Abstract

The prediction of sea surface temperature (SST) in real-time or online mode has applications in planning marine operations and forecasting climate. This paper demonstrates how SST measurements can be combined with numerical estimations with the help of neural networks and how reliable site-specific forecasts can be made accordingly. Additionally, this work demonstrates the skill of a special wavelet neural network in this task. The study was conducted at six different locations in the Indian Ocean and over three time scales (daily, weekly, and monthly). At every time step, the difference between the numerical estimation and the SST measurement was evaluated, an error time series was formed, and errors over future time steps were forecasted. The time series forecasting was affected through neural networks. The predicted errors were added to the numerical estimation, and SST predictions were made over five time steps in the future. The performance of this procedure was assessed through various error statistics, which showed a highly satisfactory functioning of this scheme. The wavelet neural network based on the particular basic or mother wavelet called the “Meyer wavelet with discrete approximation” worked more satisfactorily than other wavelets.

Abstract

The quality of daily gridded Advanced Scatterometer (ASCAT; DASCAT) blended winds is examined in the tropical Indian Ocean using 3-day running mean gridded Quick Scatterometer (QuikSCAT; QSCAT) winds and in situ daily winds from the Research Moored Array for African–Asian–Australian Monsoon Analysis and Prediction (RAMA). The primary objective of this study is to examine whether DASCAT is a reliable replacement for the widely used QSCAT wind products. Spatial distributions of DASCAT and QSCAT winds show good agreement in speed and direction, except over a few localized regions. The study finds a significant spatial coherence between rainfall and the regions of discrepancy between DASCAT and QSCAT. Comparison of DASCAT and QSCAT wind products with RAMA moorings indicates that DASCAT better captures the overall wind variability compared to QSCAT, especially during rainy and low wind (<5 m s⁻¹) conditions. The root-mean-square of the RAMA–DASCAT (RAMA–QSCAT) difference during rainfall in the zonal and meridional winds is 1.4 and 1.6 m s⁻¹ (2.7 and 2.0 m s⁻¹), respectively. The present study indicates that the DASCAT blended wind product is a reliable alternative to QSCAT in the tropical Indian Ocean.

Abstract

This study investigates sea surface temperature (SST) and precipitation variations in the eastern Arabian Sea (EAS) induced by the northward-propagating Indian summer monsoon (ISM) intraseasonal oscillations (MISOs) through analyzing satellite observations and the Climate Forecast System Reanalysis (CFSR) and performing ocean general circulation model (OGCM) experiments. MISOs in the EAS achieve the largest intensity in the developing stage (May–June) of the ISM. The MISOs induce intraseasonal SST variability primarily through surface heat flux forcing, contributed by both shortwave radiation and turbulent heat flux, and secondarily through mixed layer entrainment. The shallow mixed layer depth (MLD < 40 m) in the developing stage and decaying stage (September–October) of the ISM significantly amplifies the heat flux forcing effect on SST and causes large intraseasonal SST variability. Meanwhile, the high SST (>29°C) in the developing stage leads to enhanced response of MISO convection to SST anomaly. It means that the ocean state of the EAS region during the developing stage favors active two-way air–sea interaction and the formation of the strong first-pulse MISO event. These results provide compelling evidence for the vital role played by the ocean in the MISO mechanisms and have implications for understanding and forecasting the ISM onset. Compared to satellite observation, MISOs in CFSR data have weaker SST variability by ~50% and biased SST–precipitation relation. Reducing these biases in CFSR, which provides initial conditions of the National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2), may help improve the ISM rainfall forecast.

Abstract

There is considerable controversy in the literature concerning fog formation. One set of observations suggests that fog forms during a lull in turbulence, white another set of observations suggests that increased turbulence leads to fog formation.

A number of first-order closure techniques are applied to numerical simulations. The results show that fog formation and development is directly correlated with the magnitude of the eddy mixing coefficients. Larger turbulence generation leads to more rapid fog development and to larger liquid water contents. The rate at which the fog top grows is directly related to the rate at which turbulence lifts the inversion.

During the mature fog stage, a series of fog dissipation and redevelopment episodes occur. Liquid water develops in the upper regions of the fog during the turbulently quiet periods. Subsequent destabilization of the atmosphere increases turbulence generation and mixes the upper-level liquid water to the surface, creating surface fog intensification. Quasi-periodic oscillations in fog parameters are largest in the upper regions of the fog and become progressively damped in the lower regions of a thick fog.

These results are in qualitative agreement with the observations reported by Jiusto and Lala and support the hypothesis that there are distinct stages of fog development.

Abstract

Northward-propagating Indian summer monsoon intraseasonal oscillations (MISOs) are a major origin of the active–break spells of the monsoon rainfall. Forecast results for 28 active and 27 break spells from the National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2 (CFSv2), during 1999–2010 are analyzed. CFSv2 forecasts are able to represent the propagation of MISOs from the equator to central India, showing improvements in many aspects compared to its previous version. Systematic biases for MISOs, however, still exist, exhibiting apparently weaker amplitude and slower northward propagation compared to observations. The eastern Arabian Sea (EAS)–western Bay of Bengal (WBB) region (EAS–WBB region; 12°–20°N, 65°–85°E) is found to be critical for the MISO prediction. In that region, the forecast and observed MISO trajectories begin to bifurcate from each other, and forecast errors grow rapidly. Further diagnosis reveals that local air–sea interaction in that region is severely underrepresented in CFSv2. Sea surface temperature (SST) response to surface heat flux forcing and convection response to SST forcing are both too weak, leading to the underestimated MISO amplitude. The relationship between precipitation and SST in CFSv2 is much more chaotic than in observation. The misrepresentation of air–sea coupling results in longer MISO periods in the EAS–WBB region, manifesting as slower propagation and delayed arrival of MISOs in central India. Refining the air–sea coupling processes is crucial for improving the CFSv2 forecast. This includes taking into account the ocean skin layer, better resolving the diurnal cycle, and improving the ocean model physics.

Abstract

Cyclone Phailin, which developed over the Bay of Bengal in October 2013, was one of the strongest tropical cyclones to make landfall in India. We study the response of the salinity-stratified north Bay of Bengal to Cyclone Phailin with the help of hourly observations from three open-ocean moorings 200 km from the cyclone track, a mooring close to the cyclone track, daily sea surface salinity (SSS) from Aquarius, and a one-dimensional model. Before the arrival of Phailin, moored observations showed a shallow layer of low-salinity water lying above a deep, warm “barrier” layer. As the winds strengthened, upper-ocean mixing due to enhanced vertical shear of storm-generated currents led to a rapid increase of near-surface salinity. Sea surface temperature (SST) cooled very little, however, because the prestorm subsurface ocean was warm. Aquarius SSS increased by 1.5–3 psu over an area of nearly one million square kilometers in the north Bay of Bengal. A one-dimensional model, with initial conditions and surface forcing based on moored observations, shows that cyclone winds rapidly eroded the shallow, salinity-dominated density stratification and mixed the upper ocean to 40–50-m depth, consistent with observations. Model sensitivity experiments indicate that changes in ocean mixed layer temperature in response to Cyclone Phailin are small. A nearly isothermal, salinity-stratified barrier layer in the prestorm upper ocean has two effects. First, near-surface density stratification reduces the depth of vertical mixing. Second, mixing is confined to the nearly isothermal layer, resulting in little or no SST cooling.

Abstract

Previous studies have talked about the existence of a relation between the Atlantic meridional mode (AMM) and Atlantic zonal mode (AZM) via the meridional displacement of the intertropical convergence zone (ITCZ) in the Atlantic during boreal spring and the resulting cross-equatorial zonal winds. However, why the strong relation between the ITCZ (or AMM) and zonal winds does not translate into a strong relation between the ITCZ and AZM has not been explained. This question is addressed here, and it is found that there is a skewness in the relation between ITCZ and AZM: while a northward migration of ITCZ during spring in general leads to a cold AZM event in the ensuing summer, the southward migration of the ITCZ is less likely to lead to a warm event. This is contrary to what the previous studies imply. The skewness is attributed to the Atlantic seasonal cycle and to the strong seasonality of the AZM. All those cold AZM events preceded by a northward ITCZ movement during spring are found to strictly adhere to typical timings and evolution of the different Bjerknes feedback components involved. It is also observed that the causative mechanisms of warm events are more diverse than those of the cold events. These results can be expected to enhance our understanding of the AZM as well as that of chronic model biases and contribute to the predictability of the Indian summer monsoon through the links between the two as shown in our earlier studies.

Abstract

A global analysis of latent heat flux (LHF) sensitivity to sea surface temperature (SST) is performed, with focus on the tropics and the north Indian Ocean (NIO). Sensitivity of LHF state variables (surface wind speed W _s and vertical humidity gradients Δq) to SST give rise to mutually interacting dynamical (W _s driven) and thermodynamical (Δq driven) coupled feedbacks. Generally, LHF sensitivity to SST is pronounced over tropics where SST increase causes W _s (Δq) changes, resulting in a maximum decrease (increase) of LHF by ~15 W m⁻² (°C)⁻¹. But the Bay of Bengal (BoB) and north Arabian Sea (NAS) remain an exception that is opposite to the global feedback relationship. This uniqueness is attributed to strong seasonality in monsoon W _s and Δq variations, which brings in warm (cold) continental air mass into the BoB and NAS during summer (winter), producing a large seasonal cycle in air–sea temperature difference ΔT (and hence in Δq). In other tropical oceans, surface air is mostly of marine origin and blows from colder to warmer waters, resulting in a constant ΔT ~ 1°C throughout the year, and hence a constant Δq. Thus, unlike other basins, when the BoB and NAS are warming, air temperature warms faster than SST. The resultant decrease in ΔT and Δq contributes to decrease the LHF with increased SST, contrary to other basins. This analysis suggests that, in the NIO, LHF variability is largely controlled by thermodynamic processes, which peak during the monsoon period. These observed LHF sensitivities are then used to speculate how the surface energetics and coupled feedbacks may change in a warmer world.

Abstract

Intraseasonal sea surface temperature (SST) variability over the Seychelles–Chagos thermocline ridge (SCTR; 12°–4°S, 55°–85°E) induced by boreal wintertime Madden–Julian oscillations (MJOs) is investigated with a series of OGCM experiments forced by the best available atmospheric data. The impact of the ocean interannual variation (OIV), for example, the thermocline depth changes in the SCTR, is assessed. The results show that surface shortwave radiation (SWR), wind speed–controlled turbulent heat fluxes, and wind stress–driven ocean processes are all important in causing the MJO-related intraseasonal SST variability. The effect of the OIV is significant in the eastern part of the SCTR (70°–85°E), where the intraseasonal SSTs are strengthened by about 20% during the 2001–11 period. In the western part (55°–70°E), such effect is relatively small and not significant. The relative importance of the three dominant forcing factors is adjusted by the OIV, with increased (decreased) contribution from wind stress (wind speed and SWR). The OIV also tends to intensify the year-to-year variability of the intraseasonal SST amplitude. In general, a stronger (weaker) SCTR favors larger (smaller) SST responses to the MJO forcing. Because of the nonlinearity of the upper-ocean thermal stratification, especially the mixed layer depth (MLD), the OIV imposes an asymmetric impact on the intraseasonal SSTs between the strong and weak SCTR conditions. In the eastern SCTR, both the heat flux forcing and entrainment are greatly amplified under the strong SCTR condition, but only slightly suppressed under the weak SCTR condition, leading to an overall strengthening effect by the OIV.

Abstract

We use profiles from a Lagrangian float in the north Indian Ocean to explore the usefulness of Thorpe analysis methods to measure vertical scales and dissipation rates in the ocean surface boundary layer. An rms Thorpe length scale L _T and an energy dissipation rate ε _T were computed by resorting the measured density profiles. These are compared to the mixed layer depth (MLD) computed with different density thresholds, the Monin–Obukhov (MO) length L _MO computed from the ERA5 reanalysis values of wind stress, and buoyancy flux B ₀ and dissipation rates ε from historical microstructure data. The Thorpe length scale L _T is found to accurately match MLD for small (<0.005 kg m⁻³) density thresholds, but not for larger thresholds, because these do not detect the warm diurnal layers. We use ξ = L _T /|L _MO| to classify the boundary layer turbulence during nighttime convection. In our data, 90% of points from the Bay of Bengal (Arabian Sea) satisfy ξ < 1 (1 < ξ <10), indicating that wind forcing is (both wind forcing and convection are) driving the turbulence. Over the measured range of ξ, ε _T decreases with decreasing ξ, i.e., more wind forcing, while ε increases, clearly showing that ε/ε _T decreases with increasing ξ. This is explained by a new scaling for ξ ≪ 1, ε _T = 1.15B ₀ ξ ^0.5 compared to the historical scaling ε = 0.64B ₀ + 1.76ξ ⁻¹. For ξ ≪ 1 we expect ε = ε _T . Similar calculations may be possible using routine Argo float and ship data, allowing more detailed global measurements of ε _T , thereby providing large-scale tests of turbulence scaling in boundary layers.