K. Bai, Ke Li, Mingliang Ma, Kaitao Li, Zhengqiang Li, Jianping Guo, N. Chang, Zhuo Tan, Di Han
Abstract. Developing a big data analytics framework for generating a Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. By synergistically integrating multimodal aerosol data acquired from diverse sources via a tensor flow based data fusion method, a gap-free aerosol optical depth (AOD) dataset with daily 1-km resolution covering the period of 2000–2020 in China was generated. Specifically, data gaps in daily AOD imageries from MODIS aboard Terra were reconstructed based on a set of AOD data tensors acquired from satellites, numerical analysis, and in situ air quality data via integrative efforts of spatial pattern recognition for high dimensional gridded image analysis and knowledge transfer in statistical data mining. To our knowledge, this is the first long-term gap-free high resolution AOD dataset in China, from which spatially contiguous PM2.5 and PM10 concentrations were estimated using an ensemble learning approach. Ground validation results indicate that the LGHAP AOD data are in a good agreement with in situ AOD observations from AERONET, with R of 0.91 and RMSE equaling to 0.21. Meanwhile, PM2.5 and PM10 estimations also agreed well with ground measurements, with R of 0.95 and 0.94 and RMSE of 12.03 and 19.56 μg m−3, respectively. Overall, the LGHAP provides a suite of long-term gap free gridded maps with high-resolution to better examine aerosol changes in China over the past two decades, from which three distinct variation periods of haze pollution were revealed in China. Additionally, the proportion of population exposed to unhealthy PM2.5 was increased from 50.60 % in 2000 to 63.81 % in 2014 across China, which was then drastically reduced to 34.03 % in 2020. Overall, the generated LGHAP aerosol dataset has a great potential to trigger multidisciplinary applications in earth observations, climate change, public health, ecosystem assessment, and environmental management. The daily resolution AOD, PM2.5, and PM10 datasets can be publicly accessed at https://doi.org/10.5281/zenodo.5652257 (Bai et al., 2021a), https://doi.org/10.5281/zenodo.5652265 (Bai et al., 2021b), and https://doi.org/10.5281/zenodo.5652263 (Bai et al., 2021c), respectively. Meanwhile, monthly and annual mean datasets can be found at https://doi.org/10.5281/zenodo.5655797 (Bai et al., 2021d) and https://doi.org/10.5281/zenodo.5655807 (Bai et al., 2021e), respectively. Python, Matlab, R, and IDL codes were also provided to help users read and visualize these data.
摘要构建长期无间隙高分辨率大气污染物浓度数据集(简称LGHAP)的大数据分析框架，对环境管理和地球系统科学分析具有重要意义。通过基于张量流的数据融合方法，对不同来源的多模态气溶胶数据进行协同整合，生成了2000-2020年中国无间隙气溶胶光学深度(AOD)日分辨率1 km数据集。具体而言，基于从卫星获取的AOD数据张量、数值分析和现场空气质量数据，通过高维网格图像分析的空间模式识别和统计数据挖掘的知识转移的综合努力，重构Terra上MODIS每日AOD图像的数据缺口。据我们所知，这是中国第一个长期无间隙高分辨率AOD数据集，使用集成学习方法估算了空间连续的PM2.5和PM10浓度。地面验证结果表明，lglhap AOD数据与AERONET现场AOD观测值吻合较好，R为0.91,RMSE为0.21。PM2.5和PM10的R值分别为0.95和0.94,RMSE分别为12.03和19.56 μ m−3。总体而言，LGHAP提供了一套长期无间隙高分辨率网格图，以更好地研究过去20年中国的气溶胶变化，从中揭示了中国雾霾污染的三个不同变化期。此外，全国暴露于不健康PM2.5的人口比例从2000年的50.60%上升到2014年的63.81%，然后大幅下降到2020年的34.03%。总的来说，生成的LGHAP气溶胶数据集在地球观测、气候变化、公共卫生、生态系统评估和环境管理等领域具有很大的应用潜力。日分辨率AOD、PM2.5和PM10数据集可分别在https://doi.org/10.5281/zenodo.5652257 (Bai et al.， 2021a)、https://doi.org/10.5281/zenodo.5652265 (Bai et al.， 2021b)和https://doi.org/10.5281/zenodo.5652263 (Bai et al.， 2021c)公开访问。同时，月均数据集可在https://doi.org/10.5281/zenodo.5655797 (Bai et al.， 2021d)和https://doi.org/10.5281/zenodo.5655807 (Bai et al.， 2021e)上找到。还提供了Python、Matlab、R和IDL代码来帮助用户读取和可视化这些数据。
{"title":"LGHAP: a Long-term Gap-free High-resolution Air Pollutants concentration dataset derived via tensor flow based multimodal data fusion","authors":"K. Bai, Ke Li, Mingliang Ma, Kaitao Li, Zhengqiang Li, Jianping Guo, N. Chang, Zhuo Tan, Di Han","doi":"10.5194/essd-2021-404","DOIUrl":"https://doi.org/10.5194/essd-2021-404","url":null,"abstract":"Abstract. Developing a big data analytics framework for generating a Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. By synergistically integrating multimodal aerosol data acquired from diverse sources via a tensor flow based data fusion method, a gap-free aerosol optical depth (AOD) dataset with daily 1-km resolution covering the period of 2000–2020 in China was generated. Specifically, data gaps in daily AOD imageries from MODIS aboard Terra were reconstructed based on a set of AOD data tensors acquired from satellites, numerical analysis, and in situ air quality data via integrative efforts of spatial pattern recognition for high dimensional gridded image analysis and knowledge transfer in statistical data mining. To our knowledge, this is the first long-term gap-free high resolution AOD dataset in China, from which spatially contiguous PM2.5 and PM10 concentrations were estimated using an ensemble learning approach. Ground validation results indicate that the LGHAP AOD data are in a good agreement with in situ AOD observations from AERONET, with R of 0.91 and RMSE equaling to 0.21. Meanwhile, PM2.5 and PM10 estimations also agreed well with ground measurements, with R of 0.95 and 0.94 and RMSE of 12.03 and 19.56 μg m−3, respectively. Overall, the LGHAP provides a suite of long-term gap free gridded maps with high-resolution to better examine aerosol changes in China over the past two decades, from which three distinct variation periods of haze pollution were revealed in China. Additionally, the proportion of population exposed to unhealthy PM2.5 was increased from 50.60 % in 2000 to 63.81 % in 2014 across China, which was then drastically reduced to 34.03 % in 2020. Overall, the generated LGHAP aerosol dataset has a great potential to trigger multidisciplinary applications in earth observations, climate change, public health, ecosystem assessment, and environmental management. The daily resolution AOD, PM2.5, and PM10 datasets can be publicly accessed at https://doi.org/10.5281/zenodo.5652257 (Bai et al., 2021a), https://doi.org/10.5281/zenodo.5652265 (Bai et al., 2021b), and https://doi.org/10.5281/zenodo.5652263 (Bai et al., 2021c), respectively. Meanwhile, monthly and annual mean datasets can be found at https://doi.org/10.5281/zenodo.5655797 (Bai et al., 2021d) and https://doi.org/10.5281/zenodo.5655807 (Bai et al., 2021e), respectively. Python, Matlab, R, and IDL codes were also provided to help users read and visualize these data.\u0000","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125270480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
E. Olmedo, V. González-Gambau, A. Turiel, C. González‐Haro, Aina García-Espriu, M. Grégoire, A. Álvera-Azcárate, L. Buga, M. Rio
Abstract. In the framework of the European Space Agency (ESA) regional initiative called Earth Observation data For Science and Innovation in the Black Sea (EO4SIBS), a new dedicated Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) product is generated for the Black Sea for the years 2011–2020. Three SMOS SSS fields are retrieved and distributed: a level 2 product consisting of binned SSS in daily maps at 0.25° × 0.25° spatial resolution grid by considering ascending ((Olmedo et al., 2021b), https://doi.org/10.20350/digitalCSIC/13993) and descending ((Olmedo et al., 2021c), https://doi.org/10.20350/digitalCSIC/13995) satellite overpass directions separately; a level 3 product ((Olmedo et al., 2021d), https://doi.org/10.20350/digitalCSIC/13996) consisting of binned SSS in 9-day maps at 0.25° × 0.25° grid by combining as cending and descending satellite overpass directions; and a level 4 product ((Olmedo et al., 2021e), https://doi.org/10.20350/digitalCSIC/13997) consisting of daily maps at 0.05 × 0.0505° that are computed by merging the level 3 SSS product with Sea Surface Temperature (SST) maps. The generation of SMOS SSS fields in the Black Sea requires the use of enhanced data processing algorithms for improving the Brightness Temperatures in the region since this basin is typically strongly affected by Radio Frequency Interference (RFI) sources which hinders the retrieval of salinity. Here, we describe the algorithms introduced to improve the quality of the salinity retrieval in this basin. The validation of the EO4SIBS SMOS SSS products is performed by: i) comparing the EO4SIBS SMOS SSS products with near-to-surface salinity measurements provided by in situ measurements; ii) assessing the geophysical consistency of the products by comparing them with a model and other satellite salinity measurements; iii) computing maps of SSS errors by using Correlated Triple Collocation analysis. The accuracy of the EO4SIBS SMOS SSS products depend on the time period and on the product level. The accuracy in the period 2016–2020 is better than in 2011–2015 and it is as follows for the different products: i) Level 2 ascending: 1.85 / 1.50 psu (in 2011–2015 / 2016–2020); Level 2 descending: 2.95 1.95 psu; ii) Level 3: 0.7 / 0.5 psu; and iii) Level 4: 0.6 / 0.4 psu.
{"title":"New SMOS SSS maps in the framework of the Earth Observation data For Science and Innovation in the Black Sea","authors":"E. Olmedo, V. González-Gambau, A. Turiel, C. González‐Haro, Aina García-Espriu, M. Grégoire, A. Álvera-Azcárate, L. Buga, M. Rio","doi":"10.5194/essd-2021-364","DOIUrl":"https://doi.org/10.5194/essd-2021-364","url":null,"abstract":"Abstract. In the framework of the European Space Agency (ESA) regional initiative called Earth Observation data For Science and Innovation in the Black Sea (EO4SIBS), a new dedicated Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) product is generated for the Black Sea for the years 2011–2020. Three SMOS SSS fields are retrieved and distributed: a level 2 product consisting of binned SSS in daily maps at 0.25° × 0.25° spatial resolution grid by considering ascending ((Olmedo et al., 2021b), https://doi.org/10.20350/digitalCSIC/13993) and descending ((Olmedo et al., 2021c), https://doi.org/10.20350/digitalCSIC/13995) satellite overpass directions separately; a level 3 product ((Olmedo et al., 2021d), https://doi.org/10.20350/digitalCSIC/13996) consisting of binned SSS in 9-day maps at 0.25° × 0.25° grid by combining as cending and descending satellite overpass directions; and a level 4 product ((Olmedo et al., 2021e), https://doi.org/10.20350/digitalCSIC/13997) consisting of daily maps at 0.05 × 0.0505° that are computed by merging the level 3 SSS product with Sea Surface Temperature (SST) maps. The generation of SMOS SSS fields in the Black Sea requires the use of enhanced data processing algorithms for improving the Brightness Temperatures in the region since this basin is typically strongly affected by Radio Frequency Interference (RFI) sources which hinders the retrieval of salinity. Here, we describe the algorithms introduced to improve the quality of the salinity retrieval in this basin. The validation of the EO4SIBS SMOS SSS products is performed by: i) comparing the EO4SIBS SMOS SSS products with near-to-surface salinity measurements provided by in situ measurements; ii) assessing the geophysical consistency of the products by comparing them with a model and other satellite salinity measurements; iii) computing maps of SSS errors by using Correlated Triple Collocation analysis. The accuracy of the EO4SIBS SMOS SSS products depend on the time period and on the product level. The accuracy in the period 2016–2020 is better than in 2011–2015 and it is as follows for the different products: i) Level 2 ascending: 1.85 / 1.50 psu (in 2011–2015 / 2016–2020); Level 2 descending: 2.95 1.95 psu; ii) Level 3: 0.7 / 0.5 psu; and iii) Level 4: 0.6 / 0.4 psu.\u0000","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"227 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121292829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
F. Tubiello, Kevin Karl, A. Flammini, Johannes Gütschow, Griffiths Obli-Layrea, G. Conchedda, Xueyao Pan, Sally Yue Qi, Hörn Halldórudóttir Heiðarsdóttir, Nathaniel S. Wanner, R. Quadrelli, Leonardo Rocha Souza, Philippe Benoit, M. Hayek, D. Sandalow, Erik Mencos-Contreras, C. Rosenzweig, José Rosero Moncayo, P. Conforti, M. Torero
Abstract. We present results from the FAOSTAT agri-food systems emissions database, relative to 236 countries and territories and over the period 1990–2019. We find that in 2019, world-total food systems emissions were 16.5 billion metric tonnes (Gt CO2eq yr−1), corresponding to 31 % of total anthropogenic emissions. Of the agri-food systems total, global emissions within the farm gate –from crop and livestock production processes including on-farm energy use—were 7.2 Gt CO2eq yr−1; emissions from land use change, due to deforestation and peatland degradation, were 3.5 Gt CO2eq yr−1; and emissions from pre- and post-production processes –manufacturing of fertilizers, food processing, packaging, transport, retail, household consumption and food waste disposal—were 5.8 Gt CO2eq yr−1. Over the study period 1990–2019, agri-food systems emissions increased in total by 17 %, largely driven by a doubling of emissions from pre- and post-production processes. Conversely, the FAO data show that since 1990 land use emissions decreased by 25 %, while emissions within the farm gate increased only 9 %. In 2019, in terms of single GHG, pre- and post- production processes emitted the most CO2 (3.9 Gt CO2 yr−1), preceding land use change (3.3 Gt CO2 yr−1) and farm-gate (1.2 Gt CO2 yr−1) emissions. Conversely, farm-gate activities were by far the major emitter of methane (140 Mt CH4 yr−1) and of nitrous oxide (7.8 Mt N2O yr−1). Pre-and post- processes were also significant emitters of methane (49 Mt CH4 yr−1), mostly generated from the decay of solid food waste in landfills and open-dumps. The most important trend over the 30-year period since 1990 highlighted by our analysis is the increasingly important role of food-related emissions generated outside of agricultural land, in pre- and post-production processes along food supply chains, at all scales from global, regional and national, from 1990 to 2019. In fact, our data show that by 2019, food supply chains had overtaken farm-gate processes to become the largest GHG component of agri-food systems emissions in Annex I parties (2.2 Gt CO2eq yr−1). They also more than doubled in non-Annex I parties (to 3.5 Gt CO2eq yr−1), becoming larger than emissions from land-use change. By 2019 food supply chains had become the largest agri-food system component in China (1100 Mt CO2eq yr−1); USA (700 Mt CO2eq yr−1) and EU-27 (600 Mt CO2eq yr−1). This has important repercussions for food-relevant national mitigation strategies, considering that until recently these have focused mainly on reductions of non-CO2 gases within the farm gate and on CO2 mitigation from land use change. The information used in this work is available as open data at: https://zenodo.org/record/5615082 (Tubiello et al., 2021d). It is also available to users via the FAOSTAT database (FAO, 2021a), with annual updates.
{"title":"Pre- and post-production processes along supply chains increasingly dominate GHG emissions from agri-food systems globally and in most countries","authors":"F. Tubiello, Kevin Karl, A. Flammini, Johannes Gütschow, Griffiths Obli-Layrea, G. Conchedda, Xueyao Pan, Sally Yue Qi, Hörn Halldórudóttir Heiðarsdóttir, Nathaniel S. Wanner, R. Quadrelli, Leonardo Rocha Souza, Philippe Benoit, M. Hayek, D. Sandalow, Erik Mencos-Contreras, C. Rosenzweig, José Rosero Moncayo, P. Conforti, M. Torero","doi":"10.5194/essd-2021-389","DOIUrl":"https://doi.org/10.5194/essd-2021-389","url":null,"abstract":"Abstract. We present results from the FAOSTAT agri-food systems emissions database, relative to 236 countries and territories and over the period 1990–2019. We find that in 2019, world-total food systems emissions were 16.5 billion metric tonnes (Gt CO2eq yr−1), corresponding to 31 % of total anthropogenic emissions. Of the agri-food systems total, global emissions within the farm gate –from crop and livestock production processes including on-farm energy use—were 7.2 Gt CO2eq yr−1; emissions from land use change, due to deforestation and peatland degradation, were 3.5 Gt CO2eq yr−1; and emissions from pre- and post-production processes –manufacturing of fertilizers, food processing, packaging, transport, retail, household consumption and food waste disposal—were 5.8 Gt CO2eq yr−1. Over the study period 1990–2019, agri-food systems emissions increased in total by 17 %, largely driven by a doubling of emissions from pre- and post-production processes. Conversely, the FAO data show that since 1990 land use emissions decreased by 25 %, while emissions within the farm gate increased only 9 %. In 2019, in terms of single GHG, pre- and post- production processes emitted the most CO2 (3.9 Gt CO2 yr−1), preceding land use change (3.3 Gt CO2 yr−1) and farm-gate (1.2 Gt CO2 yr−1) emissions. Conversely, farm-gate activities were by far the major emitter of methane (140 Mt CH4 yr−1) and of nitrous oxide (7.8 Mt N2O yr−1). Pre-and post- processes were also significant emitters of methane (49 Mt CH4 yr−1), mostly generated from the decay of solid food waste in landfills and open-dumps. The most important trend over the 30-year period since 1990 highlighted by our analysis is the increasingly important role of food-related emissions generated outside of agricultural land, in pre- and post-production processes along food supply chains, at all scales from global, regional and national, from 1990 to 2019. In fact, our data show that by 2019, food supply chains had overtaken farm-gate processes to become the largest GHG component of agri-food systems emissions in Annex I parties (2.2 Gt CO2eq yr−1). They also more than doubled in non-Annex I parties (to 3.5 Gt CO2eq yr−1), becoming larger than emissions from land-use change. By 2019 food supply chains had become the largest agri-food system component in China (1100 Mt CO2eq yr−1); USA (700 Mt CO2eq yr−1) and EU-27 (600 Mt CO2eq yr−1). This has important repercussions for food-relevant national mitigation strategies, considering that until recently these have focused mainly on reductions of non-CO2 gases within the farm gate and on CO2 mitigation from land use change. The information used in this work is available as open data at: https://zenodo.org/record/5615082 (Tubiello et al., 2021d). It is also available to users via the FAOSTAT database (FAO, 2021a), with annual updates.\u0000","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123959516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
K. Cohen, V. Cartelle, R. Barnett, F. Busschers, N. Barlow
Abstract. Abundant numbers of sites and studies exist that document the Last Interglacial (Eemian, Ipswichian, MIS 5e) coastal record for geographically and geomorphologically diverse NW Europe. This paper documents a database of 141 known Last Interglacial sea-level indicative data points from in and around the North Sea (35 entries in Netherlands, 10 Belgium, 16 in Germany, 17 in Denmark, 8 in Britain) and the English Channel (28 entries for British and 25 for the French side, 3 on the Channel Isles), believed to be a representative and fairly complete inventory and assessment coming from some 80 published sites. The good geographic distribution (some 1500 km SW-NE) across the near field of the Scandinavian and British Ice Sheets and the attention paid to absolute and relative age control are assets of the NW European database compilation. The research history of Last Interglacial coastal environments and sea-level position for this area is long, methodically diverse and spread over regional literature in several languages. Last Interglacial high-stand shorelines of Dutch and German Bight parts of the North Sea, were of lagoonal and estuarine type and have preserved subsurface (data entry included estimates of non-GIA vertical land motion). In contrast, Last Interglacial high-stand shorelines along the English Channel are encountered above modern sea-level (data entry includes datum definitions). Our review and database compilation effort drew from the original regional literature, and paid particular attention to distinguishing between sea-level index points (SLIPs) and marine and terrestrial limiting-points. This paper describes the dominant sea-level indicators produced from region to region, compliant to the database structure of the special issue (WALIS), referenced to original source data. The sea level proxies in majority are obtained from localities with well-developed lithostratigraphic, morpho-stratigraphic and biostratigraphical constraints. Amino-Acid Racemization information is also prominent, especially in Britain, albeit for many sites the older, lesser quality applications of that technique. The majority of European continental sites have chronostratigraphic age-control, notably through regional Pollen Association Zones of known durations. This greatly helps to separate transgression, highstand (‘stillstand’) and regression subsets from within the interglacial, useful when summarizing and/or querying the dataset. In all regions, many SLIPs and limiting points have further independent age-control from luminescence (IRSL, OSL, TL), U-series and ESR dating techniques. Main foreseen usage of this database for the near field region of the European ice sheets is in GIA modelling.
{"title":"Last Interglacial sea-level data points from Northwest Europe","authors":"K. Cohen, V. Cartelle, R. Barnett, F. Busschers, N. Barlow","doi":"10.5194/essd-2021-390","DOIUrl":"https://doi.org/10.5194/essd-2021-390","url":null,"abstract":"Abstract. Abundant numbers of sites and studies exist that document the Last Interglacial (Eemian, Ipswichian, MIS 5e) coastal record for geographically and geomorphologically diverse NW Europe. This paper documents a database of 141 known Last Interglacial sea-level indicative data points from in and around the North Sea (35 entries in Netherlands, 10 Belgium, 16 in Germany, 17 in Denmark, 8 in Britain) and the English Channel (28 entries for British and 25 for the French side, 3 on the Channel Isles), believed to be a representative and fairly complete inventory and assessment coming from some 80 published sites. The good geographic distribution (some 1500 km SW-NE) across the near field of the Scandinavian and British Ice Sheets and the attention paid to absolute and relative age control are assets of the NW European database compilation. The research history of Last Interglacial coastal environments and sea-level position for this area is long, methodically diverse and spread over regional literature in several languages. Last Interglacial high-stand shorelines of Dutch and German Bight parts of the North Sea, were of lagoonal and estuarine type and have preserved subsurface (data entry included estimates of non-GIA vertical land motion). In contrast, Last Interglacial high-stand shorelines along the English Channel are encountered above modern sea-level (data entry includes datum definitions). Our review and database compilation effort drew from the original regional literature, and paid particular attention to distinguishing between sea-level index points (SLIPs) and marine and terrestrial limiting-points. This paper describes the dominant sea-level indicators produced from region to region, compliant to the database structure of the special issue (WALIS), referenced to original source data. The sea level proxies in majority are obtained from localities with well-developed lithostratigraphic, morpho-stratigraphic and biostratigraphical constraints. Amino-Acid Racemization information is also prominent, especially in Britain, albeit for many sites the older, lesser quality applications of that technique. The majority of European continental sites have chronostratigraphic age-control, notably through regional Pollen Association Zones of known durations. This greatly helps to separate transgression, highstand (‘stillstand’) and regression subsets from within the interglacial, useful when summarizing and/or querying the dataset. In all regions, many SLIPs and limiting points have further independent age-control from luminescence (IRSL, OSL, TL), U-series and ESR dating techniques. Main foreseen usage of this database for the near field region of the European ice sheets is in GIA modelling.\u0000","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129396186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Melisa Diaz Resquin, Pablo Lichtig, Diego Alessandrello, Marcelo De Oto, Darío Gómez, Cristina Rössler, Paula S. Castesana, L. Dawidowski
Abstract. The COVID-19 (COronaVIrus Disease 2019) pandemic provided the unique opportunity to evaluate the role of a sudden and deep decline in air pollutant emissions in the ambient air of numerous cities worldwide. Argentina, in general, and the Metropolitan Area of Buenos Aires (MABA), in particular, were under strict control measures from March to May 2020. Private vehicle restrictions were intense, and primary pollutant concentrations decreased substantially. To quantify the changes in CO, NO, NO2, PM10, SO2 and O3 concentrations under the stay-at-home orders imposed against COVID-19, we compared the observations during the different lockdown phases with both observations during the same period in 2019 and concentrations that would have occurred under a business-as-usual (BAU) scenario under no restrictions. We employed a Random Forest (RF) algorithm to estimate the BAU concentration levels. This approach exhibited a high predictive performance based on only a handful of available indicators (meteorological variables, air quality concentrations and emission temporal variations) at a low computational cost. Results during testing showed that the model captured the observed daily variations and the diurnal cycles of these pollutants with a normalized mean bias (NMB) of less than 11 % and Pearson correlation coefficients of the diurnal variations of between 0.65 and 0.89 for all the pollutants considered. Based on the Random Forest results, we estimated that the lockdown implied concentration decreases of up to 47 % (CO), 60 % (NOx) and 36 % (PM10) during the strictest mobility restrictions. Higher O3 concentrations (up to 87 %) were also observed, which is consistent with the response in a VOC-limited chemical regime to the decline in NOx emissions. Relative changes with respect to the 2019 observations were consistent with those estimated with the Random Forest model, but indicated that larger decreases in primary pollutants and lower increases in O3 would have occurred. This points out to the need of accounting not only for the differences in emissions, but also in meteorological variables to evaluate the lockdown effects on air quality. The findings of this study may be valuable for formulating emission control strategies that do not disregard their implication on secondary pollutants. The data set used in this study and an introductory machine learning code are openly available at https://data.mendeley.com/datasets/h9y4hb8sf8/1 (Diaz Resquin et al., 2021).
摘要2019冠状病毒病(COVID-19)大流行为评估全球许多城市环境空气中空气污染物排放量突然大幅下降的作用提供了独特的机会。从2020年3月到5月，整个阿根廷，特别是布宜诺斯艾利斯大都会区(MABA)都处于严格的控制措施之下。机动车限行力度加大，主要污染物浓度大幅下降。为了量化在针对COVID-19实施的居家令下CO、NO、NO2、PM10、SO2和O3浓度的变化，我们将不同封锁阶段的观测结果与2019年同期的观测结果以及在没有限制的情况下“一切照旧”(BAU)情景下的浓度进行了比较。我们采用随机森林(RF)算法来估计BAU浓度水平。该方法仅基于少数可用指标(气象变量、空气质量浓度和排放时间变化)，计算成本低，具有较高的预测性能。测试结果表明，该模型捕获了观测到的这些污染物的日变化和日循环，其归一化平均偏差(NMB)小于11%，所有考虑的污染物的日变化的Pearson相关系数在0.65至0.89之间。根据随机森林的结果，我们估计在最严格的移动限制期间，封城意味着浓度降低高达47% (CO)， 60% (NOx)和36% (PM10)。还观察到更高的O3浓度(高达87%)，这与voc限制化学制度对氮氧化物排放下降的响应一致。与2019年的观测结果相比，相对变化与随机森林模型的估计结果一致，但表明将出现更大幅度的初级污染物减少和更低的O3增加。这表明，在评估封城对空气质量的影响时，不仅需要考虑排放量的差异，还需要考虑气象变量。本研究的发现可能对制定不忽视其对二次污染物的影响的排放控制策略有价值。本研究中使用的数据集和介绍性机器学习代码可在https://data.mendeley.com/datasets/h9y4hb8sf8/1上公开获取(Diaz Resquin et al.， 2021)。
{"title":"A machine learning approach to address air quality changes during the COVID-19 lockdown in Buenos Aires, Argentina","authors":"Melisa Diaz Resquin, Pablo Lichtig, Diego Alessandrello, Marcelo De Oto, Darío Gómez, Cristina Rössler, Paula S. Castesana, L. Dawidowski","doi":"10.5194/essd-2021-318","DOIUrl":"https://doi.org/10.5194/essd-2021-318","url":null,"abstract":"Abstract. The COVID-19 (COronaVIrus Disease 2019) pandemic provided the unique opportunity to evaluate the role of a sudden and deep decline in air pollutant emissions in the ambient air of numerous cities worldwide. Argentina, in general, and the Metropolitan Area of Buenos Aires (MABA), in particular, were under strict control measures from March to May 2020. Private vehicle restrictions were intense, and primary pollutant concentrations decreased substantially. To quantify the changes in CO, NO, NO2, PM10, SO2 and O3 concentrations under the stay-at-home orders imposed against COVID-19, we compared the observations during the different lockdown phases with both observations during the same period in 2019 and concentrations that would have occurred under a business-as-usual (BAU) scenario under no restrictions. We employed a Random Forest (RF) algorithm to estimate the BAU concentration levels. This approach exhibited a high predictive performance based on only a handful of available indicators (meteorological variables, air quality concentrations and emission temporal variations) at a low computational cost. Results during testing showed that the model captured the observed daily variations and the diurnal cycles of these pollutants with a normalized mean bias (NMB) of less than 11 % and Pearson correlation coefficients of the diurnal variations of between 0.65 and 0.89 for all the pollutants considered. Based on the Random Forest results, we estimated that the lockdown implied concentration decreases of up to 47 % (CO), 60 % (NOx) and 36 % (PM10) during the strictest mobility restrictions. Higher O3 concentrations (up to 87 %) were also observed, which is consistent with the response in a VOC-limited chemical regime to the decline in NOx emissions. Relative changes with respect to the 2019 observations were consistent with those estimated with the Random Forest model, but indicated that larger decreases in primary pollutants and lower increases in O3 would have occurred. This points out to the need of accounting not only for the differences in emissions, but also in meteorological variables to evaluate the lockdown effects on air quality. The findings of this study may be valuable for formulating emission control strategies that do not disregard their implication on secondary pollutants. The data set used in this study and an introductory machine learning code are openly available at https://data.mendeley.com/datasets/h9y4hb8sf8/1 (Diaz Resquin et al., 2021).\u0000","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116911839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
E. Zelenin, D. Bachmanov, S. Garipova, V. Trifonov, A. Kozhurin
Abstract. Active faults are those faults on which movement is possible in the future. It draws particular attention to active faults in geodynamic studies and seismic hazard assessment. Here we present a high-detail continental-scale geodatabase: The Active Faults of Eurasia Database (AFEAD). It comprises 46,775 objects stored in the shapefile format with spatial detail sufficient for a map of scale 1:1M. Fault sense, a rank of confidence in activity, a rank of slip rate, and a reference to source publications are provided for each database entry. Where possible, it is supplemented with a fault name, fault zone name, abbreviated fault parameters (e.g., slip rate, age of the last motion, total offset), and text information from the sources. The database was collected from 612 sources, including regional maps, databases, and research papers. AFEAD facilitates a spatial search for local studies. It provides sufficient detail for planning a study of a particular fault system and guides deeper bibliographical investigations if needed. This scenario is particularly significant for vast Central and North Asia areas, where most studies are available only in Russian and hardcopy. Moreover, the database model provides the basis for GIS-based regional and continental-scale integrative studies. The database is available at https://doi.org/10.13140/RG.2.2.10333.74726 and via web map at http://neotec.ginras.ru/index/mapbox/database_map.html (last access: July 30, 2021). Some database representations with supplementary data are hosted at http://neotec.ginras.ru/index/english/database_eng.html.
{"title":"The Database of the Active Faults of Eurasia (AFEAD): Ontology and Design behind the Continental-Scale Dataset","authors":"E. Zelenin, D. Bachmanov, S. Garipova, V. Trifonov, A. Kozhurin","doi":"10.5194/essd-2021-312","DOIUrl":"https://doi.org/10.5194/essd-2021-312","url":null,"abstract":"Abstract. Active faults are those faults on which movement is possible in the future. It draws particular attention to active faults in geodynamic studies and seismic hazard assessment. Here we present a high-detail continental-scale geodatabase: The Active Faults of Eurasia Database (AFEAD). It comprises 46,775 objects stored in the shapefile format with spatial detail sufficient for a map of scale 1:1M. Fault sense, a rank of confidence in activity, a rank of slip rate, and a reference to source publications are provided for each database entry. Where possible, it is supplemented with a fault name, fault zone name, abbreviated fault parameters (e.g., slip rate, age of the last motion, total offset), and text information from the sources. The database was collected from 612 sources, including regional maps, databases, and research papers. AFEAD facilitates a spatial search for local studies. It provides sufficient detail for planning a study of a particular fault system and guides deeper bibliographical investigations if needed. This scenario is particularly significant for vast Central and North Asia areas, where most studies are available only in Russian and hardcopy. Moreover, the database model provides the basis for GIS-based regional and continental-scale integrative studies. The database is available at https://doi.org/10.13140/RG.2.2.10333.74726 and via web map at http://neotec.ginras.ru/index/mapbox/database_map.html (last access: July 30, 2021). Some database representations with supplementary data are hosted at http://neotec.ginras.ru/index/english/database_eng.html.\u0000","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129338878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
E. Ceballos-Romero, K. Buesseler, M. Villa-Alfageme
Abstract. We present here a global oceanic compilation of 234Th measurements that collects results from researchers and laboratories over a period exceeding 50 years. The origin of the 234Th sampling in the ocean goes back to 1967, when Bhat et al. (1969) initially studied 234Th distribution relative to its parent 238U in the Indian Ocean. However, it was the seminal work of Buesseler et al. (1992) – in which it was proposed that particulate organic carbon (POC) flux could be calculated from 234Th distributions if the ratio of POC to 234Th measured on sinking particles (POC : 234Th) at the desired depth was known – that drove the extensive use of the 234Th-238U radioactive pair to evaluate the efficiency with which photosynthetically fixed carbon is exported from surface ocean by means of the biological pump. Since then, a large number of 234Th depth profiles have been collected using a variety of sampling instruments and strategies that have changed the past 50 years. The present compilation is made of a total 223 datasets: 214 from studies published either in articles in referred journals, PhD thesis or repositories, and 9 unpublished datasets. The data were compiled from over 5000 locations spanning all the oceans for total 234Th profiles, dissolved and particulate 234Th concentrations, and POC : 234Th ratios (both sediment traps and filtration methods that include two sizes classes; 1–53 µm and < 53 µm). A total of 379 oceanographic expeditions and more than 56000 234Th and 18000 238U data points have been gathered in a single open-access, long-term and dynamic repository. This paper introduces the dataset along with informative and descriptive graphics. Appropriate metadata have been included, including geographic location, date, and sample depth, among others. When available, we also include water temperature, salinity, 238U data and particulate organic nitrogen data. Data sources and methods information (including 238U and 234Th) are also detailed along with valuable information for future data analysis such as bloom stage and steady/non-steady state conditions at the sampling moment. The data are archived on PANGAEA repository, with the dataset’s DOI doi.pangaea.de/10.1594/PANGAEA.918125 (Ceballos-Romero et al., 2021). This provides a valuable resource to better understand and quantify how the contemporary oceanic carbon uptake functions and how it will change in future.
摘要我们在这里介绍了全球海洋的第234次测量汇编，收集了研究人员和实验室在超过50年的时间里的结果。海洋中第234次采样的起源可以追溯到1967年，当时Bhat et al.(1969)最初研究了234次相对于其母体238U在印度洋的分布。然而,它的开创性工作Buesseler et al .(1992),它提出了颗粒有机碳(POC)通量可以从第234分布计算如果POC的比率在沉没234测量粒子(POC: 234)所需的深度是已知的,开车的广泛使用234 th - 238 u放射性对评估光合成固定碳的效率从海洋表面通过生物泵出口。从那时起，使用各种采样工具和策略收集了大量234层深度剖面，这些方法在过去50年中发生了变化。目前的汇编由223个数据集组成:214个来自发表在参考期刊、博士论文或知识库中的研究，9个未发表的数据集。这些数据来自所有海洋的5000多个地点，包括234的总剖面、溶解和颗粒234的浓度，以及POC: 234的比率(包括沉积物捕集器和过滤方法，包括两种尺寸类别;1 ~ 53µm和< 53µm)。在一个开放、长期、动态的数据库中，共收集了379次海洋考察和56000多个234和18000多个238U数据点。本文介绍了数据集以及信息和描述性图形。包含了适当的元数据，包括地理位置、日期和样本深度等。在可用的情况下，我们还包括水温，盐度，238U数据和颗粒有机氮数据。还详细介绍了数据源和方法信息(包括238U和234)以及对未来数据分析有价值的信息，如采样时刻的开花阶段和稳态/非稳态条件。数据存档在PANGAEA存储库中，数据集的DOI DOI . PANGAEA. de/10.1594/PANGAEA.918125 (Ceballos-Romero et al.， 2021)。这为更好地理解和量化当代海洋碳吸收的功能及其未来的变化提供了宝贵的资源。
{"title":"Revisiting five decades of 234Th data: a comprehensive global oceanic compilation","authors":"E. Ceballos-Romero, K. Buesseler, M. Villa-Alfageme","doi":"10.5194/essd-2021-259","DOIUrl":"https://doi.org/10.5194/essd-2021-259","url":null,"abstract":"Abstract. We present here a global oceanic compilation of 234Th measurements that collects results from researchers and laboratories over a period exceeding 50 years. The origin of the 234Th sampling in the ocean goes back to 1967, when Bhat et al. (1969) initially studied 234Th distribution relative to its parent 238U in the Indian Ocean. However, it was the seminal work of Buesseler et al. (1992) – in which it was proposed that particulate organic carbon (POC) flux could be calculated from 234Th distributions if the ratio of POC to 234Th measured on sinking particles (POC : 234Th) at the desired depth was known – that drove the extensive use of the 234Th-238U radioactive pair to evaluate the efficiency with which photosynthetically fixed carbon is exported from surface ocean by means of the biological pump. Since then, a large number of 234Th depth profiles have been collected using a variety of sampling instruments and strategies that have changed the past 50 years. The present compilation is made of a total 223 datasets: 214 from studies published either in articles in referred journals, PhD thesis or repositories, and 9 unpublished datasets. The data were compiled from over 5000 locations spanning all the oceans for total 234Th profiles, dissolved and particulate 234Th concentrations, and POC : 234Th ratios (both sediment traps and filtration methods that include two sizes classes; 1–53 µm and < 53 µm). A total of 379 oceanographic expeditions and more than 56000 234Th and 18000 238U data points have been gathered in a single open-access, long-term and dynamic repository. This paper introduces the dataset along with informative and descriptive graphics. Appropriate metadata have been included, including geographic location, date, and sample depth, among others. When available, we also include water temperature, salinity, 238U data and particulate organic nitrogen data. Data sources and methods information (including 238U and 234Th) are also detailed along with valuable information for future data analysis such as bloom stage and steady/non-steady state conditions at the sampling moment. The data are archived on PANGAEA repository, with the dataset’s DOI doi.pangaea.de/10.1594/PANGAEA.918125 (Ceballos-Romero et al., 2021). This provides a valuable resource to better understand and quantify how the contemporary oceanic carbon uptake functions and how it will change in future.\u0000","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133596955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Junxiao Wang, Liuming Wang, Mengyao Li, Liping Zhu, Xingong Li
Abstract. The Tibetan Plateau, known as "the third pole of the Earth", is a region susceptible to climate change. With little human disturbance, lake storage changes serve as a unique indicator of climate change, but comprehensive lake storage data are rare in the region, especially for the lakes with an area less than 10 km2 which are the most sensitive to environmental changes. In this paper, we completed a census of annual lake volume change for 976 lakes larger than 1 km2 in the endorheic basin of the Tibetan Plateau (EBTP) during 1989–2019 using Landsat imagery and digital terrain models. Validation and comparison with several existing studies indicate that our data are more reliable. Lake volume in the EBTP exhibited a net increase of 193.45 km3 during the time period with an increasing rate of 6.45 km3 year−1. In general, the larger the lake area, the greater the lake volume change, though there are some exceptions. Lakes with an area less than 10 km2 have more severe volume change whether decreasing or increasing. This research complements existing lake studies by providing a comprehensive and long-term lake volume change data for the region. The dataset is available on Zenodo ( https://doi.org/10.5281/zenodo.5543615 , Wang et al., 2021).
摘要被称为“地球第三极”的青藏高原是一个易受气候变化影响的地区。在人为干扰较小的情况下，湖泊蓄水量变化是气候变化的独特指标，但该地区湖泊蓄水量的综合数据较少，特别是对环境变化最敏感的面积小于10 km2的湖泊。本文利用陆地卫星图像和数字地形模型，对1989-2019年青藏高原内陆河流域976个大于1 km2的湖泊进行了湖泊体积变化的普查。与几个现有研究的验证和比较表明，我们的数据更可靠。在此期间，青藏高原湖泊体积净增加193.45 km3，年增长率为6.45 km3。一般来说，湖泊面积越大，湖泊体积变化越大，尽管也有一些例外。面积小于10平方公里的湖泊，无论是减少还是增加，其体积变化都更为剧烈。本研究补充了现有的湖泊研究，为该地区提供了全面和长期的湖泊体积变化数据。该数据集可在Zenodo上获得(https://doi.org/10.5281/zenodo.5543615, Wang et al.， 2021)。
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Léo Seyfried, L. Biscara, F. Leckler, A. Pasquet, H. Michaud
Abstract. The French Flooding Prevention Action Program of Saint-Malo requires assessment of coastal flooding risks. The first prerequisite is a knowledge of the topography and bathymetry of the bay of Saint-Malo. In addition to existing topo-bathymetric data, the acquisition of new multibeam bathymetric data is performed. The combination of these datasets allows the generation of two high resolution topo-bathymetric digital terrain models. Then, to understand the hydrodynamic conditions which cause coastal flooding, a dense and extensive oceanographic field experiment is conducted. Oceanographic data were acquired using a network of 22 moorings with 37 sensors, during winter 2018–2019. The network included 2 directional buoys, 2 pressure tide gauges, 18 wave pressure gauges, 4 single-point current meters, 7 current profilers and 4 acoustic wave-current profilers from mid-depth (25 m) up to the upper beach and the dike system. The oceanographic dataset provides an overview of hydrodynamics in Saint-Malo bay and wave processes leading to coastal flooding. The combination of high-resolution topo-bathymetric and oceanographic datasets provides a unique capability for model validation and process studies. The topo-bathymetric and oceanographic datasets are available freely at doi : https://doi.org/10.17183/MNT_COTIER_GNB_PAPI_SM_20m_WGS84, https://doi.org/10.17183/MNT_COTIER_PORT_SM_PAPI_SM_5m_WGS84, and https://doi.org/10.17183/CAMPAGNE_OCEANO_STMALO.
{"title":"Topo-bathymetric and oceanographic datasets for coastal flooding risk assessment: French Flooding Prevention Action Program of Saint-Malo","authors":"Léo Seyfried, L. Biscara, F. Leckler, A. Pasquet, H. Michaud","doi":"10.5194/essd-2021-316","DOIUrl":"https://doi.org/10.5194/essd-2021-316","url":null,"abstract":"Abstract. The French Flooding Prevention Action Program of Saint-Malo requires assessment of coastal flooding risks. The first prerequisite is a knowledge of the topography and bathymetry of the bay of Saint-Malo. In addition to existing topo-bathymetric data, the acquisition of new multibeam bathymetric data is performed. The combination of these datasets allows the generation of two high resolution topo-bathymetric digital terrain models. Then, to understand the hydrodynamic conditions which cause coastal flooding, a dense and extensive oceanographic field experiment is conducted. Oceanographic data were acquired using a network of 22 moorings with 37 sensors, during winter 2018–2019. The network included 2 directional buoys, 2 pressure tide gauges, 18 wave pressure gauges, 4 single-point current meters, 7 current profilers and 4 acoustic wave-current profilers from mid-depth (25 m) up to the upper beach and the dike system. The oceanographic dataset provides an overview of hydrodynamics in Saint-Malo bay and wave processes leading to coastal flooding. The combination of high-resolution topo-bathymetric and oceanographic datasets provides a unique capability for model validation and process studies. The topo-bathymetric and oceanographic datasets are available freely at doi : https://doi.org/10.17183/MNT_COTIER_GNB_PAPI_SM_20m_WGS84, https://doi.org/10.17183/MNT_COTIER_PORT_SM_PAPI_SM_5m_WGS84, and https://doi.org/10.17183/CAMPAGNE_OCEANO_STMALO.\u0000","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125637413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
B. Loveday, T. Smyth, A. Akpınar, T. Hull, M. Inall, J. Kaiser, B. Queste, Matt Tobermann, C. Williams, M. Palmer
Abstract. Shelf-seas play a key role in both the global carbon cycle and coastal marine ecosystems through the drawn-down and fixing of carbon, as measured through phytoplankton net primary production (NPP). Measuring NPP in situ, and extrapolating this to the local, regional and global scale presents challenges however because of limitations with the techniques utilised (e.g. radiocarbon isotopes), data sparsity and the inherent biogeochemical heterogeneity of coastal and open-shelf waters. Here, we introduce a powerful new technique based on the synergistic use of in situ glider profiles and satellite Earth Observation measurements which can be implemented in a real-time or delayed mode system. We apply this system to a fleet of gliders successively deployed over a 19-month time-frame in the North Sea, generating an unprecedented fine scale time-series of NPP in the region (Loveday and Smyth, 2020). At the large-scale, this time-series gives close agreement with existing satellite-based estimates of NPP for the region and previous in situ estimates. What has not been elucidated before is the high-frequency, small-scale, depth-resolved variability associated with bloom phenology, mesoscale phenomena and mixed layer dynamics.
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