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3,101 results for “historic”
Historical and modern view of Vallon de Nant, Switzerland (1912 and 2021)
<p>Comparison of a historical view of the Vallon de Nant and a modern view. The postcard has been published here by Robert Di Salvo <a href="https://notrehistoire.ch/entries/lyYnl9ov8E9">https://notrehistoire.ch/entries/lyYnl9ov8E9</a> and is republished here with his authorization. The recent view has been taken by Anthony Michelon on 12 November 2021 at 13h at the position 574722E/121303N (CH1903) or 46.242682N/7.110908E (WGS84) at elevation 1785 masl. The comparison was first published in the PhD thesis of Anthony Michelon (2022, <a href="https://serval.unil.ch/en/notice/serval:BIB_0C61AF744730">link</a>)</p>
Lausanne Historical Censuses Dataset HTR 35k
<p>This training dataset includes a total of 34,913 manually transcribed text segments. It is dedicated to the handwritten text recognition (HTR) of historical sources, typically tabular records, such as censuses. This dataset is based on a sample of 83 pages from the 19th century (1805-1898) censuses of Lausanne, Switzerland. The primary language of the documents is French, although many germanic names and toponyms are also found.</p> <p>The training data are formatted and provided on the model of the Bentham dataset. The format thus simply consists in a list of jpeg images, one per text segments, and their corresponding transcription, stored in a txt file. The file naming convention is 'yyyy-ppp-n', where 'y' stands for the year of publication of the census, and 'p' for the page number.</p> <p>The digitized documents are provided by the <a href="http://www.lausanne.ch/vie-pratique/culture/bibliotheques-et-archives/archives.html">Archives of the City of Lausanne</a>.</p> <p>Please note that the annotation and extraction methodology, as well as the complete evaluation of performance, including HTR benchmark and post-correction performance is published in :</p> <ul> <li>Petitpierre R., Rappo L., Kramer M. (2023). <em>An end-to-end pipeline for historical censuses processing</em>. International Journal on Document Analysis and Recognition (IJDAR). doi: <a href="https://doi.org/10.1007/s10032-023-00428-9">10.1007/s10032-023-00428-9</a></li> </ul> <p>Tabular dataset resulting from automatic extraction are also available on Zenodo :</p> <ul> <li>Petitpierre R., Rappo L., Kramer M., di Lenardo I. (2023). <em>1805-1898 Census Records of Lausanne : a Long Digital Dataset for Demographic History</em>. Zenodo. doi: <a href="https://doi.org/10.5281/zenodo.7711640">10.5281/zenodo.7711640</a></li> </ul>
Historical (1979 - 2020) data for anthropogenic inputs to a catchment and riverine mainstem exports for carbon, nitrogen, and phosphorus
<p>We estimated the difference in Net Anthropogenic Nitrogen and Phosphorus Inputs (NANI-NAPI) at the finest scale possible (the municipality) in the <em>Rivière du Nord</em> watershed (Québec, Canada) between 1981 and 2016. The dataset here reports the delta between those two years for each municipality in the watershed.</p> <p>Three sites along the mainstem of <em>Rivière du Nord </em>have been sampled ~bi-monthly from ~1979 - 2020 for dissolved organic carbon (DOC), total nitrogen (TN), and total phosphorus (TP), from which we estimated annual riverine export at each site. We also include annual precipitation (as the sum of rain and snow), and NANI-NAPI interpolated for each sub-watershed for 1981, 1986, 1991, 1996, 2001, 2006, 2011, and 2016.</p> <p> </p> <p> </p>
Southern Hemisphere Lamb Weather Types from historical GCM experiments and various reanalyses
<p>This dataset comprises six-hourly Lamb Weather Type (LWT) time series covering the period 1979-2005 for a) historical experiments run with 61 distinct GCMs from CMIP5 and 6 (specified in "get_historical_metadata.py" published at https://doi.org/10.5281/zenodo.4555367) and b) 3 distinct reanalyses (ERA-Interim, JRA-55 and ERA5, the latter extended to 2020). The LWT time series are provided on a 2.5º regular latitude-longitude grid covering the southern hemisphere between 30ºS and 70ºS. The full LWT approach covering 27 classes is applied and the corresponding results for the Northern Hemisphere were stored in a companion dataset at https://doi.org/10.5281/zenodo.4452080. The format of the files is netCDF-4, compressed with the netCDF Kitchen Sink command "ncks -4 -L 1". The Python code used to generate this dataset is available from https://doi.org/10.5281/zenodo.4555367</p> <p> </p> <p><strong>Note</strong></p> <p>The LWT_SH.zip file contains all relevant data. Please ignore the separate netCDF files outside this zip file. These are old files that could not be deleted during the update from version 1 to 2 due to technical issues with Zenodo.</p> <p> </p> <p><strong>Historial</strong></p> <p>Version 2 is a major dataset update featuring the following improvements:</p> <p>1. The attributes from the netCDF source files "psl...nc" obtained from ESGF were copied into the files available here. These attributes are indicated with the prefix "udata...." (for "underlying data").</p> <p>2. All non-standard calenders from the underlying netCDF files from ESGF were converted into standard using the "xarray.Dataset.convert_calendar" function. The original calendar information was stored as additional netCDF attribute.</p> <p>3. The "patch" method from Python's xesmf module was used to regrid the original psl data from the native GCM grid available from ESGF to the regular lat-lon 2.5° grid common to all applied GCMs and reanalyses.</p> <p>contact: Swen Brands, brandssf@ifca.unican.es</p> <p> </p> <p><strong>Principal Research Articles, Software and Complementary Datasets Associated with this Dataset</strong></p> <p>Brands, S. (2022). A circulation-based performance atlas of the CMIP5 and<br> 6 models for regional climate studies in the Northern Hemisphere mid-to-<br> high latitudes. Geoscientific Model Development, 15 (4), 1375–1411.<br> doi: https://doi.org/10.5194/gmd-15-1375-2022</p> <p>Brands, S. (2022). A circulation-based performance atlas of the CMIP5 and 6 mod-<br> els for regional climate studies in the northern hemisphere [data set]. Zenodo.<br> doi: https://doi.org/10.5281/zenodo.4452080</p> <p>Brands, S. (2022). Common error patterns in the regional atmospheric circulation<br> simulated by the CMIP multi-model ensemble. Geophysical Research Letters,<br> 49 (23), e2022GL101446. doi: https://doi.org/10.1029/2022GL101446</p> <p>Brands, Swen, Tatebe, Hiroaki, Danek, Christopher, Fernández, Jesús, Swart, Neil C., Volodin, Evgeny, Kim, YoungHo, Collier, Mark, Bi, Dave, & Tongwen, Wu. (2022). Python code to calculate Lamb circulation types derived from historical CMIP simulations and reanalysis data. In Geoscientific Model Development: Vols. gmd-2020-418 (Version 4). Zenodo. https://doi.org/10.5281/zenodo.6390256</p> <p>Brands, S., Fernández-Granja, J. A., Bedia, J., Casanueva, A., & Fernández,<br> J. (2023). Auxiliary online material to Brands et al. (2023): A global<br> climate model performance atlas for the Southern Hemisphere extratrop-<br> ics based on regional atmospheric circulation patterns. figshare. doi:<br> https://doi.org/10.6084/m9.figshare.22193443.v1</p> <p>Brands, S., Fernández-Granja, J. A., Bedia, J., Casanueva, A., & Fernández,<br> J. (2023b). Southern Hemisphere Lamb Weather Types from historical<br> GCM experiments and various reanalyses (1.0) [data set]. Zenodo. doi:<br> https://doi.org/10.5281/zenodo.7612988</p> <p>Brands, S., Tatebe, H., Danek, C., Fernández, J., Swart, N., Volodin, E., . . . Tong-<br> wen, W. (2023). GCM metadata archive get historical metadata.py (v1.1).<br> Zenodo. doi: https://doi.org/10.5281/zenodo.7715383</p> <p>Fernández-Granja, J. A., Brands, S., Bedia, J., Casanueva, A., & Fernández, J.<br> (2023). Exploring the limits of the Jenkinson–Collison weather types clas-<br> sification scheme: a global assessment based on various reanalyses.<br> Climate Dynamics. doi: 10.1007/s00382-022-06658-7</p> <p> </p> <p><strong>References of the source GCMs</strong> <strong>and Early References of the Lamb Weather Typing Method</strong></p> <p>Bentsen, M., Bethke, I., Debernard, J. B., Iversen, T., Kirkevåg, A., Seland, Ø., . . .<br> Kristjánsson, J. E. (2013). The Norwegian Earth System Model, NorESM1-M<br> – part 1: Description and basic evaluation of the physical climate.<br> Geoscientific Model Development, 6 (3), 687–720. doi: 10.5194/gmd-6-687-2013</p> <p>Bi, D., Dix, M., Marsland, S., O’Farrell, S., Sullivan, A., Bodman, R., . . . Heerde-<br> gen, A. (2020). Configuration and spin-up of ACCESS-CM2, the new gener-<br> ation Australian Community Climate and Earth System Simulator Coupled<br> Model. Journal of Southern Hemisphere Earth Systems Science, 70 (1), 225-<br> 251. doi: doi:10.1071/ES19040</p> <p>Bi, D., Dix, M., Marsland, S. J., O’Farrell, S., Rashid, H., Uotila, P., . . . Puri, K.<br> (2013). The ACCESS coupled model: description, control climate and evaluation. Australian Meteorological and Oceanographic Journal , 63 , 41-64. doi: 0.22499/2.6301.004</p> <p>Boucher, O., Servonnat, J., Albright, A. L., Aumont, O., Balkanski, Y., Bastrikov,<br> V., . . . Vuichard, N. (2020). Presentation and evaluation of the IPSL-CM6A-<br> LR climate model. Journal of Advances in Modeling Earth Systems, 12 (7),<br> e2019MS002010. doi: 10.1029/2019MS002010</p> <p>Cao, J., Wang, B., Yang, Y.-M., Ma, L., Li, J., Sun, B., . . . Wu, L.<br> (2018). The NUIST Earth System Model (NESM) version 3: description and prelimi-<br> nary evaluation. Geoscientific Model Development, 11 (7), 2975–2993.<br> doi: 10.5194/gmd-11-2975-2018</p> <p>Cherchi, A., Fogli, P. G., Lovato, T., Peano, D., Iovino, D., Gualdi, S., . . . Navarra,<br> A. (2019). Global mean climate and main patterns of variability in the CMCC-<br> CM2 coupled model. Journal of Advances in Modeling Earth Systems, 11 (1),<br> 185-209. doi: 10.1029/2018MS001369</p> <p>Chylek, P., Li, J., Dubey, M. K., Wang, M., & Lesins, G. (2011).<br> Observed and model simulated 20th century arctic temperature variability: Canadian Earth<br> System Model CanESM2. Atmospheric Chemistry and Physics Discussions,<br> 11 , 22893–22907. doi: 10.5194/acpd-11-22893-2011</p> <p>Collins, W. J., Bellouin, N., Doutriaux-Boucher, M., Gedney, N., Halloran, P., Hinton, T., . . . Woodward, S. (2011). Development and evaluation of an Earth-System model – HadGEM2. Geoscientific Model Development, 4 (4),1051–1075.doi: 10.5194/gmd-4-1051-2011</p> <p>Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., . . .<br> Vitart, F. (2011). The ERA-Interim reanalysis: configuration and performance<br> of the data assimilation system. Q. J. R. Meteorol. Soc., 137 (656, Part a),<br> 553-597. doi: 10.1002/qj.828</p> <p>Döscher, R., Acosta, M., Alessandri, A., Anthoni, P., Arneth, A., Arsouze, T., . . .<br> Zhang, Q. (2021). The EC-Earth3 Earth System Model for the Coupled Model<br> Intercomparison Project 6. Geoscientific Model Development Discussions,<br> 2021 , 1–90. doi: 10.5194/gmd-2020-446</p> <p>Dufresne, J.-L., Foujols, M.-A., Denvil, S., Caubel, A., Marti, O., Aumont, O., . . .<br> Vuichard, N. (2013). Climate change projections using the IPSL-CM5 Earth<br> System Model: from CMIP3 to CMIP5. Clim. Dyn., 40 (9-10), 2123-2165. doi:<br> 10.1007/s00382-012-1636-1</p> <p>Dunne, J. P., Horowitz, L. W., Adcroft, A. J., Ginoux, P., Held, I. M., John, J. G.,<br> . . . Zhao, M. (2020). The GFDL Earth System Model version 4.1 (GFDL-<br> ESM 4.1): Overall coupled model description and simulation characteristics.<br> Journal of Advances in Modeling Earth Systems, 12 (11), e2019MS002015. doi:<br> https://doi.org/10.1029/2019MS002015</p> <p>Dunne, J. P., John, J. G., Adcroft, A. J., Griffies, S. M., Hallberg, R. W., Shevli-<br> akova, E., . . . Zadeh, N. (2012). GFDL’s ESM2 Global Coupled Climate-<br> Carbon Earth System Models. Part I: Physical formulation and baseline<br> simulation characteristics.Journal of Climate, 25 (19), 6646–6665.<br> doi: https://doi.org/10.1175/JCLI-D-11-00560.1</p> <p>Griffies, S., Winton, M., Donner, L., Horowitz, L., Downes, S., Farneti, R., . . .<br> Zadeh, N. (2011). The GFDL-CM3 coupled climate model: Characteristics<br> of the ocean and sea ice simulations. Journal of Climate, 24 , 3520-3544. doi:<br> 10.1175/2011JCLI3964.1</p> <p>Hajima, T., Watanabe, M., Yamamoto, A., Tatebe, H., Noguchi, M. A., Abe, M., . . .<br> Kawamiya, M. (2020). Development of the MIROC-ES2L Earth system model<br> and the evaluation of biogeochemical processes and feedbacks.<br> Geoscientific Model Development, 13 (5), 2197–2244. doi: 10.5194/gmd-13-2197-2020</p> <p>Hazeleger, W., Wang, X., Severijns, C., Briceag, S., Bintanja, R., Sterl, A., . . .<br> van der Wiel, K. (2011). Ec-earth v2.2: Description and validation of a new<br> seamless earth system prediction model.Climate Dynamics, 39 , 1-19.<br> doi: 10.1007/s00382-011-1228-5</p> <p>Held, I. M., Guo, H., Adcroft, A., Dunne, J. P., Horowitz, L. W., Krasting, J., . . .<br> Zadeh, N. (2019). Structure and performance of GFDL’s CM4.0 climate<br> model. Journal of Advances in Modeling Earth Systems, 11 (11), 3691-3727.<br> doi: 10.1029/2019MS001829</p> <p>Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater,<br> J., . . . Thépaut, J.-N. (2020). The ERA5 global reanalysis. Quarterly<br> Journal of the Royal Meteorological Society, 146 (730), 1999-2049.<br> doi:https://doi.org/10.1002/qj.3803</p> <p>Jones, P. D., Hulme, M., & Briffa, K. R. (1993). A comparison of Lamb circulation<br> types with an objective classification scheme. International Journal of Clima-<br> tology, 13 (6), 655-663. doi: https://doi.org/10.1002/joc.3370130606</p> <p>Kelley, M., Schmidt, G. A., Nazarenko, L. S., Bauer, S. E., Ruedy, R., Russell,<br> G. L., . . . Yao, M.-S. (2020). GISS-E2.1: Configurations and climatology.<br> Journal of Advances in Modeling Earth Systems, 12 (8), e2019MS002025. doi:<br> 10.1029/2019MS002025</p> <p>Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., . . . Taka-<br> hashi, K. (2015). The JRA-55 Reanalysis: General specifications and basic<br> characteristics. Journal of the Meteorological Society of Japan. Ser. II , 93 (1),<br> 5-48. doi: 10.2151/jmsj.2015-001</p> <p>Lamb, H. (1972). British Isles weather types and a register of daily sequence of cir-<br> culation patterns, 1861-1971. Geophysical Memoir , 116 , 85pp. (HMSO)</p> <p>Lee, J., Kim, J., Sun, M.-A., Kim, B.-H., Moon, H., Sung, H. M., . . . Byun, Y.-<br> H. (2019). Evaluation of the Korea Meteorological Administration Ad-<br> vanced Community Earth-System model (K-ACE). Asia-Pacific Journal of Atmospheric Sciences, 56 , 381–395.<br> doi: https://doi.org/10.1007/</p> <p>Lee, W.-L., Wang, Y.-C., Shiu, C.-J., Tsai, I., Tu, C.-Y., Lan, Y.-Y., . . . Hsu, H.-H.<br> (2020). Taiwan Earth System Model version 1: description and evaluation<br> of mean state. Geoscientific Model Development, 13 (9), 3887–3904. doi:<br> 10.5194/gmd-13-3887-2020</p> <p>Li, L., Lin, P., Yu, Y.-Q., Zhou, T., Liu, L., Liu, J., . . . Qiao, F.-L.<br> (2013). The Flexible Global Ocean-Atmosphere-Land System Model, Grid-point ver-<br> sion 2: FGOALS-g2. Advances in Atmospheric Sciences, 30 , 543-560.doi:<br> 10.1007/s00376-012-2140-6</p> <p>Li, L., Yu, Y., Tang, Y., Lin, P., Xie, J., Song, M., . . . Wei, J. (2020). The Flex-<br> ible Global Ocean-Atmosphere-Land System Model Grid-point version 3<br> (FGOALS-g3): Description and evaluation. Journal of Advances in Model-<br> ing Earth Systems, 12 (9), e2019MS002012. doi: https://doi.org/10.1029/<br> 2019MS002012</p> <p>Martin, T. H. D. T. G. M., Bellouin, N., Collins, W. J., Culverwell, I. D., Halloran,<br> P. R., Hardiman, S. C., . . . Wiltshire, A. (2011). The HadGEM2 family of Met<br> Office Unified Model climate configurations. Geoscientific Model Development,<br> 4 (3), 723–757. doi: 10.5194/gmd-4-723-2011</p> <p>Mauritsen, T., Bader, J., Becker, T., Behrens, J., Bittner, M., Brokopf, R., . . .<br> Roeckner, E. (2019). Developments in the MPI-M Earth System Model version<br> 1.2 (MPI-ESM1.2) and its response to increasing CO2. Journal of Advances in<br> Modeling Earth Systems, 11 (4), 998-1038. doi: 10.1029/2018MS001400</p> <p>Pak, G., Noh, Y., Lee, M.-I., Yeh, S.-W., Kim, D., Kim, S.-Y., . . . Kim, Y. H.<br> (2021). Korea Institute of Ocean Science and Technology Earth System Model<br> and its simulation characteristics. Ocean Science Journal , 56 , 18-45.<br> doi: 10.1007/s12601-021-00001-7</p> <p>Park, S., Shin, J., Kim, S., Oh, E., & Kim, Y.<br> (2019). Global climate simulated by the Seoul National University Atmosphere Model version 0 with a unified<br> convection scheme (SAM0-UNICON). Journal of Climate, 32 (10), 2917-2949.<br> doi: 10.1175/JCLI-D-18-0796.1</p> <p>Roberts, M., Baker, A., Blockley, E., Calvert, D., Coward, A., Hewitt, H., . . . Vi-<br> dale, P. (2019). Description of the resolution hierarchy of the global coupled<br> HadGEM3-GC3.1 model as used in CMIP6 HighResMIP experiments. Geosci-<br> entific Model Development Discussions, 1-47. doi: 10.5194/gmd-2019-148</p> <p>Schmidt, G. A., Kelley, M., Nazarenko, L., Ruedy, R., Russell, G. L., Aleinov, I.,<br> . . . Zhang, J. (2014). Configuration and assessment of the GISS ModelE2<br> contributions to the CMIP5 archive. Journal of Advances in Modeling Earth<br> Systems, 6 (1), 141-184. doi: 10.1002/2013MS000265</p> <p>Scoccimarro, E., Gualdi, S., Bellucci, A., Sanna, A., Giuseppe Fogli, P., Manzini, E.,<br> . . . Navarra, A. (2011). Effects of tropical cyclones on ocean heat transport<br> in a high-resolution coupled general circulation model.<br> Journal of Climate, 24 (16), 4368-4384. doi: 10.1175/2011JCLI4104.1</p> <p>Seland, Ø., Bentsen, M., Seland Graff, L., Olivié, D., Toniazzo, T., Gjermundsen,<br> A., . . . Schulz, M. (2020). The Norwegian Earth System Model, NorESM2 –<br> evaluation of the CMIP6 DECK and historical simulations. Geoscientific Model<br> Development, 2020 , 1–68. doi: 10.5194/gmd-2019-378</p> <p>Semmler, T., Danilov, S., Gierz, P., Goessling, H. F., Hegewald, J., Hinrichs, C.,<br> . . . Jung, T. (2020). Simulations for CMIP6 with the AWI Climate Model AWI-CM-1-1.<br> Journal of Advances in Modeling Earth Systems, 12 (9), e2019MS002009. doi: 10.1029/2019MS002009</p> <p>Swapna, P., Koll, R., Aparna, K., Kulkarni, K., Ag, P., Ashok, K., . . . Goswami,<br> B. N. (2015). The IITM Earth System Model: Transformation of a seasonal<br> prediction model to a long term climate model. Bulletin of the American<br> Meteorological Society, 96 , 1351–1367. doi: 10.1175/BAMS-D-13-00276.1</p> <p>Séférian, R., Nabat, P., Michou, M., Saint-Martin, D., Voldoire, A., Colin, J., . . .<br> Madec, G. (2019). Evaluation of CNRM Earth System Model, CNRM-ESM2-1: Role of Earth system processes in present-day and future climate. Journal of Advances in Modeling Earth Systems, 11 (12), 4182-4227.<br> doi: 10.1029/2019MS001791</p> <p>Tamura, T., Ohshima, K. I., & Nihashi, S. (2008). Mapping of sea ice production for<br> antarctic coastal polynyas. Geophysical Research Letters, 35 (7). doi: https://<br> doi.org/10.1029/2007GL032903</p> <p>Tatebe, H., Ogura, T., Nitta, T., Komuro, Y., Ogochi, K., Takemura, T., . . . Ki-<br> moto, M. (2019). Description and basic evaluation of simulated mean state,<br> internal variability, and climate sensitivity in MIROC6.<br> Geoscientific Model Development, 12 (7), 2727–2765. doi: 10.5194/gmd-12-2727-2019</p> <p>Tegen, I., Neubauer, D., Ferrachat, S., Siegenthaler-Le Drian, C., Bey, I., Schutgens,<br> N., . . . Lohmann, U. (2019). The global aerosol–climate model ECHAM6.3–<br> HAM2.3 – part 1: Aerosol evaluation. Geoscientific Model Development, 12 (4),<br> 1643–1677. doi: 10.5194/gmd-12-1643-2019</p> <p>Voldoire, A., Saint-Martin, D., Sénési, S., Decharme, B., Alias, A., Chevallier, M.,<br> . . . Waldman, R. (2019). Evaluation of CMIP6 DECK experiments with<br> CNRM-CM6-1. Journal of Advances in Modeling Earth Systems, 11 (7), 2177-<br> 2213. doi: 10.1029/2019MS001683</p> <p>Voldoire, A., Sanchez-Gomez, E., Salas y Melia, D., Decharme, B., Cassou, C., Sen-<br> esi, S., . . . Chauvin, F. (2013). The CNRM-CM5.1 global climate model:<br> description and basic evaluation. Clim. Dyn., 40 (9-10), 2091-2121.<br> doi: 10.1007/s00382-011-1259-y</p> <p>Volodin, E., Diansky, N., & Gusev, A. (2010). Simulating present-day climate<br> with the INMCM4.0 coupled model of the atmospheric and oceanic general<br> circulations. Izvestiya, Atmospheric and Oceanic Physics, 46 , 414-431. doi:<br> https://doi.org/10.1134/S000143381004002X</p> <p>Volodin, E., Mortikov, E., Kostrykin, S., Galin, V., Lykossov, V., Gritsun, A.,<br> . . . Iakovlev, N. (2017). Simulation of the present-day climate with<br> the climate model INMCM5. Climate Dynamics, 49 , 3715–3734.<br> doi: https://doi.org/10.1007/s00382-017-3539-7</p> <p>Watanabe, M., Suzuki, T., O’ishi, R., Komuro, Y., Watanabe, S., Emori, S., . . .<br> Kimoto, M. (2010). Improved climate simulation by MIROC5: Mean states,<br> variability, and climate sensitivity. Journal of Climate, 23 , 6312-6335. doi:<br> 10.1175/2010JCLI3679.1</p> <p>Watanabe, S., Hajima, T., Sudo, K., Nagashima, T., Takemura, T., Okajima, H., . . .<br> Kawamiya, M. (2011). MIROC-ESM 2010: model description and basic results<br> of CMIP5-20c3m experiments. Geoscientific Model Development, 4 , 845-872.<br> doi: 10.5194/gmd-4-845-2011</p> <p>Wu, T., Lu, Y., Fang, Y., Xin, X., Li, L., Li, W., . . . Liu, X. (2019). The Beijing<br> Climate Center Climate System Model (BCC-CSM): the main progress from<br> CMIP5 to CMIP6. Geoscientific Model Development, 12 (4), 1573–1600. doi:<br> 10.5194/gmd-12-1573-2019</p> <p>Wu, T., Song, L., Li, W., Wang, Z., Zhang, H., Xin, X., . . . Zhou, M.<br> (2014). An overview of BCC Climate System Model development and application<br> for climate change studies. Acta Meteorologica Sinica, 28 , 34–56. doi:<br> 10.1007/s13351-014-3041-7</p> <p>Yukimoto, S., Kawai, H., Koshiro, T., Oshima, N., Yoshida, K., Urakawa, S., . . .<br> Ishii, M. (2019). The Meteorological Research Institute Earth System Model<br> version 2.0, MRI-ESM2.0: Description and basic evaluation of the physical<br> component.</p> <p>Journal of the Meteorological Society of Japan. Ser. II , 97 (5),<br> 931-965. doi: 10.2151/jmsj.2019-051 Yukimoto, S., Yoshimura, H., Hosaka, M., Sakami, T., Tsujino, H., Hirabara, M.,<br> . . . Kitoh, A. (2011). Meteorological Research Institute-Earth System Model version 1 (MRI-ESM1) — model description —.Technical Reports of the Meteorological Research Institute, 64 , 1-96.</p> <p>Ziehn, T., Chamberlain, M. A., Law, R. M., Lenton, A., Bodman, R. W., Dix, M.,<br> . . . Srbinovsky, J. (2020). The Australian Earth System Model: ACCESS-<br> ESM1.5. Journal of Southern Hemisphere Earth Systems Science, 70 , 193-214.<br> doi: https://doi.org/10.1071/ES19035</p> <p> </p>
Historical Revisionism Searchs
<p>Retrieval strategy for gathering the most relevant recent literature about Information manipulations and historical revisionism: Russian disinformation and foreign interference through manipulated history-based narratives. EU-HYBNET Project.</p>
Datasets of Radwin 2023 Great Salt Lake Remote Sensing Historical Assessment
<p>Included are the culminated datasets for an article in review to be published with the Utah Geological Association. This data helps an investigator reproduce or utilize the data. There are datasets for both Landsat and Sentinel, for both the North and South arms of the Great Salt Lake. Additionally, there are datasets documenting the NDWI threshold used for each Landsat image, the outlier images not used in analyses, NDWI error assessment, and calculation of stats/facts.</p> <p>Paper abstract:</p> <p>The Great Salt Lake has been rapidly shrinking since the highstand of the mid-1980s, creating cause for concern in recent decades as the lake has reached historic lows. Many investigators have assessed the evolution of lake elevation, geochemistry, anthropogenic impacts, and links to climate and atmospheric processes; however, the use of remote sensing to study the evolution of the lake has been significantly limited. Harnessing recent advancements in cloud-processing, specifically Google Earth Engine cloud computing, this study utilizes over 600 Landsat TM/OLI and Sentinel MSI satellite images from 1984-2023 to present time-series analyses of remotely sensed Great Salt Lake water area, exposed lakebed area, surface cover types, and chlorophyll-a analyses paired with modelled estimates for water and exposed lakebed area. Results show that since the highstand of 1986-1987, the water area has declined by 45% (~3,000 km<sup>2</sup>) and the exposed lakebed area has increased to ~3,500 km<sup>2</sup> from ~500 km<sup>2</sup>. The area of unconsolidated sediments not protected by vegetation or halite crusts has risen to ~2,400 km<sup>2</sup>. Significant halite crusts are observed in the North Arm, having a max extent of ~150 km<sup>2</sup> between 2002 and 2003, while only small extents of halite crusts are observed for the South Arm. Vegetation is more prevalent in the Bear River Bay and South Arm, with surface area increases over 400% since 1990. Gypsum is widely observed independent of halite crusts. The results highlight multiple instances of land-use/water-management that led to observable changes in water/exposed lakebed area and halite crust extent. This study demonstrates the important benefits of maintaining a lake elevation above ~4,194 ft to maximize lake and halite crust area, which would help mitigate possible dust events and maintain broad lake extent.</p> <p> </p> <p>Files should be self-explanatory based on filename, where BRB means Bear River Bay. Note there are two video files animating the evolution of the North and South Arms of the Great Salt Lake using satellite imagery from 1984 to 2023. </p> <p> </p> <p>Visit https://github.com/radwinskis for details on code used for this study.</p> <p>Please contact me at markradwin@gmail.com with any questions.</p> <p> </p>
Historical Occurrence of Antarctic Icebergs within Mercantile Shipping Routes and the Exceptional Events of the 1890s
<p>This is the dataset created for the Journal of Glaciology paper "<i>Historical Occurrence of Antarctic Icebergs within Mercantile Shipping Routes and the Exceptional Events of the 1890s</i>" by Robert Headland, Nick Hughes and Jeremy Wilkinson (<a href="https://doi.org/10.1017/jog.2023.80">doi:10.1017/jog.2023.80</a>). We have endeavoured to make the data as accessible as possible by providing it in a range of formats.</p><p>Please see the README.pdf for a detailed description of the files, and the paper for the dataset. Version 1.1 contains additional reports from newspaper archives.</p>
Northern Hemisphere Lamb Weather Types from historical GCM experiments and various reanalyses
<p>This dataset contains 6-hourly instantaneous discrete Lamb circulation type time series (Lamb 1972) on a 2.5 degrees longitude-latitude grid covering the northern hemisphere extratropics between 30ºN and 70ºN for the period 1979-2005 or longer. These "Lamb catalogues" were calculated upon SLP data from the historical experiments run with 61 distinct GCMs participating in the Coupled Model Intercomparison Project phases 5 and 6, and also from three distinct reanalyses (ERA5 extended to 2020 from version 5 onwards, ERA-Interim and JRA-55). For 13 out of the aforementioned 61 GCMs, 72 additional runs are provided to explore the role of internal model variability. For more information, please refer to the following article:</p> <p>Brands, S.: A circulation-based performance atlas of the CMIP5 and 6 models for regional climate studies in the Northern Hemisphere mid-to-high latitudes, Geosci. Model Dev., 15, 1375–1411, https://doi.org/10.5194/gmd-15-1375-2022, 2022.</p> <p>or contact: brandssf@ifca.unican.es</p> <p>Reference: Lamb, H.: British Isles Weather types and a register of daily sequence of circulation patterns, 1861-1971, Geophysical Memoir, 116, 85pp., HMSO, 1972.</p> <p>CAUTION: When unpacked, this dataset occupies 110 GB of your local disk space.</p> <p>Update information:</p> <p>Version 2 of this archive includes the model_source_attributes.txt file containing the "source" attributes stored in the netCDF files obtained from ESGF. This attribute provides details about the individual component models within the coupled model configurations used in CMIP5 and 6.</p> <p>Version 3 of this archive includes 10 new GCMs, two additional runs for CNRM-CM6-1 and an updated version of model_source_attributes.txt</p> <p>Version 3.1 includes README.txt, which explains the content of the files located in the tar.gz file.</p> <p>Version 4 further includes Lamb Weather Type catalogues for the ERA5 reanalysis and 4 additional GCMs. All files have been compressed individually. The <model_source_attributes.txt> file is depreciated and no longer updated. It is replaced by the Python function <get_historical_metadata.py> available from https://doi.org/10.5281/zenodo.4555367. This function contains an exhaustive metadata archive of the 60 GCMs considered here.</p> <p>Version 4.1 The LWT catalogue for CMCC-CM2-HR4 is included for consistency with the respective Southern Hemisphere dataset published at https://doi.org/10.5281/zenodo.7612987</p> <p>Version 5 is a major dataset update featuring the following improvements:</p> <p>1. The attributes from the netCDF source files "psl...nc" obtained from ESGF were copied into the files available here. These attributes are indicated with the prefix "udata...." (for "underlying data").</p> <p>2. All non-standard calenders from the underlying netCDF files from ESGF were converted into standard using the "xarray.Dataset.convert_calendar" function. The original calendar information was stored as additional netCDF attribute.</p> <p>3. The "patch" method from Python's xesmf module was used to regrid the original psl data from the native GCM grid available from ESGF to the regular lat-lon 2.5° grid common to all applied GCMs and reanalyses.</p> <p>contact: Swen Brands, brandssf@ifca.unican.es</p> <p> </p> <p><strong>Principal Research Articles, Software and Complementary Datasets Associated with this Dataset</strong></p> <p>Brands, S. (2022). A circulation-based performance atlas of the CMIP5 and<br>6 models for regional climate studies in the Northern Hemisphere mid-to-<br>high latitudes. Geoscientific Model Development, 15 (4), 1375–1411.<br>doi: https://doi.org/10.5194/gmd-15-1375-2022</p> <p>Brands, S. (2022). A circulation-based performance atlas of the CMIP5 and 6 mod-<br>els for regional climate studies in the northern hemisphere [data set]. Zenodo.<br>doi: https://doi.org/10.5281/zenodo.4452080</p> <p>Brands, S. (2022). Common error patterns in the regional atmospheric circulation<br>simulated by the CMIP multi-model ensemble. Geophysical Research Letters,<br>49 (23), e2022GL101446. doi: https://doi.org/10.1029/2022GL101446</p> <p>Brands, Swen, Tatebe, Hiroaki, Danek, Christopher, Fernández, Jesús, Swart, Neil C., Volodin, Evgeny, Kim, YoungHo, Collier, Mark, Bi, Dave, & Tongwen, Wu. (2022). Python code to calculate Lamb circulation types derived from historical CMIP simulations and reanalysis data. In Geoscientific Model Development: Vols. gmd-2020-418 (Version 4). Zenodo. https://doi.org/10.5281/zenodo.6390256</p> <p>Brands, S., Fernández-Granja, J. A., Bedia, J., Casanueva, A., & Fernández,<br>J. (2023). Auxiliary online material to Brands et al. (2023): A global<br>climate model performance atlas for the Southern Hemisphere extratrop-<br>ics based on regional atmospheric circulation patterns. figshare. doi:<br>https://doi.org/10.6084/m9.figshare.22193443.v1</p> <p>Brands, S., Fernández-Granja, J. A., Bedia, J., Casanueva, A., & Fernández,<br>J. (2023b). Southern Hemisphere Lamb Weather Types from historical<br>GCM experiments and various reanalyses (1.0) [data set]. Zenodo. doi:<br>https://doi.org/10.5281/zenodo.7612988</p> <p>Brands, S., Tatebe, H., Danek, C., Fernández, J., Swart, N., Volodin, E., . . . Tong-<br>wen, W. (2023). GCM metadata archive get historical metadata.py (v1.1).<br>Zenodo. doi: https://doi.org/10.5281/zenodo.7715383</p> <p>Fernández-Granja, J. A., Brands, S., Bedia, J., Casanueva, A., & Fernández, J.<br>(2023). Exploring the limits of the Jenkinson–Collison weather types clas-<br>sification scheme: a global assessment based on various reanalyses.<br>Climate Dynamics. doi: 10.1007/s00382-022-06658-7</p> <p> </p> <p><strong>References of the source GCMs</strong> <strong>and Early References of the Lamb Weather Typing Method</strong></p> <p>Bentsen, M., Bethke, I., Debernard, J. B., Iversen, T., Kirkevåg, A., Seland, Ø., . . .<br>Kristjánsson, J. E. (2013). The Norwegian Earth System Model, NorESM1-M<br>– part 1: Description and basic evaluation of the physical climate.<br>Geoscientific Model Development, 6 (3), 687–720. doi: 10.5194/gmd-6-687-2013</p> <p>Bi, D., Dix, M., Marsland, S., O’Farrell, S., Sullivan, A., Bodman, R., . . . Heerde-<br>gen, A. (2020). Configuration and spin-up of ACCESS-CM2, the new gener-<br>ation Australian Community Climate and Earth System Simulator Coupled<br>Model. Journal of Southern Hemisphere Earth Systems Science, 70 (1), 225-<br>251. doi: doi:10.1071/ES19040</p> <p>Bi, D., Dix, M., Marsland, S. J., O’Farrell, S., Rashid, H., Uotila, P., . . . Puri, K.<br>(2013). The ACCESS coupled model: description, control climate and evaluation. Australian Meteorological and Oceanographic Journal , 63 , 41-64. doi: 0.22499/2.6301.004</p> <p>Boucher, O., Servonnat, J., Albright, A. L., Aumont, O., Balkanski, Y., Bastrikov,<br>V., . . . Vuichard, N. (2020). Presentation and evaluation of the IPSL-CM6A-<br>LR climate model. Journal of Advances in Modeling Earth Systems, 12 (7),<br>e2019MS002010. doi: 10.1029/2019MS002010</p> <p>Cao, J., Wang, B., Yang, Y.-M., Ma, L., Li, J., Sun, B., . . . Wu, L.<br>(2018). The NUIST Earth System Model (NESM) version 3: description and prelimi-<br>nary evaluation. Geoscientific Model Development, 11 (7), 2975–2993.<br>doi: 10.5194/gmd-11-2975-2018</p> <p>Cherchi, A., Fogli, P. G., Lovato, T., Peano, D., Iovino, D., Gualdi, S., . . . Navarra,<br>A. (2019). Global mean climate and main patterns of variability in the CMCC-<br>CM2 coupled model. Journal of Advances in Modeling Earth Systems, 11 (1),<br>185-209. doi: 10.1029/2018MS001369</p> <p>Chylek, P., Li, J., Dubey, M. K., Wang, M., & Lesins, G. (2011).<br>Observed and model simulated 20th century arctic temperature variability: Canadian Earth<br>System Model CanESM2. Atmospheric Chemistry and Physics Discussions,<br>11 , 22893–22907. doi: 10.5194/acpd-11-22893-2011</p> <p>Collins, W. J., Bellouin, N., Doutriaux-Boucher, M., Gedney, N., Halloran, P., Hinton, T., . . . Woodward, S. (2011). Development and evaluation of an Earth-System model – HadGEM2. Geoscientific Model Development, 4 (4),1051–1075.doi: 10.5194/gmd-4-1051-2011</p> <p>Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., . . .<br>Vitart, F. (2011). The ERA-Interim reanalysis: configuration and performance<br>of the data assimilation system. Q. J. R. Meteorol. Soc., 137 (656, Part a),<br>553-597. doi: 10.1002/qj.828</p> <p>Döscher, R., Acosta, M., Alessandri, A., Anthoni, P., Arneth, A., Arsouze, T., . . .<br>Zhang, Q. (2021). The EC-Earth3 Earth System Model for the Coupled Model<br>Intercomparison Project 6. Geoscientific Model Development Discussions,<br>2021 , 1–90. doi: 10.5194/gmd-2020-446</p> <p>Dufresne, J.-L., Foujols, M.-A., Denvil, S., Caubel, A., Marti, O., Aumont, O., . . .<br>Vuichard, N. (2013). Climate change projections using the IPSL-CM5 Earth<br>System Model: from CMIP3 to CMIP5. Clim. Dyn., 40 (9-10), 2123-2165. doi:<br>10.1007/s00382-012-1636-1</p> <p>Dunne, J. P., Horowitz, L. W., Adcroft, A. J., Ginoux, P., Held, I. M., John, J. G.,<br>. . . Zhao, M. (2020). The GFDL Earth System Model version 4.1 (GFDL-<br>ESM 4.1): Overall coupled model description and simulation characteristics.<br>Journal of Advances in Modeling Earth Systems, 12 (11), e2019MS002015. doi:<br>https://doi.org/10.1029/2019MS002015</p> <p>Dunne, J. P., John, J. G., Adcroft, A. J., Griffies, S. M., Hallberg, R. W., Shevli-<br>akova, E., . . . Zadeh, N. (2012). GFDL’s ESM2 Global Coupled Climate-<br>Carbon Earth System Models. Part I: Physical formulation and baseline<br>simulation characteristics.Journal of Climate, 25 (19), 6646–6665.<br>doi: https://doi.org/10.1175/JCLI-D-11-00560.1</p> <p>Griffies, S., Winton, M., Donner, L., Horowitz, L., Downes, S., Farneti, R., . . .<br>Zadeh, N. (2011). The GFDL-CM3 coupled climate model: Characteristics<br>of the ocean and sea ice simulations. Journal of Climate, 24 , 3520-3544. doi:<br>10.1175/2011JCLI3964.1</p> <p>Hajima, T., Watanabe, M., Yamamoto, A., Tatebe, H., Noguchi, M. A., Abe, M., . . .<br>Kawamiya, M. (2020). Development of the MIROC-ES2L Earth system model<br>and the evaluation of biogeochemical processes and feedbacks.<br>Geoscientific Model Development, 13 (5), 2197–2244. doi: 10.5194/gmd-13-2197-2020</p> <p>Hazeleger, W., Wang, X., Severijns, C., Briceag, S., Bintanja, R., Sterl, A., . . .<br>van der Wiel, K. (2011). Ec-earth v2.2: Description and validation of a new<br>seamless earth system prediction model.Climate Dynamics, 39 , 1-19.<br>doi: 10.1007/s00382-011-1228-5</p> <p>Held, I. M., Guo, H., Adcroft, A., Dunne, J. P., Horowitz, L. W., Krasting, J., . . .<br>Zadeh, N. (2019). Structure and performance of GFDL’s CM4.0 climate<br>model. Journal of Advances in Modeling Earth Systems, 11 (11), 3691-3727.<br>doi: 10.1029/2019MS001829</p> <p>Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater,<br>J., . . . Thépaut, J.-N. (2020). The ERA5 global reanalysis. Quarterly<br>Journal of the Royal Meteorological Society, 146 (730), 1999-2049.<br>doi:https://doi.org/10.1002/qj.3803</p> <p>Jones, P. D., Hulme, M., & Briffa, K. R. (1993). A comparison of Lamb circulation<br>types with an objective classification scheme. International Journal of Clima-<br>tology, 13 (6), 655-663. doi: https://doi.org/10.1002/joc.3370130606</p> <p>Kelley, M., Schmidt, G. A., Nazarenko, L. S., Bauer, S. E., Ruedy, R., Russell,<br>G. L., . . . Yao, M.-S. (2020). GISS-E2.1: Configurations and climatology.<br>Journal of Advances in Modeling Earth Systems, 12 (8), e2019MS002025. doi:<br>10.1029/2019MS002025</p> <p>Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., . . . Taka-<br>hashi, K. (2015). The JRA-55 Reanalysis: General specifications and basic<br>characteristics. Journal of the Meteorological Society of Japan. Ser. II , 93 (1),<br>5-48. doi: 10.2151/jmsj.2015-001</p> <p>Lamb, H. (1972). British Isles weather types and a register of daily sequence of cir-<br>culation patterns, 1861-1971. Geophysical Memoir , 116 , 85pp. (HMSO)</p> <p>Lee, J., Kim, J., Sun, M.-A., Kim, B.-H., Moon, H., Sung, H. M., . . . Byun, Y.-<br>H. (2019). Evaluation of the Korea Meteorological Administration Ad-<br>vanced Community Earth-System model (K-ACE). Asia-Pacific Journal of Atmospheric Sciences, 56 , 381–395.<br>doi: https://doi.org/10.1007/</p> <p>Lee, W.-L., Wang, Y.-C., Shiu, C.-J., Tsai, I., Tu, C.-Y., Lan, Y.-Y., . . . Hsu, H.-H.<br>(2020). Taiwan Earth System Model version 1: description and evaluation<br>of mean state. Geoscientific Model Development, 13 (9), 3887–3904. doi:<br>10.5194/gmd-13-3887-2020</p> <p>Li, L., Lin, P., Yu, Y.-Q., Zhou, T., Liu, L., Liu, J., . . . Qiao, F.-L.<br>(2013). The Flexible Global Ocean-Atmosphere-Land System Model, Grid-point ver-<br>sion 2: FGOALS-g2. Advances in Atmospheric Sciences, 30 , 543-560.doi:<br>10.1007/s00376-012-2140-6</p> <p>Li, L., Yu, Y., Tang, Y., Lin, P., Xie, J., Song, M., . . . Wei, J. (2020). The Flex-<br>ible Global Ocean-Atmosphere-Land System Model Grid-point version 3<br>(FGOALS-g3): Description and evaluation. Journal of Advances in Model-<br>ing Earth Systems, 12 (9), e2019MS002012. doi: https://doi.org/10.1029/<br>2019MS002012</p> <p>Martin, T. H. D. T. G. M., Bellouin, N., Collins, W. J., Culverwell, I. D., Halloran,<br>P. R., Hardiman, S. C., . . . Wiltshire, A. (2011). The HadGEM2 family of Met<br>Office Unified Model climate configurations. Geoscientific Model Development,<br>4 (3), 723–757. doi: 10.5194/gmd-4-723-2011</p> <p>Mauritsen, T., Bader, J., Becker, T., Behrens, J., Bittner, M., Brokopf, R., . . .<br>Roeckner, E. (2019). Developments in the MPI-M Earth System Model version<br>1.2 (MPI-ESM1.2) and its response to increasing CO2. Journal of Advances in<br>Modeling Earth Systems, 11 (4), 998-1038. doi: 10.1029/2018MS001400</p> <p>Pak, G., Noh, Y., Lee, M.-I., Yeh, S.-W., Kim, D., Kim, S.-Y., . . . Kim, Y. H.<br>(2021). Korea Institute of Ocean Science and Technology Earth System Model<br>and its simulation characteristics. Ocean Science Journal , 56 , 18-45.<br>doi: 10.1007/s12601-021-00001-7</p> <p>Park, S., Shin, J., Kim, S., Oh, E., & Kim, Y.<br>(2019). Global climate simulated by the Seoul National University Atmosphere Model version 0 with a unified<br>convection scheme (SAM0-UNICON). Journal of Climate, 32 (10), 2917-2949.<br>doi: 10.1175/JCLI-D-18-0796.1</p> <p>Roberts, M., Baker, A., Blockley, E., Calvert, D., Coward, A., Hewitt, H., . . . Vi-<br>dale, P. (2019). Description of the resolution hierarchy of the global coupled<br>HadGEM3-GC3.1 model as used in CMIP6 HighResMIP experiments. Geosci-<br>entific Model Development Discussions, 1-47. doi: 10.5194/gmd-2019-148</p> <p>Schmidt, G. A., Kelley, M., Nazarenko, L., Ruedy, R., Russell, G. L., Aleinov, I.,<br>. . . Zhang, J. (2014). Configuration and assessment of the GISS ModelE2<br>contributions to the CMIP5 archive. Journal of Advances in Modeling Earth<br>Systems, 6 (1), 141-184. doi: 10.1002/2013MS000265</p> <p>Scoccimarro, E., Gualdi, S., Bellucci, A., Sanna, A., Giuseppe Fogli, P., Manzini, E.,<br>. . . Navarra, A. (2011). Effects of tropical cyclones on ocean heat transport<br>in a high-resolution coupled general circulation model.<br>Journal of Climate, 24 (16), 4368-4384. doi: 10.1175/2011JCLI4104.1</p> <p>Seland, Ø., Bentsen, M., Seland Graff, L., Olivié, D., Toniazzo, T., Gjermundsen,<br>A., . . . Schulz, M. (2020). The Norwegian Earth System Model, NorESM2 –<br>evaluation of the CMIP6 DECK and historical simulations. Geoscientific Model<br>Development, 2020 , 1–68. doi: 10.5194/gmd-2019-378</p> <p>Semmler, T., Danilov, S., Gierz, P., Goessling, H. F., Hegewald, J., Hinrichs, C.,<br>. . . Jung, T. (2020). Simulations for CMIP6 with the AWI Climate Model AWI-CM-1-1.<br>Journal of Advances in Modeling Earth Systems, 12 (9), e2019MS002009. doi: 10.1029/2019MS002009</p> <p>Swapna, P., Koll, R., Aparna, K., Kulkarni, K., Ag, P., Ashok, K., . . . Goswami,<br>B. N. (2015). The IITM Earth System Model: Transformation of a seasonal<br>prediction model to a long term climate model. Bulletin of the American<br>Meteorological Society, 96 , 1351–1367. doi: 10.1175/BAMS-D-13-00276.1</p> <p>Séférian, R., Nabat, P., Michou, M., Saint-Martin, D., Voldoire, A., Colin, J., . . .<br>Madec, G. (2019). Evaluation of CNRM Earth System Model, CNRM-ESM2-1: Role of Earth system processes in present-day and future climate. Journal of Advances in Modeling Earth Systems, 11 (12), 4182-4227.<br>doi: 10.1029/2019MS001791</p> <p>Tamura, T., Ohshima, K. I., & Nihashi, S. (2008). Mapping of sea ice production for<br>antarctic coastal polynyas. Geophysical Research Letters, 35 (7). doi: https://<br>doi.org/10.1029/2007GL032903</p> <p>Tatebe, H., Ogura, T., Nitta, T., Komuro, Y., Ogochi, K., Takemura, T., . . . Ki-<br>moto, M. (2019). Description and basic evaluation of simulated mean state,<br>internal variability, and climate sensitivity in MIROC6.<br>Geoscientific Model Development, 12 (7), 2727–2765. doi: 10.5194/gmd-12-2727-2019</p> <p>Tegen, I., Neubauer, D., Ferrachat, S., Siegenthaler-Le Drian, C., Bey, I., Schutgens,<br>N., . . . Lohmann, U. (2019). The global aerosol–climate model ECHAM6.3–<br>HAM2.3 – part 1: Aerosol evaluation. Geoscientific Model Development, 12 (4),<br>1643–1677. doi: 10.5194/gmd-12-1643-2019</p> <p>Voldoire, A., Saint-Martin, D., Sénési, S., Decharme, B., Alias, A., Chevallier, M.,<br>. . . Waldman, R. (2019). Evaluation of CMIP6 DECK experiments with<br>CNRM-CM6-1. Journal of Advances in Modeling Earth Systems, 11 (7), 2177-<br>2213. doi: 10.1029/2019MS001683</p> <p>Voldoire, A., Sanchez-Gomez, E., Salas y Melia, D., Decharme, B., Cassou, C., Sen-<br>esi, S., . . . Chauvin, F. (2013). The CNRM-CM5.1 global climate model:<br>description and basic evaluation. Clim. 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(2011). MIROC-ESM 2010: model description and basic results<br>of CMIP5-20c3m experiments. Geoscientific Model Development, 4 , 845-872.<br>doi: 10.5194/gmd-4-845-2011</p> <p>Wu, T., Lu, Y., Fang, Y., Xin, X., Li, L., Li, W., . . . Liu, X. (2019). The Beijing<br>Climate Center Climate System Model (BCC-CSM): the main progress from<br>CMIP5 to CMIP6. Geoscientific Model Development, 12 (4), 1573–1600. doi:<br>10.5194/gmd-12-1573-2019</p> <p>Wu, T., Song, L., Li, W., Wang, Z., Zhang, H., Xin, X., . . . Zhou, M.<br>(2014). An overview of BCC Climate System Model development and application<br>for climate change studies. Acta Meteorologica Sinica, 28 , 34–56. doi:<br>10.1007/s13351-014-3041-7</p> <p>Yukimoto, S., Kawai, H., Koshiro, T., Oshima, N., Yoshida, K., Urakawa, S., . . .<br>Ishii, M. (2019). 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Lake Tahoe Historic Secchi depth
Historic Secchi depth (Water clarity) measurement of Lake Tahoe, CA/NV. There are two sampling stations Index (LTP, 39.0972 -120.155) and Mid-lake (MLTP, 39.1417 -120.0153 ). Observation at Index station started on 1967-07-28, and averaged sampling interval is around 14 days. Mid-lake station sampling has been started on 1980-04-29, and taken at approximately 33 days interval. See methods for details.
Historic salvage sale locations (1954 - 1974), Andrews Experimental Forest
Historic salvage sale areas (with buffered roads) are reconstructed. Historic salvage timber sales in the H J Andrews from 1954 - 1974 were outlined on a variety of hard copy maps. These manuscripts were digitized into four non-overlapping coverages. Each coverage was turned into a region and the UNION command was used to combine the data into a single coverage of salvage sale regions. The road layer was buffered by 50 yards (45.72 meters)and these areas were added to the coverage.
Historical photos from Niwot Ridge, 1948 - 2005.
Historical photos provide a unique insight into vegetation and land use change. This dataset contains three georeferenced images from Niwot Ridge showing vegetation change between 1949 and 2005.
Fig. 14 in Two new species of Odontostilbe historically hidden under O. microcephala (Characiformes: Cheirodontinae)
Fig. 14. Box-plot graphics of Odontostilbe avanhandava, O. microcephala and O. weitzmani: lateral line series of scales (top); and branched rays of the anal fin (bottom).
Fig. 15 in Two new species of Odontostilbe historically hidden under O. microcephala (Characiformes: Cheirodontinae)
Fig. 15. Morphometric comparison of Odontostilbe microcephala and O. weitzmani. (a) Density plot with the overlap of the variable M17 (horizontal orbit diameter), that best discriminates the two species. (b) Scatterplot of the polar coordinates obtained for both species using variables M17 and M6 (orbit to dorsal-fin origin); the arrows show the vector of variables. (c) Bivariate randomization test, showing the individual (red point) with a higher probability of belonging to O. weitzmani among all included individuals identified as O. microcephala. (d) Bivariate randomization test, showing the individual (red point) with a higher probability of belonging to O. microcephala among all individuals identified as O. weitzmani.
Fig. 13 in Two new species of Odontostilbe historically hidden under O. microcephala (Characiformes: Cheirodontinae)
Fig. 13. Principal Components Analysis (PCA) between the species Odontostilbe avanhandava (red) O. microcephala (light blue) and O. weitzmani (black).
Fig. 6 in Two new species of Odontostilbe historically hidden under O. microcephala (Characiformes: Cheirodontinae)
Fig. 6. Southern South America showing the distribution of Odontostilbe avanhandava (green diamonds), O. microcephala (yellow squares) and O. weitzmani (red circle). Type localities represented by star of respective colors.
Fig. 5 in Two new species of Odontostilbe historically hidden under O. microcephala (Characiformes: Cheirodontinae)
Fig. 5. First gill arch of Odontostilbe weitzmani: (a) left side, lateral view, showing gill gland (arrow) on anteriormost portion of lower branch of MCP 20337 male. In detail (b) gill rakers near the junction of ceratobranchial and epibranchial, and (c) gill rakers on lower branch of gill arch. Scanning electron micrographs (SEM).
FIG. 10 in On unreported historical specimens of marine arthropods from the Solnhofen and Nusplingen Lithographic Limestones (Late Jurassic, Germany) housed at the Muséum national d'Histoire naturelle, Paris
FIG. 10. — Elder ungulatus (Münster, 1839). Specimen MNHN.F.A33549 under UV light. Scale bar: 1 cm. Photograph: L. Cazes.
FIG. 7 in On unreported historical specimens of marine arthropods from the Solnhofen and Nusplingen Lithographic Limestones (Late Jurassic, Germany) housed at the Muséum national d'Histoire naturelle, Paris
FIG. 7. — Other Erymoidea from the Late Jurassic Solnhofen Lithographic Limestones (Bavaria, Germany): A-C, specimen MNHN.GG.2004/8245 of Palaeastacus fuciformis (Schlotheim, 1822) from Solnhofen: specimen in natural light (A), specimen in UV light (B) and line drawing (C); D, specimen MNHN.F.B13445 of Pustulina minuta (Schlotheim, 1822) from Solnhofen in UV light. Abbreviations: a1, antennule; a2, antenna; e, eye; Mxp3, third maxillipeds; P1-P5, periopods 1 to 5. Scale bars: A-C, 1 cm; D, 0.5 cm. Photographs: L. Cazes. Line drawing: J. Devillez.
FIG. 8 in On unreported historical specimens of marine arthropods from the Solnhofen and Nusplingen Lithographic Limestones (Late Jurassic, Germany) housed at the Muséum national d'Histoire naturelle, Paris
FIG. 8. — Mecochirus longimanatus (Schlotheim, 1820): A, B, specimen MNHN.F.A33537; A, specimen in natural light; B, specimen in UV light; C, specimen MNHN.F.B13457; D-F, specimen MNHN.F.A70929; D, general view in UV light; E, view of the carapace in natural light; F, line drawing of the carapace; G, H, specimen MNHN.F.A33539; G, specimen in natural light; H, specimen in UV light. Abbreviations: ac, antennal carina; e1e, cervical groove; gc, gastro-orbital carina; oc, orbital carina. Scale bars: 1 cm. Photographs: L. Cazes. Line drawing: G.P. Odin.
FIG. 4 in On unreported historical specimens of marine arthropods from the Solnhofen and Nusplingen Lithographic Limestones (Late Jurassic, Germany) housed at the Muséum national d'Histoire naturelle, Paris
FIG. 4. — Antrimpos undenarius Münster, 1839 from Nusplingen, housed at the MNHN: A, picture of the old label; B, C, specimen MNHN.F.A49615; B, natural light picture; C, line-drawing of the grooves; D, specimen MNHN.F.A49610; E, specimen MNHN.F.A49622; F, G, specimen MNHN.F.A49608; F, UV picture; G, line-drawing of the rostrum; H, specimen MNHN.F.A49624. Abbreviations: b, antennal groove; b1, hepatic groove; ct, cephalothorax; e1e, cervical groove; hs, hepatic spine; r, rostrum; rs, rostral spines; s1-s5, pleonal somites; ss, supraorbital spine. Scale bars: B-F, H, 1 cm; G, 2 cm. Photographs: L. Cazes (except A: G. P. Odin). Line drawing: G. P. Odin.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.