Online Resource List
Get a list of Instrument objects. Instruments have a 1:1 mapping with Observations.
GET /api/v3/onlineresources/?format=api&offset=79500
{ "count": 87212, "next": "https://api.catalogue.ceda.ac.uk/api/v3/onlineresources/?format=api&limit=100&offset=79600", "previous": "https://api.catalogue.ceda.ac.uk/api/v3/onlineresources/?format=api&limit=100&offset=79400", "results": [ { "ob_id": 87255, "function": "externalCitation", "linkage": "https://doi.org/10.1007/s00484-016-1199-7", "name": "Gommes, R., Wu, B., Zhang, N., Feng, X., Zeng, H., Li, Z. & Chen, B. (2016) CropWatch agroclimatic indicators (CWAIs) for weather impact assessment on global agriculture. International Journal of Biometeorology 61, 199–215. https://doi.org/10.1007/s00484-016-1199-7", "relatedTo": { "ob_id": 6679, "uuid": "ac4ecbd554d0dd52a9b575d9666dc42d", "short_code": "ob" } }, { "ob_id": 87256, "function": "externalCitation", "linkage": "https://doi.org/10.1038/sdata.2018.299", "name": "Werner, A.T., Schnorbus, M.A., Shrestha, R.R., Cannon, A.J., Zwiers, F.W., Dayon, G. & Anslow, F. (2019) A long-term, temporally consistent, gridded daily meteorological dataset for northwestern North America. Scientific Data 6. https://doi.org/10.1038/sdata.2018.299", "relatedTo": { "ob_id": 6679, "uuid": "ac4ecbd554d0dd52a9b575d9666dc42d", "short_code": "ob" } }, { "ob_id": 87257, "function": "externalCitation", "linkage": "https://doi.org/10.1016/j.proenv.2016.03.064", "name": "Purwaningsih, A. & Hidayat, R. (2016) Performance of Decadal Prediction in Coupled Model Intercomparisson Project Phase 5 (CMIP5) on Projecting Climate in Tropical Area. Procedia Environmental Sciences 33, 128–139. https://doi.org/10.1016/j.proenv.2016.03.064", "relatedTo": { "ob_id": 6679, "uuid": "ac4ecbd554d0dd52a9b575d9666dc42d", "short_code": "ob" } }, { "ob_id": 87258, "function": "externalCitation", "linkage": "https://doi.org/10.5194/esd-2016-24-ac3", "name": "Bayer, A. (2016) Comment to Reviewer #1. https://doi.org/10.5194/esd-2016-24-ac3", "relatedTo": { "ob_id": 6679, "uuid": "ac4ecbd554d0dd52a9b575d9666dc42d", "short_code": "ob" } }, { "ob_id": 87259, "function": "externalCitation", "linkage": "https://doi.org/10.1101/180174", "name": "Ladau, J., Shi, Y., Jing, X., He, J.-S., Chen, L., Lin, X., Fierer, N., Gilbert, J.A., Pollard, K.S. & Chu, H. (2017) Climate change will lead to pronounced shifts in the diversity of soil microbial communities. https://doi.org/10.1101/180174", "relatedTo": { "ob_id": 6679, "uuid": "ac4ecbd554d0dd52a9b575d9666dc42d", "short_code": "ob" } }, { "ob_id": 87260, "function": "externalCitation", "linkage": "https://doi.org/10.1007/s10040-017-1550-z", "name": "Bonsor, H.C., MacDonald, A.M., Ahmed, K.M., et al. (2017) Hydrogeological typologies of the Indo-Gangetic basin alluvial aquifer, South Asia. Hydrogeology Journal 25, 1377–1406. https://doi.org/10.1007/s10040-017-1550-z", "relatedTo": { "ob_id": 6679, "uuid": "ac4ecbd554d0dd52a9b575d9666dc42d", "short_code": "ob" } }, { "ob_id": 87261, "function": "externalCitation", "linkage": "https://doi.org/10.5194/hessd-12-3477-2015", "name": "Duku, C., Rathjens, H., Zwart, S.J. & Hein, L. (2015) Towards ecosystem accounting: a comprehensive approach to modelling multiple hydrological ecosystem services. https://doi.org/10.5194/hessd-12-3477-2015", "relatedTo": { "ob_id": 6679, "uuid": "ac4ecbd554d0dd52a9b575d9666dc42d", "short_code": "ob" } }, { "ob_id": 87262, "function": "externalCitation", "linkage": "https://doi.org/1871.1/c26796d2-6eeb-4486-aa51-2e4c15c5bbb4", "name": "not a doi", "relatedTo": { "ob_id": 6679, "uuid": "ac4ecbd554d0dd52a9b575d9666dc42d", "short_code": "ob" } }, { "ob_id": 87263, "function": "externalCitation", "linkage": "https://doi.org/10.1007/s10584-016-1634-0", "name": "Dirmeyer, P.A., Yu, L., Amini, S., Crowell, A.D., Elders, A. & Wu, J. (2016) Projections of the shifting envelope of Water cycle variability. Climatic Change 136, 587–600. https://doi.org/10.1007/s10584-016-1634-0", "relatedTo": { "ob_id": 6679, "uuid": "ac4ecbd554d0dd52a9b575d9666dc42d", "short_code": "ob" } }, { "ob_id": 87264, "function": "externalCitation", "linkage": "https://doi.org/10.5194/tcd-9-2745-2015", "name": "Atlaskina, K., Berninger, F. & de Leeuw, G. (2015) Satellite observations of changes in snow-covered land surface albedo during spring in the Northern Hemisphere. https://doi.org/10.5194/tcd-9-2745-2015", "relatedTo": { "ob_id": 6679, "uuid": "ac4ecbd554d0dd52a9b575d9666dc42d", "short_code": "ob" } }, { "ob_id": 87265, "function": "externalCitation", "linkage": "https://doi.org/10.1007/s00382-014-2341-z", "name": "Palazzi, E., von Hardenberg, J., Terzago, S. & Provenzale, A. (2014) Precipitation in the Karakoram-Himalaya: a CMIP5 view. Climate Dynamics 45, 21–45. https://doi.org/10.1007/s00382-014-2341-z", "relatedTo": { "ob_id": 6679, "uuid": "ac4ecbd554d0dd52a9b575d9666dc42d", "short_code": "ob" } }, { "ob_id": 87266, "function": "externalCitation", "linkage": "https://doi.org/10.1038/sdata.2015.8", "name": "Sharma, S., Gray, D.K., Read, J.S., et al. (2015) A global database of lake surface temperatures collected by in situ and satellite methods from 1985–2009. Scientific Data 2. https://doi.org/10.1038/sdata.2015.8", "relatedTo": { "ob_id": 6679, "uuid": "ac4ecbd554d0dd52a9b575d9666dc42d", "short_code": "ob" } }, { "ob_id": 87267, "function": "externalCitation", "linkage": "https://doi.org/10.48550/arxiv.1502.02536", "name": "Naesseth, C.A., Lindsten, F. & Schön, T.B. (2015) Nested Sequential Monte Carlo Methods. https://doi.org/10.48550/ARXIV.1502.02536", "relatedTo": { "ob_id": 6679, "uuid": "ac4ecbd554d0dd52a9b575d9666dc42d", "short_code": "ob" } }, { "ob_id": 87268, "function": "externalCitation", "linkage": "https://doi.org/10.1017/sus.2018.15", "name": "Creutzig, F., Bren d’Amour, C., Weddige, U., Fuss, S., Beringer, T., Gläser, A., Kalkuhl, M., Steckel, J.C., Radebach, A. & Edenhofer, O. (2019) Assessing human and environmental pressures of global land-use change 2000–2010. Global Sustainability 2. https://doi.org/10.1017/sus.2018.15", "relatedTo": { "ob_id": 6679, "uuid": "ac4ecbd554d0dd52a9b575d9666dc42d", "short_code": "ob" } }, { "ob_id": 87269, "function": "externalCitation", "linkage": "https://doi.org/10.1038/ngeo2882", "name": "Arneth, A., Sitch, S., Pongratz, J., et al. (2017) Historical carbon dioxide emissions caused by land-use changes are possibly larger than assumed. Nature Geoscience 10, 79–84. https://doi.org/10.1038/ngeo2882", "relatedTo": { "ob_id": 6679, "uuid": "ac4ecbd554d0dd52a9b575d9666dc42d", "short_code": "ob" } }, { "ob_id": 87270, "function": "externalCitation", "linkage": "https://doi.org/10.5194/hess-21-1189-2017", "name": "Robinson, E.L., Blyth, E.M., Clark, D.B., Finch, J. & Rudd, A.C. (2017) Trends in atmospheric evaporative demand in Great Britain using high-resolution meteorological data. Hydrology and Earth System Sciences 21, 1189–1224. https://doi.org/10.5194/hess-21-1189-2017", "relatedTo": { "ob_id": 6679, "uuid": "ac4ecbd554d0dd52a9b575d9666dc42d", "short_code": "ob" } }, { "ob_id": 87271, "function": "externalCitation", "linkage": "https://doi.org/10.5194/esd-2016-11", "name": "Krause, A., Pugh, T.A.M., Bayer, A.D., Lindeskog, M. & Arneth, A. (2016) Impacts of land-use history on the recovery of ecosystems after agricultural abandonment. https://doi.org/10.5194/esd-2016-11", "relatedTo": { "ob_id": 6679, "uuid": "ac4ecbd554d0dd52a9b575d9666dc42d", "short_code": "ob" } }, { "ob_id": 87272, "function": "externalCitation", "linkage": "https://doi.org/10.1371/journal.pone.0192642", "name": "Duku, C., Zwart, S.J. & Hein, L. (2018) Impacts of climate change on cropping patterns in a tropical, sub-humid watershed. ed. by P.K. Subudhi. PLOS ONE 13, e0192642. https://doi.org/10.1371/journal.pone.0192642", "relatedTo": { "ob_id": 6679, "uuid": "ac4ecbd554d0dd52a9b575d9666dc42d", "short_code": "ob" } }, { "ob_id": 87273, "function": "externalCitation", "linkage": "https://doi.org/10.5194/essdd-6-689-2013", "name": "Le Quéré, C., Peters, G.P., Andres, R.J., et al. (2013) Global carbon budget 2013. https://doi.org/10.5194/essdd-6-689-2013", "relatedTo": { "ob_id": 6679, "uuid": "ac4ecbd554d0dd52a9b575d9666dc42d", "short_code": "ob" } }, { "ob_id": 87274, "function": "externalCitation", "linkage": "https://doi.org/10.1128/msystems.00167-18", "name": "Ladau, J., Shi, Y., Jing, X., He, J.-S., Chen, L., Lin, X., Fierer, N., Gilbert, J.A., Pollard, K.S. & Chu, H. (2018) Existing Climate Change Will Lead to Pronounced Shifts in the Diversity of Soil Prokaryotes. ed. by O. Mason. mSystems 3. https://doi.org/10.1128/msystems.00167-18", "relatedTo": { "ob_id": 6679, "uuid": "ac4ecbd554d0dd52a9b575d9666dc42d", "short_code": "ob" } }, { "ob_id": 87275, "function": "externalCitation", "linkage": "https://doi.org/10.1371/journal.pone.0199383", "name": "Wu, Z., Boke-Olén, N., Fensholt, R., Ardö, J., Eklundh, L. & Lehsten, V. (2018) Effect of climate dataset selection on simulations of terrestrial GPP: Highest uncertainty for tropical regions. ed. by J.M. Dias. PLOS ONE 13, e0199383. https://doi.org/10.1371/journal.pone.0199383", "relatedTo": { "ob_id": 6679, "uuid": "ac4ecbd554d0dd52a9b575d9666dc42d", "short_code": "ob" } }, { "ob_id": 87276, "function": "externalCitation", "linkage": "https://doi.org/10.1126/science.aan5028", "name": "Myers-Smith, I.H. & Myers, J.H. (2018) Comment on “Precipitation drives global variation in natural selection”. Science 359. https://doi.org/10.1126/science.aan5028", "relatedTo": { "ob_id": 6679, "uuid": "ac4ecbd554d0dd52a9b575d9666dc42d", "short_code": "ob" } }, { "ob_id": 87277, "function": "externalCitation", "linkage": "https://doi.org/1983/0ba390c0-7c3d-42c2-98ef-9535325acef3", "name": "not a doi", "relatedTo": { "ob_id": 6679, "uuid": "ac4ecbd554d0dd52a9b575d9666dc42d", "short_code": "ob" } }, { "ob_id": 87278, "function": "externalCitation", "linkage": "https://doi.org/10.5194/bgd-12-4331-2015", "name": "Hashimoto, S., Carvalhais, N., Ito, A., Migliavacca, M., Nishina, K. & Reichstein, M. (2015) Global spatiotemporal distribution of soil respiration modeled using a global database. https://doi.org/10.5194/bgd-12-4331-2015", "relatedTo": { "ob_id": 6679, "uuid": "ac4ecbd554d0dd52a9b575d9666dc42d", "short_code": "ob" } }, { "ob_id": 87279, "function": "externalCitation", "linkage": "https://doi.org/10.25932/publishup-43788", "name": "Hellwig, N., Walz, A. & Markovic, D. (2019) Climatic and socioeconomic effects on land cover changes across Europe. Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe, 764. https://doi.org/10.25932/PUBLISHUP-43788", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87280, "function": "externalCitation", "linkage": "https://doi.org/10.5194/bg-15-4495-2018", "name": "Haughton, N., Abramowitz, G., De Kauwe, M.G. & Pitman, A.J. (2018) Does predictability of fluxes vary between FLUXNET sites? Biogeosciences 15, 4495–4513. https://doi.org/10.5194/bg-15-4495-2018", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87281, "function": "externalCitation", "linkage": "https://doi.org/10.1007/s11069-021-04962-9", "name": "Dar, M.A., Ahmed, R., Latif, M. & Azam, M. (2021) Climatology of dust storm frequency and its association with temperature and precipitation patterns over Pakistan. Natural Hazards 110, 655–677. https://doi.org/10.1007/s11069-021-04962-9", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87282, "function": "externalCitation", "linkage": "https://doi.org/10.1029/2018jd028378", "name": "Careto, J.A.M., Cardoso, R.M., Soares, P.M.M. & Trigo, R.M. (2018) Land‐Atmosphere Coupling in CORDEX‐Africa: Hindcast Regional Climate Simulations. Journal of Geophysical Research: Atmospheres 123. https://doi.org/10.1029/2018jd028378", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87283, "function": "externalCitation", "linkage": "https://doi.org/10.17169/refubium-26076", "name": "Russo, E., Kirchner, I., Pfahl, S., Schaap, M. & Cubasch, U. (2019) Sensitivity studies with the regional climate model COSMO-CLM 5.0 over the CORDEX Central Asia Domain. Freie Universität Berlin. https://doi.org/10.17169/REFUBIUM-26076", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87284, "function": "externalCitation", "linkage": "https://doi.org/10.3390/rs14133151", "name": "Guo, Z., Lou, W., Sun, C. & He, B. (2022) Trend Changes of the Vegetation Activity in Northeastern East Asia and the Connections with Extreme Climate Indices. Remote Sensing 14, 3151. https://doi.org/10.3390/rs14133151", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87285, "function": "externalCitation", "linkage": "https://doi.org/1887/81918", "name": "not a doi", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87286, "function": "externalCitation", "linkage": "https://doi.org/10.1007/s00704-021-03730-w", "name": "Peng, Q., Wang, R., Jiang, Y., Li, C. & Guo, W. (2021) The change of hydrological variables and its effects on vegetation in Central Asia. Theoretical and Applied Climatology 146, 741–753. https://doi.org/10.1007/s00704-021-03730-w", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87287, "function": "externalCitation", "linkage": "https://doi.org/10.1016/j.jhydrol.2019.01.044", "name": "Kumar, A., Sanyal, P. & Agrawal, S. (2019) Spatial distribution of δ18O values of water in the Ganga river basin: Insight into the hydrological processes. Journal of Hydrology 571, 225–234. https://doi.org/10.1016/j.jhydrol.2019.01.044", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87288, "function": "externalCitation", "linkage": "https://doi.org/10.3390/atmos11121334", "name": "Centella-Artola, A., Bezanilla-Morlot, A., Taylor, M.A., et al. (2020) Evaluation of Sixteen Gridded Precipitation Datasets over the Caribbean Region Using Gauge Observations. Atmosphere 11, 1334. https://doi.org/10.3390/atmos11121334", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87289, "function": "externalCitation", "linkage": "https://doi.org/10.48550/arxiv.1907.09725", "name": "Ise, T. & Oba, Y. (2019) VARENN: Graphical representation of spatiotemporal data and application to climate studies. https://doi.org/10.48550/ARXIV.1907.09725", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87290, "function": "externalCitation", "linkage": "https://doi.org/10.1016/j.jssas.2021.07.006", "name": "Ibanga, O.A., Idehen, O.F. & Omonigho, M.G. (2022) Spatiotemporal variability of soil moisture under different soil groups in Etsako West Local Government Area, Edo State, Nigeria. Journal of the Saudi Society of Agricultural Sciences 21, 125–147. https://doi.org/10.1016/j.jssas.2021.07.006", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87291, "function": "externalCitation", "linkage": "https://doi.org/10.1002/joc.7621", "name": "Zermeño‐Díaz, D.M. (2022) Diagnostics of observed dry trends in Caribbean precipitation. International Journal of Climatology 42, 6927–6943. https://doi.org/10.1002/joc.7621", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87292, "function": "externalCitation", "linkage": "https://doi.org/10.5194/cp-15-307-2019", "name": "Bothe, O., Wagner, S. & Zorita, E. (2019) Inconsistencies between observed, reconstructed, and simulated precipitation indices for England since the year 1650 CE. Climate of the Past 15, 307–334. https://doi.org/10.5194/cp-15-307-2019", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87293, "function": "externalCitation", "linkage": "https://doi.org/10.20944/preprints202007.0317.v1", "name": "EL CHAMI, D. & Galli, F. (2020) A Preliminary Assessment of Growth Regulators in Agricultural: Innovation for Sustainable Vegetable Nutrition. https://doi.org/10.20944/preprints202007.0317.v1", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87294, "function": "externalCitation", "linkage": "https://doi.org/10.1038/s41467-020-17710-7", "name": "Burrell, A.L., Evans, J.P. & De Kauwe, M.G. (2020) Anthropogenic climate change has driven over 5 million km2 of drylands towards desertification. Nature Communications 11. https://doi.org/10.1038/s41467-020-17710-7", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87295, "function": "externalCitation", "linkage": "https://doi.org/10.1007/s00382-021-05992-6", "name": "Chinta, V., Chen, Z., Du, Y. & Chowdary, J.S. (2021) Influence of the Interdecadal Pacific Oscillation on South Asian and East Asian summer monsoon rainfall in CMIP6 models. Climate Dynamics 58, 1791–1809. https://doi.org/10.1007/s00382-021-05992-6", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87296, "function": "externalCitation", "linkage": "https://doi.org/10.1057/s41599-019-0302-1", "name": "Lyu, H., Dong, Z., Roobavannan, M., Kandasamy, J. & Pande, S. (2019) Rural unemployment pushes migrants to urban areas in Jiangsu Province, China. Palgrave Communications 5. https://doi.org/10.1057/s41599-019-0302-1", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87297, "function": "externalCitation", "linkage": "https://doi.org/10.1186/s40663-020-00239-y", "name": "Xu, K., Wang, X., Jiang, C. & Sun, O.J. (2020) Assessing the vulnerability of ecosystems to climate change based on climate exposure, vegetation stability and productivity. Forest Ecosystems 7. https://doi.org/10.1186/s40663-020-00239-y", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87298, "function": "externalCitation", "linkage": "https://doi.org/10.1080/02626667.2020.1802030", "name": "Lyu, H., Dong, Z., Roobavannan, M., Kandasamy, J. & Pande, S. (2020) Prospects of interventions to alleviate rural–urban migration in Jiangsu Province, China based on sensitivity and scenario analysis. Hydrological Sciences Journal 65, 2175–2184. https://doi.org/10.1080/02626667.2020.1802030", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87299, "function": "externalCitation", "linkage": "https://doi.org/10.1016/j.watres.2020.116276", "name": "Rabaey, K., Vandekerckhove, T., de Walle, A.V. & Sedlak, D.L. (2020) The third route: Using extreme decentralization to create resilient urban water systems. Water Research 185, 116276. https://doi.org/10.1016/j.watres.2020.116276", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87300, "function": "externalCitation", "linkage": "https://doi.org/10.5194/hess-2020-191", "name": "Hulsman, P., Savenije, H.H.G. & Hrachowitz, M. (2020) Learning from satellite observations: increased understanding of catchment processes through stepwise model improvement. https://doi.org/10.5194/hess-2020-191", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87301, "function": "externalCitation", "linkage": "https://doi.org/10.1021/acs.est.2c01274", "name": "Sinha, E., Michalak, A.M., Balaji, V. & Resplandy, L. (2022) India’s Riverine Nitrogen Runoff Strongly Impacted by Monsoon Variability. Environmental Science & Technology 56, 11335–11342. https://doi.org/10.1021/acs.est.2c01274", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87302, "function": "externalCitation", "linkage": "https://doi.org/10.1002/ecs2.3315", "name": "Sun, G. & Mu, M. (2021) Impacts of two types of errors on the predictability of terrestrial carbon cycle. Ecosphere 12. https://doi.org/10.1002/ecs2.3315", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87303, "function": "externalCitation", "linkage": "https://doi.org/10.5194/hess-24-3331-2020", "name": "Hulsman, P., Winsemius, H.C., Michailovsky, C.I., Savenije, H.H.G. & Hrachowitz, M. (2020) Using altimetry observations combined with GRACE to select parameter sets of a hydrological model in a data-scarce region. Hydrology and Earth System Sciences 24, 3331–3359. https://doi.org/10.5194/hess-24-3331-2020", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87304, "function": "externalCitation", "linkage": "https://doi.org/10.1007/s00376-021-1075-1", "name": "Li, J., Xie, T., Tang, X., Wang, H., Sun, C., Feng, J., Zheng, F. & Ding, R. (2021) Influence of the NAO on Wintertime Surface Air Temperature over East Asia: Multidecadal Variability and Decadal Prediction. Advances in Atmospheric Sciences 39, 625–642. https://doi.org/10.1007/s00376-021-1075-1", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87305, "function": "externalCitation", "linkage": "https://doi.org/10.1175/waf-d-19-0023.1", "name": "Ardilouze, C., Batté, L., Decharme, B. & Déqué, M. (2019) On the Link between Summer Dry Bias over the U.S. Great Plains and Seasonal Temperature Prediction Skill in a Dynamical Forecast System. Weather and Forecasting 34, 1161–1172. https://doi.org/10.1175/waf-d-19-0023.1", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87306, "function": "externalCitation", "linkage": "https://doi.org/10.5194/cp-16-1043-2020", "name": "Rezsöhazy, J., Goosse, H., Guiot, J., Gennaretti, F., Boucher, E., André, F. & Jonard, M. (2020) Application and evaluation of the dendroclimatic process-based model MAIDEN during the last century in Canada and Europe. Climate of the Past 16, 1043–1059. https://doi.org/10.5194/cp-16-1043-2020", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87307, "function": "externalCitation", "linkage": "https://doi.org/10.1038/s41612-020-0129-x", "name": "Ise, T. & Oba, Y. (2020) VARENN: graphical representation of periodic data and application to climate studies. npj Climate and Atmospheric Science 3. https://doi.org/10.1038/s41612-020-0129-x", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87308, "function": "externalCitation", "linkage": "https://doi.org/10.1038/s41598-018-34993-5", "name": "Lausier, A.M. & Jain, S. (2018) Overlooked Trends in Observed Global Annual Precipitation Reveal Underestimated Risks. Scientific Reports 8. https://doi.org/10.1038/s41598-018-34993-5", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87309, "function": "externalCitation", "linkage": "https://doi.org/10.1038/s41893-019-0220-7", "name": "Chen, C., Park, T., Wang, X., et al. (2019) China and India lead in greening of the world through land-use management. Nature Sustainability 2, 122–129. https://doi.org/10.1038/s41893-019-0220-7", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87310, "function": "externalCitation", "linkage": "https://doi.org/10.1038/s41598-018-29286-w", "name": "Grotjahn, R. & Huynh, J. (2018) Contiguous US summer maximum temperature and heat stress trends in CRU and NOAA Climate Division data plus comparisons to reanalyses. Scientific Reports 8. https://doi.org/10.1038/s41598-018-29286-w", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87311, "function": "externalCitation", "linkage": "https://doi.org/10.5194/cp-15-1275-2019", "name": "Weitzel, N., Hense, A. & Ohlwein, C. (2019) Combining a pollen and macrofossil synthesis with climate simulations for spatial reconstructions of European climate using Bayesian filtering. Climate of the Past 15, 1275–1301. https://doi.org/10.5194/cp-15-1275-2019", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87312, "function": "externalCitation", "linkage": "https://doi.org/10.5194/gmd-14-2977-2021", "name": "Wu, T., Yu, R., Lu, Y., et al. (2021) BCC-CSM2-HR: a high-resolution version of the Beijing Climate Center Climate System Model. Geoscientific Model Development 14, 2977–3006. https://doi.org/10.5194/gmd-14-2977-2021", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87313, "function": "externalCitation", "linkage": "https://doi.org/10.1038/s41597-020-00587-y", "name": "Karger, D.N., Schmatz, D.R., Dettling, G. & Zimmermann, N.E. (2020) High-resolution monthly precipitation and temperature time series from 2006 to 2100. Scientific Data 7. https://doi.org/10.1038/s41597-020-00587-y", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87314, "function": "externalCitation", "linkage": "https://doi.org/10.1038/s41559-018-0647-7", "name": "Craven, D., Eisenhauer, N., Pearse, W.D., et al. (2018) Multiple facets of biodiversity drive the diversity–stability relationship. Nature Ecology & Evolution 2, 1579–1587. https://doi.org/10.1038/s41559-018-0647-7", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87315, "function": "externalCitation", "linkage": "https://doi.org/10.1111/gcbb.12671", "name": "Shepherd, A., Littleton, E., Clifton‐Brown, J., Martin, M. & Hastings, A. (2020) Projections of global and UK bioenergy potential from Miscanthus × giganteus—Feedstock yield, carbon cycling and electricity generation in the 21st century. GCB Bioenergy 12, 287–305. https://doi.org/10.1111/gcbb.12671", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87316, "function": "externalCitation", "linkage": "https://doi.org/10.1007/978-3-030-70238-0_15", "name": "Kharuk, V.I., Im, S.T. & Petrov, I.A. (2021) Conifer Growth During Warming Hiatus in the Altay-Sayan Mountain Region, Siberia. Mountain Landscapes in Transition, 385–401. https://doi.org/10.1007/978-3-030-70238-0_15", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87317, "function": "externalCitation", "linkage": "https://doi.org/10.6084/m9.figshare.12153837.v1", "name": "Xu, K., Wang, X., Jiang, C. & Sun, O.J. (2020) Additional file 1 of Assessing the vulnerability of ecosystems to climate change based on climate exposure, vegetation stability and productivity. figshare. https://doi.org/10.6084/M9.FIGSHARE.12153837.V1", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87318, "function": "externalCitation", "linkage": "https://doi.org/10.1007/s00704-018-2476-7", "name": "Hong, T., Dong, W., Ji, D., Dai, T., Yang, S. & Wei, T. (2018) The response of vegetation to rising CO2 concentrations plays an important role in future changes in the hydrological cycle. Theoretical and Applied Climatology 136, 135–144. https://doi.org/10.1007/s00704-018-2476-7", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87319, "function": "externalCitation", "linkage": "https://doi.org/10.5194/gmd-14-3215-2021", "name": "Glotfelty, T., Ramírez-Mejía, D., Bowden, J., Ghilardi, A. & West, J.J. (2021) Limitations of WRF land surface models for simulating land use and land cover change in Sub-Saharan Africa and development of an improved model (CLM-AF v. 1.0). Geoscientific Model Development 14, 3215–3249. https://doi.org/10.5194/gmd-14-3215-2021", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87320, "function": "externalCitation", "linkage": "https://doi.org/10.1007/978-3-030-70238-0_16", "name": "Kharuk, V.I., Im, S.T., Petrov, I.A., Shushpanov, A.S. & Dvinskaya, M.L. (2021) Climate-Induced Fir (Abies sibirica Ledeb.) Mortality in the Siberian Mountains. Mountain Landscapes in Transition, 403–416. https://doi.org/10.1007/978-3-030-70238-0_16", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87321, "function": "externalCitation", "linkage": "https://doi.org/10.1029/2018jd029945", "name": "Ge, J., Pitman, A.J., Guo, W., Wang, S. & Fu, C. (2019) Do Uncertainties in the Reconstruction of Land Cover Affect the Simulation of Air Temperature and Rainfall in the CORDEX Region of East Asia? Journal of Geophysical Research: Atmospheres 124, 3647–3670. https://doi.org/10.1029/2018jd029945", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87322, "function": "externalCitation", "linkage": "https://doi.org/10.5194/cp-17-1119-2021", "name": "Parker, S.E., Harrison, S.P., Comas-Bru, L., Kaushal, N., LeGrande, A.N. & Werner, M. (2021) A data–model approach to interpreting speleothem oxygen isotope records from monsoon regions. Climate of the Past 17, 1119–1138. https://doi.org/10.5194/cp-17-1119-2021", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87323, "function": "externalCitation", "linkage": "https://doi.org/10.1007/s00376-021-0348-z", "name": "Tian, Y., Gao, Y. & Guo, D. (2021) The Relationship between Melt Season Sea Ice over the Bering Sea and Summer Precipitation over Mid-Latitude East Asia. Advances in Atmospheric Sciences 38, 918–930. https://doi.org/10.1007/s00376-021-0348-z", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87324, "function": "externalCitation", "linkage": "https://doi.org/10.1002/joc.7146", "name": "Makula, E.K. & Zhou, B. (2021) Changes in March to May rainfall over Tanzania during 1978–2017. International Journal of Climatology 41, 5663–5675. https://doi.org/10.1002/joc.7146", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87325, "function": "externalCitation", "linkage": "https://doi.org/10.1007/s10021-019-00339-z", "name": "Serra-Maluquer, X., Gazol, A., Sangüesa-Barreda, G., Sánchez-Salguero, R., Rozas, V., Colangelo, M., Gutiérrez, E. & Camarero, J.J. (2019) Geographically Structured Growth decline of Rear-Edge Iberian Fagus sylvatica Forests After the 1980s Shift Toward a Warmer Climate. Ecosystems 22, 1325–1337. https://doi.org/10.1007/s10021-019-00339-z", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87326, "function": "externalCitation", "linkage": "https://doi.org/10.1029/2019jd030332", "name": "Yang, R., Gui, S. & Cao, J. (2019) Bay of Bengal‐East Asia‐Pacific Teleconnection in Boreal Summer. Journal of Geophysical Research: Atmospheres 124, 4395–4412. https://doi.org/10.1029/2019jd030332", "relatedTo": { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "short_code": "ob" } }, { "ob_id": 87327, "function": "externalCitation", "linkage": "https://doi.org/10.5194/esd-2021-43", "name": "Lalande, M., Ménégoz, M., Krinner, G., Naegeli, K. & Wunderle, S. (2021) Climate change in the High Mountain Asia in CMIP6. https://doi.org/10.5194/esd-2021-43", "relatedTo": { "ob_id": 20372, "uuid": "edf8febfdaad48abb2cbaf7d7e846a86", "short_code": "ob" } }, { "ob_id": 87328, "function": "externalCitation", "linkage": "https://doi.org/10.1002/joc.7278", "name": "Lu, S. & Zuo, H. (2021) Sensitivity of South Asian summer monsoon simulation to land surface schemes in Weather Research and Forecasting model. International Journal of Climatology 41, 6805–6824. https://doi.org/10.1002/joc.7278", "relatedTo": { "ob_id": 20372, "uuid": "edf8febfdaad48abb2cbaf7d7e846a86", "short_code": "ob" } }, { "ob_id": 87329, "function": "externalCitation", "linkage": "https://doi.org/10.5194/hess-2020-112", "name": "Koné, B., Diedhiou, A., Diawara, A., Anquetin, S., Touré, N.E., Bamba, A. & Kobea, A.T. (2020) Influence of initial soil moisture in a Regional Climate Model study over West Africa: Part 1: Impact on the climate mean. https://doi.org/10.5194/hess-2020-112", "relatedTo": { "ob_id": 20372, "uuid": "edf8febfdaad48abb2cbaf7d7e846a86", "short_code": "ob" } }, { "ob_id": 87330, "function": "externalCitation", "linkage": "https://doi.org/10.5194/cp-15-335-2019", "name": "Dallmeyer, A., Claussen, M. & Brovkin, V. (2019) Harmonising plant functional type distributions for evaluating Earth system models. Climate of the Past 15, 335–366. https://doi.org/10.5194/cp-15-335-2019", "relatedTo": { "ob_id": 20372, "uuid": "edf8febfdaad48abb2cbaf7d7e846a86", "short_code": "ob" } }, { "ob_id": 87331, "function": "externalCitation", "linkage": "https://doi.org/10.5194/hess-2019-303", "name": "Baudouin, J.-P., Herzog, M. & Petrie, C.A. (2019) Cross-validating precipitation datasets in the Indus River basin. https://doi.org/10.5194/hess-2019-303", "relatedTo": { "ob_id": 20372, "uuid": "edf8febfdaad48abb2cbaf7d7e846a86", "short_code": "ob" } }, { "ob_id": 87332, "function": "externalCitation", "linkage": "https://doi.org/10.1371/journal.pone.0223362", "name": "Park, S., Park, H., Im, J., Yoo, C., Rhee, J., Lee, B. & Kwon, C. (2019) Delineation of high resolution climate regions over the Korean Peninsula using machine learning approaches. ed. by M. Huang. PLOS ONE 14, e0223362. https://doi.org/10.1371/journal.pone.0223362", "relatedTo": { "ob_id": 20372, "uuid": "edf8febfdaad48abb2cbaf7d7e846a86", "short_code": "ob" } }, { "ob_id": 87333, "function": "externalCitation", "linkage": "https://doi.org/10.5194/gmd-15-3121-2022", "name": "Sato, H. & Ise, T. (2022) Predicting global terrestrial biomes with the LeNet convolutional neural network. Geoscientific Model Development 15, 3121–3132. https://doi.org/10.5194/gmd-15-3121-2022", "relatedTo": { "ob_id": 20372, "uuid": "edf8febfdaad48abb2cbaf7d7e846a86", "short_code": "ob" } }, { "ob_id": 87334, "function": "externalCitation", "linkage": "https://doi.org/10.3390/f9050267", "name": "Schwab, N., Kaczka, R.J., Janecka, K., Böhner, J., Chaudhary, R.P., Scholten, T. & Schickhoff, U. (2018) Climate Change-Induced Shift of Tree Growth Sensitivity at a Central Himalayan Treeline Ecotone. Forests 9, 267. https://doi.org/10.3390/f9050267", "relatedTo": { "ob_id": 20372, "uuid": "edf8febfdaad48abb2cbaf7d7e846a86", "short_code": "ob" } }, { "ob_id": 87335, "function": "externalCitation", "linkage": "https://doi.org/10.1016/j.jag.2017.11.015", "name": "Becker, M., Papa, F., Frappart, F., Alsdorf, D., Calmant, S., da Silva, J.S., Prigent, C. & Seyler, F. (2018) Satellite-based estimates of surface water dynamics in the Congo River Basin. International Journal of Applied Earth Observation and Geoinformation 66, 196–209. https://doi.org/10.1016/j.jag.2017.11.015", "relatedTo": { "ob_id": 20372, "uuid": "edf8febfdaad48abb2cbaf7d7e846a86", "short_code": "ob" } }, { "ob_id": 87336, "function": "externalCitation", "linkage": "https://doi.org/10.1038/sdata.2017.191", "name": "Abatzoglou, J.T., Dobrowski, S.Z., Parks, S.A. & Hegewisch, K.C. (2018) TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Scientific Data 5. https://doi.org/10.1038/sdata.2017.191", "relatedTo": { "ob_id": 20372, "uuid": "edf8febfdaad48abb2cbaf7d7e846a86", "short_code": "ob" } }, { "ob_id": 87337, "function": "externalCitation", "linkage": "https://doi.org/10.3390/f9070440", "name": "Herrera-Soto, G., González-Cásares, M., Pompa-García, M., Camarero, J.J. & Solís-Moreno, R. (2018) Growth of Pinus cembroides Zucc. in Response to Hydroclimatic Variability in Four Sites Forming the Species Latitudinal and Longitudinal Distribution Limits. Forests 9, 440. https://doi.org/10.3390/f9070440", "relatedTo": { "ob_id": 20372, "uuid": "edf8febfdaad48abb2cbaf7d7e846a86", "short_code": "ob" } }, { "ob_id": 87338, "function": "externalCitation", "linkage": "https://doi.org/10.1016/j.srs.2021.100035", "name": "Lewińska, K.E., Buchner, J., Bleyhl, B., Hostert, P., Yin, H., Kuemmerle, T. & Radeloff, V.C. (2021) Changes in the grasslands of the Caucasus based on Cumulative Endmember Fractions from the full 1987–2019 Landsat record. Science of Remote Sensing 4, 100035. https://doi.org/10.1016/j.srs.2021.100035", "relatedTo": { "ob_id": 20372, "uuid": "edf8febfdaad48abb2cbaf7d7e846a86", "short_code": "ob" } }, { "ob_id": 87339, "function": "externalCitation", "linkage": "https://doi.org/10.1002/2017wr021970", "name": "Milly, P.C.D., Kam, J. & Dunne, K.A. (2018) On the Sensitivity of Annual Streamflow to Air Temperature. Water Resources Research 54, 2624–2641. https://doi.org/10.1002/2017wr021970", "relatedTo": { "ob_id": 20372, "uuid": "edf8febfdaad48abb2cbaf7d7e846a86", "short_code": "ob" } }, { "ob_id": 87340, "function": "externalCitation", "linkage": "https://doi.org/10.1007/s00382-019-04804-2", "name": "Peng, D., Zhou, T., Zhang, L. & Zou, L. (2019) Detecting human influence on the temperature changes in Central Asia. Climate Dynamics 53, 4553–4568. https://doi.org/10.1007/s00382-019-04804-2", "relatedTo": { "ob_id": 20372, "uuid": "edf8febfdaad48abb2cbaf7d7e846a86", "short_code": "ob" } }, { "ob_id": 87341, "function": "externalCitation", "linkage": "https://doi.org/10.1016/j.jhydrol.2018.04.024", "name": "Sidibe, M., Dieppois, B., Mahé, G., Paturel, J.-E., Amoussou, E., Anifowose, B. & Lawler, D. (2018) Trend and variability in a new, reconstructed streamflow dataset for West and Central Africa, and climatic interactions, 1950–2005. Journal of Hydrology 561, 478–493. https://doi.org/10.1016/j.jhydrol.2018.04.024", "relatedTo": { "ob_id": 20372, "uuid": "edf8febfdaad48abb2cbaf7d7e846a86", "short_code": "ob" } }, { "ob_id": 87342, "function": "externalCitation", "linkage": "https://doi.org/10.1007/s00704-018-2644-9", "name": "Salunke, P., Jain, S. & Mishra, S.K. (2018) Performance of the CMIP5 models in the simulation of the Himalaya-Tibetan Plateau monsoon. Theoretical and Applied Climatology 137, 909–928. https://doi.org/10.1007/s00704-018-2644-9", "relatedTo": { "ob_id": 20372, "uuid": "edf8febfdaad48abb2cbaf7d7e846a86", "short_code": "ob" } }, { "ob_id": 87343, "function": "externalCitation", "linkage": "https://doi.org/10.1007/978-981-15-4712-6_22", "name": "Saikia, P., Kumar, A., Diksha, Lal, P., Nikita & Khan, M.L. (2020) Ecosystem-Based Adaptation to Climate Change and Disaster Risk Reduction in Eastern Himalayan Forests of Arunachal Pradesh, Northeast India. Nature-based Solutions for Resilient Ecosystems and Societies, 391–408. https://doi.org/10.1007/978-981-15-4712-6_22", "relatedTo": { "ob_id": 20372, "uuid": "edf8febfdaad48abb2cbaf7d7e846a86", "short_code": "ob" } }, { "ob_id": 87344, "function": "externalCitation", "linkage": "https://doi.org/10.5194/esd-12-1061-2021", "name": "Lalande, M., Ménégoz, M., Krinner, G., Naegeli, K. & Wunderle, S. (2021) Climate change in the High Mountain Asia in CMIP6. Earth System Dynamics 12, 1061–1098. https://doi.org/10.5194/esd-12-1061-2021", "relatedTo": { "ob_id": 20372, "uuid": "edf8febfdaad48abb2cbaf7d7e846a86", "short_code": "ob" } }, { "ob_id": 87345, "function": "externalCitation", "linkage": "https://doi.org/10.5194/gmd-10-4105-2017", "name": "Wozniak, M.C. & Steiner, A.L. (2017) A prognostic pollen emissions model for climate models (PECM1.0). Geoscientific Model Development 10, 4105–4127. https://doi.org/10.5194/gmd-10-4105-2017", "relatedTo": { "ob_id": 20372, "uuid": "edf8febfdaad48abb2cbaf7d7e846a86", "short_code": "ob" } }, { "ob_id": 87346, "function": "externalCitation", "linkage": "https://doi.org/10.5194/hess-26-711-2022", "name": "Koné, B., Diedhiou, A., Diawara, A., Anquetin, S., Touré, N.E., Bamba, A. & Kobea, A.T. (2022) Influence of initial soil moisture in a regional climate model study over West Africa – Part 1: Impact on the climate mean. Hydrology and Earth System Sciences 26, 711–730. https://doi.org/10.5194/hess-26-711-2022", "relatedTo": { "ob_id": 20372, "uuid": "edf8febfdaad48abb2cbaf7d7e846a86", "short_code": "ob" } }, { "ob_id": 87347, "function": "externalCitation", "linkage": "https://doi.org/10.1007/978-981-19-4476-5_5", "name": "Schickhoff, U., Bobrowski, M., Böhner, J., Bürzle, B., Chaudhary, R.P., Müller, M., Scholten, T., Schwab, N. & Weidinger, J. (2023) The Treeline Ecotone in Rolwaling Himal, Nepal: Pattern-Process Relationships and Treeline Shift Potential. Ecology of Himalayan Treeline Ecotone, 95–145. https://doi.org/10.1007/978-981-19-4476-5_5", "relatedTo": { "ob_id": 20372, "uuid": "edf8febfdaad48abb2cbaf7d7e846a86", "short_code": "ob" } }, { "ob_id": 87348, "function": "externalCitation", "linkage": "https://doi.org/10.1038/s41598-020-80609-2", "name": "Forryan, A., Naveira Garabato, A.C., Vic, C., Nurser, A.J.G. & Hearn, A.R. (2021) Galápagos upwelling driven by localized wind–front interactions. Scientific Reports 11. https://doi.org/10.1038/s41598-020-80609-2", "relatedTo": { "ob_id": 25390, "uuid": "9c334fbe6d424a708cf3c4cf0c6a53f5", "short_code": "coll" } }, { "ob_id": 87349, "function": "externalCitation", "linkage": "https://doi.org/10.5194/os-2019-108", "name": "von Appen, W.-J., Strass, V.H., Bracher, A., Xi, H., Hörstmann, C., Iversen, M.H. & Waite, A.M. (2019) High-resolution physical-biogeochemical structure of a filament and an eddy of upwelled water off Northwest Africa. https://doi.org/10.5194/os-2019-108", "relatedTo": { "ob_id": 25390, "uuid": "9c334fbe6d424a708cf3c4cf0c6a53f5", "short_code": "coll" } }, { "ob_id": 87350, "function": "externalCitation", "linkage": "https://doi.org/10.1029/2018gb006118", "name": "Mouw, C.B., Ciochetto, A.B. & Yoder, J.A. (2019) A Satellite Assessment of Environmental Controls of Phytoplankton Community Size Structure. Global Biogeochemical Cycles 33, 540–558. https://doi.org/10.1029/2018gb006118", "relatedTo": { "ob_id": 25390, "uuid": "9c334fbe6d424a708cf3c4cf0c6a53f5", "short_code": "coll" } }, { "ob_id": 87351, "function": "externalCitation", "linkage": "https://doi.org/10.5194/os-15-819-2019", "name": "Garnesson, P., Mangin, A., Fanton d’Andon, O., Demaria, J. & Bretagnon, M. (2019) The CMEMS GlobColour chlorophyll <i>a</i> product based on satellite observation: multi-sensor merging and flagging strategies. Ocean Science 15, 819–830. https://doi.org/10.5194/os-15-819-2019", "relatedTo": { "ob_id": 25390, "uuid": "9c334fbe6d424a708cf3c4cf0c6a53f5", "short_code": "coll" } }, { "ob_id": 87352, "function": "externalCitation", "linkage": "https://doi.org/10.3390/s19194285", "name": "Sathyendranath, S., Brewin, R., Brockmann, C., et al. (2019) An Ocean-Colour Time Series for Use in Climate Studies: The Experience of the Ocean-Colour Climate Change Initiative (OC-CCI). Sensors 19, 4285. https://doi.org/10.3390/s19194285", "relatedTo": { "ob_id": 25390, "uuid": "9c334fbe6d424a708cf3c4cf0c6a53f5", "short_code": "coll" } }, { "ob_id": 87353, "function": "externalCitation", "linkage": "https://doi.org/10.23689/fidgeo-4799", "name": "Pradhan, H.K., Völker, C., Losa, S.N., Bracher, A. & Nerger, L. (2020) Global Assimilation of Ocean-Color Data of Phytoplankton Functional Types: Impact of Different Data Sets. FID GEO. https://doi.org/10.23689/FIDGEO-4799", "relatedTo": { "ob_id": 25390, "uuid": "9c334fbe6d424a708cf3c4cf0c6a53f5", "short_code": "coll" } }, { "ob_id": 87354, "function": "externalCitation", "linkage": "https://doi.org/10.3390/ijgi8080354", "name": "Wilson, C. & Robinson, D.H. (2019) Lessons Learned from the NOAA CoastWatch Ocean Satellite Course Developed for Integrating Oceanographic Satellite Data into Operational Use. ISPRS International Journal of Geo-Information 8, 354. https://doi.org/10.3390/ijgi8080354", "relatedTo": { "ob_id": 25390, "uuid": "9c334fbe6d424a708cf3c4cf0c6a53f5", "short_code": "coll" } } ] }