Atmospheric Science Letters最新文献

Forecasting precipitation accurately poses significant challenges due to various factors affecting its distribution and intensity, including but not limited to subgrid variability. Although higher resolution simulations are often considered to improve precipitation forecasts, it is crucial to note that simply increasing resolution may not suffice without appropriate adjustments to parameterization schemes or tuning. Traditionally, ensembles of simulations are used to generate uncertainty predictions associated with precipitation forecasts, but this approach can be computationally intensive. As an alternative, there is a growing trend towards leveraging neural networks for precipitation prediction, which offers potential computational advantages. We propose a new approach to generating ensemble weather predictions for high-resolution precipitation without requiring high-resolution training data. The method uses generative adversarial networks to learn the complex patterns of precipitation and produce diverse and realistic precipitation fields, allowing to generate realistic precipitation ensemble members using only the available control forecast. We demonstrate the feasibility of generating realistic precipitation ensemble members on unseen higher resolutions. We use evaluation metrics such as RMSE, CRPS, rank histogram and ROC curves to demonstrate that our generated ensemble is almost identical to the ECMWF IFS ensemble, on which our model was trained on.

Accurate air temperatures underpin environmental research. Most professional meteorological air temperature measurements still expose thermometers within traditional, naturally ventilated screens. Their representation of true air temperature depends on screen airflow, and therefore local winds. Accuracies of daily maximum (T_max) and minimum (T_min) air temperatures are assessed by comparison between a naturally ventilated large conventional screen and a co-located aspirated reference screen. In over 1200 days' data, the naturally ventilated T_min and T_max both showed small (median < 0.06°C) cold bias, but, in 1% of cases, warm T_max bias and cold T_min bias >|1°C|. The T_min cold bias is associated with calm clear nights, and the T_max warm bias events with calm winter days at low sun angles, allowing solar heating of the screen. The prevalence of poor natural ventilation, potentially affecting T_min and T_max, is estimated across European sites. Poor ventilation occurred at T_min for 12% of values, and at T_max for 4%. Climatological averaging will reduce these effects, but, without corroborating wind data, statistical changes in T_min or T_max, including identifying “Tropical Nights” (T_min > 20°C) or occurrences of winter extremes, may have limited value. Wider adoption of aspirated thermometer screens, with an initial overlap period, will largely eliminate these effects.

In July 2021, a cut-off low-pressure system brought extreme precipitation to Western Europe. Record daily rainfall totals led to flooding that caused loss of life and substantial damage to infrastructure. Climate change can amplify rainfall extremes via thermodynamic processes, but the role of dynamical changes is uncertain. We assess how the dynamics involved in this particular event are changing using flow analogues. Using past and present periods in reanalyses and large ensemble climate model data of the present-day climate and 2°C warmer climate, we find that the best flow analogues become more similar to the cut-off low-pressure system observed over Western Europe in 2021. This may imply that extreme rain events will occur more frequently in the future. Moreover, the magnitude of the analogue lows has deepened, and the associated air masses contain more precipitable water. Simulations of future climate show similar events of the future could lead to intense rainfall further east than in the current climate, due to a shift of the pattern. Such unprecedented events can have large consequences for society, we need to mitigate and adapt to reduce future impacts.

The equivalent depth of an atmospheric layer is of importance in determining the phase speed of gravity waves and characterizing wave phenomena. The value of the equivalent depth can be obtained from the eigenvalues of the vertical structure equation (the vertical part of the primitive equations) where the mean temperature profile is a coefficient. Both numerical solutions of the vertical structure equation and analytical considerations are employed to calculate the equivalent depth, $��h_n�$, as a function of the atmospheric layer's thickness, $��\Delta �z�$. Our solutions for layers of thickness 100 $��\le �\Delta �z�\le �$ 2000 m show that for baroclinic modes, $��h_n�$ can be over two orders of magnitudes smaller than $��\Delta �z�$. Analytic expressions are derived for $��h_n�$ in layers of uniform temperature and numerical solutions are derived for layers in which the temperature changes linearly with height. A comparison between the two cases shows that a slight temperature gradient (of say 0.65 K across a 100 m layer) decreases $��h_n�$ by a factor of 3 (but can reach a factor of 10 for larger gradients) compared with its value in a layer of uniform temperature, while a change of 10 K in the layer's uniform temperature hardly changes $��h_n�$. The $��n�=�0�$ baroclinic mode exists in all combinations of boundary conditions top and bottom while the barotropic mode only exists when the vertical velocity vanishes at both boundaries of the layer.

Climatological monthly mean tropical cyclone (TC) genesis location in the western North Pacific exhibits a marked meridional shift from June to November and the physical cause of such a shift was investigated through the diagnosis of observational and reanalysis datasets. Two genesis potential indices were used to examine key environmental parameters affecting the meridional shift. The diagnosis results indicate that dynamic parameters such as vertical and meridional wind shears play a dominant role in promoting both the northward advance and southward retreat of the mean genesis latitude, while the effect of vertical velocity is negligible. The thermodynamic parameters such as relative humidity and potential intensity in general play a minor role, except that the latter helps promote northward advance during the early TC season. The change of the environmental parameters is closely linked to the sub-seasonal evolution of the monsoon trough and subtropical high in the western North Pacific. Maximum synoptic-scale variability also experiences a similar meridional shift, contributing to the TC genesis location shift.

This letter raises the possibility that ergodicity concerns might have some bearing on the signal-to-noise paradox. This is explored by applying the ergodic theorem to the theory behind ensemble weather forecasting and the ensemble mean. Using the ensemble mean as our best forecast of observations amounts to interpreting it as the most likely phase-space trajectory, which relies on the ergodic theorem. This can fail for ensemble forecasting systems if members are not perfectly exchangeable with each other, the averaging window is too short and/or there are too few members. We argue these failures can occur in cases such as the winter North Atlantic Oscillation (NAO) forecasts due to intransitivity or regime behaviour for regions such as the North Atlantic and Arctic. This behaviour, where different ensemble members may become stuck in different relatively persistent flow states (intransitivity) or multi-modality (regime behaviour), can in certain situations break the ergodic theorem. The problem of non-ergodic systems and models in the case of weather forecasting is discussed, as are potential mitigation methods and metrics for ergodicity in ensemble systems.

This study analyses the landfall intensity of tropical cyclones (TCs) affecting the Pacific coast of Japan and found that the proportion of strong typhoons increased significantly in the second 22 years from 1977 to 2020. With an objective cluster analysis of TC tracks, one could isolate a cluster of TCs originating from the southeastern part of the western North Pacific (WNP), which plays a dominant role in increasing landfalls of strong typhoons. These TCs are characterized by a long-recurving track and could achieve significantly higher intensity and larger size. Further analysis of TC trajectories and the environmental steering flow show a greater tendency for TCs originating from the southeastern WNP to approach the Pacific coast of Japan, even though there was a dramatic decrease in TC genesis number during autumn. Meanwhile, a notable earlier onset of strong typhoons occurred within this cluster of TCs due to more favorable atmospheric and oceanic conditions in summer. The results of this study emphasize the impacts of TC track change and seasonal advance of strong typhoons on the variation of intensity and potential destructiveness of landfalling TCs.

The storms over the Bay of Bengal (BoB) often combine with the weather systems such as the South Branch Trough (SBT) and the West Pacific Subtropical High (WPSH) to transport plenty of moisture inducing extreme precipitation on the Tibetan Plateau (TP). Determining the fine moisture structures of storms helps understand mechanism of this kind of extreme precipitation. An extreme precipitation occurred on the TP influenced by storm Rashmi (2008). A Lagrangian approach is scrutinized the forward and backward moisture transport trajectories of Rashmi and the TP, respectively. The moisture source of this extreme precipitation is relatively clear, which comes from the collaborative influence of Rashmi with the southwest jet generated by the SBT and the WPSH. Utilizing a three-dimensional K-means clustering method devised in this study, the Rashmi's forward trajectories are classified into three categories, the particles ascending with the northward movement of Rashmi (45%), consistently below 1 km (37.5%), and rapidly ascending into the southwest jet stream (17.5%). Notably, 97.5%, 1.2%, and 91% of these categories impact the TP, respectively. The moisture transport structure of storm is verified by backward tracking of moisture over the TP. In addition, the three-dimensional moisture trajectories classification method is recommended when trajectories suffer rapid altitude changes.

The upper reaches of the Yangtze River Basin (UYRB) are famous for their hydropower generation and water resources in China, relying greatly on precipitation. The UYRB summer precipitation has decreased since the early 2000s and has been exposed to a particularly dry period in the most recent two decades. By analyzing the long-term variability from the beginning of the 20th Century, we found that the precipitation exhibits a profound periodic interdecadal variation, with a significant cycle of 30–50 years. The interdecadal variability of precipitation is shown to be significantly modulated by both the Atlantic Multidecadal Oscillation (AMO) and Pacific Decadal Oscillation (PDO), exerting their impact through related circulation anomalies. Moreover, the periodical nature of the UYRB precipitation variation and its correlation with AMO/PDO suggest that the UYRB might enter a wet period in the forthcoming decade, i.e. rich in hydroelectric resources but a high risk of flood.

Global mean near surface temperature change is the key metric by which our warming climate is monitored and for which international climate policy is set. At the end of each year the Met Office issues a global mean temperature forecast for the coming year. Following on from the new record in 2023, we predict that 2024 will likely (76% chance) be a new record year with a 1-in-3 chance of exceeding 1.5°C above pre-industrial. Whilst a one-year temporary exceedance of 1.5°C would not constitute a breach of the Paris Agreement target, our forecast highlights how close we are now to this. Our 2024 forecast is primarily driven by the strong warming trend of +0.2°C/decade (1981–2023) and secondly by the lagged warming effect of a strong tropical Pacific El Niño event. We highlight that 2023 itself was significantly warmer than the Met Office DePreSys3 forecast, with much of this additional observed warming coming from the southern hemisphere, the cause of which requires further understanding.