Find research datasets worth reusing

openCC (other)Aug 2023View details →

Fall 2020 plant monitoring survey -- biomass calculated from shoot height and flowering status of plants in permanent plots at GCE sampling sites 1-10

The biomass of plants surveyed in permanent plots at 10 GCE LTER sampling sites in October 2019 was estimated based on allometric relationships between biomass and shoot height and flowering status derived for each site, zone, and species in October 2002 and October 2008. Biomass was calculated for dominant species, including Spartina alterniflora, S. cynosuroides, Juncus roemerianus, and Zizaniopsis miliacea, as well as rarer species including Scirpus spp, Panicum spp. And Typha angustifolia. This data set is based on GCE plant monitoring survey data set PLT-GCEM-2011a, and allometric relationships were based on GCE data sets PLT-GCEM-0211b, PLT-GCEM-0711, and PLT-GCEM-2011.

openCC (other)Jan 2023View details →

Fall 2021 plant monitoring survey -- biomass calculated from shoot height and flowering status of plants in permanent plots at GCE sampling sites 1-10

The biomass of plants surveyed in permanent plots at 10 GCE LTER sampling sites in October 2021 was estimated based on allometric relationships between biomass and shoot height and flowering status derived for each site, zone, and species in October 2002 and October 2008. Biomass was calculated for dominant species, including Spartina alterniflora, S. cynosuroides, Juncus roemerianus, and Zizaniopsis miliacea, as well as rarer species including Scirpus spp, Panicum spp. And Typha angustifolia. This data set is based on GCE plant monitoring survey data set PLT-GCEM-2111a, and allometric relationships were based on GCE data sets PLT-GCEM-0211b, PLT-GCEM-0711, and PLT-GCEM-2011.

openCC (other)Feb 2024View details →

Fall 2022 plant monitoring survey -- biomass calculated from shoot height and flowering status of plants in permanent plots at GCE sampling sites 1-10

The biomass of plants surveyed in permanent plots at 10 GCE LTER sampling sites in October 2022 was estimated based on allometric relationships between biomass and shoot height and flowering status derived for each site, zone, and species in October 2002 and October 2008. Biomass was calculated for dominant species, including Spartina alterniflora, S. cynosuroides, Juncus roemerianus, and Zizaniopsis miliacea, as well as rarer species including Scirpus spp, Panicum spp. And Typha angustifolia. This data set is based on GCE plant monitoring survey data set PLT-GCEM-2111a, and allometric relationships were based on GCE data sets PLT-GCEM-0211b, PLT-GCEM-0711, and PLT-GCEM-2011.

openCC (other)Feb 2025View details →

Fall 2023 plant monitoring survey -- biomass calculated from shoot height and flowering status of plants in permanent plots at GCE sampling sites 1-10

The biomass of plants surveyed in permanent plots at 10 GCE LTER sampling sites in October 2023 was estimated based on allometric relationships between biomass and shoot height and flowering status derived for each site, zone, and species in October 2002 and October 2008. Biomass was calculated for dominant species, including Spartina alterniflora, S. cynosuroides, Juncus roemerianus, and Zizaniopsis miliacea, as well as rarer species including Scirpus spp, Panicum spp. And Typha angustifolia. This data set is based on GCE plant monitoring survey data set PLT-GCEM-2111a, and allometric relationships were based on GCE data sets PLT-GCEM-0211b, PLT-GCEM-0711, and PLT-GCEM-2011.

openCC (other)Jan 2026View details →

Fall 2024 plant monitoring survey -- biomass calculated from shoot height and flowering status of plants in permanent plots at GCE sampling sites 1-10

The biomass of plants surveyed in permanent plots at 10 GCE LTER sampling sites in October 2024 was estimated based on allometric relationships between biomass and shoot height and flowering status derived for each site, zone, and species in October 2002 and October 2008. Biomass was calculated for dominant species, including Spartina alterniflora, S. cynosuroides, Juncus roemerianus, and Zizaniopsis miliacea, as well as rarer species including Scirpus spp, Panicum spp. And Typha angustifolia. This data set is based on GCE plant monitoring survey data set PLT-GCEM-2111a, and allometric relationships were based on GCE data sets PLT-GCEM-0211b, PLT-GCEM-0711, and PLT-GCEM-2011.

openCC (other)Dec 2022View details →

Pelagic, epilimnetic production estimates in Sparkling, Trout (Wisconsin), Acton (Ohio), and Castle (California) Lakes (USA) calculated using 14C and free-water O2 metabolism methods, 2007 - 2017

Concurrent daily estimates of pelagic, eplilimnetic production (mmol C m3 d) generated from 14C incubations and diel changes in high frequency dissolved oxygen data (free-water). Original data derived from the North Temperate Lakes Long Term Ecological Research program (Sparkling [2007-2013], Trout [2007-2012] Lakes), Castle Lake Research Station (Castle Lake [2014-2017]), and Center for Aquatic and Watershed Sciences (Acton Lake [2010-2014]). 14C production estimates were generated as part of each research programs core data collection. Free-water production estimates generated using high frequency sensor data provided by research programs and Phillips (2020) time-varying, Bayesian metabolism model.

edi60/100

Soil volumetric water content calculated from neutron hydroprobe data at 15 NPP study locations at the Jornada Basin LTER site, 1989-ongoing

This data package contains soil water content data calculated from monthly neutron hydroprobe count measurements made at 15 net primary production (NPP) study locations on Jornada Experimental Range (JER) and Chihuahuan Desert Rangeland Research Center (CDRRC) lands. Once a month, neutron probe measurements are made at 10 depths (where possible) at each of 10 access tubes at each of the 15 NPP sites using a neutron probe (CPN Model 503DR Hydroprobe and CPN Model 503 Elite Hydroprobe). The raw dataset, also on EDI (knb-lter-jrn.210013001), consists of the count of thermalized neutrons at 30 cm depth intervals to a maximum depth of 300 cm. In this data package, the raw neutron counts have been adjusted for radioactive decay of the neutron source, then converted to volumetric water content (VWC) to a maximum depth of 270 cm using calibration equations (deepest probe depths are excluded from VWC calculations). The NPP sites these measurements are made at represent the 5 dominant vegetation types of the Jornada Basin, which consist of 3 shrub (creosotebush, mesquite dune, and tarbush) and 2 grass (upland grassland and playa) types. Three NPP sites are located in each of the types. This data collection is ongoing with new data collected monthly (updates to the EDI package may occur less frequently). NOTE: This version of the dataset includes calibrated data from a new hydroprobe unit that has recently been put into service. Repair parts were no longer available for the older unit.

openCC (other)Mar 2025View details →

edi56/100

FAO and SAGARPA. (2012). Baseline of the Natural Resources Sustainability Program. Sustainable Land Use Subindex - Calculation Methodology. Mexico City (53 pp.) (FAO y SAGARPA. (2012). Línea de Base del Programa de Sustentabilidad de los Recursos Naturales. Subíndice de Uso Sustentable del Suelo - Metodología de Cálculo (53 pág.). Ciudad de México)

The lack of soil data is a complication that most soil scientists will encounter throughout their career; this critical aspect is exacerbated due to the excessive cost of soil surveying. Consequently, it is essential to develop strategies that guarantee the permanent accessibility of past soil sampling efforts. The main objective of this contribution is to release an entire dataset of soil samples surveyed by the Secretary of Agriculture, Livestock, Rural Development, Fisheries, and Food (SAGARPA) in collaboration with the Food and Agriculture Organization of the United Nations (FAO) in the year 2012, the dataset consists of more that 4000 compound samples surveyed on managed cropland. SAGARPAS's main objective was to generate a new index to assess and monitor the current and future state of soil when sustainable soil practices take place. A complete set of physicochemical properties were determined via laboratory analysis for all record in the dataset, namely: pH, EC, OM, BD, P, Sand, Silt, Clay, Texture, N, K, Ca, Mg, Na, CEC, Sodium Adsorption Ratio (SAR), Exchangeable Sodium Percentage (ESP), including the calculation of the Sustainable Soil Land Use Subindex (SSLUS). We presented a reviewed dataset with the potential to contribute to a large variety of studies, ranging from: agricultural pinpointing of the best conditions for crop production to digital soil mapping and modeling and agronomical studies. We also provide the original report on the developement of the dataset, indicating the names of creators and colaborators, as well as the methods of analysis and interpretation of the results. The new information is appealing for a wide diversity of users interested in soil traits across agricultural systems of Mexico.

openCC0Apr 2025View details →

opencc-by-4.0Dec 2021View details →

Global Extra-tropical Circulation Database based on the Jenkinson-Collison Classification calculated with 6-hourly mean sea-level pressure fields from various reanalysis datasets

<h1>Dataset Description</h1> Global Extra-tropical Circulation Database based on the Jenkinson-Collison Classification calculated with 6-hourly mean sea-level pressure fields from several reanalysis datasets. This dataset is the result of an extension of the Jenkinson-Collison circulation type classification to the entire globe, including a modification of its original formulation for the southern hemisphere. A modified version of the IPCC-AR6 Reference Regions that excludes the intertropical range where the method is not applicable is also included, as used in the reference paper for global assessment. Further details in <a href="https://doi.org/10.1007/s00382-022-06658-7" target="_blank" rel="noopener">https://doi.org/10.1007/s00382-022-06658-7 </a> <h2>Note for version 1.1.0</h2> This version corrects an issue in the previous release, which was incorrectly labeled as version 0.1. That version was incomplete due to the omission of previously existing files, and should be considered incomplete. Version 1.1.0 restores all original files alongside the newly added one, ensuring the dataset is now complete and consistent. We apologize for any inconvenience this may have caused and appreciate your understanding.

opencc-by-4.0May 2024View details →

Calculation of parameter values based on observations for the herbaceous biomass plantation PFT representing Miscanthus in JSBACH3.2

This dataset provides the calculation of parameter values and the observational data that was collected from literature used in these calculations for the re-implementation of a herbaceous biomass plantation (HBP) PFT representing Miscanthus in the dynamic global vegetation model (DGVM) JSBACH3.2 (Egerer et al. subm., Nützel et al. in prep.). The parameters included are the maximum rubisco capacity (Vmax) at 25°C, the PEPcase CO2 specificity (k) and specific leaf area (SLA). Some of the observed parameter values were already compiled in a dataset by Li et al. (2018). These observations were therefore re-used in this dataset (which is specified within the dataset sheets) and complemented with additional observed values from literature that has become available since then or was not included in the study by Li et al. (2018). A detailed methodology of the parameter calculations for JSBACH3.2 can be found on the first sheet of the dataset.

opencc-by-4.0Jul 2024View details →

Dataset of "Asparagine-Modified Magnetic Graphene Oxide: An Efficient and Green Nanocatalyst for Synthesis of 5-oxodihydropyrano[3,2-c]chromenes and dihydropyrano[2,3- c]pyrazole derivatives and the Density functional theory calculation".

The primary focus of this study involved the fabrication of a novel nanocatalyst Fe3O4-supported asparagine functionalized graphene oxide (Fe3O4@GO-N-(Asparagine)). The catalyst was synthesized through a four-step procedure.

opencc-by-4.0Oct 2024View details →

The mass of the lowermost stratosphere (LMS): LMS mass calculation and trends in five reanalyses for the time period 1979–2019

Description Python code to calculate the mass of the lowermost stratosphere (LMS) and investigate LMS mass trends with the dynamic linear regression model (DLM, Laine et al. 2014, Alsing 2019) as presented in Weyland et al. (2024). The LMS mass is calculated via a three dimensioal integral, following Appenzeller et al. (1996), given an upper and lower LMS boundary surface (4D pressure fields). Here, the lateral boundary is determined via the intersection of the tropopause with the 350K isentrope (4D pressure field). The upper LMS boundary can be defined by the isentrope according to the potential temperature at the tropical lapse rate tropopause (PPT10mean) or the cold point (PPTcp10mean) or approximated by the 380K isentrope. See Weyland et. al (2024) for further description and context. The mass calculation is performed with calc_LMS_mass.py. The DLM trend analysis is conducted with dlm_LMS_mass.py, using dlm_modules.py. In order to be able to use the provided code, the dlmmc model code has to be downloaded from <a href="https://github.com/justinalsing/dlmmc">https://github.com/justinalsing/dlmmc</a> (Alsing 2019). The neccesary 3D (time, lat, lon) pressure fields to define the LMS boundaries are provided for the time period 1979–20191 from five modern reanalyses: ERA52 (Hersbach et al., 2020), ERA-Interim (Dee et al., 2011), MERRA-2 (Gelaro et al., 2017) and JRA-55 (Kobayashi et al., 2015) and JRA3Q (Kosaka et al., 2024): <ul> <li>lrtp*.nc : <ul> <li>3D (time, lat, lon) pressure, temperature and potential temperature at the WMO lapse rate tropopause for the time period 1979-20191, derived from monthly mean data on pressure levels from the respective reanalysis. The lapse rate detection algorithm closely follows that of Birner et al. (2010), based on the work of Reichler et al. (2003). The lapse rate tropopause can serve as the lower LMS boundary. The potential temperature at the lapse rate tropopause between 10°N-10°S is used to define a &bdquo;dynamic“ upper LMS boundary (PPT10mean).</li> </ul> </li> </ul> <ul> <li>cp*.nc : <ul> <li>3D (time, lat, lon) pressure, temperature and potential temperature at the cold point for the time period 1979–20191 , derived from monthly mean data on pressure levels from the respective reanalysis. The cold point here is defined by the pressure corresponding to a lapse rate of 0K/km. The potential temperature at the cold point between 10°N–10°S is used to define a &bdquo;dynamic“ upper LMS boundary (PPTcp10mean).</li> </ul> </li> </ul> <ul> <li>ppt10mean*.nc : <ul> <li>3D (time, lat, lon) pressure at the isentrope accroding to the potential temperature at the tropical (10°N–10°S) lapse rate tropopause (PPT10mean) for the time period 1979–20191, derived from lrtp*.nc. PPT10mean can be used to define the upper LMS boundary.</li> </ul> </li> </ul> <ul> <li>pptcp10mean*.nc : <ul> <li>3D (time, lat, lon) pressure at the isentrope accroding to the potential temperature at the cold point between 10°N-10°S (PPTcp10mean) for the time period 1979–20191, derived from cp*.nc. PPTcp10mean can be used to define the upper LMS boundary.</li> </ul> </li> </ul> <ul> <li>p380K*.nc : <ul> <li>3D (time, lat, lon) pressure at the 380K isentrope for the time period 1979–20191, derived from monthly mean data on pressure levels from the respective reanalysis. The 380K isentropic pressure field can be used to approximate the upper LMS boundary.</li> </ul> </li> </ul> <ul> <li>p350K*.nc : <ul> <li>3D (time, lat, lon) pressure at the 350K isentrope for the time period 1979–20191, derived from monthly mean data on pressure levels from the respective reanalysis. The 350K isentrope is used to determine the lateral LMS boundaries via its intersection with the tropopause. This intersection approximates the location of the subtropical jet streams and the maximum PV-gradient, marking a transport barrier. It is determined by the sign change of the pressure difference between the tropopause and the 350K isentrope.</li> </ul> </li> </ul> <ul> <li>my_enso_79-19.txt : <ul> <li>Regressor to account for El-Niño/Southern Oscillation for the time period 1979–2019. Source: <a href="https://psl.noaa.gov/enso/mei/">https://psl.noaa.gov/enso/mei/</a>, last accessed: 11 July 2023. The data has been normalized and centered around zero. The use of regressors is optional.</li> </ul> </li> </ul> <ul> <li>my_qbo30_79-19.txt and my_qbo50_79-19.txt : <ul> <li>Regressor to account for the quasi-biennial oscillation at 30 and 50 hPa for the time period 1979–2019. Source: <a href="https://www.geo.fu-berlin.de/met/ag/strat/produkte/qbo/qbo.dat">https://www.geo.fu-berlin.de/met/ag/strat/produkte/qbo/qbo.dat</a>, last accessed: 11 July 2023. The data has been normalized and centered around zero. The use of regressors is optional.</li> </ul> </li> </ul> <ul> <li>my_SAOD_79-19.txt : <ul> <li>Regressor to account for stratospheric (volcanic) aerosol optical depth for the time period 1979-2019. Source: <a href="https://asdc.larc.nasa.gov/project/GloSSAC/GloSSAC_1.0">https://asdc.larc.nasa.gov/project/GloSSAC/GloSSAC_1.0</a>, last accessed: 11 July 2023. The data has been normalized. The use of regressors is optional.</li> </ul> </li> </ul>   For further details see Weyland et al. (2024). 1Note that the ERA-Interim time series ends in 2018 and that the MERRA-2 time series starts in 1980. 2For the time period 2000–2006, the sub-reanalysis ERA5.1 replaces ERA5, correcting the reanalysis for a cold bias in the lower stratosphere (Simmons et al., 2020).   How to use – example: Assuming you are interested in the LMS mass between a lower boundary (-lb, e.g., the lapse rate tropopause) and an upper boundary (-ub, e.g., the 380K isentrope) in ERA5 for the entire Northern hemisphere (-lat=NH) covering the time period 1979-2019:   <ul> <li> Calculate the respective LMS mass timeseries: $ python calc_LMS_mass.py -lb=lrtp_ERA5.nc -ub=p380K_ERA5.nc -latb=p350K_ERA5.nc -lat=NH -fout=LMS_mass_ERA5_lrtp_p380K_NH.nc Isentropic pressure at 350K (-latb) is required to determine the lateral LMS boundary. The LMS mass time series together with an uncertainty estimate is saved to a netCDF file (-fout), e.g. &bdquo;LMS_mass_ERA5_lrtp_p380K_NH.nc“. </li> </ul>   <ul> <li> Perform a DLM trend analysis for your LMS mass time series, here LMS_mass_ERA5_lrtp_p380K_NH.nc (-mf) : Download the DLM model code (dlmmc) from <a href="https://github.com/justinalsing/dlmmc">https://github.com/justinalsing/dlmmc</a> (Alsing 2019) and save the &bdquo;dlmmc“ folder, containing the DLM modules in your working directory. </li> </ul> $ python dlm_lms_mass.py -mf=LMS_mass_ERA5_lrtp_p380K_NH.nc -s=2000 In this example, the DLM will provide 2000 samples (-s) after an additional 1000 warm-up samples. The DLM time series, containing 2000 samples (-s) per time step, is saved to a netCDF file. The name of the output file can be specifyed with -fout. Default is &bdquo;dlm_“ + mf, i.e. &bdquo;dlm_ LMS_mass_ERA5_lrtp_p380K_NH.nc“ in this example. The function dlm_lms_mass.dlm_lms_mass contains an option to visualize the DLM result (plot=True). Furthermore, it can be specified whether the DLM should be run with regressors (use_regressors=True) or without regressors (use_regressors=False). See the DLM documentation (Laine et al. 2014, Alsing 2019) for further options.   Funding: This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – TRR 301 – Project-ID 428312742: “The tropopause region in a changing atmosphere”.

opencc-by-4.0May 2023View details →

Calculated moisture sources for the Yangtse River Valley for past, present and future climate using a Lagrangian moisture source diagnostic

This dataset contains calculated moisture sources for the Yangtse River Valley (110–122°E and 27–33°N, eastern China) for past, present and future climate using a Lagrangian moisture source diagnostic. The dataset comprises gridded monthly moisture source data files and monthly time series files for a Last Glacial Maximum (LGM) simulation and a Pre-Industrial reference simulation (PRE) with CAM5.1 using prescribed sea surface temperatures, and a control simulation (CTL, 2001-2010) and a climate scenario run with representative concentration pathway 6 (RCP, 2061-2070) with the coupled NorESM-1M model. Each file covers a 10-year time period, computed with the Lagrangian moisture source diagnostic WaterSip (Sodemann et al., 2008).

opencc-by-4.0Oct 2023View details →

Climate change velocity metrics calculated for three climate variables across Finland

This dataset contains files that show the climate change velocity metrics calculated for three climate variables across Finland. The climate velocities were used to study the magnitude of projected climatic changes in a nation-wide Natura 2000 protected area (PA) network (Heikkinen et al., 2020). Using fine-resolution climate data that describes the present-day and future topoclimates and their spatio-temporal variation, the study explored the rate of climatic changes in protected areas on an ecologically relevant, but yet poorly explored scale. The velocities for the three climate variables were developed in the following work, where in-depth description of the different steps in velocity metrics calculation and a number of visualisations of their spatial variation across Finland are provided:Risto K. Heikkinen 1, Niko Leikola 1, Juha Aalto 2,3, Kaisu Aapala 1, Saija Kuusela 1, Miska Luoto 2 & Raimo Virkkala 1 2020: Fine-grained climate velocities reveal vulnerability of protected areas to climate change. Scientific Reports 10:1678. https://doi.org/10.1038/s41598-020-58638-81 Finnish Environment Institute, Biodiversity Centre, Latokartanonkaari 11, FI-00790 Helsinki, Finland2 Department of Geosciences and Geography, University of Helsinki, FI-00014, Helsinki, Finland3 Finnish Meteorological Institute, FI-00101, Helsinki, Finland The dataset includes GIS compatible geotiff files describing the nine spatial climate velocity surfaces calculated across the whole of Finland at 50 m × 50 m spatial resolution. These nine different velocity surfaces consist of velocity metric values measured for each 50-m grid cell separately for the three different climate variables and in relation to the three different future climate scenarios (RCP2.6, RCP4.5 and RCP8.5). The baseline climate data for the study were the monthly temperature and precipitation data averaged for the period from 1981 to 2010 modelled at a resolution of 50-m, based on which estimates for the annual temperature sum above 5 °C (growing degree days, GDD, °C), the mean January temperature (TJan, °C) and the annual climatic water balance (WAB, the difference between annual precipitation and potential evapotranspiration; mm) were calculated. Corresponding future climate surfaces were produced using an ensemble of 23 global climate models for the years 2070–2099 (Taylor et al. 2012) and the three RCPs. The data for the three climate variables for 1981–2010 and under the three RCPs will be made available in separately via METIS - FMI's Research Data repository service (Aalto et al., in prep.). The climate velocity surfaces included in the present data repository were developed using climate-analog approach (Hamann et al. 2015; Batllori et al. 2017; Brito-Morales et al. 2018), whereby velocity metrics for the 50-m grid cells were measured based on the distance between climatically similar cells under the baseline and the future climates, calculated separately for the three climate variables. In Heikkinen et al. (2020), the spatial data for the Natura 2000 protected areas were used to assess their exposure to climate change. The full data on N2K areas can be downloaded from the following link: https://ckan.ymparisto.fi/dataset/%7BED80465E-135B-4391-AA8A-FE2038FB224D%7D. However, note that the N2K areas including multiple physically separate patches were treated as separate polygons in Heikkinen et al. (2020), and a minimum size requirement of 2 hectares were requested. Moreover, the digital elevation model (DEM) data for Finland (which were dissected to Natura 2000 polygons to examine their elevational variation and its relationships to topoclimatic variation) can be downloaded from the following link: https://ckan.ymparisto.fi/en/dataset/dem25_astergdem25. The coordinate system for the climate velocity data files is: ETRS-TM35FIN (EPSG: 3067) (or YKJ Finland/Finnish Uniform Coordinate System (EPSG: 2393)). Summary of the key settings and elements of the study are provided below. A detailed treatment is provided in Heikkinen et al. (2020).Code to the files (four files per each velocity layer: *.tif, *.tfw. *.ovr and *.tif.aux.xml) in the dataset: (a) Velocity of GDD with respect to RCP2.6 future climate (Fig 2a in Heikkinen et al. 2020). Name of the file: GDDRCP26.*(b) Velocity of GDD with respect to RCP4.5 future climate (Fig. 2b in Heikkinen et al. 2020). Name of the file: GDDRCP45.*(c) Velocity of GDD with respect to RCP8.5 future climate (Fig. 2c in Heikkinen et al. 2020). Name of the file: GDDRCP85.*(d) Velocity of mean January temperature with respect to RCP2.6 future climate (Fig. 2d in Heikkinen et al. 2020). Name of the file: TJanRCP26.*(e) Velocity of mean January temperature with respect to RCP4.5 future climate (Fig. 2e in Heikkinen et al. 2020). Name of the file: TJanRCP45.*(f) Velocity of mean January temperature with respect to RCP8.5 future climate (Fig. 2f in Heikkinen et al. 2020). Name of the file: TJanRCP85.*(g) Velocity of climatic water balance with respect to RCP2.6 future climate (Fig. 2g in Heikkinen et al. 2020). Name of the file: WABRCP26.*(h) Velocity of climatic water balance with respect to RCP4.5 future climate (Fig. 2h in Heikkinen et al. 2020). Name of the file: WABRCP45.*(i) Velocity of climatic water balance with respect to RCP8.5 future climate (Fig. 2i in Heikkinen et al. 2020). Name of the file: WABRCP85.*Note that velocity surfaces e and f include disappearing climate conditions.Summary of the study:Climate velocity is a generic metric which provides useful information for climate-wise conservation planning to identify regions and protected areas where climate conditions are changing most rapidly, exposing them to high rates of climate displacement (Batllori et al. 2017), causing potential carry-over impacts to community structure and ecosystem functions (Ackerly et al. 2010). Climate velocity has been typically used to assess the climatic risks for species and their populations, but velocity metrics can also be used to identify protected areas which face overall difficulties in retaining ecological conditions that promote present-day biodiversity. Earlier climate velocity assessments have focussed on the domains of the mesoclimate (resolutions of 1–100 km) or macroclimate (>100 km scales), and fine-grained (<100 m) local climatic conditions created by variation in topography ('topoclimate'; Ackerly et al. 2010; 2020) have largely been overlooked (Heikkinen et al. 2020). This omission may lead to biased exposure assessments especially in rugged terrain (Dobrowski et al. 2013; Franklin et al. 2013), as well as a limited ability to detect sites decoupled from the regional climate (Aalto et al. 2017; Lenoir et al. 2017). This study provided the first assessment of the climatic exposure risks across a national PA (Natura 2000) network based on very fine-grained velocities of three established drivers of high latitude biodiversity. The produce fine-grain climate velocity measures, 50-m resolution monthly temperature and precipitation data averaged for 1981–2010 were first developed, and based on it, the three bioclimatic variables (growing degree days, mean January temperature and annual climatic water balance) were calculated for the whole study domain. In the next phase, similar future climate surfaces were produced based on data from an ensemble of 23 global climate models, extracted from the CMIP5 archives for the years 2070–2099 and the three RCP scenarios (RCP2.6, RCP4.5 and RCP8.5)26. In the final step, climate velocities for each the 50 x 50 m grid cells were measured using climate-analog velocity method (Hamann et al. 2015) and based on the distance between climatically similar cells under the baseline and future climates.The results revealed notable spatial differences in the high velocity areas for the three bioclimatic variables, indicating contrasting exposure risks in protected areas situated in different areas. Moreover, comparisons of the 50-m baseline and future climate surfaces revealed a potential wholesale disappearance of current topoclimatic temperature conditions from almost all the studied PAs by the end of this century.Calculation of climate change velocity metrics for the three climate variablesThe overall process of calculation of climate velocities included three main steps. (1) In the first step, we developed high-resolution monthly average temperature and precipitation data averaged over the years 1981–2010 and across the study domain at a spatial resolution of 50 × 50 m. This was done by building topoclimatic models based on climate data sourced from 313 meteorological stations (European Climate Assessment and Dataset [ECA&D]) (Klok et al. 2009). Our station network and modelling domain covered the whole of Finland with an additional 100 km buffer. However, it was also extended to cover large parts of northern Sweden and Norway for areas >66.5°N, as well as selected adjacent areas in Russia (for details see Heikkinen et al. 2020). This was done to capture the present-day climate spaces in Finland which are projected to move in the future beyond the country borders but have analogous climate areas in neighbouring areas; this was done to avoid developing a large number of velocity values deemed as infinite or unknown in the data for Finland. The 50-m resolution average air temperature data were developed for the study domain using generalized additive modelling (GAM), as implemented in the R-package mgcv version 1.8–7 (R Development Core Team 2011; Wood 2011). In this modelling we utilised variables of geographical location (latitude and longitude, included as an anisotropic interaction), topography (elevation, potential incoming solar radiation, relative elevation) and water cover (sea and lake proximity), and subsequent leave-one-out cross-validation tests to assess model performance (for full process description, see Aalto et al. 2017; Heikkinen et al. 2020). The resulting topoclimate data effectively captured the physiographic effects of solar radiation and cold-air pooling.To produce gridded precipitation data, we applied global kriging interpolation to the data from 343 rain gauges from the ECA&D dataset. The interpolation was carried out using information on geographical location, topography (elevation and eastness index) and proximity to the sea and R package gstat. The eastness index was obtained from a sine-transforming aspect raster surface calculated from a 50 m × 50 m digital elevation model to capture the effect of prevailing westerly winds on the accumulated precipitation on windward slopes. The gridding was first run at a resolution of 500 × 500 m, whereafter gridded precipitation values were bilinearly interpolated into the same 50 × 50 m resolution as the air temperature data. Next, the three bioclimatic variables ((i) growing degree days (GDD, °C days) indicating the accumulated warmth during the growing season; (ii) mean January air temperature -  TJan, °C; (iii) climatic water balance - WAB, mm) were calculated for each 50 x 50 grid cell from the high-resolution gridded 1981–2010 ('baseline') climate data. Earlier research has demonstrated the ecological relevance of these three complementary variables which provide estimations of winter cold, seasonal warmth and moisture availability (Sykes et al. 1996; Luoto et al. 2006; Huntley et al. 2007, 2008). Following Carter et al. (1991), GDD was calculated as the effective temperature sum above the base temperature of 5 °C as follows:GDD5 = ∑ni (Ti - Tb),  if Ti -Tb > 5where Ti denotes the mean temperature at day i, Tb represents the base temperature, and n is the length of the summation period. However, because the daily air temperature data was not available, here the GDD was estimated using monthly data as in Araújo & Luoto (2007). The WAB is the difference between the total annual precipitation sum and the potential evapotranspiration (PET), which was estimated from the monthly air temperatures following Skov and Svenning (2004): PET = 58.93 × Tabove 0°C / 12(2) In the second step we developed data on future climates by using the climate projections from the ensemble of 23 global climate models (GCMs), derived from the Coupled Model Intercomparison Project phase 5 archives (Taylor et al. 2012). From these archives, we processed to predicted averaged changes in mean temperature and precipitation with respect to the baseline 1981–2010 for the years 2070–2099, and the three RCP scenarios (cf. Moss et al. 2010). As the Coupled Model Intercomparison Project phase 5 climate scenario data represent coarse-scale resolution data, we converted it to match our fine-resolution baseline climate data by interpolation. For this, the climate model data depicting the predicted change in mean temperatures and precipitation with respect to the baseline climate were bilinearly interpolated to the 50 × 50 m grid system, and the change predicted by the GCMs was added to the spatially detailed baseline climate data. After this, the bioclimatic variables were recalculated for each RCP scenario to allow the calculation of climate change velocities across the whole country and the Natura 2000 protected areas.(3) In the third step we developed climate change velocities for the three bioclimatic variables using the climate-analog approach (Hamann et al. 2015) where velocity is calculated by measuring the distance between present-day locations with certain climatic conditions and their future climate analogues, divided by the number of years between the two points in time. Thus, we calculated climate-analog velocities for the 50-m resolution grid climate data by measuring the distance between climatically similar grid cells for the present and future climates under RCP2.6, RCP4.5 and RCP8.5. Prior the actual climate-analog velocity measurements, the climate variable surfaces were converted from continuous values into classified variable surfaces. For this, we defined the boundary values for the variable classes so that the climatically matching grid cells had their within-class ranges as small as possible but, at the same time, avoided artefactual extreme precision. After a set of pilot reclassifications, the following within-class ranges were applied: GDD, within-class range 50 °C with 51 categories; TJan, within-class range 0.5 °C with 60 categories; WAB, within-class range 50 mm with 55 categories. Next, using the reclassified present-day and future climate surfaces the search of the minimum distances between grid cells with similar present-day and future GDD/TJan/WAB climates were executed. The search was carried out using the ArcGIS software (Desktop 10.5.1.) by employing the Euclidean distance function. The minimum distances measured for each 50-m grid cell were divided by the difference between the mean points in the two time slices, 1981–2010 and 2070–2099. The resulting 50-m resolution climate velocity surfaces for the three climate variables are provided in the zipped files included this data repository. In Heikkinen et al. (2020), these climate velocity data were employed in a series of subsequent analyses. For example, high-velocity areas ('velocity hotspots') of the three climate variables were visually compared with each other based on maps showing their 50-m resolution velocities across mainland Finland and the degree of overlap between the present-day range and projected future range of the three climate variables were investigated in each of the 5,068 Natura 2000 polygons included in the study.ReferencesAalto, J., Riihimäki, H., Meineri, E., Hylander, K., Luoto, M., 2017. Revealing topoclimatic heterogeneity using meteorological station data. International Journal of Climatology 37, 544-556.Ackerly, D.D., Loarie, S.R., Cornwell, W.K., Weiss, S.B., Hamilton, H., Branciforte, R., Kraft, N.J.B., 2010. The geography of climate change: implications for conservation biogeography. Diversity and Distributions 16, 476-487.Ackerly, D.D., Kling, M.M., Clark, M.L., Papper, P., Oldfather, M.F., Flint, A.L., Flint, L.E., 2020. Topoclimates, refugia, and biotic responses to climate change. Frontiers in Ecology and the Environment 18, 288-297.Araujo, M.B., Luoto, M., 2007. The importance of biotic interactions for modelling species distributions under climate change. Global Ecology and Biogeography 16.Batllori, E., Parisien, M.-A., Parks, S.A., Moritz, M.A., Miller, C., 2017. Potential relocation of climatic environments suggests high rates of climate displacement within the North American protection network. Global Change Biology 23, 3219-3230.Brito-Morales, I., García Molinos, J., Schoeman, D.S., Burrows, M.T., Poloczanska, E.S., Brown, C.J., Ferrier, S., Harwood, T.D., Klein, C.J., McDonald-Madden, E., Moore, P.J., Pandolfi, J.M., Watson, J.E.M., Wenger, A.S., Richardson, A.J., 2018. Climate Velocity Can Inform Conservation in a Warming World. Trends in Ecology & Evolution 33, 441-457.Carter, T.R., Porter, J.H., Parry, M.L., 1991. Climatic warming and crop potential in Europe: Prospects and uncertainties. Global Environmental Change 1, 291-312.Dobrowski, S.Z., Abatzoglou, J., Swanson, A.K., Greenberg, J.A., Mynsberge, A.R., Holden, Z.A., Schwartz, M.K., 2013. The climate velocity of the contiguous United States during the 20th century. Global Change Biology 19, 241-251.Franklin, J., Davis, F.W., Ikegami, M., Syphard, A.D., Flint, L.E., Flint, A.L., Hannah, L., 2013. Modeling plant species distributions under future climates: how fine scale do climate projections need to be? Global Change Biology 19, 473-483.Hamann, A., Roberts, D.R., Barber, Q.E., Carroll, C., Nielsen, S.E., 2015. Velocity of climate change algorithms for guiding conservation and management. Global Change Biology 21, 997-1004. Heikkinen, R.K., Leikola, N., Aalto, J., Aapala, K., Kuusela, S., Luoto, M., Virkkala, R., 2020. Fine-grained climate velocities reveal vulnerability of protected areas to climate change. Scientific Reports 10:1678.Huntley, B., Green, R.E., Collingham, Y.C., Willis, S.G., 2007. A climatic atlas of European breeding birds. Durham University, The RSPB and Lynx Edicions, Barcelona.Huntley, B., Collingham, Y.C., Willis, S.G., Green, R.E., 2008. Potential Impacts of Climatic Change on European Breeding Birds. Plos One 3.Klok, E.J., Klein Tank, A.M.G., 2009. Updated and extended European dataset of daily climate observations. International Journal of Climatology 29, 1182-1191.Lenoir, J., Hattab, T., Pierre, G., 2017. Climatic microrefugia under anthropogenic climate change: implications for species redistribution. Ecography 40, 253-266.Luoto, M., Heikkinen, R.K., Pöyry, J., Saarinen, K., 2006. Determinants of biogeographical distribution of butterflies in boreal regions. Journal of Biogeography 33, 1764-1778.Moss, R.H., Edmonds, J.A., Hibbard, K.A., Manning, M.R., Rose, S.K., van Vuuren, D.P., Carter, T.R., Emori, S., Kainuma, M., Kram, T., Meehl, G.A., Mitchell, J.F.B., Nakicenovic, N., Riahi, K., Smith, S.J., Stouffer, R.J., Thomson, A.M., Weyant, J.P., Wilbanks, T.J., 2010. The next generation of scenarios for climate change research and assessment. Nature 463, 747-756.R Development Core Team, 2011. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing).Skov, F., Svenning, J.-C., 2004. Potential impact of climatic change on the distribution of forest herbs in Europe. Ecography 27, 366-380.Sykes, M.T., Prentice, I.C., Cramer, W., 1996. A bioclimatic model for the potential distributions of north European tree species under present and future climates. Journal of Biogeography 23, 203-233.Taylor, K.E., Stouffer, R.J., Meehl, G.A., 2012. An Overview of CMIP5 and the Experiment Design. Bulletin of the American meteorological Society 93, 485-498.Wood, S.N., 2011. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society Series B 73, 3-36.

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Vegetation indices calculated from reflectance spectra collected at LTER plots at Toolik Lake, Alaska during the 2007-2019 growing seasons.

Vegetation indices calculated from reflectance spectra collected at Arctic LTER experimental plots at Toolik Lake, Alaska during the 2007-2019 growing seasons. Long term experimental plots span several different vegetation types: Heath (HTH89), Moist Acidic Tussock (MAT89 and Low Fert), Moist Non-Acidic Tussock (MNAT), Non-Acidic Non-Tussock (NANT), Shrub (SHB), and Wet Sedge (WSG). Plots are differentiated by their experimental treatment and are located in replicate blocks.Canopy reflectance is measured by hand-held spectrophotometer and several indices of interest (NDVI, EVI, EVI2, PRI, WBI, and Chlorophyll index) are calculated.

Fall 2007 plant monitoring survey -- biomass calculated from shoot height and flowering status of plants in permanent plots at GCE sampling sites 1-10

The biomass of plants surveyed in permanent plots at 10 GCE LTER sampling sites in October 2007 was estimated based on allometric relationships between biomass and shoot height and flowering status derived for each site, zone, and species in October 2002 and October 2007. Biomass was only calculated for dominant species, including Spartina alterniflora, S. cynosuroides, Juncus roemerianus, and Zizaniopsis miliacea. This data set is based on GCE plant monitoring survey data set PLT-GCEM-0801a, and allometric relationships were based on GCE data sets PLT-GCEM-0211b, PLT-GCEM-0711, and PLT-GCEM-2011.

Fall 2008 plant monitoring survey -- biomass calculated from shoot height and flowering status of plants in permanent plots at GCE sampling sites 1-10

The biomass of plants surveyed in permanent plots at 10 GCE LTER sampling sites in October 2008 was estimated based on allometric relationships between biomass and shoot height and flowering status derived for each site, zone, and species in October 2002 and October 2008. Biomass was only calculated for dominant species, including Spartina alterniflora, S. cynosuroides, Juncus roemerianus, and Zizaniopsis miliacea, as well as rarer species including Scirpus spp, Panicum spp. and Typha angustifolia. This data set is based on GCE plant monitoring survey data set PLT-GCEM-0812a, and allometric relationships were based on GCE data sets PLT-GCEM-0211b, PLT-GCEM-0711 and PLT-GCEM-2011.

Fall 2009 plant monitoring survey -- biomass calculated from shoot height and flowering status of plants in permanent plots at GCE sampling sites 1-10

The biomass of plants surveyed in permanent plots at 10 GCE LTER sampling sites in October 2009 was estimated based on allometric relationships between biomass and shoot height and flowering status derived for each site, zone, and species in October 2002 and October 2008. Biomass was only calculated for dominant species, including Spartina alterniflora, S. cynosuroides, Juncus roemerianus, and Zizaniopsis miliacea, as well as rarer species including Scirpus spp, Panicum spp. and Typha angustifolia. This data set is based on GCE plant monitoring survey data set PLT-GCEM-1004a, and allometric relationships were based on GCE data sets PLT-GCEM-0211b, PLT-GCEM-0711, and PLT-GCEM-2011.

Fall 2010 plant monitoring survey -- biomass calculated from shoot height and flowering status of plants in permanent plots at GCE sampling sites 1-10

The biomass of plants surveyed in permanent plots at 10 GCE LTER sampling sites in October 2010 was estimated based on allometric relationships between biomass and shoot height and flowering status derived for each site, zone, and species in October 2002 and October 2008. Biomass was only calculated for dominant species, including Spartina alterniflora, S. cynosuroides, Juncus roemerianus, and Zizaniopsis miliacea, as well as rarer species including Scirpus spp, Panicum spp. and Typha angustifolia. This data set is based on GCE plant monitoring survey data set PLT-GCEM-1010a, and allometric relationships were based on GCE data sets PLT-GCEM-0211b, PLT-GCEM-0711, and PLT-GCEM-2011.

Fall 2011 plant monitoring survey -- biomass calculated from shoot height and flowering status of plants in permanent plots at GCE sampling sites 1-10

The biomass of plants surveyed in permanent plots at 10 GCE LTER sampling sites in October 2011 was estimated based on allometric relationships between biomass and shoot height and flowering status derived for each site, zone, and species in October 2002 and October 2008. Biomass was only calculated for dominant species, including Spartina alterniflora, S. cynosuroides, Juncus roemerianus, and Zizaniopsis miliacea, as well as rarer species including Scirpus spp, Panicum spp. and Typha angustifolia. This data set is based on GCE plant monitoring survey data set PLT-GCEM-1112a, and allometric relationships were based on GCE data sets PLT-GCEM-0211b, PLT-GCEM-0711, and PLT-GCEM-2011.