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.

Recent reports of warming trends in the Red Sea raise concerns about the response of the basin's fragile ecosystem under an increasingly warming climate. Using a variety of available Sea Surface Temperature (SST) datasets, we investigate the evolution of Red Sea SST in relation to natural climate variability. Analysis of long-term SST datasets reveals a sequence of alternating positive and negative trends, with similar amplitudes and a periodicity of nearly 70 years associated with the Atlantic Multidecadal Oscillation (AMO). High warming rates reported recently appear to be a combined effect of global warming and a positive phase of natural SST oscillations. Over the next decades, the SST trend in the Red Sea purely related to global warming is expected to be counteracted by the cooling AMO phase. Regardless of the current positive trends, projections incorporating long-term natural oscillations suggest a possible decreasing effect on SST in the near future.

The analysis of ocean and atmospheric datasets offers a unique set of challenges to scientists working in different application areas. These challenges include dealing with extremely large volumes of multidimensional data, supporting interactive visual analysis, ensembles exploration and visualization, exploring model sensitivities to inputs, mesoscale ocean features analysis, predictive analytics, heterogeneity and complexity of observational data, representing uncertainty, and many more. Researchers across disciplines collaborate to address such challenges, which led to significant research and development advances in ocean and atmospheric sciences, and also in several relevant areas such as visualization and visual analytics, big data analytics, machine learning and statistics. In this report, we perform an extensive survey of research advances in the visual analysis of ocean and atmospheric datasets. First, we survey the task requirements by conducting interviews with researchers, domain experts, and end users working with these datasets on a spectrum of analytics problems in the domain of ocean and atmospheric sciences. We then discuss existing models and frameworks related to data analysis, sense-making, and knowledge discovery for visual analytics applications. We categorize the techniques, systems, and tools presented in the literature based on the taxonomies of task requirements, interaction methods, visualization techniques, machine learning and statistical methods, evaluation methods, data types, data dimensions and size, spatial scale and application areas. We then evaluate the task requirements identified based on our interviews with domain experts in the context of categorized research based on our taxonomies, and existing models and frameworks of visual analytics to determine the extent to which they fulfill these task requirements, and identify the gaps in current research. In the last part of this report, we summarize the trends, challenges, and opportunities for future research in this area. (see http://www.acm.org/about/class/class/2012).

Phytoplankton size structure impacts ocean food-web dynamics and biogeochemical cycling, and is thus an important ecological indicator that can be utilised to quantitatively evaluate the state of marine ecosystems. Potential alterations to size structure are predicted to occur in tropical regions under future scenarios of climate change. Therefore, there is an increasing requirement for the synoptic monitoring of phytoplankton size structure in marine systems. The Red Sea remains a comparatively unexplored tropical marine ecosystem, particularly with regards to its large-scale biological dynamics. Using an in situ pigment dataset acquired in the Red Sea, we parameterise a two-component, abundance-based phytoplankton size model and apply it to remotely-sensed observations of chlorophyll-a (Chl-a) concentration, to infer Chl-a in two size classes of phytoplankton, small cells <2 μm in size (picophytoplankton) and large cells >2 μm in size. Satellite-derived estimates of phytoplankton size structure are in good agreement with corresponding in situ measurements and also capture the spatial variability related to regional mesoscale dynamics. Our analysis reveals that, for the estimation of Chl-a in the two size classes, the model performs comparably or in some cases better, to validations in other oceanic regions. Our model parameterisation will be useful for future studies on the seasonal and interannual variability of phytoplankton size classes in the Red Sea, which may ultimately be relevant for understanding trophic linkages between phytoplankton size structure and fisheries, and the development of marine management strategies.

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- (<2 μm), nano- (2-20 μm) and micro-phytoplankton (>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 (D p and D p,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 D p and D p,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.

As the Earth's temperature continues to rise, coral bleaching events become more frequent. Some of the most affected reef ecosystems are located in poorly-monitored waters, and thus, the extent of the damage is unknown. We propose the use of Marine Heatwaves (MHWs) as a new approach for detecting coral reef zones susceptible to bleaching, using the Red Sea as a model system. Red Sea corals are exceptionally heat-resistant, yet bleaching events have increased in frequency. By applying a strict definition of MHWs on >30-year satellite-derived sea surface temperature observations (1985-2015), we provide an atlas of MHW hotspots over the Red Sea coral reef zones, which includes all MHWs that caused major coral bleaching. We found that: 1) if tuned to a specific set of conditions, MHWs identify all areas where coral bleaching has previously been reported; 2) those conditions extended farther and occurred more often than bleaching was reported; and 3) an emergent pattern of extreme warming events is evident in the northern Red Sea (since 1998), a region until now thought to be a thermal refuge for corals. We argue that bleaching in the Red Sea may be vastly underrepresented. Additionally, although northern Red Sea corals exhibit remarkably high thermal resistance, the rapidly rising incidence of MHWs of high intensity indicates this region may not remain a thermal refuge much longer. As our regionally-tuned MHW algorithm was capable of isolating all extreme warming events that have led to documented coral bleaching in the Red Sea, we propose that this approach could be used to reveal bleaching-prone regions in other data-limited tropical regions. It may thus prove a highly valuable tool for policy-makers to optimise the sustainable management of coastal economic zones. This article is protected by copyright. All rights reserved.

This study presents a high-resolution spatial and temporal assessment of the solar energy resources over the Arabian Peninsula (AP) from 38 years (1980–2017) reanalysis data generated using an assimilative Weather Research and Forecasting Solar model. The simulations are performed based on two, two-way nested domains with 15 km and 5 km resolutions using the European Centre for Medium-Range Weather Forecasts as initial and boundary conditions and assimilating most of available observations in the region. Simulated solar energy resources, such as the Global Horizontal Irradiance (GHI), Direct Normal Irradiance (DNI), and the Diffusive Horizontal Irradiance (DHI), are first validated with daily observations collected at 46 in-situ radiometer stations over Saudi Arabia for a period of four years (2013–2016). Observed and modelled data are in good agreement with high correlation coefficients, index of agreements, and low normalized biases. The total mean annual GHI (DNI) over the AP ranges from 6000 to 8500 Wh m−2 (3000 to 6500 Wh m−2) with significant seasonal variations. The diffuse fraction (the ratio of the DHI to the GHI) is high (low) over the northern (southern) AP in winter whereas it is high (low) over the central to southern (northern) AP during summer, indicating a significant modulation of the sky clearness over the region. Clouds over the northern AP in winter and the aerosol loading due to desert dust over the central and southern AP in summer are the major factors driving the variability of the DHI. The effects of dust and clouds are more pronounced in the diurnal variability of the solar radiation parameters. Our analysis of various solar radiation parameters and the aerosol properties suggest a significant potential for solar energy harvesting in the AP. In particular, the southeastern to northwestern Saudi Arabia are identified as the most suitable areas to exploit solar energy with a minimum cloud coverage over the region.

The timing of phytoplankton growth (phenology) in tropical oceans is a crucial factor influencing the survival rates of higher trophic levels, food web structure and the functioning of coral reef ecosystems. Phytoplankton phenology is thus categorised as an 'ecosystem indicator', which can be utilised to assess ecosystem health in response to environmental and climatic perturbations. Ocean-colour remote sensing is currently the only technique providing global, long-term, synoptic estimates of phenology. However, due to limited available in situ datasets, studies dedicated to the validation of satellite-derived phenology metrics are sparse. The recent development of autonomous oceanographic observation platforms provides an opportunity to bridge this gap. Here, we use satellite-derived surface chlorophyll-a (Chl-a) observations, in conjunction with a Biogeochemical-Argo dataset, to assess the capability of remote sensing to estimate phytoplankton phenology metrics in the northern Red Sea - a typical tropical marine ecosystem. We find that phenology metrics derived from both contemporary platforms match with a high degree of precision (within the same 5-day period). The remotely-sensed surface signatures reflect the overall water column dynamics and successfully capture Chl-a variability related to convective mixing. Our findings offer important insights into the capability of remote sensing for monitoring food availability in tropical marine ecosystems, and support the use of satellite-derived phenology as an ecosystem indicator for marine management strategies in regions with limited data availability.

The southern Red Sea is genetically distinct from the rest of the basin; yet the reasons responsible for this genetic separation remain unclear. Connectivity is a vital process for the exchange of individuals and genes among geographically separated populations, and is necessary for maintaining biodiversity and resilience in coral reef ecosystems. Here, using long-term, high-resolution, 3-D backward particle tracking simulations, we investigate the physical connectivity of coral reefs in the southern Red Sea with neighbouring regions. Overall, the simulation results reveal that the southern Red Sea coral reefs are more physically connected with regions in the Indian Ocean (e.g., the Gulf of Aden) than with the northern part of the basin. The identified connectivity exhibits a distinct monsoon-related seasonality. Though beyond the country boundaries, relatively remote regions of the Indian Ocean may have a substantial impact on the southern Red Sea coral reef regions, and this should be taken into consideration when establishing conservation strategies for these vulnerable biodiversity hot-spots.

Using available satellite observations and Modern-Era Retrospective analysis for Research and Applications Version-2 (MERRA2) datasets for the period 1980-2016, this study reveals a summertime dust amplification over the Arabian Peninsula (AP) during about past two decades. Our results demonstrate a significant positive trend in summertime dust loading over the AP since the year 2002, with a maximum increase of 21% over the southern Red Sea. The increased summertime dust over the southern AP is attributed to the intensification of the remote dust transport from the Sahara Desert through Sudan by a strengthened Tokar Gap westerly jet, and a general increased gustiness in the AP. Furthermore, increased both air and soil temperature and reduction in the soil moisture along with increased sensible heat flux led to increased local dryness in the AP, and played a significant role in enhancing wind-induced localized dust emissions therein. The associated changes in the AP include an enhancement of net radiative fluxes, particularly the long wave radiative flux.