Atmospheric Science Letters最新文献

Ensemble forecasts have been shown to better predict observed Atlantic climate variability than that of their own ensemble members. This phenomenon—termed the signal-to-noise paradox—is found to be widespread across models, timescales, and climate variables, and has wide implications. The signal-to-noise paradox can be interpreted as forecasts underestimating the amplitude of predictable signals on seasonal-to-decadal timescales. The cause of this remains unknown. Here, we examine sea level pressure variability from a very large multi-model ensemble of uninitialized atmosphere-only simulations, focusing on boreal winter. To assess signal-to-noise errors, the ratio of predictable components (RPC) is examined globally, as well as for regional climate indices: the North Atlantic Oscillation, Arctic Oscillation, Southern Annular Mode, and an Arctic index. Our analyses reveal significant correlations between the multi-model ensemble-mean and observations over large portions of the globe, particularly the tropics, North Atlantic, and North Pacific. However, RPC values greater than one are apparent over many extratropical regions and in all four climate indices. Higher-resolution models produce greater observation-model correlations and greater RPC values than lower-resolution models in all four climate indices. We find that signal-to-noise errors emerge more clearly at higher resolution, but the amplitudes of predictable signals do not increase with resolution, at least across the range of resolutions considered here. Our results suggest that free-running atmospheric models underestimate predictable signals in the absence of sea surface temperature biases, implying that signal-to-noise errors originate in the atmosphere or in ocean–atmosphere coupling.

Observations show that mesospheric clouds (MCs) have been increasing in recent decades, presumably due to increased mesospheric water vapor which is mainly caused by greater methane (CH₄) oxidation in the middle atmosphere. Past warm climates such as those of the early Cretaceous and Paleogene periods are thought to have had higher CH₄ concentrations than present day, and future CH₄ concentrations will also likely continue to rise. Here, idealized atmosphere chemistry-climate model experiments forced with strong polar-amplified sea-surface temperatures and elevated carbon dioxide (CO₂) and CH₄ concentrations predict a substantial spreading of MCs to middle and low latitudes, well beyond regions where they are currently found. Sensitivity tests show that increased water vapor from CH₄ oxidation and cooling from increased CO₂ and CH₄ concentrations create favorable conditions for cloud formation, producing MC fractions of 0.02 in the low latitudes and 0.1 in the mid-latitudes in the Northern Hemisphere when CH₄ concentration is 16× higher than pre-industrial. Further increases in CH₄ result in a monotonic increase in low- and mid-latitude MCs. A uniform surface ocean warming, changes in polar amplification, or the solar constant do not significantly affect our results. While the appearance of these clouds is interesting, their ice and liquid water content is not sufficient to cause a significant radiative effect. On the other hand, dehydration of the mesosphere due to these low- and mid-latitude MCs could potentially lead to a reduction in atomic hydrogen, thereby affecting mesospheric ozone concentration, although further study is required to confirm this.

Satellite remote sensing enables the study of atmospheric aerosols at large spatial scales, with geostationary platforms making this possible at sub-daily frequencies. High-temporal-resolution aerosol observations can be made from geostationary data by using robust numerical inversion methods such as the widely-used optimal estimation (OE) theory. This is the case of the instantaneous Aerosol and surfacE Retrieval Using Satellites in GEOstationary orbit (iAERUS-GEO) algorithm, which successfully retrieves aerosol optical depth (AOD) maps from the Meteosat Second Generation weather satellite based on a simple implementation of the OE approach combined with the Levenberg–Marquardt method. However, the exact gain in inversion performances that can be obtained from the multiple and more advanced possibilities offered by OE is not well documented in the current literature. Against this background, this article presents the quantitative assessment of OE for the future improvement of the iAERUS-GEO algorithm. To this end, we use a series of comprehensive experiments based on AOD maps retrieved by iAERUS-GEO using different OE implementations, and ground-based observations used as reference data. First, we assess the varying importance in the inversion process of satellite observations and a priori information according to the content of satellite aerosol information. Second, we quantify the gain of AOD estimation in log space versus linear space in terms of accuracy, AOD distribution and number of successful retrievals. Finally, we evaluate the accuracy improvement of simultaneous AOD and surface reflectance retrieval as a function of the regions covered by the Meteosat Earth's disk.

Aerosols significantly affect cloud microphysics and energy budget in different ways. The contribution of the direct, semi-direct, and indirect effects of aerosols on radiation are here investigated over the North Atlantic tropical ocean under different aerosol loadings. The Weather Research and Forecasting Model is used to perform a set of numerical idealized experiments, which are forced with prescribed aerosol profiles. We evaluate the effects of aerosols on modeled shallow clouds and surface radiative budget. The results indicate that large aerosol loadings are associated with enhanced cloudiness and reduced precipitation. While the change in rainfall is mainly due to the larger number of smaller droplets, the change in cloudiness is attributed to the effects of absorbing aerosols, mainly dust particles, which are responsible for a rise of temperature that feeds back onto specific humidity. As in the boundary layer the increase of moisture dominates, the net effect is a higher relative humidity, which favors the formation of thin low non-precipitating clouds. The feedback accounts for a dynamical change in the lower troposphere: shortwave radiation absorption increases temperature at the top of the marine atmospheric boundary-layer and reduces entrainment of warm and dry air, increasing low level moisture content. Despite the overall increase in cloudiness, daytime cloud cover is reduced. The semi-direct effect of aerosols on clouds results in a warming of the surface, opposite to the indirect effect.

The southwest vortices (SWVs) are a unique type of mesoscale vortex that frequently induce torrential rainfall in China. In this study, we focused a long-lived quasi-stationary SWV, which was the primary system for producing an extremely heavy rainstorm within/around Sichuan (the maximum hourly precipitation was ~103.8 mm) in Mid July 2021. After reproduced the SWV's formation by using Weather Research and Forecasting model, we conducted trajectory analyses and topography sensitivity simulations to understand the effects of complicated topography on the vortex's formation. It is found that, the regions south and southwest of the SWV acted as the most important source regions for the air clusters that formed the SWV (proportion ≥ 65%), and the air clusters originated from the upper layer contributed the most (≥60%). Of these, the air clusters sourced from the upper layer southwest and south of the SWV played the most important role in the SWV's formation, as their increase in cyclonic vorticity and their contributions to trajectory number and vorticity were all much larger than those of the others. Sensitivity simulations indicated that, detailed topography features around the Sichuan Basin were crucial in determining the structure, intensity and precipitation of the SWV, whereas, the topography features were not a decisive factor for the SWV's formation. In summary, our findings are useful to enrich the current understanding of the SWVs' formation, which would be helpful to improve the related forecasts.

Many meteorological centers have operationally implemented global model-based ensemble prediction systems (GEPSs), making tropical cyclone (TC) forecasts from these systems available. The relatively low resolution of these GEPSs means that limits previous studies primarily focused on TC track forecasting. However, recent GEPS upgrades mean that TC intensity predictions from GEPSs are now also becoming of interest. This study focuses on the verification and comparison of the latest generation of GEPSs for TC intensity forecasts, particularly during the rapid intensification (RI) period over the western North Pacific (WP), eastern North Pacific (EP), and North Atlantic (NA) basins in 2021–2022. On average, the National Centers for Environmental Prediction (NCEP) GEPS performed best in predicting both TC intensity and RI across all three basins. Nevertheless, the exact timing of RI remains highly uncertain for these GEPS, indicating significant limitations in using GEPSs to forecast RI.

In the southwest Pacific, a meandering jet-stream in the upper troposphere is sometimes found at ~30° S during austral winters and is usually treated as a sub-tropical jet (STJ) due to its low latitude. For two contrasting cases, we have conducted analyses from two perspectives to identify the STJ and PFJ: first, using previously published qualitative criteria to identify jet-cores and second, investigating the jet-stream axes of STJ and PFJ identified using 2-PVU curves. The results showed that the chosen meandering jet-stream case at ~30° S was a merged, and for a time, a superposed STJ and PFJ. Downstream of the jet-streak, the PFJ split to the south and the STJ to the east. This is in significant contrast to the horizontally well-separated jet-stream case chosen in this study. Some processes likely contributing to the superposition of the STJ and PFJ were analyzed and discussed. The movement of PFJ that was closely associated with the movement of the low over the Tasman Sea and the convection in and near the tropical region may have played dominant roles.

When conducting large-eddy simulations (LESs) of plume dispersion in the atmosphere, crucial issue is to prescribe time-dependent turbulent inflow data. Therefore, several techniques for driving LESs have been proposed. For example, in the original recycling (OR) method developed by Kataoka and Mizuno (Wind and Structures, 2002, 5, 379–392), a mean wind profile is prescribed at the inlet boundary, the only fluctuating components extracted at the downstream position are recycled to the inlet boundary. Although the basic turbulence characteristics are reproduced with a short development section, it is difficult to generate target turbulent fluctuations consistent with realistic atmospheric turbulence. In this study, we proposed a dynamically controlled recycling (DCR) method that is a simple extension of the OR procedure. In this method, the magnitude of turbulent fluctuations is dynamically controlled to match with the target turbulent boundary layer (TBL) flow using a turbulence enhancement coefficient based on the ratio of the target turbulence statistics to the computed ones. When compared to the recommended data of Engineering Science Data Unit (ESDU) 85020, the turbulence characteristics generated by our proposed method were quantitatively reproduced well. Furthermore, the spanwise and vertical plume spreads were also simulated well. It is concluded that the DCR method successfully simulates plume dispersion in neutral TBL flows.

We investigate the impact of seasonal forecast biases in the Tropical Atlantic on the North Atlantic. The analysis uses a novel ensemble-based method to estimate the impact of tropical rainfall bias on forecasts of the Extratropical North Atlantic. The inter-ensemble spread of the forecast model is used to estimate the impact of the bias in Tropical Atlantic rainfall on the North Atlantic by selecting model members that happen to produce forecast anomalies that most closely resemble the tropical rainfall bias and using these as a proxy for the model error. The Tropical Atlantic rainfall bias impacts Rossby wave sources over the Subtropical Atlantic and there is a clear Rossby wave pattern originating from this area which is comparable to the mean bias in hindcasts. We argue that Tropical Atlantic rainfall errors explain a significant amount of the bias in seasonal forecasts over the Extratropical North Atlantic.

Convectively coupled equatorial waves are a significant source of atmospheric variability in the tropics. Current numerical models continue to struggle in simulating the coupled diabatic heating fields that are responsible for the development and maintenance of these waves. This study investigates how the diabatic fields associated with Mixed Rossby–Gravity waves (MRGs) are represented in four reanalysis products by using a unique observational dataset from the TRMM-KWAJEX (Tropical Rainfall Measuring Mission-Kwajalein Experiment) field campaign. These reanalyses include ERA5, Japanese 55-year Reanalysis (JRA-55), Climate Forecast System Reanalysis (CFSR), and Modern-Era Retrospective Analysis for Research and Applications (MERRA). We found that all four reanalyses captured the MRG structures in winds and temperature, and to a lesser degree in the humidity field except in the boundary layer. However, only the ERA5 and MERRA reanalyses captured the gradual rise and succession of the diabatic heating from boundary layer turbulence, shallow convection, cumulus congestus, and deep convection within the waves. ERA5 is the only product that also captured the gradual rise of the subgrid-scale vertical transport of moist static energy. All reanalysis products underestimated the diabatic heating from cumulus congestus. Results provide observational basis on what aspects of MRG can be trusted and what cannot in the reanalysis products.