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1. Data for Revisiting the Last Ice Area Projections from a High-Resolution Global Earth System Model

   https://doi.org/10.5281/zenodo.14042445  

   Fol, Madeleine (January 2025, Zenodo)

   This dataset contains data used in the paper: Revisiting the Last Ice Area Projections from a High-Resolution Global Earth System Model - Fol et al (2025). Results are organized in excel files or numpy arrays with the dataset name, variable and ensemble member (for simulations) in the name of the file. See below for more information on what variables are included in the files and their structure.  CESM_HR :  CESM_HR_SIAFluxes - per ensemble member: Timeseries of monthly SIA flux per gate. CESM_HR_fluxesCAADiv.npy, _fluxesQEIDiv.npy, _fuxesQEIDivMeltSeason.npy : Timeseries of annual divergence over the Queen Elizabeth Islands and the Southern Canadian Arctic Archipelago derived from monthly SIA fluxes at the entry and exit gates. CESM_HR_ThicknessDistribution.xlsx : Thickness distribution for the LIA-N, QEI, and CAA-S computed from the simulated thickness distribution (aicen001, aicen002, aicen003, aicen004, aicen005). CESM_HR_tendencies - per ensemble member- per region (LIA-N, QEI, CAA-S). Timeseries of melt season integrated thermodynamic, dynamic (advection and ridging terms) sea ice area loss. CESH_HR_sitPanArctic - per ensemble member: Timeseries of pan-Arctic mean may sea ice thickness. CESM_HR_sieSept and CESM_HR_sieMarch - per ensemble member- per region (LIA-N, QEI, CAA-S) and pan-Arctic : Timeseries of March or September sea ice extent, sea ice area. CESM_HR_sic - per ensemble member- per region (LIA-N, QEI, CAA-S): Timeseries of mean sea ice concentration for grid cells having more than 15% of SIC (no open water).  CESM_HR_meltSeason - per ensemble member- per region (LIA-N, QEI, CAA-S): Timeseries of annual freeze and melt onset dates allowing the definition of the melt season based on the thermodynamic sea ice area tendency crossing 0.  CESM_HR_mean_mayThickness.npy and CESM_HR_meanseptconc.npy: Results for map of the mean september sea ice concentration and may ice thickness for 1981-2000, 2001-2020, 2021-2040 and 2041-2060. CESM_LR :  CESM_LR_sieSept - per region (LIA-N, QEI, CAA-S) and pan-Arctic: Timeseries of September sea ice extent and sea ice area. CESM_LR_sitPanArctic.xlsx: Timeseries of pan-Arctic mean May sea ice thickness. CESM_LR_tendencies - per region (LIA-N, QEI, CAA-S). Timeseries of melt season integrated thermodynamic, dynamic (advection and ridging terms) sea ice area loss. CESM_LR_meltSeason - per region (LIA-N, QEI, CAA-S): Timeseries of annual freeze and melt onset dates allowing the definition of the melt season based on the thermodynamic sea ice area tendency crossing 0. CESM2_LE:  CESM2_LE_CAA_sept, _LIAN_sept, _QEI_sept, panArctic: Mean September sea ice extent and sea ice area per region. There is one excel tab per ensemble member in each file. CESM2_LE_pan_Arctic_hi_may: Pan-Arctic mean May sea ice thickness. There is one excel tab per ensemble member in each file. PIOMAS:  PIOMAS_panArctic_hi.xlsx: Timeseries of mean may sea ice thickness. PIOMAS_mean_1981_2000_mean_mayThickness.npy : Results for map of the mean May ice thickness for 1981-2000 and 2001-2020. Observations: CIS_marchSept_1982_1990_sie - per region (QEI and CAA-S) :  Timeseries of March and September mean sea ice extent and area. CIS ice charts do not fully cover the LIA-N.  NSDICCDR_1979_2023_sia - per region (LIA-N, QEI, CAA-S) and pan-Arctic : Timeseries of March and September mean sea ice extent and area. NSDICCDR_1981_2000_mean_septConc.npy: Results for map of the mean September sea ice concentration for 1981-2000 and 2001-2020. NSDICCDR_1981_2000_LIAN_sic.xlsx: Timeseries of monthly mean sea ice concentration in the LIA-N. NSDICCDR_1981_2000_QEI_sic.xlsx: Timeseries of monthly mean sea ice concentration in the QEI. These results are derived from the following datasets:  Ensemble members 1 and 3 of simulations from the high-resolution Community Earth System Model version 1.3 (CESM1.3-HR) produced for the International Laboratory for High-Resolution Earth System Prediction (iHESP) by the Qingdao National Laboratory for Marine Science and Technology (QNLM), Texas A&M University (TAMU), and the U.S. National Center for Atmospheric Research (NCAR). The lower resolution simulation is also used (CESM1.3-LR). (Chang et al., 2020; Zhang et al., 2020). The 100-ensemble members Community Earth System Model (version 2) Large Ensemble (CESM2-LE) (Danabasoglu et al., 2020). Satellite-derived monthly mean SIA fluxes through entry and exit gates of the CAA and Nares Strait (Howell et a., 2019; 2021; 2023; 2024; Smedsrud et al., 2017; Kwok, 2006). The National Snow and Ice Data Center (NSIDC) Climate Data Record (CDR) (version 4) sea ice concentration, stored on a 25 x 25 km polar stereographic grid centered on the North Pole from 1979 to 2023 (Meier et al., 2021). The gridded version of the regional Canadian Ice Service (CIS) Digital Archive ice charts from the Eastern and Western Arctic regions (Tivy et al., 2011). The Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) assimilated sea ice concentration and derived ice thickness distribution estimates in the Arctic from 1978 to 2022 (Zhang et al., 2000).   

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2. A mapped dataset of surface ocean acidification indicators in large marine ecosystems of the United States

   https://doi.org/10.1038/s41597-024-03530-7  

   Sharp, Jonathan D; Jiang, Li-Qing; Carter, Brendan R; Lavin, Paige D; Yoo, Hyelim; Cross, Scott L (December 2024, Scientific Data)

   Abstract Mapped monthly data products of surface ocean acidification indicators from 1998 to 2022 on a 0.25° by 0.25° spatial grid have been developed for eleven U.S. large marine ecosystems (LMEs). The data products were constructed using observations from the Surface Ocean CO₂Atlas, co-located surface ocean properties, and two types of machine learning algorithms: Gaussian mixture models to organize LMEs into clusters of similar environmental variability and random forest regressions (RFRs) that were trained and applied within each cluster to spatiotemporally interpolate the observational data. The data products, called RFR-LMEs, have been averaged into regional timeseries to summarize the status of ocean acidification in U.S. coastal waters, showing a domain-wide carbon dioxide partial pressure increase of 1.4 ± 0.4 μatm yr⁻¹and pH decrease of 0.0014 ± 0.0004 yr⁻¹. RFR-LMEs have been evaluated via comparisons to discrete shipboard data, fixed timeseries, and other mapped surface ocean carbon chemistry data products. Regionally averaged timeseries of RFR-LME indicators are provided online through the NOAA National Marine Ecosystem Status web portal. 

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3. ATMOSPHERIC RADIOCARBON FOR THE PERIOD 1910–2021 RECORDED BY ANNUAL PLANTS

   https://doi.org/10.1017/Rdc.2023.5  

   Carbone, Mariah S; Ayers, Tina J; Ebert, Christopher H; Munson, Seth M; Schuur, Edward A; Richardson, Andrew D (April 2023, Radiocarbon)

   ABSTRACT We present a timeseries of¹⁴CO₂for the period 1910–2021 recorded by annual plants collected in the southwestern United States, centered near Flagstaff, Arizona. This timeseries is dominated by five commonly occurring annual plant species in the region, which is considered broadly representative of the southern Colorado Plateau. Most samples (1910–2015) were previously archived herbarium specimens, with additional samples harvested from field experiments in 2015–2021. We used this novel timeseries to develop a smoothed local record with uncertainties for “bomb spike”¹⁴C dating of recent terrestrial organic matter. Our results highlight the potential importance of local records, as we document a delayed arrival of the 1963–1964 bomb spike peak, lower values in the 1980s, and elevated values in the last decade in comparison to the most current Northern Hemisphere Zone 2 record. It is impossible to retroactively collect atmospheric samples, but archived annual plants serve as faithful scribes: samples from herbaria around the Earth may be an under-utilized resource to improve understanding of the modern carbon cycle. 

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4. Daily mean air temperature data for the North American Great Lakes based on coastal weather stations; 1897-2023 (NCEI Accession 0291722)

   https://doi.org/10.25921/ws45-s314  

   Fujisaki-Manome, Ayumi; Cannon, David; King, Katelyn (April 2024, NOAA National Centers for Environmental Information)

   This dataset contains a record of daily mean air temperature for each of the U.S. Great Lakes from January 1, 1897 to October 22, 2023. These temperatures were derived using the following method. Daily maximum and minimum air temperature data were obtained from the Global Historical Climatology Network-Daily (GHCNd, Menne, et al. 2012) and the Great Lakes Air Temperature/Degree Day Climatology, 1897-1983 (Assel et al. 1995). Daily air temperature was calculated by taking a simple average of daily maximum and minimum air temperature. Following Cohn et al. (2021), a total of 24 coastal locations along the Great Lakes were selected. These 24 locations had relatively consistent station data records since the 1890s. Each of the selected locations had multiple weather stations in their proximity covering the historical period from 1890s to 2023, representing the weather conditions around the location. For most of the locations, datasets from multiple stations in the proximity of each location were combined to create a continuous data record from the 1890s to 2023. When doing so, data consistency was verified by comparing the data during the period when station datasets overlap. This procedure resulted in almost continuous timeseries, except for a few locations that still had temporal gaps of one to several days. Any temporal data gap less than 10 days in the combined timeseries were filled based on the linear interpolation. This resulted in completely continuous timeseries for all the locations. Average daily air temperature was calculated from by simply making an average of timeseries data from corresponding locations around each lake. This resulted in daily air temperature records for all five Great Lakes (Lake Superior, Lake Huron, Lake Michigan, Lake Erie, and Lake Ontario). 

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5. Foraging history of individual elephants using DNA metabarcoding

   https://doi.org/10.1098/rsos.230337  

   Gill, Brian A.; Wittemyer, George; Cerling, Thure E.; Musili, Paul M.; Kartzinel, Tyler R. (July 2023, Royal Society Open Science)

   Individual animals should adjust diets according to food availability. We used DNA metabarcoding to construct individual-level dietary timeseries for elephants from two family groups in Kenya varying in habitat use, social position and reproductive status. We detected at least 367 dietary plant taxa, with up to 137 unique plant sequences in one fecal sample. Results matched well-established trends: elephants tended to eat more grass when it rained and other plants when dry. Nested within these switches from ‘grazing’ to ‘browsing’ strategies, dietary DNA revealed seasonal shifts in food richness, composition and overlap between individuals. Elephants of both families converged on relatively cohesive diets in dry seasons but varied in their maintenance of cohesion during wet seasons. Dietary cohesion throughout the timeseries of the subdominant ‘Artists’ family was stronger and more consistently positive compared to the dominant ‘Royals’ family. The greater degree of individuality within the dominant family's timeseries could reflect more divergent nutritional requirements associated with calf dependency and/or priority access to preferred habitats. Whereas theory predicts that individuals should specialize on different foods under resource scarcity, our data suggest family bonds may promote cohesion and foster the emergence of diverse feeding cultures reflecting links between social behaviour and nutrition. 

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