Atmospheric Science Letters最新文献

Rokko-oroshi is a northerly local wind blowing in the mega-city Kobe, Japan. This wind blows from the Rokko Mountains. This study analyzed the three-dimensional structure of Rokko-oroshi observed with a near-surface anemometer and Doppler lidar on January 16, 2023. Furthermore, numerical simulations using the Weather Research and Forecasting (WRF) model revealed the factors responsible for the strong winds. The results showed that Rokko-oroshi on January 16, 2023 was a bora-type downslope windstorm. The Doppler lidar observed the strong winds of Rokko-oroshi and a stagnant layer immediately above them. Numerical simulation results indicated the stagnant layer was formed by mountain-wave breaking. Under this stagnant layer, the airflow transitioned from subcritical to supercritical, resulting in the strong winds of Rokko-oroshi. This Rokko-oroshi was accompanied by a hydraulic jump. The occurrence of the Rokko-oroshi was supported by an upper-level critical layer and a lower-level strong stable layer on the windward side of the Rokko Mountains.

Observational evidence has shown that Compound Wind and Precipitation Extremes (CWPEs) can cause substantial disruptions to natural and economic systems under climate change. This study conducts a historical assessment and future projection of CWPEs characteristics in the climate vulnerable region of Southeast Asia (SEA) based on two Shared Socioeconomic Pathways (SSPs) from Scenario Model Intercomparison Project (ScenarioMIP) in Coupled Model Intercomparison Project Phase 6 (CMIP6). Results reveal that the northern Philippines, the eastern and northwestern coastal areas of the Indochina Peninsula have experienced the most frequent, strongest CWPEs during the period of 1985–2014. SEA is projected to experience a frequency increase of 14.4% (22.5%) and intensity increase of 9.4% (19.5%) under the SSP2-4.5 (SSP5-8.5) scenario at the end of 21st century (2070–2099). Kalimantan appears to replace the Philippines as the most affected area, particularly under high emission scenario. In addition, the changes in CWPEs are primarily driven by the changes in precipitation, with the average contribution of precipitation changes across the whole region is 62.8% (70.4%) under the SSP2-4.5 (SSP5-8.5) scenario. For precipitation uncertainties, the contribution from model uncertainty decreases over time (from 73.9% to 42.7%), while scenario uncertainty increases (from 20.3% to 55.0%). In contrast, for wind projections, model uncertainty remains the dominant factor (from 81.3% to 87.6%) with little change. The present study reveals the high sensitivity of the CWPEs over SEA under global warming and highlighting the risks of future disaster impact in such vulnerable regions.

Atmospheric models used for weather forecasting and climate predictions discretise the atmosphere onto a vertical grid. There are however atmospheric phenomena that occur on scales smaller than the thickness of those model layers. The formation of low-level clouds due to temperature inversions is an example. This leads to atmospheric models underestimating, or even missing, these clouds and their radiative effects. Using radiosonde observations as training data, a machine learning model is used to improve the vertical detail of modelled profiles of temperature and specific humidity. In addition, a physics-informed machine learning model is developed and compared to the traditional approach; showing improvements in the cloud fraction profiles calculated from its predictions. The vertically enhanced profiles also improve the representation of layers of convective inhibition and anomalous refractivity gradients. This work facilitates targeted improvements to the representation of certain atmospheric processes without the burden of increased memory and computational cost from increasing vertical resolution throughout the whole model.

Extreme value theory (EVT) is used as univariate extreme value analysis (EVA) in order to analyze and model the covariates temperature, relative humidity (RH) and the thermal comfort index (humidex) issued from a dataset of 38 years in Tunis. It is a South Mediterranean area known as a hotspot for climate change. The best approach is to reduce the data considerably by taking annual block maxima from mean monthly data. It will converge to a generalized extreme value distribution in order to estimate the return levels of the studied parameters. The stationarity of the series are checked by augmented Dickey-Fuller test. The modeling of the three parameters shows a Weibull distribution pattern. The extreme/maximum monthly means temperature of 30.2°C and humidex of 39.4 have a common return level between 300 and 350 years. The highest mean monthly RH of 86.0% is expected to be exceeded every 50 years. For the next 38 years, the maxima monthly mean temperatures are expected to be stable, and the maxima monthly mean RH values, as well as the humidex monthly mean maxima are expected to decrease. The percentile air temperature hot day (TX90p) and night (TN90p) indices show globally linear upward trends and the ones of cold days (TX10p) and cold nights (TN10p) have a downward trend. The diurnal yearly temperature range shows an almost flat trend for its evolution through the years of study.

Climate models are increasingly used to derive localised, specific information to guide adaptation to climate change. Model projections of future scenarios are conferred credibility by evaluating model skill in reproducing large-scale properties of the observed climate system. Model evaluation at fine spatial and temporal scales and for rare extreme events is critical for provision of reliable adaptation-relevant information, but may be challenging given significant internal variability and limited observed data in this setting. Comparing distributions of physical variables from historical simulations of Coupled Model Intercomparison Project models against observed distributions provides a comprehensive, concise and physically-justified skill measure. Calculating divergence between distributions requires aggregation of data spatially or temporally. The spatial and temporal scales at which a divergence measure converges to a consistent value can indicate the scales at which a well-defined climate signal emerges from internal variability. Below this threshold, there may be insufficient data for robust evaluation, particularly for rare extremes. Here, the behaviour of several divergence measures in response to spatial and temporal aggregation is analysed empirically to give a novel evaluation of CMIP6 daily maximum temperature simulations against reanalysis. Some key insights presented here can inform methodological choices made when deriving adaptation-relevant information. Convergence varies according to model, geographic region and divergence measure; selection of the level of precision at which models can provide reliable information therefore requires a context-specific understanding. For this purpose, an interactive tool provided alongside this study demonstrates scale-dependent evaluation across several geographic regions. Commonly applied measures are found to be only weakly sensitive to discrepancies in the tails of distributions.

Whilst permafrost change is widely concerned in the context of global warming, lack of observations becomes one of major limitations for conducting large-scale and long-term permafrost change research. Reanalysis/assimilation data in theory can make up for the lack of observations, but how they characterize permafrost extent and active layer thickness remains unclear. Here, we investigate the near-surface permafrost extent and active layer thickness characterized by seven reanalysis/assimilation datasets (CFSR, MERRA-2, ERA5, ERA5-Land, GLDAS-CLSMv20, GLDAS-CLSMv21, and GLDAS-Noah). Results indicate that most of reanalysis/assimilation data have limited abilities in characterizing near-surface permafrost extent and active layer thickness. GLDAS-CLSMv20 is overall optimal in terms of comprehensive performance in characterizing both present-day near-surface permafrost extent and active layer thickness change. The GLDAS-CLSMv20 indicates that near-surface permafrost extent decreases by −0.69 × 10⁶ km² decade⁻¹ and active layer deepens by 0.06 m decade⁻¹ from 1979 to 2014. Change in active layer is significantly correlated to air temperature, precipitation, and downward longwave radiation in summer, but the correlations show regional differences. Our study implies an imperative to advance reanalysis/assimilation data's abilities to reproduce permafrost, especially for reanalysis data.

In this study, a novel sea surface evaporation scheme, along with its corresponding bulk aerodynamic formulation, is proposed to estimate sea surface evaporation, columnar humidity, and precipitation distribution within the atmosphere. The scheme is based on three distinct functions, each dependent on a single variable: zonal wind velocity, tropospheric (potential) temperature, and free convection. It is shown that the normalized Clausius–Clapeyron formula requires an adjustable scaling factor for real-world applications, calibrated using empirical fitness curves. To validate the proposed approach, we employ a model based on the pseudo-spectral moist-convective thermal rotating shallow water model, with minimal parameterization over the entire sphere. ECMWF Reanalysis 5th Generation (ERA5) reanalysis data are used to compare the model's results with observations. The model is tested across different seasons to assess its reliability under various weather conditions. The Dedalus algorithm, which handles spin-weighted spherical harmonics, is employed to address the pseudo-spectral problem-solving tasks of the model.

Raindrop size distribution (DSD) is fundamental for understanding precipitation processes. This study utilized a two-dimensional simulation with bin cloud microphysics parameterizations to investigate the spatiotemporal variability of DSDs owing to the influence of mesoscale circulation associated with the precipitation system. The simulated multicellular convection went through developing, mature, and dissipating stages, with updraft weakening and rainfall area expanding through these stages. The width of the DSD narrowed as rainfall weakened. In addition, a significant bimodal DSD was observed during the dissipating stage. Furthermore, we investigated the spatial distribution of the number density of raindrops corresponding to the maximum, local minimum, and local maximum of the significant bimodal DSD in the dissipating stage. According to the results, the raindrops constituting the maximum, local minimum, and local maximum followed different advection processes. This size-dependent advection effect may have contributed to the bimodal DSD formation.

It is difficult to model changes in the likelihood of tropical cyclones under climate change to date. We do this, for the first time, by a applying a stochastic tropical cyclone event set generated by the Imperial College Storm Model to attribute the contribution of climate change to the case of Typhoon Haiyan in 2013. Compared to a pre-industrial baseline, we estimate that a typhoon with a landfall maximum wind speed like Haiyan was larger by +3.5 m/s. This is in good agreement with previous full physics numerical model estimates. A Haiyan type of event has a current return period of 850 years, and the fractional attributable risk due to climate change is 98%. Without climate change, this event was very unlikely. The type of information available from the IRIS model could inform subsidizing of catastrophe bond yield in the context of the loss and damage fund.

On 7–8 September 2023, Hong Kong was hit by a historical and record-breaking rainstorm associated with the remnant of Tropical Cyclone Haikui (2311). The hourly rainfall recorded at the Hong Kong Observatory Headquarters once reached 158.1 mm, the highest since record began in 1884. The 24-h rainfall even exceeded 600 mm in some parts of the territory. The historical rainstorm resulted in heavy flooding and landslides, bringing significant societal impact to Hong Kong. This paper aims to review this unprecedented heavy rain event from the aspects of diagnosis, forecasting and nowcasting. Early indicators of such events over Hong Kong with substantial lead time are limited from the dynamics and thermodynamics consideration, the numerical weather prediction models, given the present technology. The only indication may come from the climatologically extreme total precipitable water. While recent research of developing a regional risk-based alerting system on the higher impact event of flooding associated with heavy rain might have potential to enhance the weather service, and emerging AI model showed some promising post-simulations, predicting historical and record-breaking rainstorms remains a challenge for operational weather forecasting and warning services.