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opencc-by-4.0May 2023View details →

Raw Data for Publication: Fungal colonisation on wood surfaces weathered at diverse climatic conditions

Colour_Changes_Izola.csv This file contains CIE Lab* color coordinates measured on the surface of Scots pine during the natural weathering test in Izola, Slovenia. Colour_Changes_Skelleftea.csv This file contains CIE Lab* color coordinates measured on the surface of Scots pine during the natural weathering test in Skelleftea, Sweden. Contact_Angles_Izola.csv This file contains dynamic contact angle with distilled water measured on the surface of Scots pine during the natural weathering test in Izola, Slovenia. Contact_Angles_Skelleftea.csv This file contains dynamic contact angle with distilled water measured on the surface of Scots pine during the natural weathering test in Skelleftea, Sweden. Gloss.csv This file contains the gloss value measured on the surface of Scots pine during the natural weathering test in Izola, Slovenia and Skelleftea, Sweden. Note: The sample IDs are structured as follows: The first letter represents the treatment condition, with "R" indicating untreated wood. The second letter (A, B, C) represents the board's ID. The third letter represents the location, with "S" indicating Skelleftea and "I" representing Izola, Slovenia. The number indicates the exposure time in weeks. FUNGAL STRAIS_DNA sequence analysis.xlsx This file contains the Genomic DNA of the fungal strains detected on the surface of Scots pine during the natural weathering test in Izola, Slovenia and Skelleftea, Sweden. Weather_Data_Izola.csv This file contains hourly local weather conditions in Izola, Slovenia Weather_Data_Skelleftea.csv This file contains hourly local weather conditions in Skelleftea, Sweden Note: The weather conditions including the following parameters:1. Air temperature (°C), 2. Dew point (°C), 3. Relative humidity (%), 4. One-hour precipitation total (mm), 5. Snow depth (mm), 6. Wind direction (°), 7. Average wind speed (km/h), 8.Sea-level air pressure (hPa)

opencc-by-4.0Mar 2023View details →

Northern Hemisphere Lamb Weather Types from historical GCM experiments and various reanalyses

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: 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. or contact: brandssf@ifca.unican.es Reference: Lamb, H.: British Isles Weather types and a register of daily sequence of circulation patterns, 1861-1971, Geophysical Memoir, 116, 85pp., HMSO, 1972. CAUTION: When unpacked, this dataset occupies 110 GB of your local disk space. Update information: 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. 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 Version 3.1 includes README.txt, which explains the content of the files located in the tar.gz file. 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. 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 Version 5 is a major dataset update featuring the following improvements: 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"). 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. 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. contact: Swen Brands, brandssf@ifca.unican.es   Principal Research Articles, Software and Complementary Datasets Associated with this Dataset Brands, S. (2022). A circulation-based performance atlas of the CMIP5 and 6 models for regional climate studies in the Northern Hemisphere mid-to- high latitudes. Geoscientific Model Development, 15 (4), 1375–1411. doi: https://doi.org/10.5194/gmd-15-1375-2022 Brands, S. (2022). A circulation-based performance atlas of the CMIP5 and 6 mod- els for regional climate studies in the northern hemisphere [data set]. Zenodo. doi: https://doi.org/10.5281/zenodo.4452080 Brands, S. (2022). Common error patterns in the regional atmospheric circulation simulated by the CMIP multi-model ensemble. Geophysical Research Letters, 49 (23), e2022GL101446. doi: https://doi.org/10.1029/2022GL101446 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 Brands, S., Fernández-Granja, J. A., Bedia, J., Casanueva, A., & Fernández, J. (2023). Auxiliary online material to Brands et al. (2023): A global climate model performance atlas for the Southern Hemisphere extratrop- ics based on regional atmospheric circulation patterns. figshare. doi: https://doi.org/10.6084/m9.figshare.22193443.v1 Brands, S., Fernández-Granja, J. A., Bedia, J., Casanueva, A., & Fernández, J. (2023b). Southern Hemisphere Lamb Weather Types from historical GCM experiments and various reanalyses (1.0) [data set]. Zenodo. doi: https://doi.org/10.5281/zenodo.7612988 Brands, S., Tatebe, H., Danek, C., Fernández, J., Swart, N., Volodin, E., . . . Tong- wen, W. (2023). GCM metadata archive get historical metadata.py (v1.1). Zenodo. doi: https://doi.org/10.5281/zenodo.7715383 Fernández-Granja, J. A., Brands, S., Bedia, J., Casanueva, A., & Fernández, J. (2023). Exploring the limits of the Jenkinson–Collison weather types clas- sification scheme: a global assessment based on various reanalyses. Climate Dynamics. doi: 10.1007/s00382-022-06658-7   References of the source GCMs and Early References of the Lamb Weather Typing Method Bentsen, M., Bethke, I., Debernard, J. B., Iversen, T., Kirkevåg, A., Seland, Ø., . . . Kristjánsson, J. E. (2013). The Norwegian Earth System Model, NorESM1-M – part 1: Description and basic evaluation of the physical climate. Geoscientific Model Development, 6 (3), 687–720. doi: 10.5194/gmd-6-687-2013 Bi, D., Dix, M., Marsland, S., O’Farrell, S., Sullivan, A., Bodman, R., . . . Heerde- gen, A. (2020). Configuration and spin-up of ACCESS-CM2, the new gener- ation Australian Community Climate and Earth System Simulator Coupled Model. Journal of Southern Hemisphere Earth Systems Science, 70 (1), 225- 251. doi: doi:10.1071/ES19040 Bi, D., Dix, M., Marsland, S. J., O’Farrell, S., Rashid, H., Uotila, P., . . . Puri, K. (2013). The ACCESS coupled model: description, control climate and evaluation. Australian Meteorological and Oceanographic Journal , 63 , 41-64. doi: 0.22499/2.6301.004 Boucher, O., Servonnat, J., Albright, A. L., Aumont, O., Balkanski, Y., Bastrikov, V., . . . Vuichard, N. (2020). Presentation and evaluation of the IPSL-CM6A- LR climate model. Journal of Advances in Modeling Earth Systems, 12 (7), e2019MS002010. doi: 10.1029/2019MS002010 Cao, J., Wang, B., Yang, Y.-M., Ma, L., Li, J., Sun, B., . . . Wu, L. (2018). The NUIST Earth System Model (NESM) version 3: description and prelimi- nary evaluation. Geoscientific Model Development, 11 (7), 2975–2993. doi: 10.5194/gmd-11-2975-2018 Cherchi, A., Fogli, P. G., Lovato, T., Peano, D., Iovino, D., Gualdi, S., . . . Navarra, A. (2019). Global mean climate and main patterns of variability in the CMCC- CM2 coupled model. Journal of Advances in Modeling Earth Systems, 11 (1), 185-209. doi: 10.1029/2018MS001369 Chylek, P., Li, J., Dubey, M. K., Wang, M., & Lesins, G. (2011). Observed and model simulated 20th century arctic temperature variability: Canadian Earth System Model CanESM2. Atmospheric Chemistry and Physics Discussions, 11 , 22893–22907. doi: 10.5194/acpd-11-22893-2011 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 Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., . . . Vitart, F. (2011). The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc., 137 (656, Part a), 553-597. doi: 10.1002/qj.828 Döscher, R., Acosta, M., Alessandri, A., Anthoni, P., Arneth, A., Arsouze, T., . . . Zhang, Q. (2021). The EC-Earth3 Earth System Model for the Coupled Model Intercomparison Project 6. Geoscientific Model Development Discussions, 2021 , 1–90. doi: 10.5194/gmd-2020-446 Dufresne, J.-L., Foujols, M.-A., Denvil, S., Caubel, A., Marti, O., Aumont, O., . . . Vuichard, N. (2013). Climate change projections using the IPSL-CM5 Earth System Model: from CMIP3 to CMIP5. Clim. Dyn., 40 (9-10), 2123-2165. doi: 10.1007/s00382-012-1636-1 Dunne, J. P., Horowitz, L. W., Adcroft, A. J., Ginoux, P., Held, I. M., John, J. G., . . . Zhao, M. (2020). The GFDL Earth System Model version 4.1 (GFDL- ESM 4.1): Overall coupled model description and simulation characteristics. Journal of Advances in Modeling Earth Systems, 12 (11), e2019MS002015. doi: https://doi.org/10.1029/2019MS002015 Dunne, J. P., John, J. G., Adcroft, A. J., Griffies, S. M., Hallberg, R. W., Shevli- akova, E., . . . Zadeh, N. (2012). GFDL’s ESM2 Global Coupled Climate- Carbon Earth System Models. Part I: Physical formulation and baseline simulation characteristics.Journal of Climate, 25 (19), 6646–6665. doi: https://doi.org/10.1175/JCLI-D-11-00560.1 Griffies, S., Winton, M., Donner, L., Horowitz, L., Downes, S., Farneti, R., . . . Zadeh, N. (2011). The GFDL-CM3 coupled climate model: Characteristics of the ocean and sea ice simulations. Journal of Climate, 24 , 3520-3544. doi: 10.1175/2011JCLI3964.1 Hajima, T., Watanabe, M., Yamamoto, A., Tatebe, H., Noguchi, M. A., Abe, M., . . . Kawamiya, M. (2020). Development of the MIROC-ES2L Earth system model and the evaluation of biogeochemical processes and feedbacks. Geoscientific Model Development, 13 (5), 2197–2244. doi: 10.5194/gmd-13-2197-2020 Hazeleger, W., Wang, X., Severijns, C., Briceag, S., Bintanja, R., Sterl, A., . . . van der Wiel, K. (2011). Ec-earth v2.2: Description and validation of a new seamless earth system prediction model.Climate Dynamics, 39 , 1-19. doi: 10.1007/s00382-011-1228-5 Held, I. M., Guo, H., Adcroft, A., Dunne, J. P., Horowitz, L. W., Krasting, J., . . . Zadeh, N. (2019). Structure and performance of GFDL’s CM4.0 climate model. Journal of Advances in Modeling Earth Systems, 11 (11), 3691-3727. doi: 10.1029/2019MS001829 Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., . . . Thépaut, J.-N. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146 (730), 1999-2049. doi:https://doi.org/10.1002/qj.3803 Jones, P. D., Hulme, M., & Briffa, K. R. (1993). A comparison of Lamb circulation types with an objective classification scheme. International Journal of Clima- tology, 13 (6), 655-663. doi: https://doi.org/10.1002/joc.3370130606 Kelley, M., Schmidt, G. A., Nazarenko, L. S., Bauer, S. E., Ruedy, R., Russell, G. L., . . . Yao, M.-S. (2020). GISS-E2.1: Configurations and climatology. Journal of Advances in Modeling Earth Systems, 12 (8), e2019MS002025. doi: 10.1029/2019MS002025 Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., . . . Taka- hashi, K. (2015). The JRA-55 Reanalysis: General specifications and basic characteristics. Journal of the Meteorological Society of Japan. Ser. II , 93 (1), 5-48. doi: 10.2151/jmsj.2015-001 Lamb, H. (1972). British Isles weather types and a register of daily sequence of cir- culation patterns, 1861-1971. Geophysical Memoir , 116 , 85pp. (HMSO) Lee, J., Kim, J., Sun, M.-A., Kim, B.-H., Moon, H., Sung, H. M., . . . Byun, Y.- H. (2019). Evaluation of the Korea Meteorological Administration Ad- vanced Community Earth-System model (K-ACE). Asia-Pacific Journal of Atmospheric Sciences, 56 , 381–395. doi: https://doi.org/10.1007/ Lee, W.-L., Wang, Y.-C., Shiu, C.-J., Tsai, I., Tu, C.-Y., Lan, Y.-Y., . . . Hsu, H.-H. (2020). Taiwan Earth System Model version 1: description and evaluation of mean state. Geoscientific Model Development, 13 (9), 3887–3904. doi: 10.5194/gmd-13-3887-2020 Li, L., Lin, P., Yu, Y.-Q., Zhou, T., Liu, L., Liu, J., . . . Qiao, F.-L. (2013). The Flexible Global Ocean-Atmosphere-Land System Model, Grid-point ver- sion 2: FGOALS-g2. Advances in Atmospheric Sciences, 30 , 543-560.doi: 10.1007/s00376-012-2140-6 Li, L., Yu, Y., Tang, Y., Lin, P., Xie, J., Song, M., . . . Wei, J. (2020). The Flex- ible Global Ocean-Atmosphere-Land System Model Grid-point version 3 (FGOALS-g3): Description and evaluation. Journal of Advances in Model- ing Earth Systems, 12 (9), e2019MS002012. doi: https://doi.org/10.1029/ 2019MS002012 Martin, T. H. D. T. G. M., Bellouin, N., Collins, W. J., Culverwell, I. D., Halloran, P. R., Hardiman, S. C., . . . Wiltshire, A. (2011). The HadGEM2 family of Met Office Unified Model climate configurations. Geoscientific Model Development, 4 (3), 723–757. doi: 10.5194/gmd-4-723-2011 Mauritsen, T., Bader, J., Becker, T., Behrens, J., Bittner, M., Brokopf, R., . . . Roeckner, E. (2019). Developments in the MPI-M Earth System Model version 1.2 (MPI-ESM1.2) and its response to increasing CO2. Journal of Advances in Modeling Earth Systems, 11 (4), 998-1038. doi: 10.1029/2018MS001400 Pak, G., Noh, Y., Lee, M.-I., Yeh, S.-W., Kim, D., Kim, S.-Y., . . . Kim, Y. H. (2021). Korea Institute of Ocean Science and Technology Earth System Model and its simulation characteristics. Ocean Science Journal , 56 , 18-45. doi: 10.1007/s12601-021-00001-7 Park, S., Shin, J., Kim, S., Oh, E., & Kim, Y. (2019). Global climate simulated by the Seoul National University Atmosphere Model version 0 with a unified convection scheme (SAM0-UNICON). Journal of Climate, 32 (10), 2917-2949. doi: 10.1175/JCLI-D-18-0796.1 Roberts, M., Baker, A., Blockley, E., Calvert, D., Coward, A., Hewitt, H., . . . Vi- dale, P. (2019). Description of the resolution hierarchy of the global coupled HadGEM3-GC3.1 model as used in CMIP6 HighResMIP experiments. Geosci- entific Model Development Discussions, 1-47. doi: 10.5194/gmd-2019-148 Schmidt, G. A., Kelley, M., Nazarenko, L., Ruedy, R., Russell, G. L., Aleinov, I., . . . Zhang, J. (2014). Configuration and assessment of the GISS ModelE2 contributions to the CMIP5 archive. Journal of Advances in Modeling Earth Systems, 6 (1), 141-184. doi: 10.1002/2013MS000265 Scoccimarro, E., Gualdi, S., Bellucci, A., Sanna, A., Giuseppe Fogli, P., Manzini, E., . . . Navarra, A. (2011). Effects of tropical cyclones on ocean heat transport in a high-resolution coupled general circulation model. Journal of Climate, 24 (16), 4368-4384. doi: 10.1175/2011JCLI4104.1 Seland, Ø., Bentsen, M., Seland Graff, L., Olivié, D., Toniazzo, T., Gjermundsen, A., . . . Schulz, M. (2020). The Norwegian Earth System Model, NorESM2 – evaluation of the CMIP6 DECK and historical simulations. Geoscientific Model Development, 2020 , 1–68. doi: 10.5194/gmd-2019-378 Semmler, T., Danilov, S., Gierz, P., Goessling, H. F., Hegewald, J., Hinrichs, C., . . . Jung, T. (2020). Simulations for CMIP6 with the AWI Climate Model AWI-CM-1-1. Journal of Advances in Modeling Earth Systems, 12 (9), e2019MS002009. doi: 10.1029/2019MS002009 Swapna, P., Koll, R., Aparna, K., Kulkarni, K., Ag, P., Ashok, K., . . . Goswami, B. N. (2015). The IITM Earth System Model: Transformation of a seasonal prediction model to a long term climate model. Bulletin of the American Meteorological Society, 96 , 1351–1367. doi: 10.1175/BAMS-D-13-00276.1 Séférian, R., Nabat, P., Michou, M., Saint-Martin, D., Voldoire, A., Colin, J., . . . 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. doi: 10.1029/2019MS001791 Tamura, T., Ohshima, K. I., & Nihashi, S. (2008). Mapping of sea ice production for antarctic coastal polynyas. Geophysical Research Letters, 35 (7). doi: https:// doi.org/10.1029/2007GL032903 Tatebe, H., Ogura, T., Nitta, T., Komuro, Y., Ogochi, K., Takemura, T., . . . Ki- moto, M. (2019). Description and basic evaluation of simulated mean state, internal variability, and climate sensitivity in MIROC6. Geoscientific Model Development, 12 (7), 2727–2765. doi: 10.5194/gmd-12-2727-2019 Tegen, I., Neubauer, D., Ferrachat, S., Siegenthaler-Le Drian, C., Bey, I., Schutgens, N., . . . Lohmann, U. (2019). The global aerosol–climate model ECHAM6.3– HAM2.3 – part 1: Aerosol evaluation. Geoscientific Model Development, 12 (4), 1643–1677. doi: 10.5194/gmd-12-1643-2019 Voldoire, A., Saint-Martin, D., Sénési, S., Decharme, B., Alias, A., Chevallier, M., . . . Waldman, R. (2019). Evaluation of CMIP6 DECK experiments with CNRM-CM6-1. Journal of Advances in Modeling Earth Systems, 11 (7), 2177- 2213. doi: 10.1029/2019MS001683 Voldoire, A., Sanchez-Gomez, E., Salas y Melia, D., Decharme, B., Cassou, C., Sen- esi, S., . . . Chauvin, F. (2013). The CNRM-CM5.1 global climate model: description and basic evaluation. Clim. Dyn., 40 (9-10), 2091-2121. doi: 10.1007/s00382-011-1259-y Volodin, E., Diansky, N., & Gusev, A. (2010). Simulating present-day climate with the INMCM4.0 coupled model of the atmospheric and oceanic general circulations. Izvestiya, Atmospheric and Oceanic Physics, 46 , 414-431. doi: https://doi.org/10.1134/S000143381004002X Volodin, E., Mortikov, E., Kostrykin, S., Galin, V., Lykossov, V., Gritsun, A., . . . Iakovlev, N. (2017). Simulation of the present-day climate with the climate model INMCM5. Climate Dynamics, 49 , 3715–3734. doi: https://doi.org/10.1007/s00382-017-3539-7 Watanabe, M., Suzuki, T., O’ishi, R., Komuro, Y., Watanabe, S., Emori, S., . . . Kimoto, M. (2010). Improved climate simulation by MIROC5: Mean states, variability, and climate sensitivity. Journal of Climate, 23 , 6312-6335. doi: 10.1175/2010JCLI3679.1 Watanabe, S., Hajima, T., Sudo, K., Nagashima, T., Takemura, T., Okajima, H., . . . Kawamiya, M. (2011). MIROC-ESM 2010: model description and basic results of CMIP5-20c3m experiments. Geoscientific Model Development, 4 , 845-872. doi: 10.5194/gmd-4-845-2011 Wu, T., Lu, Y., Fang, Y., Xin, X., Li, L., Li, W., . . . Liu, X. (2019). The Beijing Climate Center Climate System Model (BCC-CSM): the main progress from CMIP5 to CMIP6. Geoscientific Model Development, 12 (4), 1573–1600. doi: 10.5194/gmd-12-1573-2019 Wu, T., Song, L., Li, W., Wang, Z., Zhang, H., Xin, X., . . . Zhou, M. (2014). An overview of BCC Climate System Model development and application for climate change studies. Acta Meteorologica Sinica, 28 , 34–56. doi: 10.1007/s13351-014-3041-7 Yukimoto, S., Kawai, H., Koshiro, T., Oshima, N., Yoshida, K., Urakawa, S., . . . Ishii, M. (2019). The Meteorological Research Institute Earth System Model version 2.0, MRI-ESM2.0: Description and basic evaluation of the physical component. Journal of the Meteorological Society of Japan. Ser. II , 97 (5), 931-965. doi: 10.2151/jmsj.2019-051 Yukimoto, S., Yoshimura, H., Hosaka, M., Sakami, T., Tsujino, H., Hirabara, M., . . . 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. Ziehn, T., Chamberlain, M. A., Law, R. M., Lenton, A., Bodman, R. W., Dix, M., . . . Srbinovsky, J. (2020). The Australian Earth System Model: ACCESS- ESM1.5. Journal of Southern Hemisphere Earth Systems Science, 70 , 193-214. doi: https://doi.org/10.1071/ES19035

opencc-by-4.0Jul 2023View details →

Test clustered weather dataset for s2spy workflow

This dataset contains sea surface temperature (SST) over the Pacific and clustered 2 meter temperature (T2M) over North America. It is used in the example workflow of s2spy/lilio packages. The fields used here are processed outputs from the original ERA5 dataset. More details about how this dataset was generated can be found via this link: https://github.com/AI4S2S/cookbook/tree/main/data About the usage of this dataset in the example machine learning workflow of s2spy/lilio, check this link:https://github.com/AI4S2S/cookbook Data used here is generated using Copernicus Climate Change Service information and for more information about licensing, please check the Licence Agreement (https://cds.climate.copernicus.eu/cdsapp/#!/terms/licence-to-use-copernicus-products) for Copernicus Products.

opencc-by-4.0Jul 2023View details →

Data for "A New Year-Round Weather Regime Classification for North America"

This dataset supports the work presented in "A New Year-Round Weather Regime Classification for North America" by Lee et al. (2023), published in Journal of Climate: <a href="https://doi.org/10.1175/JCLI-D-23-0214.1">https://doi.org/10.1175/JCLI-D-23-0214.1</a>  The dataset includes the Z500 climatology and anomalies, the EOFs/PCs, and the parameters required to attribute each day to a regime, alongside the regimes as assigned over 1979-2022 (time series in netCDF and csv formats). In addition, we also supply the standardized projection of each day onto the cluster-mean normalized Z500 anomalies for each regime, termed the Weather Regime Index (WRI).

opencc-by-4.0Dec 2022View details →

Space weather disrupts nocturnal bird migration

Our paper tests for the effects of space weather-induced geomagnetic disturbances on radar-detected nocturnal bird migration. We find evidence for a ~10% decrease of migration intensity after controlling for weather variables and spatiotemporal autocorrelation, and also for a decrease in the effort birds spent flying against the wind in the fall, especially under overcast conditions. This repository provides the data and the code used to arrive at these conclusions and plot the main results. Weather radar data was processed from the NOAA NEXRAD network, weather data was accessed from the North American Regional Reanalysis, and magnetometer data was accessed from the SuperMAG inventory.

opencc-by-4.0Jul 2023View details →

Dataset for "A new method for identifying weather-induced power system stress using shadow prices"

These are data accompanying "A new method for identifying weather-induced power system stress using shadow prices". They consist of <ul> <li>solved network files (generated with <a href="https://github.com/PyPSA/pypsa-eur/">PyPSA-Eur</a>, here v0.6.1), used for the analysis,</li> <li>necessary data to reproduce the figures in the paper and supplementary material.</li> </ul> The optimised network files are of the form `workflow_data/results/stressful-weather/optimum/{weather_year}_181_90m_c1.25_Co2L0.0-1H.nc` (for weather_years in {1980,...,2019}). Unsolved ones can be found in `workflow_data/networks/...`. The filenames in `plot_data/` indicate which figure the data are associated to (e.g. `plot_data/fig_1_hourly_costs.csv` contains the hourly electricity costs during the winter of all networks and is necessary for Figure 1). We also added weather data for all system-defining events (mean surface level pressure, 10m wind speed anomaly, 2m temperature anomaly) in .nc files. Find more information about how to use these data and how they were generated in the README of the GitHub repository: <a href="https://github.com/koen-vg/stressful-weather/tree/v0">https://github.com/koen-vg/stressful-weather/tree/v0</a>.

opencc-by-4.0Sep 2023View details →

Extreme Weather Event database over Aotearoa New Zealand

The Aotearoa New Zealand (ANZ) Extreme Weather Events (EWE) database (EWE_database_V1.0.0.xlsx) is a comprehensive record of extreme weather events in ANZ. The events listed in this database have been carefully assessed and categorized based on their meteorological significance, considering their rarity and whether they broke records or triggered official weather warnings. Some of the metrics used to classify each event rely on subjective judgment and expert opinions. The database captures meteorologically significant events, including those that have caused substantial damage to properties or led to casualties, and, in some cases, includes supplementary information about their socioeconomic impacts. The information in the EWE database is primarily sourced from the Meteorological Service of New Zealand Ltd (MetService) and the National Institute of Water and Atmospheric Research (NIWA). Additional impact data have been added from various media sources, with insured loss data for some events sourced from the Insurance Council of New Zealand (ICNZ). Note - For more information about the database and the other additional files, please look into the Metadata (Metadata_EWE_V.1.0.0.docx)  and the supplementary document (Supplementary document on EWE_V.1.0.0.docx).

openCC0Jul 2022View details →

Understanding the Influences of Forest Type, Cover Board Type and Weather on Salamanders

Salamanders are vital bioindicators that function to support a terrestrial forest ecosystem. The continuous loss of amphibian species and their habitat can have profound impacts on terrestrial systems. In terrestrial environments, salamanders use natural cover for refuge, foraging, and maintaining moisture; however, artificial cover has commonly been used to survey and conserve these species. The objective of this study was to assess terrestrial salamander preference for natural versus artificial coverboards in relation to forest stands in two successional stages located within the James H. Barrow Biological Field Station (Hiram, Ohio). Ten artificial (particle board, 30 x 33 cm) and ten natural (white ash, 30 x 30 cm) coverboards were placed in two 50 m parallel transects arranged 2 m apart within transitional and mature forests. Surveys were conducted weekly between the second week of September and the second week of November from 2018 to 2021. Average weakly precipitation and max temperature were recorded. Both abundance and species richness were significantly higher under natural coverboards and in the transitional forest. There were also correlation between species richness and abundance with daily max temperature and weakly precipitation. 678 individuals across five species were found: Eastern Red-Backed Salamander, Spotted Salamander, Four-Toed Salamander, Red-Spotted Newt, and Northern Two-Lined Salamander. Eastern Red-Backed Salamanders were the most abundant species within both mature and transitional forests. Natural coverboards may be a better method to survey terrestrial salamanders because artificial coverboards are comprised of wood chippings, wax and adhesives which may alter soil permeability for less favorable conditions.

openCC (other)Nov 2022View details →

Data for “Herbivory damage but not plant disease under experimental warming is dependent on weather for three subalpine grass species”, Rocky Mountain Biological Laboratory, Gothic, Colorado, 2015-2017.

Both theory and prior studies predict that climate warming should increase attack rates by herbivores and pathogens on plants. However, past work has often assumed that variation in abiotic conditions other than temperature (e.g., precipitation) do not alter warming responses of plant damage by natural enemies. Studies over short time periods span low variation in weather, and studies over long-time scales often neglect to account for fine-scale weather conditions. Here, we used a 20+ year field warming experiment to investigate if warming affects herbivory and disease are dependent on variation in ambient weather observed over three years. We studied three common grass species in a subalpine meadow in the Colorado Rocky Mountains, USA. We visually estimated herbivory and disease every two-weeks during the growing season and evaluated weather conditions during the previous two- or four-week time interval (two-week average air temperature, two- and four-week cumulative precipitation) as predictors of the probability and amount of damage. Herbivore attack was 13% more likely and amount of damage was 29% greater in warmed plots than controls across the focal species, but warming treatment had little affect on plant disease. Herbivory presence and damage increased the most with experimental warming when preceded by wetter, rather than drier, fine-scale weather, but preceding ambient temperature did not strongly interact with elevated warming to influence herbivory. Disease presence and damage increased, on average, with warmer weather and more precipitation regardless of warming. The effect of warming over reference climate on herbivore damage is dependent on and amplified by fine-scale weather variation, suggesting more boom-and-bust damage dynamics with increasing climate variability. However, the mean effect of regional climate change is likely reduced monsoon rainfall, for which we predict a reduction in insect herbivore damage. Plant disease was generally unrelated

openCC (other)Jan 2023View details →

Weather data for the period 2009 to 2022 from the Open Field location at University Farms, Case Western Reserve University

Data from the Open Field weather station at University Farms of Case Western Reserve University include observations from 2009 to 2022. University Farms is located in Hunting Valley, Ohio. From 10/20/2009 to 10/30/2014, the weather station was located at N 41.496883, W 81.436117, when it was relocated to N 41.49759, W81.43738. Data include date/time (in 15-minute intervals), wind speed, wind gust speed, wind direction, air temperature, relative humidity, solar radiation, rainfall, soil moisture, soil temperatures at 0, 2, and 5 cm soil depth, and data logger battery charge.

openCC (other)Jan 2023View details →

Weather data for the period 2009 to 2022 from the North Woodlot location at University Farms, Case Western Reserve University

Data from the North Woodlot weather station at University Farms of Case Western Reserve University include observations from 2009 to 2022. University Farms is located in Hunting Valley, Ohio. The North Woodlot weather station is at N 41 29.969, W 81 25.234. Data include Date and time (in 15-minute intervals), wind speed, wind gust speed, air temperature, relative humidity, solar radiation, soil moisture, soil temperatures at 0, 2, and 5 cm soil depth, and data logger battery charge.

openCC (other)Jun 2025View details →

Harmful Algal Bloom Reports for 405 lakes in New York State (USA), with antecedent weather conditions and trophic status for each lake.

Cyanobacterial harmful algal blooms (cyanoHABs) are often associated with warm water temperatures and low wind speeds, but quantifying thresholds in meteorological conditions is challenging. In an attempt to better quantify antecedent weather conditions associated with cyanoHABs, we assembled a dataset to include cyanoHAB reports from individual lakes, as well as air temperature and wind speed data on the day of, and 5 days preceding each cyanoHAB report. CyanoHAB reports were provided by the New York State Department of Environmental Conservation (NYSDEC) for 405 lakes for an 11 year period spanning 2012-2023. We used 4km gridded surface meteorological data to calculate antecedent weather conditions leading up to each report. The resulting dataset includes 4,208 cyanoHAB reports from lakes of varying trophic states in New York State (USA). We calculated anomalies in daily wind speed and air temperature for each report and the five days preceding it. The dataset is formatted as a wide table, with individual cyanoHAB reports listed by row. Each row provides report-specific data to include the date, site, trophic state of the lake, and the cyanoHAB report status. Each row contains weather data specific to the date and location of each report, with weather variables calculated from the closest grid point for each lake site. We provide 36 columns containing weather variables for each report to include air temperature and wind speed for the day of the HAB as well as the 5 days preceding it. We also provide a 24-year mean value for the day of the year (DoY), as well as the calculated difference between the day in question and the 24-year mean for the DoY.

openCC (other)Oct 2020View details →

Ecosystem metabolism estimates from Lake Sunapee, NH, USA and meteorological driver data at the Newport, NH, USA NOAA NCDC weather station from August 2007 – December 2008

In August 2007, the Lake Sunapee Protective Association (LSPA) deployed a buoy in Lake Sunapee, NH with a meteorological station, dissolved oxygen sensor at 1 m, and a string of thermistor water temperature sensors collecting data every 10 minutes. For this dataset, the Lake Sunapee buoy was located at a location near Loon Island all year (August 2007–December 2008) including during the winter. We used high-frequency dissolved oxygen, photosynthetically active radiation (PAR), wind speed, and water temperature profile data to estimate daily rates of gross primary production, respiration, and net ecosystem production as metrics of ecosystem metabolism and carbon cycling. This dataset included under-ice data, and we compared seasonal changes in ecosystem metabolism rates for an entire lake year from the start of fall mixing in 2007 through the end of summer stratification in 2008. Under-ice and mixing periods are infrequently sampled due to logistical challenges and assumptions of low biological activity, resulting in limited understanding of the contribution of winter epilimnetic metabolism to annual carbon cycling. Using a continuous year of high-frequency dissolved oxygen data, we found that on average, under-ice net ecosystem production (NEP) was negative, in contrast to positive NEP for the spring and summer periods. Importantly, under-ice respiration was 1.2 times higher than summer respiration. Gross primary production was low but not absent under ice and increased during the last under-ice phase. Including winter metabolism estimates flipped annual NEP from autotrophy to heterotrophy, highlighting the importance of estimating metabolism year-round. Our methods for the data analysis are described in Brentrup et al. (accepted at Inland Waters) and were similar to Richardson et al. (2016) with some modifications described in the methods. This dataset also includes derived thermocline depth calculations from the water temperature thermistor data on the Lake Sunape

Hourly weather data from Toolik Field Station, ARC LTER, Alaska for 2007.

Hourly weather data from the Arctic Tundra LTER site at Toolik Lake. The following parameters are measured every minute and averaged or totaled every hour: air temperature and relative humidity at 1 and 5 meters, wind speed at 1 and 5 meters, wind direction at 5 meters, global solar radiation, photosynthetically active radiation, barometric pressure, and unfrozen precipitation.

Hourly weather data from Toolik Field Station, ARC LTER, Alaska for 2008.

Daily weather summaries from Toolik Field Station Weather Station, Toolik Lake ARC LTER, Alaska for 2008.

Daily weather summaries from Toolik Field Station Meteorological Station, Toolik Lake, Alaska. Included are daily averages and/or maximums and minimums of air, soil and lake temperature, wind speed, vapor pressure, and sum of global radiation and precipitation recorded near Toolik Lake.

openOpenMar 2016View details →

Hourly weather data from the Arctic LTER Moist Acidic Tussock Experimental plots from 2000 to 2010, Toolik Filed Station, North Slope, Alaska.

Hourly weather data from the LTER Moist Acidic Tussock Experimental plots. The station was installed in 1990 in block 2 of the Toolik LTER experimental moist acidic tussock plots. The plots are located on a hillside near Toolik Lake (68 38' N, 149 36'W). Global solar radiation, photosynthetic active radiation, unfrozen precipitation, air temperature, relative humidity, wind speed, and wind direction are measured at 3 meters. Additional sensors in greenhouses and shade houses plots measure air temperature, relative humidity and photosynthetic active radiation during the growing season. The sensors are read every minute and averaged or totaled every hour.

openOpenDec 2015View details →

Daliy weather data (wind, temperatrue, humididty, pressure, precipitation) from Roche Mountonnee , in the northern foothills of the Brooks Range, Alaska, summers 2010-2014.

Daily weather data from mid May to late July 2011 to 2013 from Roche Moutonnee (south of Toolik Field Station and Arctic LTER), in the northern foothills of the Brooks Range, Alaska. Parameters measured include: wind speed, wind directions, temperature, humidity, pressure and precipitation.

openOpenDec 2015View details →

Daliy weather data from Sagavanirktok River DOT site, in the northern foothills of the Brooks Range, Alaska, May-July 2010-2014.

Daliy weather data from mid May to late July 2011 to 2013 from Sagavanirktok Department of Transport (DOT) site (south of Toolik Field Station and Arctic LTER), in the northern foothills of the Brooks Range, Alaska. Parameters measured include: wind speed, wind directions, temperatrue, humididty, pressure and precipitation. (Rich, et al 2013).

Daily weather data file for Arctic Tundra LTER site at Toolik Lake, Arctic LTER 2000.

Daily weather data file for Arctic Tundra LTER site at Toolik Lake. Included are daily averages and/or maximums and minimums of air, soil and lake temperature, wind speed, vapor pressure, and sum of global radiation and unfrozen precipitation recorded near Toolik Lake.