Altimetry for the future: Building on 25 years of progress

In 2018 we celebrated 25 years of development of radar altimetry, and the progress achieved by this methodology in the fields of global and coastal oceanography, hydrology, geodesy and cryospheric sciences. Many symbolic major events have celebrated these developments, e.g., in Venice, Italy, the 15th (2006) and 20th (2012) years of progress and more recently, in 2018, in Ponta Delgada, Portugal, 25 Years of Progress in Radar Altimetry. On this latter occasion it was decided to collect contributions of scientists, engineers and managers involved in the worldwide altimetry community to depict the state of altimetry and propose recommendations for the altimetry of the future. This paper summarizes contributions and recommendations that were collected and provides guidance for future mission design, research activities, and sustainable operational radar altimetry data exploitation. Recommendations provided are fundamental for optimizing further scientific and operational advances of oceanographic observations by altimetry, including requirements for spatial and temporal resolution of altimetric measurements, their accuracy and continuity. There are also new challenges and new openings mentioned in the paper that are particularly crucial for observations at higher latitudes, for coastal oceanography, for cryospheric studies and for hydrology. The paper starts with a general introduction followed by a section on Earth System Science including Ocean Dynamics, Sea Level, the Coastal Ocean, Hydrology, the Cryosphere and Polar Oceans and the “Green” Ocean, extending the frontier from biogeochemistry to marine ecology. Applications are described in a subsequent section, which covers Operational Oceanography, Weather, Hurricane Wave and Wind Forecasting, Climate projection. Instruments’ development and satellite missions’ evolutions are described in a fourth section. A fifth section covers the key observations that altimeters provide and their potential complements, from other Earth observation measurements to in situ data. Section 6 identifies the data and methods and provides some accuracy and resolution requirements for the wet tropospheric correction, the orbit and other geodetic requirements, the Mean Sea Surface, Geoid and Mean Dynamic Topography, Calibration and Validation, data accuracy, data access and handling (including the DUACS system). Section 7 brings a transversal view on scales, integration, artificial intelligence, and capacity building (education and training). Section 8 reviews the programmatic issues followed by a conclusion

Coastal pH variability in the Balearic Sea

[Description of methods used for collection/generation of data] In both stations a SAMI-pH (Sunburst Sensors LCC) was attached, at 1 m in the Bay of Palma and at 4 m depth in Cabrera. The pH sensors were measuring pH, in the total scale (pHT), hourly since December 2018 in the Bay of Palma and since November 2019 in Cabrera. The sensor precision and accuracy are < 0.001 pH and ± 0.003 pH units, respectively. Monthly maintenance of the sensors was performed including data download and surface cleaning. Temperature and salinity for the Cabrera mooring line was obtained starting November 2019 with a CT SBE37 (Sea-Bird Scientific©). Accuracy of the CT is ± 0.002 ∘C for temperature and ± 0.003 mS cm−1−1 for conductivity. Additionally, oxygen data from a SBE 63 (Sea-Bird Scientific ©) sensor attached to the CT in Cabrera were used. Accuracy of oxygen sensors is ± 2% for the SBE 63.[Methods for processing the data] Periodically water samplings for dissolved oxygen (DO), pH in total scale at 25 ∘C (pH25) and total alkalinity (TA) were obtained during the sensor maintenance campaigns. DO and (pH25) samples were collected in order to validate the data obtained by the sensors. DO concentrations were evaluated with the Winkler method modified by Benson and Krause by potentiometric titration with a Metrohm 808 Titrando with a accuracy of the method of ± 2.9 μmol kg−1μmol kg−1 and with an obtained standard deviation from the sensors data and the water samples collected of ± 5.9 μmol kg−1μmol kg−1. pH25T25 data was obtained by the spectrophotometric method with a Shimadzu UV-2501 spectrophotometer containing a 25 ∘C-thermostated cells with unpurified m-cresol purple as indicator following the methodology established by Clayton and Byrne by using Certified Reference Material (CRM Batch #176 supplied by Prof. Andrew Dickson, Scripps Institution of Oceanography, La Jolla, CA, USA). The accuracy obtained from the CRM Batch was of ± 0.0051 pH units and the precision of the method of ± 0.0034 pH units. The mean difference between the SAMI-pH and discrete samples was of 0.0017 pH units.Funding for this work was provided by the projects RTI2018-095441-B-C21 (SuMaEco) and, the BBVA Foundation project Posi-COIN and the Balearic Islands Government projects AAEE111/2017 and SEPPO (2018). SF was supported by a “Margalida Comas” postdoctoral scholarship, also from the Balearic Islands Government. FFP was supported by the BOCATS2 (PID2019-104279GB-C21) project funded by MCIN/AEI/10.13039/501100011033.This work is a contribution to CSIC’s Thematic Interdisciplinary Platform PTI WATER:iOS.Peer reviewe

Cell death in lake phytoplankton communities

1. The fraction of living and dead phytoplankton cells in seven Florida lakes was assessed by using the cell digestion assay, a non-staining membrane permeability test. The cell digestion assay is an effective method to analyse cell viability in complex natural phytoplankton communities. 2. The lakes examined ranged widely in phytoplankton abundance and community composition. The variability in the percentage of living cells (% LC) was high among the taxonomic groups forming the different phytoplankton communities, ranging from 19.7% to 98% LC. 3. All cells within single cyanobacteria filaments were determined to be either dead or alive, suggesting physiological integration of the cells within colonies. 4. Within each lake, the dominant taxa generally exhibited the highest proportion of living cells. A high proportion of living cells was found to be a characteristic of the different taxa forming the communities of eutrophic lakes. The average value for the % LC for all groups comprising the phytoplankton communities in each of the lakes ranged from 29.9 ± 7.2 to 80.4 ± 4.0 (mean ± SE) and varied strongly and positively with chlorophyll a concentration. 5. These results suggest phytoplankton cell death to be an important process structuring phytoplankton communities in lakes, particularly in oligotrophic ones. © 2006 The Authors.Peer Reviewe

ASLO ASM 2021 in Palma, Spain: Tips to Enjoy the Amazingly Beautiful Spots while on the Island of Mallorca and Surroundings

[eng] The next Aquatic Sciences Meeting will be held in Palma, Spain. We anticipate that the topic "Aquatic Sciences for a Sustainable Future: Nurturing Cooperation" and the location of the meeting will incite researchers from all continents working in all aquatic fields to come over, communicate, interact, and share. While attending the meeting for the stimulating science, one has the opportunity to enjoy the astounding beauty of Mallorca, the larger island of the Balearics archipelago, located about 200 km (~125 miles) off the mainland Spain. The archipelago is bathed by the transparent waters of the Mediterranean Sea, harboring some of the best preserved endemic Posidonia oceanica seagrass meadows (Fig. 1) including those declared as UNESCO World Heritage between Ibiza and Formentera. The small Cabrera Archipelago, off the coast of Mallorca, was the first declared "Sea and Land National Park" in Spain, and shelters a diversity of sea life, including the last Spanish catalogued specimens of the highly endangered pen shell, Pinna nobilis (Fig. 1)

Coastal pH variability reconstructed through machine learning in the Balearic Sea

[Description of methods used for collection/generation of data] Data was acquired in both stations using a SAMI-pH (Sunburst Sensors LCC) was attached, at 1 m in the Bay of Palma and at 4 m depth in Cabrera. The pH sensors were measuring pH, in the total scale (pH), hourly since December 2018 in the Bay of Palma and since November 2019 in Cabrera. The sensor precision and accuracy are < 0.001 pH and ± 0.003 pH units, respectively. Monthly maintenance of the sensors was performed including data download and surface cleaning. Temperature and salinity for the Cabrera mooring line was obtained starting November 2019 with a CT SBE37 (Sea-Bird Scientific©). Accuracy of the CT is ± 0.002 ∘C for temperature and ± 0.003 mS cm−1−1 for conductivity. Additionally, oxygen data from a SBE 63 (Sea-Bird Scientific ©) sensor attached to the CT in Cabrera were used. Accuracy of oxygen sensors is ± 2% for the SBE 63.[Methods for processing the data] Once data (available at https://doi.org/XXX/DigitalCSIC/XXX) was validated, several processing steps were performed to ensure an optimal training process for the neural network models. First, all the data of the time series were re-sampled by averaging the data points obtaining a daily frequency. Afterwards, a standard feature-scaling procedure (min-max normalization) was applied to every feature (temperature, salinity and oxygen) and to pHT. Finally, we built our training and validations sets as tensors with dimensions (batchsize, windowsize, features), where batchsize is the number of examples to train per iteration, windowsize is the number of past and future points considered and features is the number of features used to predict the target series. Temperature values below =12.5T=12.5 °C were discarded as they are considered outliers in sensor data outside the normal range in the study area. A BiDireccional Long Short-Term Memory (BD-LSTM) neural network was selected as the best architecture to reconstruct the pHT time series, with no signs of overfitting and achieving less than 1% error in both training and validation sets. Data corresponding to the Bay of Palma were used in the selection of the best neural network architecture. The code and data used to determine the best neural network architecture can be found in a GitHub repository mentioned in the context information.Funding for this work was provided by the projects RTI2018-095441-B-C21, RTI2018-095441-B-C22 (SuMaEco) and Grant MDM-2017-0711 (María de Maeztu Excellence Unit) funded by MCIN/AEI/10.13039/501100011033 and by the “ERDF A way of making Europe", the BBVA Foundation project Posi-COIN and the Balearic Islands Government projects AAEE111/2017 and SEPPO (2018). SF was supported by a “Margalida Comas” postdoctoral scholarship, also from the Balearic Islands Government. FFP was supported by the BOCATS2 (PID2019-104279GB-C21) project funded by MCIN/AEI/10.13039/501100011033.This work is a contribution to CSIC’s Thematic Interdisciplinary Platform PTI WATER:iOS (https://pti-waterios.csic.es/).Peer reviewe

pH trends and seasonal cycle in the coastal Balearic Sea reconstructed through machine learning

11 pages, 2 tables, 4 figures.-- This article is licensed under a Creative Commons Attribution 4.0 International License,The decreasing seawater pH trend associated with increasing atmospheric carbon dioxide levels is an issue of concern due to possible negative consequences for marine organisms, especially calcifiers. Globally, coastal areas represent important transitional land-ocean zones with complex interactions between biological, physical and chemical processes. Here, we evaluated the pH variability at two sites in the coastal area of the Balearic Sea (Western Mediterranean). High resolution pH data along with temperature, salinity, and also dissolved oxygen were obtained with autonomous sensors from 2018 to 2021 in order to determine the temporal pH variability and the principal drivers involved. By using environmental datasets of temperature, salinity and dissolved oxygen, Recurrent Neural Networks were trained to predict pH and fill data gaps. Longer environmental time series (2012–2021) were used to obtain the pH trend using reconstructed data. The best predictions show a rate of −0.0020±0.00054 pH units year−1, which is in good agreement with other observations of pH rates in coastal areas. The methodology presented here opens the possibility to obtain pH trends when only limited pH observations are available, if other variables are accessible. Potentially, this could be a way to reliably fill the unavoidable gaps present in time series data provided by sensorsFunding for this work was provided by the projects RTI2018-095441-B-C21, RTI2018-095441-B-C22 (SuMaEco) and Grant MDM-2017-0711 (María de Maeztu Excellence Unit) funded by MCIN/AEI/10.13039/501100011033 and by the “ERDF A way of making Europe”, the BBVA Foundation project Posi-COIN and the Balearic Islands Government projects AAEE111/2017 and SEPPO (2018). SF was supported by a “Margalida Comas” postdoctoral scholarship, also from the Balearic Islands Government. FFP was supported by the BOCATS2 (PID2019-104279GB-C21) project funded by MCIN/AEI/10.13039/501100011033. This work is a contribution to CSIC’s Thematic Interdisciplinary Platform PTI WATER:iOSPeer reviewe

Spatial and temporal variation of methane emissions in the coastal Balearic Sea, Western Mediterranean

13 pages, 2 tables, 6 figures.-- Under a Creative Commons licenseMethane (CH4) gas is the most important GHG after carbon dioxide, with open ocean areas acting as discreet CH4 sources and coastal regions as intense but variable CH4 sources to the atmosphere. Here, we report CH4 concentrations and air-sea fluxes in the coastal area of the Balearic Islands Archipelago (Western Mediterranean Basin). CH4 levels and related biogeochemical variables were measured in three coastal sampling sites between 2018 and 2021, with two located close to the densely populated island of Mallorca and one in a pristine area in the Cabrera Archipelago National Park. CH4 concentrations in seawater during the study period ranged from 2.7 to 10.9 nM, without significant differences between the sampling sites. Averaged estimated CH4 fluxes during the sampling period for the three stations oscillated between 0.2 and 9.7 μmol m-2 d-1 according to a seasonal pattern and in general all sites behaved as weak CH4 sources throughout the sampling period.Funding for this work was provided by the Spanish Ministry of Science (SumaEco, RTI2018–095441-B-C21), the Government of the Balearic Islands through la Consellería d'Innovació, Recerca i Turisme (Projecte de recerca científica i tecnològica SEPPO, PRD2018/18) and the 2018 call of BBVA Foundation “Ayudas a equipos de investigación científica” as the Posi-COIN Project. SF acknowledges the financial support of a “Margalida Comas” and “Vicenç Munt Estabilitat” postdoctoral contracts and project AAEE111/2017 from the Balearic Islands Government and the PTA2018–015585-I funded by the Spanish Ministry of Science, DRC was supported by the JAE-Intro 2021 CSIC fellowship Programme. MP acknowledges the financial support during the study period to the contracts funded by the Spanish Ministry of Science CTM2015–74510-JIN and PTA2019–017983-I. This work is a contribution to CSIC's Thematic Interdisciplinary Platform PTI WATER:iOSPeer reviewe

Spatial and temporal variation of methane emissions in the coastal area of Majorca and Cabrera

Trabajo presentado en el ASLO Aquatic Sciences Meeting, celebrado de forma virtual del 22 al 27 de junio de 2021Methane (CH4) is the second most important greenhouse gas after carbon dioxide (CO2), and is responsible for approximately 20% of the radiative forcing by well-mixed greenhouse gases in the lower atmosphere. In oceanic systems, shelf regions and estuaries are a minor source of atmospheric CH4 emitting around 2.3 - 15.6 Tg CH4 yr -1, while their contribution is probably higher due to sedimentary sources in coastal areas. However, these estimates show high uncertainties and seasonal variability, and results are mostly limited to the specific study area, especially in marginal areas. In this study, we sampled a range of three different locations along the Mallorca coast, including the archipelago of Cabrera (Bay of Palma, Cap Ses Salines, and Cala Santa Maria in PN Cabrera). Samples are taken from surface water and apart from CH4 additional variables were monitored such as temperature, salinity, DOC, alkalinity, and Chl a. To determine water-atmosphere fluxes, we used the transfer coefficient and the difference in concentrations between water and atmospheric values. The samples presented a concentration range of 2.7 - 4.5 nmol L -1 CH4 with an average of 3.9 nmol L -1. Fluxes were seasonal with the highest flow estimates for summer whereas the lowest estimates were obtained in winter. Finally, we determined the relationship between different environmental variables and their effect on the estimation of CH4 fluxes and discuss spatial patterns