Dynamics of Atmospheres and Oceans最新文献

The climate prediction system is an essential tool for predicting climatological state and variability. Systematic evaluation of the output is critical for assessing the prediction performance and making improvement. In this study, we evaluate the prediction capability of the First Institute of Oceanography-Climate Prediction System version 2.0 (FIO-CPS v2.0), a short-term climate prediction system, on the 2-meter air temperature over China using five criteria, namely prediction score (PS), prediction consistency (PC), correlation coefficient (CC), root mean square error (RMSE), and distance between indices of simulation and observation (DISO). The results showed that FIO-CPS v2.0 has higher accuracy in summer, and its performance varies with different lead times depending on the evaluation criteria used. Higher overall prediction skill was mostly found in the northeastern region during July and September, and the southeastern coastal region during June–September. Our findings provide insights into the prediction ability of the FIO-CPS v2.0 on air temperature and may help to facilitate its development.

The Indian Summer Monsoon (ISM) significantly impacts the climate of the Asian continent. During the summer of 2022, the penetration of monsoonal waves towards higher latitudes led to severe and unprecedented floods in various parts of Iran, Pakistan, and southern Afghanistan. In this study, we utilized meteorological data from weather stations, satellite remote sensing, reanalysis data, and teleconnection indices to investigate the penetration of monsoonal waves at higher latitudes in Iran. We also employed outputs from two global models, the Global Forecast System (GFS) and Climate Forecast System (CFS), and the Weather Research and Forecasting Model (WRF) regional model, to examine their forecasts of heavy monsoon rains. Our analysis of teleconnection indices revealed that La Niña, combined with a negative or neutral Dipole Mode Index (DMI) and a positive Indian Monsoon Index (IMI), intensified monsoon-related rainfall in the region. The low-pressure system over India weakened, while the system over central Iran strengthened. Additionally, we observed a meridional rotation of the Somali low-level jet. Generally, southern to southwestern Iran, as well as central and eastern regions, receive moisture from the Arabian Sea due to southerly and easterly winds from water surfaces. Comparing forecasts with 2–7 days lead times and extended 10–15 days from the CFS and GFS global models demonstrated that neither of models accurately predicted the observed range of rainfall over Iran in the extended period. However, the WRF regional model predictions were significantly better. We also discovered that the 48-hour forecast from the WRF model outperformed other forecasts for this case study.

The exchange of air-sea CO₂ plays a significant role in regulating the Earth’s climate. The errors associated with the estimations of air-sea CO₂ fluxes during extreme transient events like tropical cyclones (TCs) are important for climate research. In this study, we assess the estimates of CO₂ gas transfer velocity and the corresponding air-sea flux derived by employing five wind-dependent and two wave-dependent parameterizations for eight TCs in the Bay of Bengal using mooring observations and reanalysis datasets. To start with, we analyze drag coefficient ($C_D$) and associated frictional velocity ($u^{*}$) derived from two globally very commonly used bulk flux algorithms, COARE 3.0 and the updated version COARE 3.6, with the estimates from wind-wave tank experiments for moderate and high wind speeds for all TCs. The analysis indicates that COARE 3.6 provides the best estimate of drag coefficient. Further, we find that the wave-dependent parameterization by Woolf (2005) provides the best estimates of CO₂ gas transfer velocity compared to existing estimates of laboratory-based wind-wave tank experiments for high winds. Among all wind-only parameterizations, the hybrid parameterization proposed by Nightingale et al. (2000) performs best for high winds. We find that for winds < 20 m/s, the resultant fluxes of CO₂ estimated using these seven parameterizations vary within 5 mmol CO₂ m^-2 d^-1. However, for winds > 20 m/s, the difference between wind- and wave-parameterized fluxes are significant (∼50 mmol CO₂ m^-2 d^-1). The percentage of variation in CO₂ flux explained by transfer velocity (difference in sea and air pCO₂) during TC conditions is nearly 78 (15)%.

Tropical air-sea interactions, near-surface salinity (Ss), and sea surface temperature (SST) fluctuations are studied via ocean reanalysis products in the period 1980–2020. The statistical work considers how the net heat and water balance affects mixed layer depth (MLD) and coupling between the upper ocean and atmosphere. Field correlations of Ss – SST exhibit significant negative values in tropical latitudes 10S-15 N, between the semi-permanent marine anticyclones and the equatorial trough. Highlighting contrasts in two areas: the tropical east Atlantic and subtropical southwest Indian Ocean, annual cycles, inter-annual fluctuations of 6 – 8 yr, and long-term downward trends of Ss emerge. The SW Indian Ocean exhibits large swings of net heat and water balance as the monsoon reverses, and steady running-correlations of Ss and SST. The E Atlantic has a subdued annual cycle, and running-correlations are weak and unsteady. In both areas the Ss lags SST by a month. A fresh minus salty composite analysis reveals that the two contrasting areas respond to opposing phases of the Southern Oscillation: E Atlantic (La Nina) and SW Indian (El Nino). Projected long-term trends for increased tropical marine rainfall could be neutralized by declining runoff from continental monsoons. Statistical outcomes infer that a 0.1 ppt reduction of near-surface salinity leads to a 5 m reduction of MLD and a 0.4 C increase of tropical SST, contributing to deeper atmospheric convection. Limitations of the study derive from inferences based on infrequent salinity measurements.

A spectral cumulus parameterization (spectral scheme) is implemented in Scale Interaction Experiment Frontier version 2 (SINTEX-F2) seasonal prediction system, and the impact on the El Niño Southern Oscillation (ENSO) prediction is examined. By conducting hindcast experiments using the original convection scheme (Tiedtke scheme) and the spectral scheme, and comparing the ENSO prediction skill, the impact of the spectral scheme is analyzed in detail. It was found that prediction skill in terms of ENSO phase and the sea surface temperature (SST) persistence were improved by using the spectral scheme, but the root-mean-square error (RMSE) increased. The ENSO feedback was also changed by changing the convection scheme. The original scheme failed to predict the zonal wind stress anomaly toward the Niño 3.4 region, whereas the spectral scheme simulated it over the equatorial eastern Pacific with narrowing the meridional width, indicating that the spectral scheme strengthened the ENSO feedback. The spectral scheme also improved zonal-vertical atmospheric response to the Niño 3.4 index due to its advantageous features. Analysis of the ENSO feedback terms revealed that strengthened forcing in the eastern Pacific improved the thermocline feedback of ENSO, as its reversed timing of positive and negative tendencies for the mixed layer temperature matched that estimated from the reanalysis data. In conclusion, the spectral scheme can improve ENSO prediction through the atmospheric forcing and mean state in the eastern Pacific which impacted the ocean properties. It improved the phase error by improving thermocline feedback, but did not improve the RMSE. Tuning of the original scheme to obtain additional improvements to ENSO prediction would be difficult, since it requires modification of detailed convective cloud properties to correct the phase error. The spectral scheme tends to overestimate the ENSO amplitude, i.e., large RMSE, but this drawback can be mitigated by tuning the convection scheme so that it suppresses the warm SST climate drift, and this is considered the more promising method to further improve ENSO prediction.

Significant wave height (SWH) is one of the core parameters for wave and accurate prediction of SWH is of great importance for ocean resource assessment. In this paper, we propose a new multi-characteristic and multi-node SWH prediction model(MCMN). The model considers the lead–lag effect among ocean characteristics and utilizes time lag correlation to automatically learn advanced indication information. For the temporal features, temporal correlations are extracted from high-dimensional spatial features efficiently in parallel using Temporal Convolutional Network(TCN). Additionally, the dependencies between nodes are modeled as the joint result of stable long-term patterns and dynamic short-term patterns. To obtain these dependencies, we introduce a novel dynamic graph neural network. Compared to previous SWH predictions focused solely on individual nodes, this model allows us to more fully explore the spatio-temporal dependencies between the nodes by capturing both long-term and short-term spatio-temporal relationship patterns among the nodes. Experiments were conducted with 120 nodes in the South China Sea and East China Sea, respectively. The results show that the model provides reliable predictions. Finally, we compare with five deep learning models, and the results show that our model has better performance in multi-node and multi-step SWH prediction.

To study potentially turbulent water motions near the deepest point on Earth in the Challenger Deep of the Mariana Trench, a 588-m long string equipped with specially designed sensitive temperature sensors was moored for nearly three years. Detailed analysis of one year of good data distinguishes ubiquitous internal tidal waves and hundreds of meters slanted convection turbulent spurs due to internal waves’ breaking from above. The spurs, or intrusions of anomalous waters, can occur on a tidal periodicity. Some tidal wave breaking including 100-m tall turbulent overturns reaching the trench floor is associated with warm waters that push from above, and of which the largest occurred during the passing of a tropical storm. The various turbulence types prevent the hadal, below 6000 m, waters from being stagnant, which is an important necessity for deep-trench life.