Showing papers by "Ibrahim Hoteit published in 2019"

Multiple stressor effects on coral reef ecosystems

Abstract: Global climate change has profound implications on species distributions and ecosystem functioning. In the coastal zone, ecological responses may be driven by various biogeochemical and physical environmental factors. Synergistic interactions can occur when the combined effects of stressors exceed their individual effects. The Red Sea, characterized by strong gradients in temperature, salinity, and nutrients along the latitudinal axis provides a unique opportunity to study ecological responses over a range of these environmental variables. Using multiple linear regression models integrating in situ, satellite and oceanographic data, we investigated the response of coral reef taxa to local stressors and recent climate variability. Taxa and functional groups responded to a combination of climate (temperature, salinity, air-sea heat fluxes, irradiance, wind speed), fishing pressure and biogeochemical (chlorophyll a and nutrients - phosphate, nitrate, nitrite) factors. The regression model for each species showed interactive effects of climate, fishing pressure and nutrient variables. The nature of the effects (antagonistic or synergistic) was dependent on the species and stressor pair. Variables consistently associated with the highest number of synergistic interactions included heat flux terms, temperature, and wind speed followed by fishing pressure. Hard corals and coralline algae abundance were sensitive to changing environmental conditions where synergistic interactions decreased their percentage cover. These synergistic interactions suggest that the negative effects of fishing pressure and eutrophication may exacerbate the impact of climate change on corals. A high number of interactions were also recorded for algae, however for this group, synergistic interactions increased algal abundance. This study is unique in applying regression analysis to multiple environmental variables simultaneously to understand stressor interactions in the field. The observed responses have important implications for understanding climate change impacts on marine ecosystems and whether managing local stressors, such as nutrient enrichment and fishing activities, may help mitigate global drivers of change.

Marine heatwaves reveal coral reef zones susceptible to bleaching in the Red Sea

Abstract: This publication is supported by the Office of Sponsored Research (OSP) at King Abdullah University of Science and Technology (KAUST) under the Virtual Red Sea Initiative (REP/1/3268-01-01). We are grateful to the Met Office, the Group for High Resolution SST (GHRSST), the Global Telecommunications System (GTS), and EUMETSAT Ocean and Sea Ice Satellite Applications Facility (OSI-SAF) for making the OSTIA database available, and to UNEP-WCMC for providing the Global distribution of warm-water coral reefs database. We thank Ute Langner for processing the coral reef locations, and John A. Gittings for his initial constructive comments.

The role of the Indian Summer Monsoon variability on Arabian Peninsula summer climate

Abstract: This study investigates the influence of the Indian Summer Monsoon (ISM) on the atmospheric circulation over the Arabian Peninsula (AP) using the European Centre for Medium Range Weather Forecasts’ twentieth century reanalysis (ERA-20C) for the period 1901–2010. After describing the summer climate of the AP using various dynamic and thermodynamic parameters, we investigate the link between extreme ISMs and atmospheric circulation over the AP on inter-annual time scale. Analysis of composites of different parameters during extreme monsoon (strong and weak) years reveals that the ISM plays an important role in the summer circulation over the AP and adjoining regions. The major noticeable changes in modulating circulation during extreme monsoons are: (1) a strengthening of lower tropospheric northerly winds, westerly winds passing through the Tokar Gap, Shamal winds, and the upper tropospheric easterly jet stream during strong ISM; (2) a northward (southward) shift of the subtropical westerly jet stream during strong (weak) monsoon years; (3) the development of strong upper level ridge above the surface thermal low during strong ISM years, which result in a baroclinic structure over the AP and adjoining regions; (4) an increase in adiabatic warming, and hence aridity, over the AP during strong monsoon years, caused by intense subsidence of the middle to upper troposphere due to zonal overturning circulation; and (5) convective instability during strong monsoon years caused by an intensification of the upward motion over the southern AP. Furthermore, during strong monsoons, the availability of excess moisture leads to atmospheric instability, which in turn triggers the formation of clouds that lead to more rainfall over the southwestern AP. Finally, the westward propagation of a Gill-type Rossby waves induced by the ISM play an important role in the variations of the AP summer climate by enhancing the warm core structure over the AP and through their interaction with the midlatitude westerlies during strong monsoons.

Observational Needs for Improving Ocean and Coupled Reanalysis, S2S Prediction, and Decadal Prediction

Abstract: 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.

Evaluating tropical phytoplankton phenology metrics using contemporary tools

Abstract: 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.

Natural Climate Oscillations may Counteract Red Sea Warming Over the Coming Decades

Abstract: The study was supported by King Abdullah University of Science and Technology (KAUST) under the "Virtual Red Sea Initiative", Award Number REP/1/3268-01-01. We acknowledge all data providers that made their datasets available for this study, and specifically thank the UK MetOffice Ocean for providing the OSTIA and HadISST products, NASA for providing the AVHRR-OI data, and NOAA for providing the ERSST data and the AMO time series.The SSA analysis was performed with routines developed in Matlab by Eric Breitenberger. We are also grateful to Dionysios Raitsos, John Gittings and Sabique Langodan for the valuable discussions during the preparation of the manuscript.

Factors Regulating the Relationship Between Total and Size-Fractionated Chlorophyll-a in Coastal Waters of the Red Sea.

Abstract: Phytoplankton biomass and size structure are recognised as key ecological indicators. With the aim to quantify the relationship between these two ecological indicators in tropical waters and understand controlling factors, we analysed the total chlorophyll-\textit{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-\textit{a} concentration, and physical-chemical variables were collected near Thuwal in Saudi Arabia. We fitted a three-component model to the size-fractionated chlorophyll-\textit{a} data to quantify the relationship between total chlorophyll and that in three size classes of phytoplankton (pico-, nano- and micro-phytoplankton). The model has an advantage over other more empirical methods in that its parameters are interpretable, expressed as the maximum chlorophyll-\textit{a} concentration of small phytoplankton (pico- and combined pico-nanophytoplankton, $C^m_p$ and $C^m_{p,n}$ respectively) and the fractional contribution of these two size classes to total chlorophyll-\textit{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-\textit{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 $C^m_p$ and $C^m_{p,n}$ 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.

The Red Sea

Abstract: The Red Sea is one of the warmest and saltiest seas of the world. Water exchanges occur only in the south, and there is moderate nutrient variability at macroecological scales. The Red Sea contains extensive areas of coral reefs, supporting high levels of diversity with numerous endemic species. Seagrass meadows and mangrove stands are also common. Despite the relatively low population density along most of the Red Sea shores, coastal development has intensified over the last few decades and is not expected to slow down. Marine communities face increasing pressures from fishing, shipping, oil exploration, aquaculture, desalination discharges, growing human population, terrestrial run-off, plastic waste, and climatic changes. Projections of increasing tourism add further pressures and, if sound marine spatial planning tools are not in place, could result in adverse effects for the sustainable management of resources. Current levels of contamination are overall low but coral reefs show signs of overfishing and there are also signs of homogenization of coral reef communities along the entire latitudinal gradient, probably due to anthropogenic pressures. Ecosystem-based management approaches are urgently needed for sustainable management.

On the Recent Amplification of Dust over the Arabian Peninsula during 2002 – 2012

Abstract: This research was supported by the King Abdulla University of Science and Technology (KAUST) under the Virtual Red Sea Initiative (Grant # REP/1/3268-01-01),the General Commission for Survey (under award number RGC/3/1612-01-0) and the Saudi ARAMCO-KAUST Marine Environmental Observatory(SAKMEO)at KAUST.All the data we used in this manuscript are publicly available. MERRA2 dataset was downloaded from https://disc.gsfc.nasa.gov/and ERA-interimdataset from https://apps.ecmwf.int/.MODIS, MISR and SeaWIFS satellite data products of AOD were downloaded from https://giovanni.gsfc.nasa.gov/giovanni/. The authors would like to thank three anonymous reviewers for their constructive and helpful comments.

Efficient Dynamical Downscaling of General Circulation Models Using Continuous Data Assimilation

Abstract: Continuous data assimilation (CDA) is successfully implemented for the first time for efficient dynamical downscaling of a global atmospheric reanalysis. A comparison of the performance of CDA with the standard grid and spectral nudging techniques for representing long- and short-scale features in the downscaled fields using the Weather Research and Forecast (WRF) model is further presented and analyzed. The WRF model is configured at 25km horizontal resolution and is driven by 250km initial and boundary conditions from NCEP/NCAR reanalysis fields. Downscaling experiments are performed over a one-month period in January, 2016. The similarity metric is used to evaluate the performance of the downscaling methods for large and small scales. Similarity results are compared for the outputs of the WRF model with different downscaling techniques, NCEP reanalysis, and Final Analysis. Both spectral nudging and CDA describe better the small-scale features compared to grid nudging. The choice of the wave number is critical in spectral nudging; increasing the number of retained frequencies generally produced better small-scale features, but only up to a certain threshold after which its solution gradually became closer to grid nudging. CDA maintains the balance of the large- and small-scale features similar to that of the best simulation achieved by the best spectral nudging configuration, without the need of a spectral decomposition. The different downscaled atmospheric variables, including rainfall distribution, with CDA is most consistent with the observations. The Brier skill score values further indicate that the added value of CDA is distributed over the entire model domain. The overall results clearly suggest that CDA provides an efficient new approach for dynamical downscaling by maintaining better balance between the global model and the downscaled fields.

Efficient Dynamical Downscaling of General Circulation Models Using Continuous Data Assimilation

Abstract: Author(s): Desamsetti, Srinivas; Dasari, Hari Prasad; Langodan, Sabique; Titi, Edriss S; Knio, Omar; Hoteit, Ibrahim | Abstract: Continuous data assimilation (CDA) is successfully implemented for the first time for efficient dynamical downscaling of a global atmospheric reanalysis. A comparison of the performance of CDA with the standard grid and spectral nudging techniques for representing long- and short-scale features in the downscaled fields using the Weather Research and Forecast (WRF) model is further presented and analyzed. The WRF model is configured at 25km horizontal resolution and is driven by 250km initial and boundary conditions from NCEP/NCAR reanalysis fields. Downscaling experiments are performed over a one-month period in January, 2016. The similarity metric is used to evaluate the performance of the downscaling methods for large and small scales. Similarity results are compared for the outputs of the WRF model with different downscaling techniques, NCEP reanalysis, and Final Analysis. Both spectral nudging and CDA describe better the small-scale features compared to grid nudging. The choice of the wave number is critical in spectral nudging; increasing the number of retained frequencies generally produced better small-scale features, but only up to a certain threshold after which its solution gradually became closer to grid nudging. CDA maintains the balance of the large- and small-scale features similar to that of the best simulation achieved by the best spectral nudging configuration, without the need of a spectral decomposition. The different downscaled atmospheric variables, including rainfall distribution, with CDA is most consistent with the observations. The Brier skill score values further indicate that the added value of CDA is distributed over the entire model domain. The overall results clearly suggest that CDA provides an efficient new approach for dynamical downscaling by maintaining better balance between the global model and the downscaled fields.

Remotely sensing harmful algal blooms in the Red Sea.

Abstract: 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.

Physical connectivity simulations reveal dynamic linkages between coral reefs in the southern Red Sea and the Indian Ocean.

Abstract: 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.

Ocean Observations to Improve our Understanding, Modeling, and Forecasting of Subseasonal-to-Seasonal Variability

Abstract: 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 to extract their observation potential in forecast applications. S2S forecasts will benefit significantly from higher spatiotemporal 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.

Interannual Variability of the Exchange Flow Through the Strait of Bab-al-Mandeb

Abstract: The authors acknowledge the valuable and constructive comments and suggestions of two anonymous reviewers. This research was funded by King Abdullah University of Science and Technology (KAUST) under the Competitive Research Grants (CRG) Program award URF/1/3408-01-01. The research made extensive use of the supercomputing resources laboratory at KAUST. The data set used in this research can be available via the link https://figshare.com/s/4d2432bd00c7e94ce79e.

SKRIPS v1.0: a regional coupled ocean–atmosphere modeling framework (MITgcm–WRF) using ESMF/NUOPC, description and preliminary results for the Red Sea

Abstract: . A new regional coupled ocean–atmosphere model is developed and its implementation is presented in this paper. The coupled model is based on two open-source community model components: the MITgcm ocean model and the Weather Research and Forecasting (WRF) atmosphere model. The coupling between these components is performed using ESMF (Earth System Modeling Framework) and implemented according to National United Operational Prediction Capability (NUOPC) protocols. The coupled model is named the Scripps–KAUST Regional Integrated Prediction System (SKRIPS). SKRIPS is demonstrated with a real-world example by simulating a 30 d period including a series of extreme heat events occurring on the eastern shore of the Red Sea region in June 2012. The results obtained by using the coupled model, along with those in forced stand-alone oceanic or atmospheric simulations, are compared with observational data and reanalysis products. We show that the coupled model is capable of performing coupled ocean–atmosphere simulations, although all configurations of coupled and uncoupled models have good skill in modeling the heat events. In addition, a scalability test is performed to investigate the parallelization of the coupled model. The results indicate that the coupled model code scales well and the ESMF/NUOPC coupler accounts for less than 5 % of the total computational resources in the Red Sea test case. The coupled model and documentation are available at https://library.ucsd.edu/dc/collection/bb1847661c (last access: 26 September 2019), and the source code is maintained at https://github.com/iurnus/scripps_kaust_model (last access: 26 September 2019).

The State of the Art in Visual Analysis Approaches for Ocean and Atmospheric Datasets

Abstract: This study was supported by the Office of Sponsored Research (OSR) at King Abdullah University of Science and Technology (KAUST) under the “Virtual Red Sea Initiative” (award #REP/1/3268-01-01) We want to acknowledge Dr Hari Prasad Dasari for his help and contribution in organizing the interviews with domain experts We also like to thank KAUST Visualization Core Lab for their help and support

Abnormal events detection using deep neural networks: application to extreme sea surface temperature detection in the Red Sea

Abstract: We present a method based on deep learning for detecting and localizing abnormal/extreme events in sea surface temperature (SST) of the Red Sea images using training samples of normal events only. The method operates in two stages; the first one involves features extraction from each patch of the SST input image using the first two convolutional layers extracted from a pretrained convolutional neural network. In the second stage, two methods are used for training the model from the normal training data. The first method uses one-class support vector machine (1-SVM) classifier that allows a fast and robust abnormal detection in the presence of outliers in the training dataset. In the second method, a Gaussian model is defined on the Mahalanobis distances between all normal training data. Experimental tests are conducted on satellite-derived SST data of the Red Sea spanning for a period of 31 years (1985–2015). Our results suggest that the Gaussian model of Mahalanobis distances outperformed 1-SVM by providing better performance in terms of sensitivity and specificity.

Impact of dynamical representational errors on an Indian Ocean ensemble data assimilation system

Abstract: The authors would like to thank INCOIS Director for supporting this research. All the experiments were conducted on the high performance computer Aditya, IITM, Pune, India. The support from Aditya-HPC team is highly appreciated. SSR and AP acknowledge the training on LETKF-MOM by Prof. Eugenia Kalnay and her team Travis Sluka and Dr. Steve Penny at the University of Maryland under the Monsoon Mission-I project. Authors thank Dr. Steve Penny for providing 20CR ensemble forcing. The authors would like to thank also Dr.Munmun DasGupta, NCMRWF, India for providing the ocean observations. SSR would like to thank Dr. Rajesh Sikhakolli, ISRO, India for his valuable general suggestions. Authors hereby declare no conflict of interest.

Comparison of 4DVAR and EnKF state estimates and forecasts in the Gulf of Mexico

Abstract: We gratefully acknowledge the ECCO consortium, including MIT, JPL, and the University of Hamburg, and the NCAR Data Assimilation Research Section (DAReS). The MITgcm code used in this study is checkpoint 64Y, and was obtained from http://mitgcm.org/. The Ssalto/Duacs altimeter product AVISO is produced and distributed by the Copernicus Marine and Environment Monitoring Service (CMEMS: http://marine.copernicus.eu/). The HYCOM/NCODA 1/12° global analysis data were obtained from the HYCOM consortium (http://hycom.org/dataserver/). The along-track altimetry data were obtained from the Radar Altimeter Database System (RADS: http://rads.tudelft.nl/rads/index.shtml). The SST data were obtained from Remote Sensing Systems Inc. (http://www.remss.com/). The NCEP/NCAR-Reanalysis-1 atmospheric forcings were obtained from http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html. The MITgcm–DART EnKF and MITgcm–ECCO 4DVAR state estimates, input files including observations and error fields, are available from the authors upon request (email:ggopalakrishnan@ucsd.edu). BDC gratefully acknowledges support from the Office of Naval Research grants N000141512285 and N000141512598. Research reported in this publication was supported by the Gulf Research Program of the National Academy of Sciences under award number 2000006422. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Gulf Research Program or the National Academy of Sciences.

Combining Hybrid and One-Step-Ahead Smoothing for Efficient Short-Range Storm Surge Forecasting with an Ensemble Kalman Filter

Abstract: This work combines two auxiliary techniques, namely the one-step-ahead (OSA) smoothing and the hybrid formulation, to boost the forecasting skills of a storm surge ensemble Kalman filter (E...