Advances in Atmospheric Sciences最新文献

Accurate soil moisture (SM) prediction is critical for understanding hydrological processes. Physics-based (PB) models exhibit large uncertainties in SM predictions arising from uncertain parameterizations and insufficient representation of land-surface processes. In addition to PB models, deep learning (DL) models have been widely used in SM predictions recently. However, few pure DL models have notably high success rates due to lacking physical information. Thus, we developed hybrid models to effectively integrate the outputs of PB models into DL models to improve SM predictions. To this end, we first developed a hybrid model based on the attention mechanism to take advantage of PB models at each forecast time scale (attention model). We further built an ensemble model that combined the advantages of different hybrid schemes (ensemble model). We utilized SM forecasts from the Global Forecast System to enhance the convolutional long short-term memory (ConvLSTM) model for 1–16 days of SM predictions. The performances of the proposed hybrid models were investigated and compared with two existing hybrid models. The results showed that the attention model could leverage benefits of PB models and achieved the best predictability of drought events among the different hybrid models. Moreover, the ensemble model performed best among all hybrid models at all forecast time scales and different soil conditions. It is highlighted that the ensemble model outperformed the pure DL model over 79.5% of in situ stations for 16-day predictions. These findings suggest that our proposed hybrid models can adequately exploit the benefits of PB model outputs to aid DL models in making SM predictions.

A previously developed hybrid coupled model (HCM) is composed of an intermediate tropical Pacific Ocean model and a global atmospheric general circulation model (AGCM), denoted as HCM^AGCM. In this study, different El Niño flavors, namely the Eastern-Pacific (EP) and Central-Pacific (CP) types, and the associated global atmospheric teleconnections are examined in a 1000-yr control simulation of the HCM^AGCM. The HCM^AGCM indicates profoundly different characteristics among EP and CP El Niño events in terms of related oceanic and atmospheric variables in the tropical Pacific, including the amplitude and spatial patterns of sea surface temperature (SST), zonal wind stress, and precipitation anomalies. An SST budget analysis indicates that the thermocline feedback and zonal advective feedback dominantly contribute to the growth of EP and CP El Niño events, respectively. Corresponding to the shifts in the tropical rainfall and deep convection during EP and CP El Niño events, the model also reproduces the differences in the extratropical atmospheric responses during the boreal winter. In particular, the EP El Niño tends to be dominant in exciting a poleward wave train pattern to the Northern Hemisphere, while the CP El Niño tends to preferably produce a wave train similar to the Pacific North American (PNA) pattern. As a result, different climatic impacts exist in North American regions, with a warm-north and cold-south pattern during an EP El Niño and a warm-northeast and cold-southwest pattern during a CP El Niño, respectively. This modeling result highlights the importance of internal natural processes within the tropical Pacific as they relate to the genesis of ENSO diversity because the active ocean–atmosphere coupling is allowed only in the tropical Pacific within the framework of the HCM^AGCM.

Abstract

In this study, we aim to assess dynamical downscaling simulations by utilizing a novel bias-corrected global climate model (GCM) data to drive a regional climate model (RCM) over the Asia-western North Pacific region. Three simulations were conducted with a 25-km grid spacing for the period 1980–2014. The first simulation (WRF_ERA5) was driven by the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) dataset and served as the validation dataset. The original GCM dataset (MPI-ESM1-2-HR model) was used to drive the second simulation (WRF_GCM), while the third simulation (WRF_GCMbc) was driven by the bias-corrected GCM dataset. The bias-corrected GCM data has an ERA5-based mean and interannual variance and long-term trends derived from the ensemble mean of 18 CMIP6 models. Results demonstrate that the WRFGCMbc significantly reduced the root-mean-square errors (RMSEs) of the climatological mean of downscaled variables, including temperature, precipitation, snow, wind, relative humidity, and planetary boundary layer height by 50%–90% compared to the WRF_GCM. Similarly, the RMSEs of interannual-to-interdecadal variances of downscaled variables were reduced by 30%–60%. Furthermore, the WRFGCMbc better captured the annual cycle of the monsoon circulation and intraseasonal and day-to-day variabilities. The leading empirical orthogonal function (EOF) shows a monopole precipitation mode in the WRFGCM. In contrast, the WRF_GCMbc successfully reproduced the observed tri-pole mode of summer precipitation over eastern China. This improvement could be attributed to a better-simulated location of the western North Pacific subtropical high in the WRF_GCMbc after GCM bias correction.

Abstract

The rapidly changing Antarctic sea ice has garnered significant interest. To enhance the prediction skill for sea ice and respond to the Sea Ice Prediction Network-South’s latest call, this study presents the reforecast results of Antarctic sea-ice area and extent from December to June of the coming year with a Convolutional Long Short-Term Memory (ConvLSTM) Network. The reforecast experiments demonstrate that ConvLSTM captures the interannual and interseasonal variability of Antarctic sea ice successfully, and performs better than the European Centre for Medium-Range Weather Forecasts. Based on this, we present the prediction from December 2023 to June 2024, indicating that the Antarctic sea ice will remain at lows, but may not create a new record low. This research highlights the promising application of deep learning in Antarctic sea-ice prediction.

This study investigates the relationship between the persistence and the zonal scale of atmospheric dipolar modes (DMs). Results from the daily data of ERA5 and the long-term output of an idealized atmospheric model show that the atmospheric DMs with a broader (narrower) zonal scale dipolar structure possess a longer (shorter) persistence. A detailed vorticity budget analysis indicates that the persistence of a hemispheric-scale DM (1/1 DM) and a regional or sectoral DM (1/8 DM) in the model both largely rely on the persistence of the nonlinear eddy forcing. Linear terms can indirectly reduce the persistence of the anomalous nonlinear eddy forcing in a 1/8 DM by modifying the baroclinicity via the arousal of anomalous vertical motions. Therefore, the atmospheric DMs with a broader (narrower) zonal scale possess a longer (shorter) persistence because the effects of the linear terms are less (more) pronounced when the atmospheric DMs have better (worse) zonal symmetry. Further analyses show that the positive eddy feedback effect is weak or even absent in a 1/8 DM and the high-frequency eddy forcing acts more like a concomitant phenomenon rather than a leading driving factor for a 1/8 DM. Thus, the hemispheric-scale DM and the regional or sectoral DMs are different, not only in their persistence but also in their dynamics.

The variations of the frontogenetic trend of a cold filament induced by the cross-filament wind and wave fields are studied by a non-hydrostatic large eddy simulation. Five cases with different strengths of wind and wave fields are studied. The results show that the intense wind and wave fields further break the symmetries of submesoscale flow fields and suppress the levels of filament frontogenesis. The changes of secondary circulation directions—that is, the conversion between the convergence and divergence of the surface cross-filament currents with the downwelling and upwelling jets in the filament center—are associated with the inertial oscillation. The filament frontogenesis and frontolysis caused by the changes of secondary circulation directions may periodically sharpen and smooth the gradient of submesoscale flow fields. The lifecycle of the cold filament may include multiple stages of filament frontogenesis and frontolysis.

Based on the TRMM dataset, this paper compares the applicability of the improved MCE (minimum circumscribed ellipse), MBR (minimum bounding rectangle), and DIA (direct indexing area) methods for rain cell fitting. These three methods can reflect the geometric characteristics of clouds and apply geometric parameters to estimate the real dimensions of rain cells. The MCE method shows a major advantage in identifying the circumference of rain cells. The circumference of rain cells identified by MCE in most samples is smaller than that identified by DIA and MBR, and more similar to the observed rain cells. The area of rain cells identified by MBR is relatively robust. For rain cells composed of many pixels (N > 20), the overall performance is better than that of MCE, but the contribution of MBR to the best identification results, which have the shortest circumference and the smallest area, is less than that of MCE. The DIA method is best suited to small rain cells with a circumference of less than 100 km and an area of less than 120 km², but the overall performance is mediocre. The MCE method tends to achieve the highest success at any angle, whereas there are fewer “best identification” results from DIA or MBR and more of the worst ones in the along-track direction and cross-track direction. Through this comprehensive comparison, we conclude that MCE can obtain the best fitting results with the shortest circumference and the smallest area on behalf of the high filling effect for all sizes of rain cells.

If an explicit time scheme is used in a numerical model, the size of the integration time step is typically limited by the spatial resolution. This study develops a regular latitude–longitude grid-based global three-dimensional tracer transport model that is computationally stable at large time-step sizes. The tracer model employs a finite-volume flux-form semi-Lagrangian transport scheme in the horizontal and an adaptively implicit algorithm in the vertical. The horizontal and vertical solvers are coupled via a straightforward operator-splitting technique. Both the finite-volume scheme’s one-dimensional slope-limiter and the adaptively implicit vertical solver’s first-order upwind scheme enforce monotonicity. The tracer model permits a large time-step size and is inherently conservative and monotonic. Idealized advection test cases demonstrate that the three-dimensional transport model performs very well in terms of accuracy, stability, and efficiency. It is possible to use this robust transport model in a global atmospheric dynamical core.

Atmospheric ammonia (NH₃) is a chemically active trace gas that plays an important role in the atmospheric environment and climate change. Satellite remote sensing is a powerful technique to monitor NH₃ concentration based on the absorption lines of NH₃ in the thermal infrared region. In this study, we establish a retrieval algorithm to derive the NH₃ column from the Hyperspectral Infrared Atmospheric Sounder (HIRAS) onboard the Chinese FengYun (FY)-3D satellite and present the first atmospheric NH₃ column global map observed by the HIRAS instrument. The HIRAS observations can well capture NH₃ hotspots around the world, e.g., India, West Africa, and East China, where large NH₃ emissions exist. The HIRAS NH₃ columns are also compared to the space-based Infrared Atmospheric Sounding Interferometer (IASI) measurements, and we find that the two instruments observe a consistent NH₃ global distribution, with correlation coefficient (R) values of 0.28–0.73. Finally, some remaining issues about the HIRAS NH₃ retrieval are discussed.

Understanding the structure of tropical cyclone (TC) hydrometeors is crucial for detecting the changes in the distribution and intensity of precipitation. In this study, the GMI brightness temperature and cloud-dependent 1DVAR algorithm were used to retrieve the hydrometeor profiles and surface rain rate of TC Nanmadol (2022). The Advanced Radiative Transfer Modeling System (ARMS) was used to calculate the Jacobian and degrees of freedom (ΔDOF) of cloud water, rainwater, and graupel for different channels of GMI in convective conditions. The retrieval results were compared with the Dual-frequency Precipitation Radar (DPR), GMI 2A, and IMERG products. It is shown that from all channels of GMI, rain water has the highest ΔDOF, at 1.72. According to the radiance Jacobian to atmospheric state variables, cloud water emission dominates its scattering. For rain water, the emission of channels 1–4 dominates scattering. Compared with the GMI 2A precipitation product, the 1DVAR precipitation rate has a higher correlation coefficient (0.713) with the IMERG product and can better reflect the location of TC precipitation. Near the TC eyewall, the highest radar echo top indicates strong convection. Near the melting layer where Ka-band attenuation is strong, the double frequency difference of DPR data reflects the location of the melting. The DPR drop size distribution (DSD) product shows that there is a significant increase in particle size below the melting layer in the spiral rain band. Thus, the particle size may be one of the main reasons for the smaller rain water below the melting layer retrieved from 1DVAR.