{"count":87423,"next":"https://api.catalogue.ceda.ac.uk/api/v3/onlineresources/?format=json&limit=100&offset=81300","previous":"https://api.catalogue.ceda.ac.uk/api/v3/onlineresources/?format=json&limit=100&offset=81100","results":[{"ob_id":89083,"function":"externalCitation","linkage":"https://doi.org/10.14288/1.0422982","name":"Simon, L. (2023) Seasonal variation in marine bird distribution in the northeast Pacific Ocean. University of British Columbia. https://doi.org/10.14288/1.0422982","relatedTo":{"ob_id":27532,"uuid":"62c0f97b1eac4e0197a674870afe1ee6","short_code":"ob"}},{"ob_id":89084,"function":"externalCitation","linkage":"https://doi.org/10.3389/fmars.2022.837772","name":"von Hammerstein, H., Setter, R.O., van Aswegen, M., Currie, J.J. & Stack, S.H. (2022) High-Resolution Projections of Global Sea Surface Temperatures Reveal Critical Warming in Humpback Whale Breeding Grounds. Frontiers in Marine Science 9. https://doi.org/10.3389/fmars.2022.837772","relatedTo":{"ob_id":27532,"uuid":"62c0f97b1eac4e0197a674870afe1ee6","short_code":"ob"}},{"ob_id":89085,"function":"externalCitation","linkage":"https://doi.org/10.5194/egusphere-2022-25-ac1","name":"Pauthenet, E. (2022) Reply on RC1. https://doi.org/10.5194/egusphere-2022-25-ac1","relatedTo":{"ob_id":27532,"uuid":"62c0f97b1eac4e0197a674870afe1ee6","short_code":"ob"}},{"ob_id":89086,"function":"externalCitation","linkage":"https://doi.org/10.5194/egusphere-2022-1119-ac1","name":"Guinaldo, T. (2023) Reply on RC1. https://doi.org/10.5194/egusphere-2022-1119-ac1","relatedTo":{"ob_id":27532,"uuid":"62c0f97b1eac4e0197a674870afe1ee6","short_code":"ob"}},{"ob_id":89087,"function":"externalCitation","linkage":"https://doi.org/10.1038/s43247-023-00791-9","name":"error occurred","relatedTo":{"ob_id":27532,"uuid":"62c0f97b1eac4e0197a674870afe1ee6","short_code":"ob"}},{"ob_id":89088,"function":"externalCitation","linkage":"https://doi.org/10.3289/eurosea_d7.4","name":"McAdam, R. (2022) Skills of the user-relevant ocean indicators. EuroSea. https://doi.org/10.3289/eurosea_d7.4","relatedTo":{"ob_id":27532,"uuid":"62c0f97b1eac4e0197a674870afe1ee6","short_code":"ob"}},{"ob_id":89089,"function":"externalCitation","linkage":"https://doi.org/10.5194/gmd-16-6127-2023","name":"error occurred","relatedTo":{"ob_id":27532,"uuid":"62c0f97b1eac4e0197a674870afe1ee6","short_code":"ob"}},{"ob_id":89090,"function":"externalCitation","linkage":"https://doi.org/10.3389/fmars.2023.1052166","name":"van Oostende, M., Hieronymi, M., Krasemann, H. & Baschek, B. (2023) Global ocean colour trends in biogeochemical provinces. Frontiers in Marine Science 10. https://doi.org/10.3389/fmars.2023.1052166","relatedTo":{"ob_id":27532,"uuid":"62c0f97b1eac4e0197a674870afe1ee6","short_code":"ob"}},{"ob_id":89091,"function":"externalCitation","linkage":"https://doi.org/10.5194/egusphere-2022-1119-ac2","name":"Guinaldo, T. (2023) Reply on RC2. https://doi.org/10.5194/egusphere-2022-1119-ac2","relatedTo":{"ob_id":27532,"uuid":"62c0f97b1eac4e0197a674870afe1ee6","short_code":"ob"}},{"ob_id":89092,"function":"externalCitation","linkage":"https://doi.org/10.14465/2023.reu08.tem","name":"Cornes, R.C., Tinker, J., Hermanson, L., Oltmanns, M., Hunter, W.R., Lloyd-Hartley, H., Kent, E.C., Rabe, B. & Renshaw, R. (2023) Climate change impacts on sea temperature around the UK and Ireland. MCCIP Rolling Evidence Updates. https://doi.org/10.14465/2023.REU08.TEM","relatedTo":{"ob_id":27532,"uuid":"62c0f97b1eac4e0197a674870afe1ee6","short_code":"ob"}},{"ob_id":89093,"function":"externalCitation","linkage":"https://doi.org/10.1038/s41598-022-13568-5","name":"error occurred","relatedTo":{"ob_id":27532,"uuid":"62c0f97b1eac4e0197a674870afe1ee6","short_code":"ob"}},{"ob_id":89094,"function":"externalCitation","linkage":"https://doi.org/10.5194/egusphere-2022-25-ac2","name":"Pauthenet, E. (2022) Reply on RC2. https://doi.org/10.5194/egusphere-2022-25-ac2","relatedTo":{"ob_id":27532,"uuid":"62c0f97b1eac4e0197a674870afe1ee6","short_code":"ob"}},{"ob_id":89095,"function":"externalCitation","linkage":"https://doi.org/10.1029/2020ea001427","name":"error occurred","relatedTo":{"ob_id":27532,"uuid":"62c0f97b1eac4e0197a674870afe1ee6","short_code":"ob"}},{"ob_id":89096,"function":"externalCitation","linkage":"https://doi.org/10.5194/essd-2021-194-ac4","name":"Chamberlain, M.A. (2021) Reply on RC4. https://doi.org/10.5194/essd-2021-194-ac4","relatedTo":{"ob_id":27532,"uuid":"62c0f97b1eac4e0197a674870afe1ee6","short_code":"ob"}},{"ob_id":89097,"function":"externalCitation","linkage":"https://doi.org/10.5194/essd-2022-129","name":"Land, P.E., Findlay, H.S., Shutler, J.D., Piolle, J.-F., Sims, R., Green, H., Kitidis, V., Polukhin, A. & Pipko, I.I. (2022) OceanSODA-MDB: a standardised surface ocean carbonate system dataset for model-data intercomparisons. https://doi.org/10.5194/essd-2022-129","relatedTo":{"ob_id":27532,"uuid":"62c0f97b1eac4e0197a674870afe1ee6","short_code":"ob"}},{"ob_id":89098,"function":"externalCitation","linkage":"https://doi.org/10.1029/2024jc021134","name":"error occurred","relatedTo":{"ob_id":32135,"uuid":"1dbe7a109c0244aaad713e078fd3059a","short_code":"coll"}},{"ob_id":89099,"function":"externalCitation","linkage":"https://doi.org/10.5194/egusphere-2022-430-ac1","name":"Kostadinov, T. (2022) Reply on RC1. https://doi.org/10.5194/egusphere-2022-430-ac1","relatedTo":{"ob_id":32135,"uuid":"1dbe7a109c0244aaad713e078fd3059a","short_code":"coll"}},{"ob_id":89100,"function":"externalCitation","linkage":"https://doi.org/10.5194/os-19-703-2023","name":"error occurred","relatedTo":{"ob_id":32135,"uuid":"1dbe7a109c0244aaad713e078fd3059a","short_code":"coll"}},{"ob_id":89101,"function":"externalCitation","linkage":"https://doi.org/10.5194/tc-2022-246-ac1","name":"Lei, R. (2023) Reply on RC1. https://doi.org/10.5194/tc-2022-246-ac1","relatedTo":{"ob_id":32135,"uuid":"1dbe7a109c0244aaad713e078fd3059a","short_code":"coll"}},{"ob_id":89102,"function":"externalCitation","linkage":"https://doi.org/35013282","name":"not a doi","relatedTo":{"ob_id":32135,"uuid":"1dbe7a109c0244aaad713e078fd3059a","short_code":"coll"}},{"ob_id":89103,"function":"externalCitation","linkage":"https://doi.org/10.3389/frsen.2022.882418","name":"error occurred","relatedTo":{"ob_id":32135,"uuid":"1dbe7a109c0244aaad713e078fd3059a","short_code":"coll"}},{"ob_id":89104,"function":"externalCitation","linkage":"https://doi.org/10.5194/bg-2022-246","name":"Belcher, A., Henley, S., Hendry, K., Wootton, M., Friberg, L., Dallman, U., Wang, T. & Manno, C. (2023) Seasonal cycles of biogeochemical fluxes in the Scotia Sea, Southern Ocean: A stable isotope approach. https://doi.org/10.5194/bg-2022-246","relatedTo":{"ob_id":32135,"uuid":"1dbe7a109c0244aaad713e078fd3059a","short_code":"coll"}},{"ob_id":89105,"function":"externalCitation","linkage":"https://doi.org/10.5194/egusphere-2022-430-ac2","name":"Kostadinov, T. (2022) Reply on RC2. https://doi.org/10.5194/egusphere-2022-430-ac2","relatedTo":{"ob_id":32135,"uuid":"1dbe7a109c0244aaad713e078fd3059a","short_code":"coll"}},{"ob_id":89106,"function":"externalCitation","linkage":"https://doi.org/10.5194/egusphere-2022-430-rc2","name":"Boss, E. (2022) Comment on egusphere-2022-430. https://doi.org/10.5194/egusphere-2022-430-rc2","relatedTo":{"ob_id":32135,"uuid":"1dbe7a109c0244aaad713e078fd3059a","short_code":"coll"}},{"ob_id":89107,"function":"externalCitation","linkage":"https://doi.org/10.1364/hmise.2023.hw4c.5","name":"Smith, M.E., Thomalla, S.J. & Matlakala, L. (2023) Coccolithophore bloom classification in coastal waters from multi-decade satellite ocean colour data. Optica Sensing Congress 2023 (AIS, FTS, HISE, Sensors, ES), HW4C.5. https://doi.org/10.1364/hmise.2023.hw4c.5","relatedTo":{"ob_id":32135,"uuid":"1dbe7a109c0244aaad713e078fd3059a","short_code":"coll"}},{"ob_id":89108,"function":"externalCitation","linkage":"https://doi.org/10.3389/fmars.2023.1129876","name":"error occurred","relatedTo":{"ob_id":32135,"uuid":"1dbe7a109c0244aaad713e078fd3059a","short_code":"coll"}},{"ob_id":89109,"function":"externalCitation","linkage":"https://doi.org/10.5194/egusphere-2022-31","name":"Lima, M.M., Gouveia, C.M. & Trigo, R.M. (2022) Upper Ocean Response on the Passage of Tropical Cyclones in the Azores Region. https://doi.org/10.5194/egusphere-2022-31","relatedTo":{"ob_id":32135,"uuid":"1dbe7a109c0244aaad713e078fd3059a","short_code":"coll"}},{"ob_id":89110,"function":"externalCitation","linkage":"https://doi.org/10.5194/gmd-2023-25","name":"error occurred","relatedTo":{"ob_id":32135,"uuid":"1dbe7a109c0244aaad713e078fd3059a","short_code":"coll"}},{"ob_id":89111,"function":"externalCitation","linkage":"https://doi.org/10.1029/2023gl106577","name":"error occurred","relatedTo":{"ob_id":32135,"uuid":"1dbe7a109c0244aaad713e078fd3059a","short_code":"coll"}},{"ob_id":89112,"function":"externalCitation","linkage":"https://doi.org/10.1029/2024ea003748","name":"error occurred","relatedTo":{"ob_id":32135,"uuid":"1dbe7a109c0244aaad713e078fd3059a","short_code":"coll"}},{"ob_id":89113,"function":"externalCitation","linkage":"https://doi.org/10.1029/2024jc020898","name":"error occurred","relatedTo":{"ob_id":32135,"uuid":"1dbe7a109c0244aaad713e078fd3059a","short_code":"coll"}},{"ob_id":89114,"function":"externalCitation","linkage":"https://doi.org/10.1029/2024jc021254","name":"error occurred","relatedTo":{"ob_id":32135,"uuid":"1dbe7a109c0244aaad713e078fd3059a","short_code":"coll"}},{"ob_id":89115,"function":"externalCitation","linkage":"https://doi.org/10.5285/cb47cc464c5a41de8c718d117437b4e6","name":"Met Office (2019) MIDAS Open: UK daily rainfall data, v201908. https://doi.org/10.5285/CB47CC464C5A41DE8C718D117437B4E6","relatedTo":{"ob_id":27821,"uuid":"9972bc173ef94068b2070d4b26f849a7","short_code":"ob"}},{"ob_id":89116,"function":"externalCitation","linkage":"https://doi.org/10.5285/8bcf6925cddc4681b96f94d424537b9e","name":"Met Office (2022) MIDAS Open: UK daily temperature data, v202207. https://doi.org/10.5285/8BCF6925CDDC4681B96F94D424537B9E","relatedTo":{"ob_id":27821,"uuid":"9972bc173ef94068b2070d4b26f849a7","short_code":"ob"}},{"ob_id":89117,"function":"externalCitation","linkage":"https://doi.org/10.5285/d6bcf4171c2f4754a7455d00deda0f72","name":"Met Office (2021) MIDAS Open: UK daily rainfall data, v202107. https://doi.org/10.5285/D6BCF4171C2F4754A7455D00DEDA0F72","relatedTo":{"ob_id":27821,"uuid":"9972bc173ef94068b2070d4b26f849a7","short_code":"ob"}},{"ob_id":89118,"function":"externalCitation","linkage":"https://doi.org/10.5285/a39add95a8dc49709f6e984c020c8dbc","name":"Met Office (2019) MIDAS Open: UK daily temperature data, v201901. https://doi.org/10.5285/A39ADD95A8DC49709F6E984C020C8DBC","relatedTo":{"ob_id":27821,"uuid":"9972bc173ef94068b2070d4b26f849a7","short_code":"ob"}},{"ob_id":89119,"function":"externalCitation","linkage":"https://doi.org/10.5285/ec54d5e5288a4ebb8c7ad2a1ef6aec42","name":"Met Office (2019) MIDAS Open: UK daily rainfall data, v201901. https://doi.org/10.5285/EC54D5E5288A4EBB8C7AD2A1EF6AEC42","relatedTo":{"ob_id":27821,"uuid":"9972bc173ef94068b2070d4b26f849a7","short_code":"ob"}},{"ob_id":89120,"function":"externalCitation","linkage":"https://doi.org/10.5285/b37382e8c1e74b849831a5fa13afdcae","name":"Met Office (2019) MIDAS Open: UK daily temperature data, v201908. https://doi.org/10.5285/B37382E8C1E74B849831A5FA13AFDCAE","relatedTo":{"ob_id":27821,"uuid":"9972bc173ef94068b2070d4b26f849a7","short_code":"ob"}},{"ob_id":89121,"function":"externalCitation","linkage":"https://doi.org/10.5285/a7ba08d073eb40a9aab5e312f371d007","name":"Met Office (2019) MIDAS Open: UK soil temperature data, v201901. https://doi.org/10.5285/A7BA08D073EB40A9AAB5E312F371D007","relatedTo":{"ob_id":27821,"uuid":"9972bc173ef94068b2070d4b26f849a7","short_code":"ob"}},{"ob_id":89122,"function":"externalCitation","linkage":"https://doi.org/10.5285/4ecbf3fa1b084c5a9080248433275124","name":"Met Office (2022) MIDAS Open: UK soil temperature data, v202207. https://doi.org/10.5285/4ECBF3FA1B084C5A9080248433275124","relatedTo":{"ob_id":27821,"uuid":"9972bc173ef94068b2070d4b26f849a7","short_code":"ob"}},{"ob_id":89123,"function":"externalCitation","linkage":"https://doi.org/10.5285/6ad6792f44c84c228651b01d182d9d73","name":"Met Office (2019) MIDAS Open: UK daily weather observation data, v201908. https://doi.org/10.5285/6AD6792F44C84C228651B01D182D9D73","relatedTo":{"ob_id":27821,"uuid":"9972bc173ef94068b2070d4b26f849a7","short_code":"ob"}},{"ob_id":89124,"function":"externalCitation","linkage":"https://doi.org/10.5285/ec9e894089434b03bd9532d7b343ec4b","name":"Met Office (2020) MIDAS Open: UK daily rainfall data, v202007. https://doi.org/10.5285/EC9E894089434B03BD9532D7B343EC4B","relatedTo":{"ob_id":27821,"uuid":"9972bc173ef94068b2070d4b26f849a7","short_code":"ob"}},{"ob_id":89125,"function":"externalCitation","linkage":"https://doi.org/10.5285/0049795739e44310a4982e26d8e26748","name":"Met Office (2019) MIDAS Open: UK daily weather observation data, v201901. https://doi.org/10.5285/0049795739E44310A4982E26D8E26748","relatedTo":{"ob_id":27821,"uuid":"9972bc173ef94068b2070d4b26f849a7","short_code":"ob"}},{"ob_id":89126,"function":"externalCitation","linkage":"https://doi.org/10.5285/cabc37d867fa4f2a84302350df908693","name":"Met Office (2021) MIDAS Open: UK soil temperature data, v202107. https://doi.org/10.5285/CABC37D867FA4F2A84302350DF908693","relatedTo":{"ob_id":27821,"uuid":"9972bc173ef94068b2070d4b26f849a7","short_code":"ob"}},{"ob_id":89127,"function":"externalCitation","linkage":"https://doi.org/10.5285/85b9dad7af814bfa9047a525927257f4","name":"Met Office (2020) MIDAS Open: UK soil temperature data, v202007. https://doi.org/10.5285/85B9DAD7AF814BFA9047A525927257F4","relatedTo":{"ob_id":27821,"uuid":"9972bc173ef94068b2070d4b26f849a7","short_code":"ob"}},{"ob_id":89128,"function":"externalCitation","linkage":"https://doi.org/10.1144/geoenergy2023-040","name":"Receveur, M., McDermott, C.I., Fraser-Harris, A., Gilfillan, S. & Watson, I. (2024) Quantifying vertical borehole heat exchanger sustainability from a geothermal resource perspective in the UK. Geoenergy 2. https://doi.org/10.1144/geoenergy2023-040","relatedTo":{"ob_id":27821,"uuid":"9972bc173ef94068b2070d4b26f849a7","short_code":"ob"}},{"ob_id":89129,"function":"externalCitation","linkage":"https://doi.org/10.26848/rbgf.v14.6.p3711-3735","name":"error occurred","relatedTo":{"ob_id":26434,"uuid":"58f00d8814064b79a0c49662ad3af537","short_code":"ob"}},{"ob_id":89130,"function":"externalCitation","linkage":"https://doi.org/10.1088/1748-9326/ad2b29","name":"error occurred","relatedTo":{"ob_id":26434,"uuid":"58f00d8814064b79a0c49662ad3af537","short_code":"ob"}},{"ob_id":89131,"function":"externalCitation","linkage":"https://doi.org/10.18257/raccefyn.1514","name":"error occurred","relatedTo":{"ob_id":26434,"uuid":"58f00d8814064b79a0c49662ad3af537","short_code":"ob"}},{"ob_id":89132,"function":"externalCitation","linkage":"https://doi.org/10.1038/s41561-021-00763-8","name":"error occurred","relatedTo":{"ob_id":26434,"uuid":"58f00d8814064b79a0c49662ad3af537","short_code":"ob"}},{"ob_id":89133,"function":"externalCitation","linkage":"https://doi.org/10.3390/rs12132162","name":"Nolde, M., Plank, S. & Riedlinger, T. (2020) An Adaptive and Extensible System for Satellite-Based, Large Scale Burnt Area Monitoring in Near-Real Time. Remote Sensing 12, 2162. https://doi.org/10.3390/rs12132162","relatedTo":{"ob_id":26434,"uuid":"58f00d8814064b79a0c49662ad3af537","short_code":"ob"}},{"ob_id":89134,"function":"externalCitation","linkage":"https://doi.org/10.1088/2632-2153/acc008","name":"Shmuel, A. & Heifetz, E. (2023) Developing novel machine-learning-based fire weather indices. Machine Learning: Science and Technology 4, 015029. https://doi.org/10.1088/2632-2153/acc008","relatedTo":{"ob_id":26434,"uuid":"58f00d8814064b79a0c49662ad3af537","short_code":"ob"}},{"ob_id":89135,"function":"externalCitation","linkage":"https://doi.org/10.5194/gmd-2023-14-ac1","name":"Jin, M. (2023) Reply on CEC1. https://doi.org/10.5194/gmd-2023-14-ac1","relatedTo":{"ob_id":26434,"uuid":"58f00d8814064b79a0c49662ad3af537","short_code":"ob"}},{"ob_id":89136,"function":"externalCitation","linkage":"https://doi.org/10.3390/rs14225826","name":"Cimdins, R., Krasovskiy, A. & Kraxner, F. (2022) Regional Variability and Driving Forces behind Forest Fires in Sweden. Remote Sensing 14, 5826. https://doi.org/10.3390/rs14225826","relatedTo":{"ob_id":26434,"uuid":"58f00d8814064b79a0c49662ad3af537","short_code":"ob"}},{"ob_id":89137,"function":"externalCitation","linkage":"https://doi.org/10.3390/fire6080319","name":"Shmuel, A. & Heifetz, E. (2023) A Machine-Learning Approach to Predicting Daily Wildfire Expansion Rate. Fire 6, 319. https://doi.org/10.3390/fire6080319","relatedTo":{"ob_id":26434,"uuid":"58f00d8814064b79a0c49662ad3af537","short_code":"ob"}},{"ob_id":89138,"function":"externalCitation","linkage":"https://doi.org/10.1007/s41324-022-00478-x","name":"error occurred","relatedTo":{"ob_id":26434,"uuid":"58f00d8814064b79a0c49662ad3af537","short_code":"ob"}},{"ob_id":89139,"function":"externalCitation","linkage":"https://doi.org/10.3390/rs14061446","name":"Yan, J., He, H., Wang, L., Zhang, H., Liang, D. & Zhang, J. (2022) Inter-Comparison of Four Models for Detecting Forest Fire Disturbance from MOD13A2 Time Series. Remote Sensing 14, 1446. https://doi.org/10.3390/rs14061446","relatedTo":{"ob_id":26434,"uuid":"58f00d8814064b79a0c49662ad3af537","short_code":"ob"}},{"ob_id":89140,"function":"externalCitation","linkage":"https://doi.org/10.5194/gmd-2023-14-cec1","name":"error occurred","relatedTo":{"ob_id":26434,"uuid":"58f00d8814064b79a0c49662ad3af537","short_code":"ob"}},{"ob_id":89141,"function":"externalCitation","linkage":"https://doi.org/10.1038/s43247-023-00893-4","name":"error occurred","relatedTo":{"ob_id":26434,"uuid":"58f00d8814064b79a0c49662ad3af537","short_code":"ob"}},{"ob_id":89142,"function":"externalCitation","linkage":"https://doi.org/10.35595/2414-9179-2022-1-28-346-358","name":"error occurred","relatedTo":{"ob_id":26434,"uuid":"58f00d8814064b79a0c49662ad3af537","short_code":"ob"}},{"ob_id":89143,"function":"externalCitation","linkage":"https://doi.org/10.21046/2070-7401-2022-19-1-143-157","name":"error occurred","relatedTo":{"ob_id":26434,"uuid":"58f00d8814064b79a0c49662ad3af537","short_code":"ob"}},{"ob_id":89144,"function":"externalCitation","linkage":"https://doi.org/10.5194/essd-15-541-2023","name":"error occurred","relatedTo":{"ob_id":26434,"uuid":"58f00d8814064b79a0c49662ad3af537","short_code":"ob"}},{"ob_id":89145,"function":"externalCitation","linkage":"https://doi.org/10.3390/fire6080314","name":"Boubekraoui, H., Maouni, Y., Ghallab, A., Draoui, M. & Maouni, A. (2023) Wildfires Risk Assessment Using Hotspot Analysis and Results Application to Wildfires Strategic Response in the Region of Tangier-Tetouan-Al Hoceima, Morocco. Fire 6, 314. https://doi.org/10.3390/fire6080314","relatedTo":{"ob_id":26434,"uuid":"58f00d8814064b79a0c49662ad3af537","short_code":"ob"}},{"ob_id":89146,"function":"externalCitation","linkage":"https://doi.org/10.1016/j.oneear.2023.05.024","name":"error occurred","relatedTo":{"ob_id":26434,"uuid":"58f00d8814064b79a0c49662ad3af537","short_code":"ob"}},{"ob_id":89147,"function":"externalCitation","linkage":"https://doi.org/10.1134/s2079096123010122","name":"error occurred","relatedTo":{"ob_id":26434,"uuid":"58f00d8814064b79a0c49662ad3af537","short_code":"ob"}},{"ob_id":89148,"function":"externalCitation","linkage":"https://doi.org/10.24189/ncr.2021.035","name":"Shinkarenko, S.S., Ivanov, N.M. & Berdengalieva, A.N. (2021) Spatio-temporal dynamics of burnt areas in federal Protected Areas in the south-east of European Russia. Nature Conservation Research 6. https://doi.org/10.24189/ncr.2021.035","relatedTo":{"ob_id":26434,"uuid":"58f00d8814064b79a0c49662ad3af537","short_code":"ob"}},{"ob_id":89149,"function":"externalCitation","linkage":"https://doi.org/10.1038/s41467-023-42597-5","name":"Qu, Y., Miralles, D.G., Veraverbeke, S., Vereecken, H. & Montzka, C. (2023) Wildfire precursors show complementary predictability in different timescales. Nature Communications 14. https://doi.org/10.1038/s41467-023-42597-5","relatedTo":{"ob_id":26434,"uuid":"58f00d8814064b79a0c49662ad3af537","short_code":"ob"}},{"ob_id":89150,"function":"externalCitation","linkage":"https://doi.org/10.5327/z2176-94781303","name":"error occurred","relatedTo":{"ob_id":26434,"uuid":"58f00d8814064b79a0c49662ad3af537","short_code":"ob"}},{"ob_id":89151,"function":"externalCitation","linkage":"https://doi.org/10.1038/s41467-023-44321-9","name":"Boonman, C.C.F., Serra-Diaz, J.M., Hoeks, S., Guo, W.-Y., Enquist, B.J., Maitner, B., Malhi, Y., Merow, C., Buitenwerf, R. & Svenning, J.-C. (2024) More than 17,000 tree species are at risk from rapid global change. Nature Communications 15. https://doi.org/10.1038/s41467-023-44321-9","relatedTo":{"ob_id":26434,"uuid":"58f00d8814064b79a0c49662ad3af537","short_code":"ob"}},{"ob_id":89152,"function":"externalCitation","linkage":"https://doi.org/10.48550/arxiv.2312.07199","name":"Karasante, I., Alonso, L., Prapas, I., Ahuja, A., Carvalhais, N. & Papoutsis, I. (2023) SeasFire as a Multivariate Earth System Datacube for Wildfire Dynamics. https://doi.org/10.48550/ARXIV.2312.07199","relatedTo":{"ob_id":26434,"uuid":"58f00d8814064b79a0c49662ad3af537","short_code":"ob"}},{"ob_id":89153,"function":"externalCitation","linkage":"https://doi.org/10.3354/meps14571","name":"error occurred","relatedTo":{"ob_id":25390,"uuid":"9c334fbe6d424a708cf3c4cf0c6a53f5","short_code":"coll"}},{"ob_id":89154,"function":"externalCitation","linkage":"https://doi.org/10.5194/os-2020-125","name":"Giddings, J., Heywood, K.J., Matthews, A.J., Joshi, M.M., Webber, B.G.M., Sanchez-Franks, A., King, B.A. & Vinayachandran, P.N. (2021) Spatial and temporal variability of solar penetration depths in the\nBay of Bengal and its impact on SST during the summer monsoon. https://doi.org/10.5194/os-2020-125","relatedTo":{"ob_id":25390,"uuid":"9c334fbe6d424a708cf3c4cf0c6a53f5","short_code":"coll"}},{"ob_id":89155,"function":"externalCitation","linkage":"https://doi.org/10.5194/os-2021-35","name":"Wang, B., Fennel, K. & Yu, L. (2021) Can assimilation of satellite observations improve subsurface biological properties in a numerical model? A case study for the Gulf of Mexico. https://doi.org/10.5194/os-2021-35","relatedTo":{"ob_id":25390,"uuid":"9c334fbe6d424a708cf3c4cf0c6a53f5","short_code":"coll"}},{"ob_id":89156,"function":"externalCitation","linkage":"https://doi.org/10.5194/os-2018-155","name":"error occurred","relatedTo":{"ob_id":25390,"uuid":"9c334fbe6d424a708cf3c4cf0c6a53f5","short_code":"coll"}},{"ob_id":89157,"function":"externalCitation","linkage":"https://doi.org/10.5194/os-2021-35-ac4","name":"Wang, B. (2021) Reply on RC4. https://doi.org/10.5194/os-2021-35-ac4","relatedTo":{"ob_id":25390,"uuid":"9c334fbe6d424a708cf3c4cf0c6a53f5","short_code":"coll"}},{"ob_id":89158,"function":"externalCitation","linkage":"https://doi.org/10.3390/w10111527","name":"Lemmen, C. (2018) North Sea Ecosystem-Scale Model-Based Quantification of Net Primary Productivity Changes by the Benthic Filter Feeder Mytilus edulis. Water 10, 1527. https://doi.org/10.3390/w10111527","relatedTo":{"ob_id":25390,"uuid":"9c334fbe6d424a708cf3c4cf0c6a53f5","short_code":"coll"}},{"ob_id":89159,"function":"externalCitation","linkage":"https://doi.org/10.5194/bg-2019-169","name":"Sankar, S., Thondithala Ramachandran, A., Franck Eitel, K.G., Kondrik, D., Sen, R., Madipally, R. & Pettersson, L.H. (2019) The influence of tropical Indian Ocean warming and Indian Ocean Dipole on the surface chlorophyll concentration in the eastern Arabian Sea. https://doi.org/10.5194/bg-2019-169","relatedTo":{"ob_id":25390,"uuid":"9c334fbe6d424a708cf3c4cf0c6a53f5","short_code":"coll"}},{"ob_id":89160,"function":"externalCitation","linkage":"https://doi.org/10.5194/os-2020-125-ac1","name":"Giddings, J. (2021) AC1: Response to RC1. https://doi.org/10.5194/os-2020-125-ac1","relatedTo":{"ob_id":25390,"uuid":"9c334fbe6d424a708cf3c4cf0c6a53f5","short_code":"coll"}},{"ob_id":89161,"function":"externalCitation","linkage":"https://doi.org/10.5194/os-2021-35-ac1","name":"Wang, B. (2021) Reply on RC1. https://doi.org/10.5194/os-2021-35-ac1","relatedTo":{"ob_id":25390,"uuid":"9c334fbe6d424a708cf3c4cf0c6a53f5","short_code":"coll"}},{"ob_id":89162,"function":"externalCitation","linkage":"https://doi.org/10.5194/os-16-253-2020","name":"error occurred","relatedTo":{"ob_id":25390,"uuid":"9c334fbe6d424a708cf3c4cf0c6a53f5","short_code":"coll"}},{"ob_id":89163,"function":"externalCitation","linkage":"https://doi.org/10.5194/os-2021-35-ac2","name":"Wang, B. (2021) Reply on RC2. https://doi.org/10.5194/os-2021-35-ac2","relatedTo":{"ob_id":25390,"uuid":"9c334fbe6d424a708cf3c4cf0c6a53f5","short_code":"coll"}},{"ob_id":89164,"function":"externalCitation","linkage":"https://doi.org/10.5194/os-2020-125-ac2","name":"Giddings, J. (2021) AC2: Response to RC2. https://doi.org/10.5194/os-2020-125-ac2","relatedTo":{"ob_id":25390,"uuid":"9c334fbe6d424a708cf3c4cf0c6a53f5","short_code":"coll"}},{"ob_id":89165,"function":"externalCitation","linkage":"https://doi.org/10.5194/os-2021-35-ac3","name":"Wang, B. (2021) Reply on RC3. https://doi.org/10.5194/os-2021-35-ac3","relatedTo":{"ob_id":25390,"uuid":"9c334fbe6d424a708cf3c4cf0c6a53f5","short_code":"coll"}},{"ob_id":89166,"function":"externalCitation","linkage":"https://doi.org/10.5194/os-2020-125-rc1","name":"Giddy, I. (2021) Comment on os-2020-125. https://doi.org/10.5194/os-2020-125-rc1","relatedTo":{"ob_id":25390,"uuid":"9c334fbe6d424a708cf3c4cf0c6a53f5","short_code":"coll"}},{"ob_id":89167,"function":"externalCitation","linkage":"https://doi.org/10.1029/2023jc020077","name":"error occurred","relatedTo":{"ob_id":25390,"uuid":"9c334fbe6d424a708cf3c4cf0c6a53f5","short_code":"coll"}},{"ob_id":89168,"function":"externalCitation","linkage":"https://doi.org/10.1029/2024jc021275","name":"error occurred","relatedTo":{"ob_id":25390,"uuid":"9c334fbe6d424a708cf3c4cf0c6a53f5","short_code":"coll"}},{"ob_id":89169,"function":"externalCitation","linkage":"https://doi.org/10.5194/gmd-2020-193","name":"error occurred","relatedTo":{"ob_id":25066,"uuid":"58a8802721c94c66ae45c3baa4d814d0","short_code":"ob"}},{"ob_id":89170,"function":"externalCitation","linkage":"https://doi.org/10.4178/epih.e2021041","name":"Okunlola, O.A., Oyeyemi, O.T. & Lukman, A.F. (2021) Modeling the relationship between malaria prevalence and insecticide-treated bed net coverage in Nigeria using a Bayesian spatial generalized linear mixed model with a Leroux prior. Epidemiology and Health 43, e2021041. https://doi.org/10.4178/epih.e2021041","relatedTo":{"ob_id":25066,"uuid":"58a8802721c94c66ae45c3baa4d814d0","short_code":"ob"}},{"ob_id":89171,"function":"externalCitation","linkage":"https://doi.org/32737311","name":"not a doi","relatedTo":{"ob_id":25066,"uuid":"58a8802721c94c66ae45c3baa4d814d0","short_code":"ob"}},{"ob_id":89172,"function":"externalCitation","linkage":"https://doi.org/10.5194/bg-2018-179","name":"Haughton, N., Abramowitz, G., De Kauwe, M.G. & Pitman, A.J. (2018) Does predictability of fluxes vary between FLUXNET sites? https://doi.org/10.5194/bg-2018-179","relatedTo":{"ob_id":25066,"uuid":"58a8802721c94c66ae45c3baa4d814d0","short_code":"ob"}},{"ob_id":89173,"function":"externalCitation","linkage":"https://doi.org/10.5194/cp-2020-78","name":"Parker, S.E., Harrison, S.P., Comas-Bru, L., Kaushal, N., LeGrande, A.N. & Werner, M. (2020) A data-model approach to interpreting speleothem oxygen isotope records from monsoon regions on orbital timescales. https://doi.org/10.5194/cp-2020-78","relatedTo":{"ob_id":25066,"uuid":"58a8802721c94c66ae45c3baa4d814d0","short_code":"ob"}},{"ob_id":89174,"function":"externalCitation","linkage":"https://doi.org/10.1134/s1067413621050118","name":"error occurred","relatedTo":{"ob_id":25066,"uuid":"58a8802721c94c66ae45c3baa4d814d0","short_code":"ob"}},{"ob_id":89175,"function":"externalCitation","linkage":"https://doi.org/10.1007/s10640-021-00541-5","name":"error occurred","relatedTo":{"ob_id":25066,"uuid":"58a8802721c94c66ae45c3baa4d814d0","short_code":"ob"}},{"ob_id":89176,"function":"externalCitation","linkage":"https://doi.org/10.5194/bg-2019-256","name":"Basile, S.J., Lin, X., Wieder, W.R., Hartman, M.D. & Keppel-Aleks, G. (2019) Leveraging the signature of heterotrophic respiration on atmospheric CO2 for model benchmarking. https://doi.org/10.5194/bg-2019-256","relatedTo":{"ob_id":25066,"uuid":"58a8802721c94c66ae45c3baa4d814d0","short_code":"ob"}},{"ob_id":89177,"function":"externalCitation","linkage":"https://doi.org/10.3390/atmos11101075","name":"Li, T., Li, J., Au, T.F. & Zhang, D.D. (2020) Moisture Variability in the East Pearl River Basin since 1894 CE Inferred from Tree Ring Records. Atmosphere 11, 1075. https://doi.org/10.3390/atmos11101075","relatedTo":{"ob_id":25066,"uuid":"58a8802721c94c66ae45c3baa4d814d0","short_code":"ob"}},{"ob_id":89178,"function":"externalCitation","linkage":"https://doi.org/10.5194/gmd-2020-196","name":"error occurred","relatedTo":{"ob_id":25066,"uuid":"58a8802721c94c66ae45c3baa4d814d0","short_code":"ob"}},{"ob_id":89179,"function":"externalCitation","linkage":"https://doi.org/10.5285/d4e823f0172947c5ae6e6b265656c273","name":"University Of East Anglia Climatic Research Unit (CRU), Harris, I.C. & Jones, P.D. (2017) CRU CY4.01: Climatic Research Unit (CRU) year-by-year variation of selected climate variables by country (CY) version 4.01 (Jan. 1901 - Dec. 2016). https://doi.org/10.5285/D4E823F0172947C5AE6E6B265656C273","relatedTo":{"ob_id":25066,"uuid":"58a8802721c94c66ae45c3baa4d814d0","short_code":"ob"}},{"ob_id":89180,"function":"externalCitation","linkage":"https://doi.org/10.1016/j.envsoft.2019.104562","name":"Dellar, M., Topp, C., Pardo, G., del Prado, A., Fitton, N., Holmes, D., Banos, G. & Wall, E. (2019) Empirical and dynamic approaches for modelling the yield and N content of European grasslands. Environmental Modelling & Software 122, 104562. https://doi.org/10.1016/j.envsoft.2019.104562","relatedTo":{"ob_id":25066,"uuid":"58a8802721c94c66ae45c3baa4d814d0","short_code":"ob"}},{"ob_id":89181,"function":"externalCitation","linkage":"https://doi.org/10.3390/f11020132","name":"error occurred","relatedTo":{"ob_id":25066,"uuid":"58a8802721c94c66ae45c3baa4d814d0","short_code":"ob"}},{"ob_id":89182,"function":"externalCitation","linkage":"https://doi.org/10.5194/cp-2018-87","name":"Weitzel, N., Hense, A. & Ohlwein, C. (2018) Combining a pollen synthesis and climate simulations for spatial reconstructions of European climate using Bayesian modelling. https://doi.org/10.5194/cp-2018-87","relatedTo":{"ob_id":25066,"uuid":"58a8802721c94c66ae45c3baa4d814d0","short_code":"ob"}}]}