Dynamics of Atmospheres and Oceans最新文献

The precipitation in Central-East Brazil (CEB) from December to February is heavily influenced by the South Atlantic Convergence Zone (SACZ). The SACZ not only causes considerable rainfall but also has an impact on the underlying ocean. This study examines the extreme precipitation events in CEB and their relationship with the SACZ and sea surface temperature (SST). Empirical Orthogonal Function (EOF) analyses of daily precipitation and vertical velocity at 500 hPa data diagnose the extremes. The grouped events of similar positioning and intensity resulted in 170 extremely wet and 172 dry events. Results indicate that the variability of the SACZ is responsible for extremely wet precipitation events in CEB. Composites of precipitation, SST, and wind anomalies at 850-hPa and 200-hPa characterize their occurrence and resemble SACZ high-intensity variability. Conversely, extremely dry CEB conditions are associated with SACZ southern events (51 events) and SACZ inactivity (121 events). The latter refers to major drought events when upper-level cyclonic circulation favors dry air descending and inhibiting convection over CEB. SACZ southern events have similar atmospheric dynamical patterns as SACZ events but are displaced to the south. The meridional displacement of the South Atlantic Low-Level Jet (SALLJ) and its confluence with the northeasterly flow of the South Atlantic Subtropical High (SASH) are identified as the causes of the cooling or heating of the underlying ocean. The intensity of the extreme event is related to the strength of lower-level wind circulation, while upper-level wind circulation anomalies favor the lower-level effects. The persistence of the systems is related to the development of SST anomalies.

The present study deals with the vertical structure of Tropical Cyclones (TC) from 2010 to 2020 over the North Indian Ocean (NIO) using the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement Mission (GPM). We analyzed 31 TC which were characterized as severe cyclonic storms, very severe cyclonic storms, and super cyclones out of which 18 occurred over the Bay of Bengal and 13 over the Arabian Sea. The normalized intensity difference method of brightness temperature is used to distinguish the regions of the TC into eyewall, inner rainband, outer rainband, and outer region after identifying the eye. The eye radius (R_e) of the cyclone is used to define the annular regions. The vertical structure is explored using the reflectivity in each region of the cyclone and also with respect to convective precipitation, stratiform precipitation, and sea surface temperature. The intensity of the convection in the eyewall and the rainbands is strong and reaches up to an altitude of 14 km. The outer rainbands were more of stratiform nature and a distinct bimodal distribution is observed with a peak below and above the bright band. The outermost region exhibited the characteristics of both the convective and stratiform, however, the intensity is observed to be less. The convection is observed to be strong in the eyewall for high SST compared to the marginal, however, the vertical extent is large in the marginal SST. The vertical structure of the TC over the NIO contained a wider distribution of reflectivity and vertically extended compared to that of the cyclones in the other oceanic basins.

Surface layer parameterization schemes in numerical weather prediction models are based on the Monin-Obukhov similarity theory (MOST), which utilizes empirical functions to incorporate the effects of near-surface atmospheric stability. In the present study, an effort has been made to implement and evaluate the performance of recently developed similarity functions under stable stratification in the surface layer parameterization of Weather Research and Forecasting Model version 4.2.2 (WRFv4.2.2). For this purpose, the commonly used revised version of MM5 surface layer module in WRF model is updated using the similarity functions suggested by Srivastava et al. (2020). The model is configured with three nested domains around the flux tower installed at Ranchi (23.412° N, 85.440°E), India. The simulations are carried out for a complete year, and the model simulated near-surface atmospheric variables are compared with the observations. The study reveals that updated similarity functions lead to a noticeable improvement in WRF model performance. In particular, the modified scheme reduced the mean absolute error and root mean square error for 10-m wind speed (2-m temperature) by about 22 % (10 %) and 23 % (8 %), respectively, with improved correlation coefficients during January. The analysis suggests that the new similarity functions could potentially be used in weather forecast model over the Indian region.

A study of a 42-year (1979–2020) long wave time series was performed for the Mediterranean Sea to detect and quantify trends in two relevant wave parameters adopted for coastal and offshore engineering purposes: significant wave height, $H_s$ and peak period, $T_p$. The high resolution MeteOcean Re-Analysis database by the Department of Civil, Chemical and Environmental Engineering (DICCA) was used. At yearly and seasonal scales, the trends of the mean and maximum values were detected by the Mann–Kendall test, choosing a significance level equal to 90%. The slope or increase/decrease trends in $H_s$ and $T_p$ were assessed by the Theil–Sen estimator. For the mean values of $H_s$ and $T_p$, increasing trends were detected for the Libyan, Levantine and Aegean seas, while for the maximum values this increasing trend was observed in a large part of the Mediterranean Sea. Finally, for the different marginal seas of the Mediterranean basin, a running trend analysis was applied in order to quantify the effect of the time window in the trend detection. The obtained results can be significant for flooding and erosion control strategies, ship and port operations, design and verification of structures, installations of Wave Energy Converters, in a hot spot for climate change such as the Mediterranean basin.

The dominant mode of the Indian Ocean variability can be impacted by changes in the background state (multidecadal timescale) of the subsurface heat content. The multidecadal variability of subsurface ocean heat content (sub-OHC) in the Indian Ocean is examined using four reanalysis products from 1958 to 2017. The analysis reveals a meridional basin-wide dipole mode in the subsurface OHC until the late 1980s, followed by the mode embedded in the uniform basin-wide patterns. These patterns are also observed in the trends of thermocline and sea surface height. The observed patterns in the Indian Ocean are explained by two distinct mechanisms. Firstly, the multidecadal variability of dipole patterns over the Indian Ocean is influenced by local wind forcing. Wind stress trends and Ekman pumping velocity trends favor downwelling (upwelling) in the off-equatorial southern Indian Ocean region, leading to thermocline depth deepening (shallowing) during 1958–1975 and 1976–1987, respectively. Secondly, the combined effect of heat transport from the western Pacific through Indonesian Through Flow and local wind forcing accounts for the basin-wide cooling and warming trends observed during 1988–2000, and 2001–2014, respectively.

This study assesses the dynamical seasonal predictions initialized in April and May for forecasting Bangladesh summer (June to September: JJAS) monsoon rainfall (BSMR) over the 1993–2016 period. The BSMR from nine models, sourced from the North American Multi-Model Ensemble (NMME) and the Copernicus Climate Change Service (C3S), undergoes a calibration process. This calibration employs the Canonical Correlation Analysis (CCA) technique to rectify biases within each individual model's ensemble mean BSMR data (referred as predictor or X variable). These corrections are made in comparison to observed BSMR (referred as predictand or Y variable), acquired from the Climate Hazards Group InfraRed Precipitation with Station data. Subsequently, the models that undergo calibration are amalgamated to construct a calibrated multi-model ensemble (CMME), which, in turn, facilitates the generation of a forecast for BSMR. The CCA correction brings about a significant improvement in root-mean-square error, underscoring the presence of correctable systematic biases in the raw model forecasts. However, these CCA corrections weakly enhance the skill (anomaly correlation) across the region. The scores that assess discrimination (the two-alternative forced-choice: 2AFC and the area under the relative operating characteristic curve: ROC for above/below normal BSMR) for tercile-based forecasts exceeded 50 % across a substantial portion of the region. This indicates a superior level of discrimination compared to what one would anticipate based on climatology. Using the CMME approach, a probabilistic forecast for the 2022 BSMR was generated and proved quite effective in capturing the observed 2022 BSMR tercile, which includes below-normal and above-normal categories of rainfall in the central-southern and northern regions of Bangladesh respectively. Furthermore, the absence of substantial skill improvements may be attributed to inaccuracies in the teleconnection patterns of the simulated first leading principal component (PC) time series of BSMR with the El Niño Southern Oscillation. In contrast, the second PC time series exhibits a similar connection to observations. These findings emphasize the importance and utility of statistical post-processing in producing reliable seasonal climate outlooks for the region.

This paper investigates the key synoptic-scale factors that affected the forecasting of mesoscale rainfall and snowfall and their associated uncertainties in a heavy rain–snow event in northeastern China on 18–20 November 2020, using ensemble-based sensitivity analysis based on global ensemble forecasts from the European Centre for Medium-Range Weather Forecasts. The heavy precipitation event was attributed to an extratropical cyclone and experienced two stages, with the snowfall stage having a better precipitation forecast skill than the rainfall stage. The mesoscale rainfall and snowfall were caused by a mesoscale rainband over Liaoning Province and two mesoscale snowbands over Heilongjiang Province, respectively, and they showed some differences with respect to their forecast skill and related key synoptic-scale factors contributing to the precipitation centers. The precipitation amount in the two different stages was correlated significantly with the midlevel trough and sensitive to the location and intensity of the low-level vortex (surface cyclone), and particularly the low-level jets and the associated water vapor transport. However, some differences were confirmed in the two different stages: the weaker midlevel trough and accompanying weaker low-level temperature trough in the rainfall stage were related to increased precipitation because the midlevel trough was far away from the control area, while the stronger midlevel trough and accompanying stronger low-level temperature trough were associated with increased precipitation in the snowfall stage. In addition to the synoptic-scale low-level jet (SLLJ), the precipitation in the rainfall stage was also affected by a boundary layer jet (BLJ) over the ocean, while only SLLJs were present in the snowfall stage. The uncertainty of the precipitation forecast was derived mainly from the uncertainty in the strength and location of the SLLJs and BLJ. Notably, the intensity of northeasterly winds west of the low-level vortex may affect the predictability of heavy snowfall.

Waves collected in the nearshore waters off the southern tip of the Indian mainland from February 2018 to January 2019 are used to examine the wave spectral characterization. The annual mean of the significant wave height (1.23 m) in this area is slightly higher than that along the waters of the eastern Arabian Sea (0.9–1.1 m), but the maximum value in an annual cycle (3.6 m) is less than that (4.5–5 m) found in those regions. Compared to other coastal locations around India, significant seasonal variations are not observed in the wave height at this location. A maximum of four wave systems are present in the study region. 76% of the surface height variance in the region results from swells from the south-southwest, and the balance is wind-seas from the west to the southeast. In a year, 72% of the time, single peak spectra are observed, majority of them are swell-dominated. The annual mean peak period and the wave energy potential of the area are larger than that available in the rest of the locations around the mainland of India. Also, the area is not subjected to high waves. Hence, it is an ideal location for installing wave energy converters.

This study employed a regional earth system model, namely ROM over the CORDEX-SA domain, to investigate the future changes in the Marine heatwaves (MHWs) with respect to the historical baseline period (1976–2005) in the three time-slices, explicitly, near future (NRF; 2010–2039), middle future (MDF;2040–2069), and far future (FRF; 2070–2099) under two emission scenarios, Representative Concentration Pathway (RCP4.5 and RCP8.5). For the historical period, ROM showed a reasonable agreement with observed MHWs metrics and their trends and outperformed the forcing General Circulation Model and Multi-Model Ensemble of CMIP5 models. The future MHWs are expected to increase in intensity and duration. The continuous lengthening of MHWs duration leads to a permanent MHW state condition with strong spatial variability in its appearance. The first permanent MHW will emerge in both RCPs, while the absolute permanent MHW state is mainly visible in RCP8.5. The genesis and augmentation in the MHWs intensity is associated with local air-sea fluxes, however, in the long term, the increase in the mean SST in the future led to the rise of MHWs activity. The diagnosis of El Niño Southern Oscillation teleconnection and Indian Ocean Dipole on the MHWs is investigated. During the El Niño regime, not only did the proportion of the Tropical Indian Ocean experiencing MHWs increase but also an increase in the intensity is evident. IOD controls the MHWs metrics in the proximity of the western box and eastern box during its positive and negative phases.