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Abstract This study examines the spatiotemporal variability of drought and associated physical processes over the Arabian Peninsula (AP). For this purpose, we computed the standardized precipitation evapotranspiration index (SPEI) for the period 1951–2020 using the Climate Research Unit and fifth generation ECMWF atmospheric reanalysis datasets. By applying rotated empirical orthogonal function analysis on the SPEI data, we identified four homogeneous and coherent drought regions. The droughts in the northern regions follow a relatively similar temporal evolution as compared to those in the southern region. All four sub-regions of the AP exhibit a significant drying trend (p

Abstract Data assimilation (DA) plays a pivotal role in diverse applications, ranging from weather forecasting to trajectory planning for autonomous vehicles. A prime example is the widely used ensemble Kalman filter (EnKF), which relies on the Kalman filter's linear update equation to correct each of the ensemble forecast member's state with incoming observations. Recent advancements have witnessed the emergence of deep learning approaches in this domain, primarily within a supervised learning framework. However, the adaptability of such models to untrained scenarios remains a challenge. In this study, we introduce a new DA strategy that utilizes reinforcement learning (RL) to apply state corrections using full or partial observations of the state variables. Our investigation focuses on demonstrating this approach to the chaotic Lorenz 63 and 96 systems, where the agent's objective is to maximize the geometric series with terms that are proportional to the negative root‐mean‐squared error (RMSE) between the observations and corresponding forecast states. Consequently, the agent develops a correction strategy, enhancing model forecasts based on available observations. Our strategy employs a stochastic action policy, enabling a Monte Carlo‐based DA framework that relies on randomly sampling the policy to generate an ensemble of assimilated realizations. Numerical results demonstrate that the developed RL algorithm performs favorably when compared to the EnKF. Additionally, we illustrate the agent's capability to assimilate non‐Gaussian observations, addressing one of the limitations of the EnKF.

This study assesses the aerosol optical depth (AOD) from historical simulations (2003–2014) and future climate scenarios (2015–2100) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) over the Middle East and North Africa (MENA) region. Multi-model mean (MME) AOD statistics are generated as the average of those from the five best-performing CMIP6 models, which reproduce observational climate statistics. These models were selected based on the validation of various climate metrics, including strong pattern correlations with observations (>0.8). The resulting MME reproduces the observed AOD seasonal cycle well. The observed positive trends (summer and annual) over the Arabian Peninsula (AP) and negative trends (winter) over North Africa are well captured by MME, as regional meteorological drivers associated with observed AOD trends, with few discrepancies. Crucially, the MME fails to capture the AOD trends over North West Africa (NWA). For MENA and NWA regions, two high-emission scenarios, SSP370 and SSP585, project a continuous rise in the annual mean AOD until the end of the century. In contrast, the low-emission scenarios, SSP126 and SSP245, project a decreasing AOD trend. Interestingly, the projected future AOD area-averaged over the AP region varies significantly across all four scenarios in time. Notably, a substantial decrease of about 8–10% in the AOD is projected by the SSP126, SSP245, and SSP585 scenarios at the end of the century (2080–2100) relative to the current period. This projected decrease in annual-mean AOD, including the frequency of extreme AOD years under SSP585, is potentially associated with a concurrent increase in annual-mean rainfall over the AP.

Abstract A risk analysis is conducted considering an array of release sources located around the NEOM shoreline. The sources are selected close to the coast and in neighboring regions of high marine traffic. The evolution of oil spills released by these sources is simulated using the MOHID model, driven by validated, high-resolution met-ocean fields of the Red Sea. For each source, simulations are conducted over a 4-week period, starting from first, tenth and twentieth days of each month, covering five consecutive years. A total of 180 simulations are thus conducted for each source location, adequately reflecting the variability of met-ocean conditions in the region. The risk associated with each source is described in terms of amount of oil beached, and by the time required for the spilled oil to reach the NEOM coast, extending from the Gulf of Aqaba in the North to Duba in the South. To further characterize the impact of individual sources, a finer analysis is performed by segmenting the NEOM shoreline, based on important coastal development and installation sites. For each subregion, source and release event considered, a histogram of the amount of volume beached is generated, also classifying individual events in terms of the corresponding arrival times. In addition, for each subregion considered, an inverse analysis is conducted to identify regions of dependence of the cumulative risk, estimated using the collection of all sources and events considered. The transport of oil around the NEOM shorelines is promoted by chaotic circulations and northwest winds in summer, and a dominant cyclonic eddy in winter. Hence, spills originating from release sources located close to the NEOM shorelines are characterized by large monthly variations in arrival times, ranging from less than a week to more than 2 weeks. Similarly, large variations in the volume fraction of beached oil, ranging from less then 50% to more than 80% are reported. The results of this study provide key information regarding the location of dominant oil spill risk sources, the severity of the potential release events, as well as the time frames within which mitigation actions may need to deployed.

Abstract Underwater noise pollution is a significant environmental issue that can have detrimental effects on marine ecosystems. One of the main sources of underwater noise pollution is ship traffic, which has been shown to negatively impact marine animals by masking communication signals and altering their behaviors. This study represents the first comprehensive analysis of underwater ship noise in the Red Sea, wherein noise maps of ships sailing through the main shipping lane in the Red Sea were simulated by integrating both anthropogenic and environmental variables. These maps offer valuable insights for policymakers, enabling them to make informed decisions and implement targeted mitigation efforts.

Abstract The Vietnam upwelling is a crucial circulation feature in the South China Sea. Although previous studies have shown that various coastal upwellings around the world may intensify under global warming, future changes in the Vietnam upwelling remain unclear. To address this knowledge gap, we analyzed the long‐term trend in the Vietnam upwelling under a high‐emission scenario for the period 2006–2100, using simulation results from a global eddy‐resolving climate model. In this model, the summertime Vietnam upwelling is projected to intensify in the 21st century and is statistically significant between 12°N and 14°N. A volume flux budget analysis indicates that wind stress curl is the most important contributor to the intensification. The geostrophic flow, to some extent, may suppress the upwelling intensification. The projected increase in upwelling is shown to significantly reduce local ocean warming and freshening and thus may have vital impacts on the local climate and circulation.

The seasonal and spatial evolution of the mixed layer (ML) in the Red Sea (RS) and the influence of atmospheric buoyancy and momentum forcing are analyzed for the 2001–2015 period using a high-resolution (1/100°, 50 vertical layers) ocean circulation model. The simulation reveals a strong spatiotemporal variability reflecting the complex patterns associated with the air–sea buoyancy flux and wind forcing, as well as the significant impact of the basin’s general and mesoscale circulation. During the spring and summer months, buoyancy forcing intensifies stratification, resulting in a generally shallow ML throughout the basin. Nevertheless, the results reveal local maxima associated with the influence of mesoscale circulation and regular wind induced mixing. Under the influence of surface buoyancy loss, the process of deepening of the ML commences in early September, reaching its maximum depth in January and February. The northern Gulf of Aqaba and the western parts of the northern RS, exhibit the deepest ML, with a gradual shoaling toward the south, primarily due to the surface advection of relatively fresh water that enters the basin from the Gulf of Aden. The mixed layer depth (MLD) variability is primarily driven by atmospheric buoyancy forcing, especially its heat flux component. Although evaporative fluxes dominate the annually averaged surface buoyancy forcing, they exhibit weak seasonal and spatial variability. Wind induced mixing exerts a significant impact on the MLD only locally, especially during summer. Of particular importance are strong winds channeled by topography, such as those in the vicinity of the Strait of Bab-Al-Mandeb and the straits connecting the two gulfs in the north, as well as lateral jets venting through mountain gaps, such as the Tokar Jet in the central RS. The analysis highlights the complex patterns of air-sea interactions, thermohaline circulation, and mesoscale activity, all of them strongly imprinted on the MLD distribution.

The contribution of surface and lateral forcing to the observed Arabian Gulf warming trend is studied based on the results of a high-resolution (1/100°, 60 vertical layers) MIT general circulation model (MITgcm) covering the period 1993–2021. The model validation against available observations reveals that the simulation satisfactorily reproduces the main features of the Arabian Gulf’s dynamics and their variability. We show that the heat content of the Arabian Gulf generally follows the reported variability of sea surface temperature, with significant increasing trends of 0.1 × 107 J m−3 and 0.2°C per decade. The interannual variability of the heat content is dominated by the surface heat fluxes, while the long-term warming of the basin is primarily driven by lateral fluxes. The analyses of the heat exchanges through the Strait of Hormuz indicate a pronounced upward trend in the transported heat toward the Arabian Gulf, which is associated with an increase in both the volume and temperature of the exchanged waters. Considering the inflow and outflow in the Strait separately, the temperature increase is more prominent in the inflowing waters; however, the dominant factor driving the rising trend in heat content exchanges is the increase in the volume of waters being exchanged. This implies that the observed warming of the Arabian Gulf during the investigated period is directly related to the acceleration of its overturning circulation.

Abstract The accuracy of initial conditions is an important driver of the forecast skill of numerical weather prediction models. Increases in the quantity of available measurements, particularly high‐resolution remote sensing observational data products from satellites, are valuable inputs for improving those initial condition estimates. However, the traditional data assimilation methods for integrating observations into forecast models are computationally expensive. This makes incorporating dense observations into operational forecast systems challenging, and it is often prohibitively time‐consuming. Additionally, high‐resolution observations often have correlated observation errors which are difficult to estimate and create problems for assimilation systems. As a result, large quantities of data are discarded and not used for state initialization. Using the Lorenz‐96 system for testing, we demonstrate that a simple machine learning method can be trained to assimilate high‐resolution data. Using it to do so improves both initial conditions and forecast accuracy. Compared to using the Ensemble Kalman Filter with high‐resolution observations ignored, our augmented method has an average root‐mean‐squared error reduced by 37%. Ensemble forecasts using initial conditions generated by the augmented method are more accurate and reliable at up to 10 days of forecast lead time.

Abstract Climate change projections (CCPs) are based on the multimodel means of individual climate model simulations that are assumed to be independent. However, model similarity leads to projections biased toward the largest set of similar models and intermodel uncertainty underestimation. We assessed the influences of similarities in CMIP6 through CMIP3 CCPs. We ascertained model similarity from shared physics/dynamics and initial conditions by comparing simulated spatial temperature and precipitation with the corresponding observed patterns and accounting for intermodel spread relative to the observational uncertainty, which is also critical. After accounting for similarity, the information from 57 CMIP6, 47 CMIP5, and 24 CMIP3 models can be explained by just 11 independent models without significant differences in globally averaged climate change statistics. On average, independent models indicate a lower global-mean temperature rise of 0.25 °C (~0.5 °C–1 °C in some regions) relative to all models by the end of the 21st century under CMIP6’s highest emission scenario.

Precipitation seasonality is the main factor controlling vegetation phenology in many tropical and subtropical regions. Anticipating the rain onset is of paramount importance for field preparation and seeding. This is of particular importance in various African countries that rely on agriculture as a main source of food, subsistence and income. In such countries, skilful and accurate onset forecasts could also inform early warning and early actions, such as aids logistics planning, for food security. Here, we assess the skill of the seasonal forecast data provided by the Copernicus Climate Change Service in predicting the rain onset over Africa. The skill, i.e. the accuracy of the seasonal forecasts simulation ensemble compared to the climatology, is computed in a probabilistic fashion by accounting for the frequencies of normal, early and late onsets predicted by the forecast system. We compute the skill using the hindcasts (forecast simulations conducted for the past) starting at the beginning of each month in the period 1993–2016. We detect the onset timing of the rainy season using a non-parametric method that accounts for double seasonality and is suitable for the specific time-window of the seasonal forecast simulations. We find positive skills in some key African agricultural regions some months in advance. Overall, the multi-model ensemble outperforms any individual model ensemble. We provide targeted recommendations to develop a useful climate service for the agricultural sector in Africa.

Abstract Drought is a recurring hydroclimatic extreme over the Arabian Peninsula (AP). So far, no study has examined the changes in drought characteristics in recent decades, not to mention the background mechanisms for such changes. To this end, analyzing the Standardized Precipitation Evapotranspiration Index (SPEI) mainly from the European Reanalysis (ERA5) data sets, in addition to other observational/reanalysis data sets over the period of 1951–2020, we show that droughts over the AP have increased in frequency and severity over the last two decades. We show that this drought acceleration, which was not observed in the previous 40–50 years, is a combination of decadal variability and long‐term trends. Importantly, we demonstrate that the decadal SPEI variability is due to the Atlantic Multidecadal Oscillation (AMO). The unprecedented multiyear drought over the AP in recent decades is evidently associated with the current positive phase of the AMO. We also show that the recent warming of the AP is a more significant factor in the drought intensification than the concurrent weakening of local precipitation. Furthermore, we developed a machine learning model largely based on the observed AMO–SPEI relationship. This model predicts a reduced drought severity over the AP in the near future.

A 3D high-resolution general ocean circulation model was implemented and validated to study the characteristics and seasonal variability of the internal tides in the Arabian Sea (AS). Three major source locations of internal tides were identified: Socotra Island, the northeastern shelf area of AS, and the Maldives. Around Socotra Island, internal tides propagate both southward and northward, before quickly dissipating. The internal tides generated in the northeastern AS split into two branches: Branch-I propagates perpendicular to the shelf, whereas Branch-II propagates more southernly. The internal tides originated in the Maldives propagate almost latitudinally both eastwards and westwards. Generally, the internal tides in the AS are more pronounced in January as shown by the forcing function, energy flux, and conversion rate. The hourly average conversion rate for the entire domain, including the AS, the Red Sea, and the Arabian Gulf – was 34.28 GW in January and 20.51 GW in July, suggesting a slightly larger barotropic-to-baroclinic conversion rate in January, probably due to the strong stratification around 100 meters in winter.

Abstract Coastal lagoons are biodiversity hotspots that support neighboring ecosystems and various services. They can exhibit distinct biophysical characteristics compared to the adjacent open sea and act paradoxically as autonomous ecosystems. Using remotely sensed observations and state‐of‐the‐art numerical simulations, the role of water column hydrodynamics in shaping the seasonal succession of phytoplankton biomass was investigated for a non‐estuarine coastal lagoon situated in the northeastern Red Sea. Observations reveal that seasonal phytoplankton blooms inside the lagoon occur during a distinctively different period compared to the adjacent open sea. We provide evidence that this striking difference is due to the contrasting hydrodynamic conditions between inside and outside the lagoon, through their effects on stratification that regulate nutrient availability and hence favorable conditions to sustain rapid phytoplankton growth. The proposed mechanism may offer new insights into understanding the biophysical dynamics of non‐estuarine coastal lagoons in other tropical regions of the global oceans.

To support accidental spill rapid response efforts, oil spill simulations may generally need to account for uncertainties concerning the nature and properties of the spill, which compound those inherent in model parameterizations. A full detailed account of these sources of uncertainty would however require prohibitive resources needed to sample a large dimensional space. In this work, a variance-based sensitivity analysis is conducted to explore the possibility of restricting a priori the set of uncertain parameters, at least in the context of realistic simulations of oil spills in the Red Sea region spanning a two-week period following the oil release. The evolution of the spill is described using the simulation capabilities of Modelo Hidrodinâmico, driven by high-resolution metocean fields of the Red Sea (RS) was adopted to simulate accidental oil spills in the RS. Eight spill scenarios are considered in the analysis, which are carefully selected to account for the diversity of metocean conditions in the region. Polynomial chaos expansions are employed to propagate parametric uncertainties and efficiently estimate variance-based sensitivities. Attention is focused on integral quantities characterizing the transport, deformation, evaporation and dispersion of the spill. The analysis indicates that variability in these quantities may be suitably captured by restricting the set of uncertain inputs parameters, namely the wind coefficient, interfacial tension, API gravity, and viscosity. Thus, forecast variability and confidence intervals may be reasonably estimated in the corresponding four-dimensional input space.

Co-optimization of renewable power and water desalination systems for Neom, a futuristic seaside city in an arid region, results in more pronounced cost investment savings for a high share of renewable sources.

Abstract This study investigates the seasonal climatology of the three-dimensional distribution and transport pathways of dust aerosols over West Asia (WA). Dust column loading over WA exhibits strong seasonality, with markedly high (weak) loading during summer (winter). The summer dust features over WA include the (i) dust reaching up to the 500 hPa level between the Red Sea (RS) and the west coast of the Indian subcontinent (IS); (ii) a slantwise advection of dust aerosols between 850 and 700 hPa levels over the Arabian Peninsula (AP) and Arabian Sea (AS); and (iii) a prominent mid-tropospheric zonal transport of AP dust toward the IS. Maximum column integrated horizontal dust mass flux (DMF) over WA is observed in summer. The intraday changes in the intensity and spatial spread of the DMF over the AP are mediated by the out-of-phase evolution of the surface winds and low-level Shamal jets. Furthermore, the diurnal changes in the strength of the inversion layers located above the monsoon boundary layer and associated wind shear regulate the spatial patterns and intensity of the DMF over the AS. The findings will support future studies aiming at quantifying the radiative effects of dust on the regional climate.

Harmful algal blooms (HABs) have adverse effects on marine ecosystems. An effective approach for detecting, monitoring, and eventually predicting the occurrences of such events is required. By combining a singular value decomposition (SVD) approach and satellite remote sensing observations, we propose a remote sensing algorithm to detect and delineate species-specific HABs. We implemented and tested the proposed SVD algorithm to detect HABs associated with the mixed assemblages of different phytoplankton functional type (PFT) groupings in the Red Sea. The results were validated with concurrent in-situ data from surface samples, demonstrating that the SVD-model performs remarkably well at detecting and distinguishing HAB species in the Red Sea basin. The proposed SVD-model offers a cost-effective tool for implementing an automated remote-sensing monitoring system for detecting HAB species in the basin. Such a monitoring system could be used for predicting HAB outbreaks based on near real-time measurements, essential to support aquaculture industries, desalination plants, tourism, and public health.

A regional, high-resolution reanalysis was analyzed to explore extreme heat loss events in the Northern Red Sea (NRS) and their links to specific regional atmospheric circulation patterns. Such events are determinant for the overturning circulation of the Red Sea and occur frequently between November and March, with maximum frequency during December and January. During these events, the most intense heat loss, often with daily-averaged values lower than -1000 W/m2, is found over the southern half of the Gulf of Aqaba and along the western coastline of the open NRS. Analyses of the spatial modes of variability of these events suggest that the majority of them extend over the entire NRS in an almost uniform way; however, secondary, nonuniform patterns related to regional adjustment in the wind field are also identified. The uniform cold outbursts are associated with distinct atmospheric circulation patterns, which favor the transfer of cold air masses from higher latitudes over the eastern Mediterranean Sea via a strong northwest wind field. Nonuniform events affect considerable parts of the NRS and occur when cold and dry air masses reach the NRS through the Middle East and the northern part of the Arabian Peninsula. The regional sea level pressure drives a clockwise rotation of the wind field that ultimately blows from the northeast/east direction. This rotation of the wind field favors local intensification and lee areas defined by the complex topography and characteristic gaps in the mountain chain along the eastern coastline of the NRS, reflecting the differentiations in the spatial distribution of the heat flux minima.

Connectivity, the exchange of individuals and genes among geographically separated marine populations, plays a key role in coral reef biodiversity and resilience. The Red Sea is a semi-enclosed basin with dynamic circulation and abundant coral reefs, making it a natural laboratory for coral reef connectivity research. Previous studies broadly investigated Red Sea connectivity, but were spatially restricted to regional or sparsely-distributed reef sites. Here, using hydrodynamic and particle tracking models, a high-resolution circulation-driven physical connectivity atlas covering every Red Sea coral reef, including seasonality, was simulated and further validated against available in-situ genetic datasets. The simulation was conducted without incorporating larval traits to isolate and quantify the connectivity contributed by circulation. Our validation experiment suggests the importance of circulation in shaping the genetic structure of Red Sea reef species, supporting the Isolation By Circulation (IBC) theory in the Red Sea seascape genetics. The simulated atlas reveals that reefs in the northern Red Sea are better sources and destinations than those in the southern basin, regardless of season. The east-west connections between the southern reefs are identified to be weak. Complex circulation dynamics drive a regional-specific seasonality, e.g., the Farasan Islands reefs are better sources during summer while the nearby Bab-Al-Mandeb strait reefs are better sources during winter. The west-coast reefs are generally winter-intensified sources whereas the east-coast reefs are generally summer-intensified sources. The revealed seasonality of physical connectivity is important for larval dispersal processes as reef species may spawn in different seasons. This physical connectivity atlas provides a reference for designing marine conservation strategies from a circulation perspective and easy-to-access physical connectivity datasets for the future Red Sea seascape genetic studies.

Abstract This study investigates the risk from oil spills along the main shipping lane in the Red Sea based upon oil spill model trajectories forced by the outputs of validated high resolution regional met-ocean data. Following the intra-annual variations in the met-ocean conditions, the results are presented by classifying the basin into three regions: northern, central and southern Red Sea. The maximum distance traveled by the slick is presented for 1, 2, 5, 10, 14 and 20 days after the commencement of a spill. Different measures of hazard assessment in terms of the concentration of beached oil alongside the corresponding probability maps are also analyzed. The volume fractions of beached, dispersed and evaporated oil, 20 days after the commencement of a spill are quantified. The Red Sea general circulation is characterized by rich mesoscale eddies, which appear to be the most prevailing dynamics in oil transport in the basin. Two case events are analyzed to closely examine the effects of the mesoscale circulations on the fate of spilled oil. The results of this study provide a comprehensive assessment of oil spill hazards in the Red Sea, stemming its main shipping lane and identifies the areas at high risk that require timely mitigation strategies.

Abstract The lockdown measures that were taken to combat the COVID-19 pandemic minimized anthropogenic activities and created natural laboratory conditions for studying air quality. Both observations and WRF-Chem simulations show a 20–50% reduction (compared to pre-lockdown and same period of previous year) in the concentrations of most aerosols and trace gases over Northwest India, the Indo Gangetic Plain (IGP), and the Northeast Indian regions. It is shown that this was mainly due to a 70–80% increase in the height of the boundary layer and the low emissions during lockdown. However, a 60–70% increase in the pollutants levels was observed over Central and South India including the Arabian sea and Bay of Bengal during this period, which is attributed to natural processes. Elevated (dust) aerosol layers are transported from the Middle East and Africa via long-range transport, and a decrease in the wind speed (20–40%) caused these aerosols to stagnate, enhancing the aerosol levels over Central and Southern India. A 40–60% increase in relative humidity further amplified aerosol concentrations. The results of this study suggest that besides emissions, natural processes including background meteorology and dynamics, play a crucial role in the pollution concentrations over the Indian sub-continent.

Abstract The regional climate of the Arabian Gulf (AG) and its variability are examined based on a 40‐year (1980–2019), 5‐km regional reanalysis of the Arabian Peninsula (AP reanalysis). The AP reanalysis fields were first validated against the available observations over the AG, suggesting that this high‐resolution reanalysis well reproduces the spatio‐temporal features of the AG atmospheric circulations. The validated AP reanalysis fields were then analysed to examine the climatic characteristics over the AG including the monthly mean, maximum and minimum temperatures, and the seasonal variations in winds, relative humidity and rainfall over the AG. The AG climate is mostly dry between May and October, and experiences moderate rainfall between December and January. The higher (lower) pressure difference between the northwest and southeast AG during summer (winter) generates the northwesterly Shamal winds over the north (central) AG. The mean Shamal winds are relatively stronger (weaker) and prolonged (shorter) during summer (winter); however, the short lived Shamal jet events in winter can be occasionally stronger than summer. In terms of interannual variability, the Shamal winds are stronger and more persistent in summer during El Niño years and in winter during La Niña years. These differences are mainly associated with changes in temperature gradients between the eastern AG and northwestern AP.

Abstract The sensitivity of different cumulus physical parameterization schemes for simulating extreme winter precipitation events over the Arabian Peninsula (AP) is investigated using a high‐resolution weather research and forecasting (WRF) model. For winters in 2001–2016, the following three parameterization schemes are examined: (i) Kain–Fritsch (KF), (ii) Betts–Miller–Janjić (BMJ), and (iii) Grell–Freitas (GF). The simulation results suggest that the AP extreme winter rainfall events are best simulated using the KF, followed by the BMJ, in terms of spatial distribution and intensity. The spatial pattern correlation coefficient between the model‐simulated and observed rainfall is highest with KF (0.94), followed by BMJ (0.91) and GF (0.76). These results are attributed to a better representation of the moisture transport associated with upper‐tropospheric cyclonic circulation and potential vorticity intrusions. By contrast, the GF scheme fails to simulate moisture convergence and updrafts, leading to an unrealistic representation of cloud hydrometeors and an improper organization of convection and associated extreme rainfall intensities. Meanwhile, the KF and BMJ also successfully simulate the dynamics and thermodynamics of extreme rainfall events that are usually driven by synoptic forcing. The study results suggest that the choice of cumulus parameterization schemes in the WRF model is critical for reliable simulation of extreme rainfall in the hyperarid AP region.

Lockdowns imposed across the world to combat the spread of the COVID-19 pandemic also reduced the anthropogenic emissions. This study investigates the changes in the anthropogenic and natural pollution levels during the lockdown over the Arabian Peninsula (AP), a region where natural pollutants (mineral dust) dominate. In-situ and satellite observations, reanalysis products, and Weather Research and Forecasting model (WRF) coupled with Chemistry module (WRF-Chem) simulations were analyzed to investigate the influence of COVID−19 lockdown on the aerosols (PM2.5, PM10, and AOD) and trace gases (NO2 and SO2). WRF-Chem reasonably reproduced the satellite and in-situ measurements during the study period, with correlation coefficients varying between 0.6–0.8 (0.3–0.8) for PM10 (NO2 and SO2) at 95% confidence levels. During the lockdown, WRF-Chem simulations indicate a significant reduction (50–60%) in the trace gas concentrations over the entire AP compared to the pre-lockdown period. This is shown to be mostly due to a significant reduction in the emissions and an increase in the boundary layer height. An increase in the aerosol concentrations over the central and northern parts of the AP, and a decrease over the north-west AP, Red Sea, and Gulf of Aden regions are noticeable during the lockdown. WRF-Chem simulations suggest that the increase in particulate concentrations over the central and northern AP during the lockdown is mainly due to an increase in dust concentrations, manifested by the stronger convergence and upliftment of winds and warmer surface temperatures (15–25%) over the desert regions. The restricted anthropogenic activities drastically reduced the trace gas concentrations, however, the reduction in particulate concentration levels is offset by the increase in the natural processes (dust emissions).

As a semienclosed marginal sea, the Red Sea connects with the open ocean through a narrow strait at its southern end, known as the Bab-al-Mandeb (BAM) strait. The water exchange between the Red Sea and the Gulf of Aden at the BAM strait is crucial for the water mass transformations and thermohaline circulation in the Red Sea as well as for nutrient supply to the open ocean. In this study, a three-dimensional high-resolution nonhydrostatic MIT general circulation model (MITgcm) was used to investigate the tidal influence on the water exchange in the BAM strait through simulations with and without tidal forcing. We found that the tidal effects on the water exchange in winter were insignificant; however, the summer intrusion of the Gulf of Aden Intermediate Water (GAIW) was strongly affected. When the simulation includes tidal forcing, the along-axis northern extension of the GAIW intrusion is reduced by u to 100 km and the monthly mean volume transport is decreased by 20% on average. Two actors that possibly contribute to the hindrance effects of tides in summer are (i) the tidal residual currents that propagate in a direction opposite to the pathway of the GAIW intrusion currents nd (ii) the enhanced vertical mixing at the pycnocline and near the benthic topography of the BAM strait, which triggers more instabilities along the pathway of the intrusion.

Detecting mesoscale ocean eddies provides a better understanding of the oceanic processes that govern the transport of salt, heat, and carbon. Established eddy detection techniques rely on physical or geometric criteria, and they notoriously fail to predict eddies that are neither circular nor elliptical in shape. Recently, deep learning techniques have been applied for semantic segmentation of mesoscale eddies, relying on the outputs of traditional eddy detection algorithms to supervise the training of the neural network. However, this approach limits the network’s predictions because the available annotations are either circular or elliptical. Moreover, current approaches depend on the sea-surface height, temperature, or currents as inputs to the network, and these data may not provide all the information necessary to accurately segment eddies. In the present work, we have trained a neural network for the semantic segmentation of eddies using human-based—and expert-validated—annotations of eddies in the Arabian Sea. Training with human-annotated datasets enables the network predictions to include more complex geometries, which occur commonly in the real ocean. We then examine the impact of different combinations of input surface variables on the segmentation performance of the network. The results indicate that providing additional surface variables as inputs to the network improves the accuracy of the predictions by approximately 5%. We have further fine-tuned another pre-trained neural network to segment eddies and achieved a reduced overall training time and higher accuracy compared to the results from a network trained from scratch.

This study explores the characteristics of crucial dust sources and changes in their emissions over the Arabian Peninsula (AP) over the 2000–2022 period using high-resolution dust aerosol optical depth data from the Moderate Resolution Imagining Spectroradiometer (MODIS) aerosol measurements onboard Terra and Aqua platforms. The MODIS dust retrievals successfully unravel the hitherto-unexplored key dust source regions and spatial heterogeneity in dust emissions. Critically, MODIS-defined dust sources display a robust geomorphological signature. In Iraq, the Tigris and Euphrates River basins contain extensive dust sources; the Euphrates dust sources are stronger and more widespread. Localized dust sources are noticed over Syria. In the Kingdom of Saudi Arabia (KSA), the eastern province particularly facilitates extensive dust activity. Oman is the prominent dust source in the southern AP due to the presence of intruding sand dunes. Dust emissions in the Iraq and KSA regions exhibit a significant negative correlation with the Standardized Precipitation-Evapotranspiration Index, a drought index, establishing that the local droughts enhance the dust emissions in these regions. The recent sustained droughts from 2008 to 2013 caused a remarkable escalation in the dust emissions in these regions through the modification of land surface conditions.

Severe weather events and the resulting sea-level extremes are considered among the greatest threats to coastal environments. Understanding this coastal hazard is vital for various coastal developments and planning activities. This study examines the sea-level extremes of meteorological origin in the Red Sea. A high-resolution (approximately 500 m) depth-averaged numerical ocean model forced by 5 km dynamically downscaled meteorological fields was implemented to hindcast the sea level of the Red Sea over 37-year, 1980–2016. The model was first validated with observations, showing good agreement with available data. The hindcast model outputs were then analyzed to describe the spatiotemporal features of the sea-level extremes in the Red Sea. The sea-level extremes in the Red Sea developed in response to wind variability over the southern Red Sea and exhibited a basin-wide impact. The magnitudes of the maximum sea levels reached approximately 0.30–0.50 m inside the Red Sea basin, with the highest values (0.85 m) in the Gulf of Suez. The seasonal distribution of the extremes suggests that these are frequent during the winter months (January–March). Assessment of long-term changes in the annual maxima, 99th and 95th percentiles of hourly sea levels indicate no significant trends over the study period.

This study assesses a Continuous Data Assimilation (CDA) dynamical-downscaling algorithm for enhancing the simulation of the Indian summer monsoon (ISM) system. CDA is a mathematically rigorous technique that has been recently introduced to constrain the large-scale features of high-resolution atmospheric models with coarse spatial scale data. It is similar to spectral nudging but does not require any spectral decomposition for scales separation. This is expected to be particularly relevant for ISM, which involves various interactions between large-scale circulations and regional physical processes. Along with a control simulation, several downscaling simulations were conducted with the Weather Research and Forecasting (WRF) model configured over the Indian monsoon region at 10 km horizontal resolution using CDA, spectral (retaining different wavenumbers) and grid nudging for three contrasting ISM rainfall seasons: normal (2016), excess (2013), and drought (2009). The simulations are nested within the global NCEP Final Analysis data available at 1 × 1° horizontal resolution. The model outputs are evaluated against the India Meteorological Department (IMD) gridded precipitation and the fifth generation ECMWF atmospheric reanalysis (ERA-5). Compared to grid and spectral nudging, the simulations using CDA produce enhanced ISM features over the Indian subcontinent including the low-level jet, tropical easterly jet, easterly wind shear, and rainfall distributions for all investigated ISM seasons. The major ISM processes, in particular the monsoon inversion over the Arabian Sea, tropospheric temperature gradients and moist static energy over central India, and zonal wind shear over the monsoon region, are all better simulated with CDA. Spectral nudging outputs are found to be sensitive to the choice of the wavenumber, requiring careful tuning to provide robust simulations of the ISM system. In contrast, control and grid nudging generally fail to well-reproduce some of the main ISM features.

The Saudi Arabian tourism sector is growing, and its economy has flourished over the last decades. This has resulted in numerous coastal developments close to large economic centers, while many more are proposed or planned. The coastal developments have influenced the behavior of the shoreline in the past. Here we undertake a national assessment on the state of the coast of Saudi Arabia based on recent data sets on historic and future shoreline positions. While at national scale the shoreline is found to be stable over the last three decades, the Red Sea coast shows a regional-mean retreat rate while the Gulf coast shows a regional-mean prograding behavior. Detailed analysis of the temporal evolution of shoreline position at selected locations show that human interventions may have accelerated shoreline retreat along adjacent shorelines, some of which are Marine Protected Areas. Furthermore, reef-fronted coastal sections have a mean accretive shoreline change rate, while the open coast shows a mean retreat rate. Future shoreline projections under RCP 4.5 and RCP 8.5 show that large parts of the shoreline may experience an accelerated retreat or a change in its regime from either stable or sprograding to retreating. Under the high emission RCP 8.5 scenario, the length of coastline projected to retreat more than doubles along the Red Sea coast, and approximately triples along the Gulf coast in 2100. At national scale, the Saudi Arabian coastline is projected to experience regional-mean retreats of ~30 m and of ~130 m by 2050 and 2100 under both RCPs considered in this study. These results indicate that effective adaptation strategies will be required to protect areas of ecological and economic value, and that climate resilience should be a key consideration in planned or proposed coastal interventions.

Sea surface temperature (SST) is a leading factor impacting coral reefs and causing bleaching events in the Red Sea. A long-term prediction of temperature patterns with an estimate of uncertainty is thus essential for environment management of the Red Sea ecosystem. In this work, we build a data-driven Bayesian structural time series model and show its effectiveness in predicting future SST seasons with a high accuracy, and identifying the main predictive factors of future SST variability among a large number of factors, including regional SST and large-scale climate indices. The modeling scheme proposed here applies an efficient hierarchical clustering to identify interconnected subregions that display distinct SST variability over the Red Sea, and a Markov Chain Monte Carlo algorithm to simultaneously select the main predictors while the time series model is being trained. In particular, numerical results indicate that monthly SST can be reliably predicted for five months ahead.

A multi-mission satellite remote sensing (MSRS) approach is explored to detect and track leaked oil from the Sabiti oil tanker accident that occurred in the central Red Sea on 11 October 2019 (RSOS-2019). The spilled oil spread rapidly and reached the coastal land near Jeddah, the second largest city of KSA, on 17 October. Different oil spill detection algorithms were implemented on SAR and optical sensor-based satellite images to track the oil spill. Sentinel-1 SAR images were most efficient at detecting the spread and thickness of RSOS-2019, but their spatio-temporal coverage greatly limits their use for tracking the oil movement. The spread and propagation of oil were well captured by Sentinel-2 images up to three weeks after the accident day, in agreement with the SAR images. MODIS successfully detected the narrow patch of oil that was leaked on the incident day and the widespread oil patches two days after. Landsat-8 RGB composite and thermal infrared images captured the oil spill on 13 October. By filtering clouds from the Meteosat images through sequential analysis, the spread and movement of the oil patches were efficiently tracked on 13 October. PlanetScope images available between 12 and 17 October enabled tracking of the oil near the coastal waters. The inferred oil spill movements are consistent with the ocean currents as revealed by a high-resolution regional ocean reanalysis. Our results demonstrate the potential of the MSRS approach to detect and track oil spills in the open and coastal waters of the Red Sea in near real-time.

Uncertainty quantification (UQ) in weather and climate models is required to assess the sensitivity of their outputs to various parameterization schemes and thereby improve their consistency with observations. Herein, we present an efficient UQ and Bayesian inference for the cloud parameters of the NCAR Single Column Atmosphere Model (SCAM6) using surrogate models based on a polynomial chaos expansion. The use of a surrogate model enables to efficiently propagate uncertainties in parameters into uncertainties in model outputs. We investigated eight uncertain parameters: the auto-conversion size threshold for ice to snow (dcs), the fall speed parameter for stratiform cloud ice (ai), the fall speed parameter for stratiform snow (as), the fall speed parameter for cloud water (ac), the collection efficiency of aggregation ice (eii), the efficiency factor of the Bergeron effect (berg_eff), the threshold maximum relative humidity for ice clouds (rhmaxi), and the threshold minimum relative humidity for ice clouds (rhmini). We built two surrogate models using two non-intrusive methods: spectral projection (SP) and basis pursuit denoising (BPDN). Our results suggest that BPDN performs better than SP as it enables to filter out internal noise during the process of fitting the surrogate model. Five out of the eight parameters (namely dcs, ai, rhmaxi, rhmini, and eii) account for most of the variance in predicted climate variables (e.g., total precipitation, cloud distribution, shortwave and longwave cloud radiative effect, ice, and liquid water path). A first-order sensitivity analysis reveals that dcs contributes ~40–80% of the total variance of the climate variables, ai around 15–30%, and rhmaxi, rhmini, and eii around 5–15%. The second- and higher-order effects contribute ~7 and 20%, respectively. The sensitivity of the model to these parameters was further explored using response curves. A Markov chain Monte Carlo (MCMC) sampling algorithm was also implemented for the Bayesian inference of dcs, ai, as, rhmini, and berg_eff using cloud distribution data collected at the Southern Great Plains (USA). The inferred parameters suggest improvements in the global Climate Earth System Model (CESM2) simulations of the tropics and sub-tropics.

Abstract In this study, the variability and trends of the outdoor thermal discomfort index (DI) in the Kingdom of Saudi Arabia (KSA) were analyzed over the 39‐year period of 1980–2018. The hourly DI was estimated based on air temperature and relative humidity data obtained from the next‐generation global reanalysis from the European Center for Medium‐Range Weather Forecasts and in‐house high‐resolution regional reanalysis generated using an assimilative Weather Research Forecast (WRF) model. The DI exceeds 28°C, that is, the threshold for human discomfort, in all summer months (June to September) over most parts of the KSA due to a combination of consistently high temperatures and relative humidity. The DI is greater than 28°C for 8–16 h over the western parts of KSA and north of the central Red Sea. A DI of >28°C persistes for 7–9 h over the Red Sea and western KSA for 90% of summer days. The spatial extent and number of days with DI > 30°C, that is, the threshold for severe human discomfort, are significantly lower than those with DI > 28°C. Long‐term trends in the number of days with DI > 28°C indicate a reduced rate of increase or even a decrease over some parts of the southwestern KSA in recent decades (1999–2018). Areas with DI > 30°C, in particular the northwestern regions of the Arabian Gulf and its adjoining regions, also showed improved comfort levels during recent decades. Significant increases in population and urbanization have been reported throughout the KSA during the study period. Analysis of five‐years clinical data suggests a positive correlation between higher temperatures and humidity with heat‐related deaths during the Hajj pilgrimage. The information provided herein is expected to aid national authorities and policymakers in developing necessary strategies to mitigate the exposure of humans to high levels of thermal discomfort in the KSA.

Abstract Mesoscale eddies are a dominant feature of the Red Sea circulation, yet their three‐dimensional characteristics remain largely unexplored. This hinders our understanding about eddy‐induced transport in the basin. This study analyzes 14‐year outputs from a high‐resolution eddy‐resolving model to investigate the three‐dimensional signature of the Red Sea eddies, their contribution to the air‐sea flux, and the eddy‐induced transport of heat and salt. Eddies are mostly active and energetic in the central and northern Red Sea. Their variability explains ∼8% of the total variance in the surface heat flux and, particularly, ∼39% in the salt flux. The asymmetric eddy structure and meridional gradient drive significant transport of heat and salt across the basin. A negative feedback mechanism is identified that relates the eddy intensity and the meridional steepness of the mixed layer depth in the basin.

In this paper, supplementary cementitious materials are used as a substitute for cement to decrease carbon dioxide emissions. A by-product of the iron manufacturing industry, ground granulated blast-furnace slag (GGBS), known to improve some performance characteristics of concrete, is used as an effective cement replacement to manufacture mortar samples. Here, the influence of curing conditions on the durability of samples including various amounts of GGBS is investigated experimentally and numerically. Twelve high-strength Portland cement CEM I 52.5 N samples were prepared, in which 0%, 45%, 60%, and 80% of cement were substituted by GGBS. In addition, three curing conditions (standard, dry, and cold curing) were applied to the samples. Durability aspects were studied through porosity, permeability, and water absorption. Experimental results indicate that samples cured in standard conditions gave the best performance in comparison to other curing conditions. Furthermore, samples incorporating 45% of GGBS have superior durability properties. Permeability and water absorption were improved by 17% and 18%, respectively, compared to the reference sample. Thereafter, data from capillary suction experiments were used to numerically determine the hydraulic properties based on a Bayesian inversion approach, namely the Markov Chain Monte Carlo method. Finally, the developed numerical model accurately estimates the hydraulic characteristics of mortar samples and greatly matches the measured water inflow over time through the samples.

The increase of the temperature in the Red Sea basin due to global warming could have a large negative effect on its marine ecosystem. Consequently, there is a growing interest, from the scientific community and public organizations, in obtaining reliable projections of the Red Sea temperatures throughout the 21st century. However, the main tool used to do climate projections, the global climate models (GCM), may not be well suited for that relatively small region. In this work we assess the skills of the CMIP5 ensemble of GCMs in reproducing different aspects of the Red Sea 3D temperature variability. The results suggest that some of the GCMs are able to reproduce the present variability at large spatial scales with accuracy comparable to medium and high-resolution hindcasts. In general, the skills of the GCMs are better inside the Red Sea than outside, in the Gulf of Aden. Based on their performance, 8 of the original ensemble of 43 GCMs have been selected to project the temperature evolution of the basin. Bearing in mind the GCM limitations, this can be an useful benchmark once the high resolution projections are available. Those models project an averaged warming at the end of the century (2080-2100) of 3.3 ±> 0.6°C and 1.6 ±> 0.4°C at the surface under the scenarios RCP8.5 and RCP4.5, respectively. In the deeper layers the warming is projected to be smaller, reaching 2.2 ±> 0.5°C and 1.5 ±> 0.3°C at 300 m. The projected warming will largely overcome the natural multidecadal variability, which could induce temporary and moderate decrease of the temperatures but not enough to fully counteract it. We have also estimated how the rise of the mean temperature could modify the characteristics of the marine heatwaves in the region. The results show that the average length of the heatwaves would increase ~15 times and the intensity of the heatwaves ~4 times with respect to the present conditions under the scenario RCP8.5 (10 time and 3.6 times, respectively, under scenario RCP4.5).

In this paper, we address the decision-making problem of a virtual power plant (VPP) involving a self-scheduling and market involvement problem under uncertainty in the wind speed and electricity prices. The problem is modeled using a risk-neutral and two risk-averse two-stage stochastic programming formulations, where the conditional value at risk is used to represent risk. A sample average approximation methodology is integrated with an adapted L-Shaped solution method, which can solve risk-neutral and specific risk-averse problems. This methodology provides a framework to understand and quantify the impact of the sample size on the variability of the results. The numerical results include an analysis of the computational performance of the methodology for two case studies, estimators for the bounds of the true optimal solutions of the problems, and an assessment of the quality of the solutions obtained. In particular, numerical experiences indicate that when an adequate sample size is used, the solution obtained is close to the optimal one.

Abstract This study analyses the connection between extreme rainfall events in Jeddah, Saudi Arabia, and synoptic‐scale weather patterns over the Arabian Peninsula. Mean rainfall follows a decreasing trend; however, the number of rainy days has increased. Interestingly, extreme rainfall is becoming less frequent but shows an increased intensity. Here we utilize self‐organizing maps (SOMs) to identify the weather patterns of the most intense rainy days and the synoptic systems causing extreme rainfall in the Jeddah region. Three main weather patterns that cause heavy rainfall events over Jeddah during the cooler months (November–April) are identified, all reflect tropical‐extratropical interactions. Extreme events in the early period (1979–1998) are characterized by a stronger tropical influence and local precipitation patterns, while a stronger extratropical forcing and higher extreme rainfall amounts are spotted in the late period (1999–2018). Our results suggest that in recent decades, the mechanism causing extreme rainfall over the city of Jeddah has shifted toward a weather regime with stronger extratropical influence.

Abstract The state of Kerala, located on the west coast of India, experienced a record 100‐year flood that resulted in major landslides from unprecedented prolonged and extremely heavy rainfall (50–480 mm·day−1) during August 1–19, 2018, causing extensive damage and about 500 causalities. Rainfall observations indicate that the heavy rainfall occurred over two spells (August 7–10 and 14–18) in association with an offshore trough, and a depression over the Bay of Bengal (BOB). High‐resolution 38‐year climatology data (5 km) and the ERA‐Interim reanalysis dataset show a strong low‐level jet over the Arabian Sea and a depression over the BOB with a southwestward tilt during the heavy rainfall. Very high‐resolution (2‐km) mesoscale model simulations suggest that this high convective instability due to the strong westerly jet along with the formation of offshore vortex, the transport of mid‐tropospheric moisture under the presence of conducive vertical shear of horizontal wind, and transport of mid‐tropospheric moisture from the BOB are the major factors (as shown in the schematic diagram) behind the extreme heavy rainfall over Kerala.

In this paper, a mathematical model was developed to simulate SARS-CoV-2 dynamics in infected patients. The model considers both the innate and adaptive immune responses and consists of healthy cells, infected cells, viral load, cytokines, natural killer cells, cytotoxic T-lymphocytes, B-lymphocytes, plasma cells, and antibody levels. First, a mathematical analysis was performed to discuss the model’s equilibrium points and compute the basic reproduction number. The accuracy of such mathematical models may be affected by many sources of uncertainties due to the incomplete representation of the biological process and poorly known parameters. This may strongly limit their performance and prediction skills. A state-of-the-art data assimilation technique, the ensemble Kalman filter (EnKF), was then used to enhance the model’s behavior by incorporating available data to determine the best possible estimate of the model’s state and parameters. The proposed assimilation system was applied on the real viral load datasets of six COVID-19 patients. The results demonstrate the efficiency of the proposed assimilation system in improving the model predictions by up to 40%.

Internal tides play a crucial role in ocean mixing. To explore the seasonal features of mode-1 M2 internal tides in the Arabian Sea, we analyzed their propagation and energy distribution using along-track sea-level anomaly data collected by satellite altimeters. We identified four primary source regions of internal tides: Abd al Kuri Island, the Carlsberg Ridge, the northeastern Arabian Sea, and the Maldive Islands. The baroclinic signals that originate from Abd al Kuri Island propagate meridionally, whereas those originating from the west coast of India propagate southwestward. The strength and energy flux of the internal tides in the Arabian Sea exhibit significant seasonal and spatial variability. The internal tides generated during winter are more energetic and can propagate further than those generated in summer. Doppler shifting and horizontal variations in stratification can explain the differences in the internal tides’ seasonal distributions.

NEOM (short for Neo-Mustaqbal) is a $500 billion coastal city megaproject, currently under construction in the northwestern part of the Red Sea, off the coast of Tabuk province in Saudi Arabia, and its success will rely on the preservation of biodiverse marine ecosystems. Monitoring the variability of ecological indicators, such as phytoplankton, in relation to regional environmental conditions, is the foundation for such a goal. We provide a detailed description of the phytoplankton seasonal cycle of surface waters surrounding NEOM using satellite-derived chlorophyll-a (Chl-a) observations, based on a regionally-tuned product of the European Space Agency’s Ocean Colour Climate Change Initiative, at 1 km resolution, from 1997 to 2018. The analysis is also supported with in situ cruise datasets and outputs of a state-of-the-art high-resolution hydrodynamic model. The open waters of NEOM follow the oligotrophic character of the Northern Red Sea (NRS), with a peak during late winter and a minimum during late summer. Coral reef-bound regions, such as Sindala and Sharma, are characterised by higher Chl-a concentrations that peak during late summer. Most of the open waters around NEOM are influenced by the general cyclonic circulation of the NRS and local circulation features, while shallow reef-bound regions are more isolated. Our analysis provides the first description of the phytoplankton seasonality and the oceanographic conditions in NEOM, which may support the development of a regional marine conservation strategy.

Phytoplankton biomass and size structure are recognized as key ecological indicators. With the aim to quantify the relationship between these two ecological indicators in tropical waters and understand controlling factors, we analyzed the total chlorophyll-a concentration, a measure of phytoplankton biomass, and its partitioning into three size classes of phytoplankton, using a series of observations collected at coastal sites in the central Red Sea. Over a period of 4 years, measurements of flow cytometry, size-fractionated chlorophyll-a concentration, and physical-chemical variables were collected near Thuwal in Saudi Arabia. We fitted a three-component model to the size-fractionated chlorophyll-a data to quantify the relationship between total chlorophyll and that in three size classes of phytoplankton [pico- (20 μm)]. The model has an advantage over other more empirical methods in that its parameters are interpretable, expressed as the maximum chlorophyll-a concentration of small phytoplankton (pico- and combined pico-nanophytoplankton, Cpm and Cp,nm, respectively) and the fractional contribution of these two size classes to total chlorophyll-a as it tends to zero (Dp and Dp,n). Residuals between the model and the data (model minus data) were compared with a range of other environmental variables available in the dataset. Residuals in pico- and combined pico-nanophytoplankton fractions of total chlorophyll-a were significantly correlated with water temperature (positively) and picoeukaryote cell number (negatively). We conducted a running fit of the model with increasing temperature and found a negative relationship between temperature and parameters Cpm and Cp,nm and a positive relationship between temperature and parameters Dp and Dp,n. By harnessing the relative red fluorescence of the flow cytometric data, we show that picoeukaryotes, which are higher in cell number in winter (cold) than summer (warm), contain higher chlorophyll per cell than other picophytoplankton and are slightly larger in size, possibly explaining the temperature shift in model parameters, though further evidence is needed to substantiate this finding. Our results emphasize the importance of knowing the water temperature and taxonomic composition of phytoplankton within each size class when understanding their relative contribution to total chlorophyll. Furthermore, our results have implications for the development of algorithms for inferring size-fractionated chlorophyll from satellite data, and for how the partitioning of total chlorophyll into the three size classes may change in a future ocean.

Subseasonal-to-seasonal (S2S) forecasts have the potential to provide advance information about weather and climate events. The high heat capacity of water means that the subsurface ocean stores and re-releases heat (and other properties) and is an important source of information for S2S forecasts. However, the subsurface ocean is challenging to observe, because it cannot be measured by satellite. Subsurface ocean observing systems relevant for understanding, modeling, and forecasting on S2S timescales will continue to evolve with the improvement in technological capabilities. The community must focus on designing and implementing low-cost, high-value surface and subsurface ocean observations, and developing forecasting system capable of extracting their observation potential in forecast applications. S2S forecasts will benefit significantly from higher spatio-temporal resolution data in regions that are sources of predictability on these timescales (coastal, tropical, and polar regions). While ENSO has been a driving force for the design of the current observing system, the subseasonal time scales present new observational requirements. Advanced observation technologies such as autonomous surface and subsurface profiling devices as well as satellites that observe the ocean-atmosphere interface simultaneously can lead to breakthroughs in coupled data assimilation (CDA) and coupled initialization for S2S forecasts. These observational platforms should also be tested and evaluated in ocean observation sensitivity experiments with current and future generation CDA and S2S prediction systems. Investments in the new ocean observations as well as model and DA system developments can lead to substantial returns on cost savings from disaster mitigation as well as socio–economic decisions that use S2S forecast information.

Developments in observing system technologies and ocean data assimilation (DA) are symbiotic. New observation types lead to new DA methods and new DA methods, such as coupled DA, can change the value of existing observations or indicate where new observations can have greater utility for monitoring and prediction. Practitioners of DA are encouraged to make better use of observations that are already available, for example, taking advantage of strongly coupled DA so that ocean observations can be used to improve atmospheric analyses and vice versa. Ocean reanalyses are useful for the analysis of climate as well as the initialization of operational long-range prediction models. There are many remaining challenges for ocean reanalyses due to biases and abrupt changes in the ocean-observing system throughout its history, the presence of biases and drifts in models, and the simplifying assumptions made in DA solution methods. From a governance point of view, more support is needed to bring the ocean-observing and DA communities together. For prediction applications, there is wide agreement that protocols are needed for rapid communication of ocean-observing data on numerical weather prediction (NWP) timescales. There is potential for new observation types to enhance the observing system by supporting prediction on multiple timescales, ranging from the typical timescale of NWP, covering hours to weeks, out to multiple decades. Better communication between DA and observation communities is encouraged in order to allow operational prediction centers the ability to provide guidance for the design of a sustained and adaptive observing network.

Harmful Algal Blooms (HABs) are of global concern, as their presence is often associated with socio-economic and environmental issues including impacts on public health, aquaculture and fisheries. Therefore, monitoring the occurrence and succession of HABs is fundamental for managing coastal regions around the world. Yet, due to the lack of adequate in situ measurements, the detection of HABs in coastal marine ecosystems remains challenging. Sensors on-board satellite platforms have sampled the Earth synoptically for decades, offering an alternative, cost-effective approach to routinely detect and monitor phytoplankton. The Red Sea, a large marine ecosystem characterised by extensive coral reefs, high levels of biodiversity and endemism, and a growing aquaculture industry, is one such region where knowledge of HABs is limited. Here, using high-resolution satellite remote sensing observations (1km, MODIS-Aqua) and a second-order derivative approach, in conjunction with available in situ datasets, we investigate for the first time the capability of a remote sensing model to detect and monitor HABs in the Red Sea. The model is able to successfully detect and generate maps of HABs associated with different phytoplankton functional types, matching concurrent in situ data remarkably well. We also acknowledge the limitations of using a remote-sensing based approach and show that regardless of a HAB's spatial coverage, the model is only capable of detecting the presence of a HAB when the Chl-a concentrations exceed a minimum value of ~ 1 mg m-3. Despite the difficulties in detecting HABs at lower concentrations, and identifying species toxicity levels (only possible through in situ measurements), the proposed method has the potential to map the reported spatial distribution of several HAB species over the last two decades. Such information is essential for the regional economy (i.e., aquaculture, fisheries & tourism), and will support the management and sustainability of the Red Sea's coastal economic zone.

In this paper, an extended SEIR model with a vaccination compartment is proposed to simulate the novel coronavirus disease (COVID-19) spread in Saudi Arabia. The model considers seven stages of infection: susceptible (S), exposed (E), infectious (I), quarantined (Q), recovered (R), deaths (D), and vaccinated (V). Initially, a mathematical analysis is carried out to illustrate the non-negativity, boundedness, epidemic equilibrium, existence, and uniqueness of the endemic equilibrium, and the basic reproduction number of the proposed model. Such numerical models can be, however, subject to various sources of uncertainties, due to an imperfect description of the biological processes governing the disease spread, which may strongly limit their forecasting skills. A data assimilation method, mainly, the ensemble Kalman filter (EnKF), is then used to constrain the model outputs and its parameters with available data. We conduct joint state-parameters estimation experiments assimilating daily data into the proposed model using the EnKF in order to enhance the model’s forecasting skills. Starting from the estimated set of model parameters, we then conduct short-term predictions in order to assess the predicability range of the model. We apply the proposed assimilation system on real data sets from Saudi Arabia. The numerical results demonstrate the capability of the proposed model in achieving accurate prediction of the epidemic development up to two-week time scales. Finally, we investigate the effect of vaccination on the spread of the pandemic.

Phytoplankton phenology and size structure are key ecological indicators that influence the survival and recruitment of higher trophic levels, marine food web structure, and biogeochemical cycling. For example, the presence of larger phytoplankton cells supports food chains that ultimately contribute to fisheries resources. Monitoring these indicators can thus provide important information to help understand the response of marine ecosystems to environmental change. In this study, we apply the phytoplankton size model of Gittings et al. (2019b) to 20-years of satellite-derived ocean colour observations in the northern and central Red Sea, and investigate interannual variability in phenology metrics for large phytoplankton (>2 µm in cell diameter). Large phytoplankton consistently bloom in the winter. However, the timing of bloom initiation and termination (in autumn and spring, respectively) varies between years. In the autumn/winter of 2002/2003, we detected a phytoplankton bloom, which initiated ~8 weeks earlier and lasted ~11 weeks longer than average. The event was linked with an eddy dipole in the central Red Sea, which increased nutrient availability and enhanced the growth of large phytoplankton. The earlier timing of food availability directly impacted the recruitment success of higher trophic levels, as represented by the maximum catch of two commercially important fisheries (Sardinella spp. and Teuthida) in the following year. The results of our analysis are essential for understanding trophic linkages between phytoplankton and fisheries and for marine management strategies in the Red Sea.