring on sandy beaches in the Mediterranean Sea. Wave setup values are compared at beach scale between process-based modeling and empirical approaches, showing highly variable results. We also quantify the impact of wave setup on return levels of coastal sea level extremes using reconstructed sea levels. We employ various methods to calculate the wave setup component. Results show high spatial dispersion, with clear differences between the numerical and empirical approaches, especially in regions prone to the development of energetic waves. The total inter-method dispersion of 100-year return levels is often higher than 30 cm for average values of 62.4 cm. We emphasize the important limitations related to wave setup modeling (i.e., its underestimation) at large scale, and call for caution when applying empirical formulations (generally developed from local studies) at regional to global scale, which can lead to unrealistic wave setup values.

Compound dry and hot events (CDHEs) are among the most destructive compound extremes. Under global warming, changes in precipitation, temperature and their dependence make profound contributions to CDHEs. In this paper, the contributions of these three factors are explicitly quantified based on a novel mathematical method. Specifically, time series of precipitation and temperature are employed to identify CDHEs and then changes of CDHEs are attributed by using the partial derivatives-based sensitivity analysis. Based on the Climatic Research Unit Time-Series (CRU TS), a case study of CDHEs is devised for the major river basins (MRBs) of the world. The results highlight that from the period 1921–1970 to the period 1971–2020, CDHEs did occur more frequently across most MRBs. The temperature tended to make the largest contribution, followed by precipitation and the dependence between precipitation and temperature. In Africa, South America and Western Europe, the rising temperature is generally the dominant factor for increases of heatwaves that contribute to CDHEs. In Asia, increases of droughts along with increases of heatwaves raise the risk of CDHEs. For MRBs with moderate increases in temperature, increasing precipitation is shown to mitigate or even offset the risks of CDHEs. In the meantime, the increasing dependence is observed to reduce the frequency of CDHEs in the Huai He and the Mississippi even though temperature is increasing. Overall, the attributing results of CDHEs from 1921 to 2020 can serve as a reference for the preparation and mitigation of CDHEs for MRBs across the world.

The Simplified Wet Bulb Globe Temperature (sWBGT) is widely used in heat stress assessments for climate-change studies, but its limitations have not been thoroughly explored. Building on recent critiques of sWBGT's use for current climate on global scale, this study examines sWBGT's biases using dynamically-downscaled sub-daily climate projections under multiple future emission scenarios. The analysis is aimed at understanding caveats in the application of sWBGT and the uncertainties in existing climate change analysis dependent on sWBGT. Results indicate sWBGT's biases are heavily influenced by local near-surface air temperature, with overestimation of heat stress in East Asia regions, particularly hot and humid areas, due to static assumptions of radiation and wind speed. This overestimation is amplified in warmer climates, leading to exaggerated projected heat stress increases in future. In contrast, underestimations are found for heat stress levels attributed to low wind speeds and strong radiations, such as over the Tibetan Plateau and certain extreme events. Additionally, sWBGT underestimates variability in extreme heatwave events compared to WBGT in both current and future climates, irrespective of overestimation in absolute heatwave intensities. This study emphasizes the limitations of sWBGT, especially in future warmer climates. Importance of sub-daily data for capturing daily maximum heat stress level and reflecting diurnal variations in different components is also discussed. In conclusion, we recommend using Liljegren's model (i.e., physics-based calculation) with high-resolution sub-daily climate data for more accurate outdoor heat stress assessments in climate change studies.

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.