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Expand Scientific Knowledge for Climate Monitoring and Prediction

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    Precipitation measurements in the Environment and Climate Change Canada (ECCC) surface network are a necessary component for monitoring weather and climate and are required for flood and water resource forecasting, numerical weather prediction and many other applications that impact the health and safety of Canadians. Beginning in the late 1990s, the ECCC surface network began a transition from manual to automated precipitation measurements. Advantages to increased automation include enhanced capabilities for monitoring in remote locations and higher observation frequency at lower cost. However, transition to automated precipitation gauges has resulted in new challenges to data quality, accuracy, and homogenization. Automated weighing precipitation gauges used in the ECCC operational network, because of their physical profile, tend to measure less precipitation falling as snow because lighter particles (snow) are deflected away from the collector by the wind flow around the gauge orifice. This phenomenon of wind-induced systematic bias is well documented in the literature. The observation requires an adjustment depending on gauge and shield configuration, precipitation phase, temperature, and wind speed. Hourly precipitation, wind speed, and temperature for 397 ECCC automated surface weather stations were retrieved from the ECCC national archive. Climate Research Division (CRD) selected this sub-set of stations because they are critical to the continuity of various climate analysis. The observation period varies by station with the earliest data series beginning in 2001 (with most beginning in 2004). The precipitation data was quality controlled using established techniques to identify and flag outliers, remove spurious observations, and correct for previously identified filtering errors. The resulting hourly precipitation data was adjusted for wind bias using the WMO Solid Precipitation Inter-Comparison Experiment (SPICE) Universal Transfer Function (UTF) equation. A full description of this data set, including the station locations, data format, methodology, and references are included in the repository.

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    The suite of gridded precipitation datasets includes the ANUSPLIN-gridded datasets of daily precipitation since 1900 (ANUSPLIN-AdjPdly), pentad average precipitation rates since 1950 (ANUSPLIN-AdjP5d), and monthly total precipitation since 1872 (ANUSPLIN-AdjPmly), on a 0.083333ºx 0.083333º latitude-longitude grid over Canada. For version 1 of these datasets, the daily precipitation data were generated from the ANUSPLIN surfaces fitted to the Adjusted Daily Rainfall and Snowfall (AdjDlyRS) dataset version 2016, which includes 3346 stations of manual observations (Wang et al. 2017; available at https://open.canada.ca/data/en/dataset/d8616c52-a812-44ad-8754-7bcc0d8de305). The monthly and pentad gridded data were generated from the ANUSPLIN surfaces fitted respectively to the monthly and pentad station data that were derived from the corresponding daily station data. When deriving the monthly station data from the daily station data, the monthly total precipitation was set to missing if there was one or more missing daily value in the month (zero tolerance for missing). The same zero tolerance for missing observations was applied in the calculation of the pentad average precipitation rates. Details of the ANUSPLIN modelling of these three gridded precipitation datasets are provided in MacDonald et al. (2020), along with comparison to the unadjusted ANUSPLIN-gridded daily precipitation dataset (Hutchinson et al. 2009). Note that the unadjusted precipitation data significantly underestimate both the precipitation amount and the regional mean long-term trend therein (MacDonald et al. 2020). It is important to point out that these ANUSPLIN-AdjP datasets are not homogenized and thus should not be used to assess climate trends/changes before ensuring the temporal homogeneity of the data. Considering there were no or few stations in the North in the early period, the gridded values in the period before 1930 were set to missing (missing code -999.00) for all gridpoints north of 65ºN and west of 110ºW (including 110ºW) and for all gridpoints north of 55ºN and east of 110ºW. The gridded values in the period of 1930-1949 were also set to missing for all gridpoints north of 70ºN. For the period from 1950 onwards, the grid covers the whole land mass of Canada. References: (1) MacDonald, H., D.W. McKenney, X.L. Wang, J. Pedlar, P. Papadopol, K. Lawrence, and M.F. Hutchinson, 2021: Spatial models of adjusted precipitation for Canada at varying time scales. J. Appl. Meteor. Climatol., 60, 291-304. doi: 10.1175/JAMC-D-20-0041.1. (2) Wang, X.L., H. Xu, B. Qian, Y. Feng, and E. Mekis, 2017: Adjusted daily rainfall and snowfall data for Canada. Atmos.-Ocean, 55, 155–168, doi:10.1080/07055900.2017.1342163. (3) Hutchinson, M.F.,D.W. McKenney, K. Lawrence, J.H. Pedlar, R. F.Hopkinson, E.Milewska, and P. Papadopol, 2009: Development and testing of Canada-wide interpolated spatial models of daily minimum–maximum temperature and precipitation for 1961–2003. J. Appl. Meteorol. Climatol., 48, 725-741. doi:10.1175/2008JAMC1979.1.

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    The purpose of the Long-Range Forecast Transient Intercomparison Project (LRFTIP) is to provide an archive of hindcast climatologies, describing the systematic behavior of models evolving from observation-based initial states, that can inform investigations into the transient behavior of initialized subseasonal to decadal climate predictions, the development of model biases, and the relative merits of different initialization methods. The archive is based on publicly available hindcast datasets including the Subseasonal to Seasonal Prediction Project (S2S), the Climate-system Historical Forecast Project (CHFP) and the Coupled Model Intercomparison Project Phase 5 (CMIP5) and 6 (CMIP6). ECCC models include the GEPS-based contribution to S2S, the CanCM3, CanCM4, CanCM4i and GEM-NEMO seasonal prediction models, and the CanCM4 CMIP5 and CanESM5 CMIP6 decadal prediction models. Additional contributions are being added as they become available.

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    This first version of the dataset (CanHoPmlyV1) contains homogenized time series of monthly total precipitation for 425 long-term stations in Canada. As detailed in Wang et al. (2023), it is based on the quality-controlled version 2020 of the Adjusted Daily Rainfall and Snowfall (AdjDlyRS) dataset (Wang et al. 2017; available at http://open.canada.ca/data/en/dataset/d8616c52-a812-44ad-8754-7bcc0d8de305), and on daily total precipitation data from automated gauges (including Belfort, Fisher & Porter, Nipher, Geonor, and Pluvio), with some records from neighbouring stations being joined to form long-term data series. Version 1 of ANUSPLIN surfaces of the adjusted monthly precipitation (MacDonald et al. 2021, available at https://open.canada.ca/data/en/dataset/779ea77a-0ad1-42f2-853e-833e1cbb9a13) was used to infill temporal data gaps in the 425 data series. A comprehensive semi-automatic data homogenization procedure was used to homogenize the data series. The aforementioned ANUSPLIN data and the Twentieth Century Reanalysis 20CRv3 ensemble-mean series of monthly precipitation (Slivinski et al., 2021) were used as reference in the homogeneity tests (Wang et al., 2023). The homogenized dataset provides more realistic trend estimates and shows better spatial consistency of trends than does the raw dataset (Wang et al. 2023). References: Wang, X.L, Y. Feng, V. Y. S. Cheng, H. Xu, 2023: Observed precipitation trends inferred from Canada’s homogenized monthly precipitation dataset, J. Clim., in press. DOI: 10.1175/JCLI-D-23-0193.1. Wang, X. L., H. Xu, B. Qian, Y. Feng, E. Mekis, 2017: The adjusted daily rainfall and snowfall data for Canada. Atmosphere-Ocean, 55:3, 155-168, DOI:10.1080/07055900.2017.1342163. MacDonald, H., D. W. McKenney, X. L. Wang, J. Pedlar, P. Papadopol, K. Lawrence, M. F. Hutchinson, 2021: Spatial Models of adjusted precipitation for Canada at varying time scales. J. Appl. Meteor. And Climatol., 60, 291-304. DOI: 10.1175/JAMC-D-20-0041.1. Slivinski, L. and coauthors, 2019: Towards a more reliable historical reanalysis: Improvements for version 3 of the Twentieth Century Reanalysis system. Q. J. R. Meteor. Soc., 2876-2908, https://doi.org/10.1002/qj.3598.

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    This campaign took place in April 2015, with a focus on the characterization of ice clouds and pollutant levels, including black carbon, across the Arctic. The campaign was again a collaboration between NETCARE scientists and The Alfred Wegener Institute (AWI). Along with the POLAR 6, the POLAR 5 made the trans-Arctic passage with an intent to characterize sea ice levels. The campaign started in Longyearben, Spitzbergen and was to have continued in Station Nord, Greenland, but bad weather at the start required researchers to skip science activities in Station Nord and to carry on to Alert and Eureka, Nunavut and then to Inuvik, NWT. The instrumentation was similar to what was deployed in the summer of 2014, but with specific changes to better characterize the optical properties of the particles and the character of the ice clouds. Close to 10 science flights were performed, with some measurements also performed on the ferry flights between stations. Institutions Involved: ● Environment and Climate Change Canada ● University of Toronto ● University of British Columbia ● University of Calgary ● Alfred Wegener Institute ● University of Mainz ● Max Planck Institute ● University of Quebec at Montreal Data sets: ● Atmospheric gas phase species ● Atmospheric aerosol particle size and number density ● Atmospheric aerosol particle composition ● Numbers of ice cloud forming particles ● Aircraft data and meteorology

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    From September 2014 to June 2015 NETCARE scientists conducted an intensive snow sampling campaign at Alert, Nunavut, Canada. Fresh snow samples were collected every few days and analyzed for black carbon, major ions, metals, elemental carbon, and organic carbon. Site Information: Alert, Nunavut (82.49 N, 62.36 W, 201m a.m.s.l.). Snow samples were collected from two Teflon-surfaced snow tables located 1 m above ground level and in an open-air, minimal traffic site, about 6 km SSW of the Alert base camp. Institutions Involved: ● Environment and Climate Change Canada ● University of Toronto ● Desert Research Institute Study Reference: Macdonald, K. M., Sharma, S., Toom, D., Chivulescu, A., Hanna, S., Bertram, A., Platt, A., Elsasser, M., Huang, L., Chellman, N., McConnell, J. R., Bozem, H., Kunkel, D., Lei, Y. D., Evans, G. J., and Abbatt, J. P. D.: Observations of Atmospheric Chemical Deposition to High Arctic Snow, Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2016-944, in review, 2016. Data sets: ● Refractory Black Carbon Concentration and Flux ● Major Ions Concentration and Flux ● Metals Concentration and Flux ● Elemental and Organic Carbon Concentration and Flux

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    The CanESM2 large ensembles are 50-member perturbed initial condition ensembles from 1950 to 2020, with all historical forcings (historical), solar and volcanic forcings only (historicalNat), anthropogenic aerosols only (historicalMisc, p4), and ozone only (historicalMisc, p6). The model, forcings, variable names and file formats all follow those used in the Coupled Model Intercomparison Project Phase 5 (CMIP5). Each of the five members of the existing CMIP5 historical ensemble had conditions perturbed in 1950 ten times to produce a new ensemble of fifty simulations starting in 1950, and a similar procedure was applied to the natural, aerosol and ozone-only ensembles. Simulations were run to 2005 using CMIP5 historical forcings and then to 2020 using RCP 8.5 forcings. The CanESM2 large ensembles were proposed by the Canadian Sea Ice and Snow Evolution Network (CanSISE) Climate Change and Atmospheric Research (CCAR) Network project, which also coordinated much of the initial analysis of the large ensembles. Relevant publications: Description of the large ensembles: Kushner, P. J., L. R. Mudryk, W. Merryfield, J. T. Ambadan, A. Berg, A. Bichet, R. Brown, C. Derksen, S. J. Déry, A. Dirkson, G. Flato, C. G. Fletcher, J. C. Fyfe, N. Gillett, C. Haas, S. Howell, F. Laliberté, K. McCusker, M. Sigmond, R. Sospedra-Alfonso, N. F. Tandon, C. Thackeray, B. Tremblay, and F. W. Zwiers, 2018: Canadian snow and sea ice: assessment of snow, sea ice, and related climate processes in Canada’s Earth system model and climate-prediction system. The Cryosphere, 12, 1137–1156, doi:10.5194/tc-12-1137-2018. Kirchmeier-Young, M.C., F.W. Zwiers, and N.P. Gillett, 2017: Attribution of Extreme Events in Arctic Sea Ice Extent. J. Climate, 30, 553–571, https://doi.org/10.1175/JCLI-D-16-0412.1 Examples of applications of the large ensembles: Kirchmeier-Young, M.C., F.W. Zwiers, N.P. Gillett and A.J. Cannon, 2017: Attributing extreme fire risk in Western Canada to human emissions, Climatic Change, 1-15, https://doi.org/10.1007/s10584-017-2030-0 Gagné, M.-È., J. C. Fyfe, N. P. Gillett, I. V. Polyakov, and G. M. Flato, 2017: Aerosol-driven increase in Arctic sea ice over the middle of the twentieth century, Geophys. Res. Lett., 44, 7338–7346, https://doi.org/10.1002/2016GL071941 Gagné, M.-È., M. C. Kirchmeier-Young, N. P. Gillett, and J. C. Fyfe, 2017: Arctic sea ice response to the eruptions of Agung, El Chichón, and Pinatubo, J. Geophys. Res. Atmos., 122, https://doi.org/10.1002/2017JD027038 Fyfe, J.C. , C. Derksen, L. Mudryk, G.M. Flato, B.D. Santer, N.C. Swart, N.P. Molotch, X. Zhang, H. Wan, V.K. Arora, J. Scinocca, 2017: Large near-term projected snowpack loss over the western United States, Nature Comm., 8:14996, https://doi.org/10.1038/ncomms14996

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    This repository holds underlying data for publications arising from NETCARE activities. Unlike the raw measurements available elsewhere in this archive, these data are specific to a particular publication and may have undergone additional analysis and processing. Data are organized by publication and are accompanied by a complete citation for the relevant manuscript.

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    In August 2013, NETCARE scientists conducted a campaign on the west coast of Vancouver Island, British Columbia, Canada. The goal of this campaign was to characterize the nature of ice nuclei in a marine environment. Measurements were conducted on the ambient levels of both deposition and immersion ice nucleating particles. Data was also collected to measure the physical and chemical properties of the ambient particles. Site Information: Amphitrite Point (48.92N, 125.54W) is located approximately 2 km from the small town of Ucluelet on Vancouver Island, British Columbia, Canada. The research site was located approximately 100 m from the Pacific Ocean. Institutions Involved: ● Environment and Climate Change Canada ● University of Toronto ● University of British Columbia ● University of Denver ● Fisheries and Oceans Canada Data sets: ● Atmospheric aerosol particle size and number density ● Atmospheric aerosol particle hygroscopicity ● Numbers of ice cloud forming particles ● Aerosol particle ion concentration as a function of particle size

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    In 2016 NETCARE scientists conducted two measurement campaigns at the Dr. Neil Trivett Global Atmosphere Watch Observatory (the “Alert Observatory”) on the northeastern tip of Ellesmere Island in the Canadian Arctic (latitude: 82.5163, longitude: -62.3085). The first campaign happened in March and was focused on ice nucleating particles and aerosol particle composition. Further measurements were made in the summer (June- Sept) on aerosol particle size and number density, gas phase species, aerosol optical properties, aerosol particle composition, and soluble gases and ions in particulate matter. Institutions Involved ● University of Toronto ● University of British Columbia ● Environment and Climate Change Canada Data sets ● Atmospheric gas phase species ● Atmospheric aerosol particle size and number density ● Atmospheric aerosol particle composition ● Atmospheric aerosol particle optical properties ● Number of ice cloud forming particles ● Soluble gases and ions in particulate matter