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396 results for “Southern Hemisphere”
Dataset for: Wood et al Role of sea surface temperature patterns for the Southern hemisphere jet stream response to CO2 forcing
<p>This is a dataset of output from version 4 of the Reading Intermediate Global Circulation Model (IGCM4) that was used in the article Wood et al (2020) 'Role of sea surface temperature patterns for the Southern hemisphere jet stream response to CO2 forcing' published in Environmental Research Letters (<a href="https://doi.org/10.1088/1748-9326/abce27">https://doi.org/10.1088/1748-9326/abce27</a>).</p> <p>To isolate the role of sea surface temperature (SST) patterns for the Southern Hemisphere circulation response in the abrupt-4xCO2 experiments in CMIP5 and CMIP6, we perform experiments using IGCM4.</p> <p>Five 120-year long simulations were performed following a 5-year spin-up period. In the control simulation (CTRL) we prescribe an annually repeating cycle of climatological monthly mean SSTs using the multi-model mean (MMM) of the ‘ts’ field for the first 200 years of the CMIP5 piControl simulations. Following the CMIP6 protocol (Eyring et al., 2016), greenhouse gas (CO<sub>2</sub>, CH<sub>4</sub>, and N<sub>2</sub>O) concentrations are set at preindustrial (year 1850) values and ozone is prescribed as a zonally averaged monthly mean preindustrial climatology.</p> <p>In two perturbation simulations (4xCO2-FULL<sub>CMIP5</sub> and 4xCO2-FULL<sub>CMIP6</sub>) the same boundary conditions are used as in CTRL, but with an annually repeating cycle of climatological monthly mean SST anomalies added using the MMM ‘ts’ field for either the CMIP5 or CMIP6 FAST (years 4-10) responses. In both the 4xCO2-FULL<sub>CMIP5</sub> and 4xCO2-FULL<sub>CMIP6</sub> simulations CO<sub>2</sub> is quadrupled from its preindustrial concentration. This enables a like-for-like comparison with the CMIP5 and CMIP6 abrupt-4xCO2 simulations. Two further perturbation simulations (SHET-only<sub>CMIP5</sub> and SHET-only<sub>CMIP6</sub>) are used to isolate the effect of differences in SH extratropical SST patterns alone. In both simulations CO<sub>2</sub> is kept at preindustrial values, and CTRL SSTs are used with the SST anomalies from either 4xCO2-FULL<sub>CMIP5</sub> or 4xCO2-FULL<sub>CMIP6</sub> added poleward of 18°S. Similarly to McCrystall et al. (2020), the SST anomalies are smoothed between 18°S and 29°S using a cosine squared weighting function with weights of 0 at 18°S and 1 at 29°S. This minimizes sharp gradients in SST across the tropical-extratropical boundary.</p> <p>To enable a clean determination of the effects of SST patterns alone, in all perturbation simulations we keep sea ice fixed at preindustrial values by only adding SST anomalies where the MMM sea ice concentration in the CMIP5 piControl simulations is less than 15% (i.e., equatorward of the sea ice edge). Furthermore, to remove the effect of differences in the change in global mean SST, the SST anomalies in each CMIP model are normalised by the respective global mean SST anomaly and then scaled to a global mean value of 2.2 K (the pooled MMM of CMIP5 and CMIP6). The CMIP6 FAST SST anomalies are added to the CMIP5 preindustrial control SSTs, so as to isolate the effect of differences in the fast SST responses between CMIP5 and CMIP6, and not the effect of differences in the base state.</p>
Data for "Revisiting the zonally asymmetric extratropical circulation of the Southern Hemisphere spring using complex empirical orthogonal functions"
<p>Data used in "Revisiting the zonally asymmetric extratropical circulation of the Southern Hemisphere spring using complex empirical orthogonal functions"</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>
CAIRT FL2S Results of Case Study Scenario 7 (CSS7) for Southern Hemisphere Winter
<p>Results of the fast level-2 simulator (FL2S) of CAIRT developed within the Earth Explorer 11 Phase 0 Science and Requirements Consolidation Study (SciReC) – CAIRT. The files contain altitude-time cross-sections of atmospheric parameters along simulated CAIRT-orbits. The variable extensions denote the original field ('_ori'), the application of the averaging kernel ('_ak'), additional application of noise ('_aknoi'), application of systematic uncertainties ('_sys'), and application of all effects ('_aknoisys'). Further information is available from the authors.</p>
Star Trail in the Southern Hemisphere with Bortle 4 Scale Light Pollution
<p>Winner in the 2023 IAU OAE Astrophotography Contest, category Still images with smartphones-mobile devices: Star Trail in the Southern Hemisphere with Bortle 4 Scale Light Pollution, by Slamat Riyadi.</p> <p>This breathtaking photo, captured under the clear night sky of Linggamekar Village, Cilimus, Kuningan, West Java, Indonesia on 25 June 2020, displays star trails sweeping across the southern hemisphere’s heavens. The star trails are due to Earth’s rotation causing the apparent motion of stars, creating these mesmerising arcs of light when followed over extended periods. Here, the point the stars rotate around (the South Celestial Pole) is close to the horizon, as the image was taken close to the equator. The photographer used the star trail feature on a smartphone, which captured a series of images over an extended period and stacked them together. The striking tree in the foreground adds depth to the image, contrasting the celestial motion above with its Earthly stillness, while also masking some of the surrounding light pollution. Different parts of the world offer diverse and stunning perspectives on the night sky, emphasising the importance of preserving dark skies everywhere.</p> <p>Credit: Slamat Riyadi/IAU OAE (<a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC BY 4.0</a>)</p>
Fig. 1 in First discovery and a new species of Coelogynopora (Platyhelminthes, Proseriata) in the Southern Hemisphere
Fig. 1. Coelogynopora kenichii sp. nov. A. Live specimen. Scale bar = 250 µm. B. Schematic depiction of the head, showing the trilobate brain, the statocyst and the anterior intestinal diverticulum.
Fig. 4 in First discovery and a new species of Coelogynopora (Platyhelminthes, Proseriata) in the Southern Hemisphere
Fig. 4. Sclerotised copulatory system of Coelogynopora kenichii sp. nov. A. Schematic depiction of the stylet and the three pairs of spines (RMNH.VER.19980.p). B. Photomicrograph of the stylet and the three pairs of spines (RMNH.VER.19980.n). C. Photomicrograph of the distal portion of the three spines on one side of the stylet, showing their different morphologies (RMNH.VER.19980.o). Scale bars: A–B = 25 µm; C = 10 µm.
Fig. 3 in First discovery and a new species of Coelogynopora (Platyhelminthes, Proseriata) in the Southern Hemisphere
Fig. 3. Reconstruction of the reproductive complex of Coelogynopora kenichii sp. nov., based on a series of sagittal sections. For the sake of clarity, a single seminal vesicle and ovovitelline duct were drawn, and gland cells were omitted (RMNH.VER.19980.l). Scale bar = 30 µm.
Fig. 2 in First discovery and a new species of Coelogynopora (Platyhelminthes, Proseriata) in the Southern Hemisphere
Fig. 2. Posterior end of Coelogynopora kenichii sp. nov. in ventral view, from a whole mount (RMNH. VER.19980.a). Scale bar = 50 µm.
Fig. 6 in First discovery and a new species of Coelogynopora (Platyhelminthes, Proseriata) in the Southern Hemisphere
Fig. 6. Map showing the known distribution of the genus Coelogynopora based on all the available reports, which may be summarised by Karling (1966), Sopott-Ehlers (1976, 1992), Tajika (1978, 1981), Riser (1981), Ax & Armonies (1987), Ax (2008), Armonies (2017, 2018), Jouk et al. (2019), and this work.
Argo Trajectories under ice (Southern Hemisphere, version 2022.05)
<p>Argo floats sometimes sample under ice, and do not return a measured position. Here, we provide estimates of positions using the multiple-constraint method described by Oke et al. (2022). </p> <p>Each file includes longitude and latitude from GPS measurements when floats are not under ice. When floats are under ice, positions are estimated by linearly interpolating between known locations (the traditional approach used by the Argo community), and using constraints: potential vorticity, f/H; mean sea-level, and density at 1000 m. A merged trajectory is also included, but users might select their preferred estimate based on their understanding of the ocean circulation at the time of measurement.</p> <p>Oke, P. R., T. Rykova, G. S. Pilo, J. L. Lovell, 2022: Estimating Argo float trajectories under ice, Journal of Geophysical Research - Earth and Space Science, under review.</p>
Investigation of the southern hemisphere mid-high latitude thermospheric ∑O/N2 responses to the Space-X storm
<p>This data sets are the data used to plot the figures in the above mentioned paper (Figure 3 to 6)</p> <p>All files are in dimension 288*144*6, 288 stands for longitudes number from -180 to 180 with a resolution of 1.25</p> <p>144 stands for latitude numbers fro -88.75 to 88.75 with a resolution of 1.25. 6 stands for the time, 0:20, 2:20, 4:20, 7:20, 10:20 and 13:20 UT on DOY 34.</p> <p>dON2 stand for the percentage diff of column density ratio of O to N2 between DOY 34 and 32</p> <p>UN stands for zonal wind, VN stands for meridional wind, TN stands for neutral temperature</p> <p>QJO stands for Joule heating rate per unit mass near 160 km</p> <p>POTEN stands for ionosphere potential</p>
Figures 50–52 in A New Aquatic Associated Genus of Trichopezinae from the Southern Hemisphere (Diptera: Empidoidea: Brachystomatidae)
Figures 50–52. Distribution of Gondwanodromia: (50) Australian species; (51) New Zealand species; (52) South American species.
Figures 48–49 in A New Aquatic Associated Genus of Trichopezinae from the Southern Hemisphere (Diptera: Empidoidea: Brachystomatidae)
Figures 48–49. Male terminalia of Australian and New Zealand species of Gondwanodromia, lateral view: (48) G. tonnoiri sp. nov.; (49) G. wardi sp. nov. Scale bar = 0.1 mm. Abbreviations: a sur – anterior surstylus; cerc –cercus; epand – epandrium; hypd – hypandrium; p sur – posterior surstylus; ph – phallus; sur – surstylus.
Figures 43–47 in A New Aquatic Associated Genus of Trichopezinae from the Southern Hemisphere (Diptera: Empidoidea: Brachystomatidae)
Figures 43–47. New Zealand species of Gondwanodromia: (43) G. tongariro sp. nov, holotype, male terminalia, scale bar = 0.25 mm; (44) G. femorata sp. nov., female midleg, scale bar = 0.5 mm (courtesy Canterbury Museum); (45) G. femorata sp. nov., female abdomen, scale bar = 0.5 mm (courtesy Canterbury Museum); (46) G. wardi sp. nov., holotype (abdomen dissected), lateral view, scale bar = 0.5 mm (courtesy Canterbury Museum); (47) G. wardi sp. nov., holotype (abdomen dissected), dorsal view, scale bar = 0.5 mm (courtesy Canterbury Museum). Abbreviations: epand lb – epandrial lobe; sur – surstylus.
Figures 41–42 in A New Aquatic Associated Genus of Trichopezinae from the Southern Hemisphere (Diptera: Empidoidea: Brachystomatidae)
Figures 41–42. Gondwanodromia tasmanica sp. nov., lateral view: (41) male terminalia; (42) female terminalia. Scale bar = 0.1 mm. Abbreviations: cerc – cercus; epand – epandrium; hypd – hypandrium; sur – surstylus.
Figures 30–33 in A New Aquatic Associated Genus of Trichopezinae from the Southern Hemisphere (Diptera: Empidoidea: Brachystomatidae)
Figures 30–33. Gondwanodromia mikae sp. nov.: (30) male head, lateral view; (31) epandrium, dorsal view, right cercus removed; (32) terminalia, lateral view; (33) hypandrium and phallus, lateral view. Scale bars: Figs 31–33 = 0.05 mm; Fig. 30 = 0.1 mm. Abbreviations: cerc – cercus; ej apod – ejaculatory apodeme; epand – epandrium; goncx apod – gonocoxal apodeme; hypd – hypandrium; lc – lacinia; ph – phallus; plp – palpus; pped – postpedicel; sbepand scl – subepandrial sclerite; st – sternite; sur – surstylus.
Figures 34–36 in A New Aquatic Associated Genus of Trichopezinae from the Southern Hemisphere (Diptera: Empidoidea: Brachystomatidae)
Figures 34–36. Gondwanodromia mutabilis (Collin), lateral view: (34) male terminalia; (35) female terminalia; (36) spermatheca. Scale bar = 0.1 mm. Abbreviations: cerc – cercus; epand – epandrium; hypd – hypandrium; ph – phallus; sur – surstylus.
Figures 19–23 in A New Aquatic Associated Genus of Trichopezinae from the Southern Hemisphere (Diptera: Empidoidea: Brachystomatidae)
Figures 19–23. Gondwanodromia mikae sp. nov.: (19) labellum, lateral view, scale bar = 0.05 mm; (20) wing, scale bar = 1.0 mm; (21) male antenna, lateral view, scale bar = 0.1 mm; (22) tarsomere 5, dorsal view, scale bar = 0.05 mm; (23) tarsomere 5, lateral view, scale bar = 0.05 mm. Abbreviations: pped – postpedicel; psdtrch – pseudotrachea.
Figures 24–29 in A New Aquatic Associated Genus of Trichopezinae from the Southern Hemisphere (Diptera: Empidoidea: Brachystomatidae)
Figures 24–29. Gondwanodromia mikae sp. nov.: (24) epandrium, dorsal view; (25) epandrium, dorsal view; (26) hypandrium and phallus, lateral view; (27) hypandrium and phallus, posterior view; (28) female terminalia, lateral view, scale bar = 0.05 mm; (29) female abdomen, lateral view. Scale bars: Figs 24–28 = 0.05 mm; Fig. 29 = 0.1 mm. Abbreviations: cerc – cercus; epand – epandrium; hypd – hypandrium; ph – phallus; spmth – spermatheca; sur – surstylus; tg – tergite.
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.