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38 Research products

  • European Marine Science
  • 2014-2023
  • Open Access
  • Publications
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  • Academy of Finland
  • FI
  • EU
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Laiho, Raija; Lampela, Maija; Minkkinen, Kari; Straková, Petra; +5 Authors

    We estimated fine-root biomass (FRB) and production (FRP) and their depth distribution and plant functional type (PFT) composition in four forested boreal peatland site types that varied in soil nutrient and water-table level regimes, ground vegetation and tree stand characteristics. Two were pine-dominated nutrient-poor sites (dwarf-shrub pine bog, tall-sedge pine fen) and two spruce-dominated nutrient-rich sites (Vaccinium myrtillus spruce swamp, herb-rich hardwood-spruce swamp). Measurements were done in two sites per site type: one undrained site and one site that had been drained for forestry. In each of the eight sites, we established three measurement plots. FRB was estimated by separating and visually identifying roots from soil cores extending down to 50-cm depth. The cores were taken in late August, 2016. FRP was estimated using ingrowth cores covering the same depth, and the separated roots were identified using Fourier transform infrared spectroscopy (FTIR). The ingrowth cores were incubated for two years, starting in November 2015 and ending in November 2017. Tree-stand basal area and stem volume per species, and projection cover of ground vegetation per species were determined in summer 2018. We monitored the soil water-table level and soil temperatures in 5 and 30 cm depths with dataloggers. Soil pH, bulk density, and carbon, nitrogen, phosphorus, potassium, calcium, magnesium, iron, manganese, boron, zinc, and copper concentrations were measured from peat cores extending down to 50-cm depth and taken simultaneously with the FRB cores. FRB, FRP and peat properties are presented for 10-cm depth segments. FRB, FRP and peat properties are presented for 10-cm depth segments. Peat cores were taken with a box-shaped 65 mm x 37 mm peat corer, except in the wet TP site where a 60 mm x 60 mm corer was used.

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    Authors: Mander, Ülo; Krasnova, Alisa; Escuer-Gatius, Jordi; Espenberg, Mikk; +10 Authors

    1 Study site and set-up The studied hemiboreal riparian forest is a 40-year old Filipendula type grey alder (Alnus incana (L.) Moench) forest stand grown on a former agricultural agricultural land. It is situated in the Agali Village (58o17' N; 27o17' E) in eastern Estonia within the Lake Peipsi Lowland (Varep 1964). The area is characterized by a flat relief with an average elevation of 32m a.s.l., formed from the bottom of former periglacial lake systems, it is slightly inclined (1%) towards a tributary of the Kalli River. The soil is Gleyic Luvisol. The thickness of the humus layer was 15-20 cm. The content of total carbon (TC), total nitrogen (TN), nitrate (NO3- -N), ammonia NH4+-N, Ca and Mg per dry matter in 10cm topsoil was 3.8 and 0.33 %, and 2.42, 2.89, 1487 and 283 mg kg-1, respectively, which was correspondingly 6.3, 8.3, 4.4, 3.6, 2.3, and 2.0 times more than those in 20cm deep zone. The long-term average annual precipitation of the region is 650 mm, and the average temperature is 17.0 °C in July and -6.7 °C in January. The duration of the growing season is typically 175-180 days from mid-April to October (Kupper et al. 2011). The mean height of the forest stand is 17.5 m, the mean stem diameter at breast height 15.6 cm and the growing stock 245 m3 ha−1 (based on Uri et al 2014 and Becker et al 2015). In the forest floor, the following herbs dominate: Filipendula ulmaria (L.) Maxim., Aegopodium podagraria L., Cirsium oleraceum (L.) Scop., Geum rivale L., Crepis paludosa (L.) Moench,), shrubs (Rubus idaeus L., Frangula alnus L., Daphne mezereum L.) and young trees (A. incana, Prunus padus (L.)) dominate. In moss-layer Climacium dendroides (Hedw.) F. Weber & D. Mohr, Plagiomnium spp and Rhytidiadelphus triquetrus (Hedw.) Warnst. 2 Soil flux measurements Soil fluxes were measured using 12 automatic dynamic chambers located close to each studied tree and installed in June 2017. The chambers were made from polymethyl methacrylate (Plexiglas) covered with non-transparent plastic film. Each soil chamber (volume of 0.032 m³) covered a 0.16 m² soil surface. To avoid stratification of gas inside the chamber, air with a constant flow rate of 1.8 L min-1 was circulated within a closed loop between the chamber and gas analyzer unit during the measurements by a diaphragm pump. The air sample was taken from the top of the chamber headspace and pumped back by distributing it to each side of the chamber. For the measurements, the soil chambers were closed automatically for 9 minutes each. Flushing time of the whole system with ambient air between measurement periods was 1 minute. Thus, there were approximately 12 measurements per chamber per day. A Picarro G2508 (Picarro Inc., Santa Clara, CA, USA) gas analyzer using cavity ring-down spectroscopy (CRDS) technology was used to monitor N2O gas concentrations in the frequency of approximately 1.17 measurements per second. The chambers were connected to the gas analyzer using a multiplexer. Since the 9 minutes of closing each soil chamber for measurements consisted of two minutes for stabilization the trend in the beginning and about two minutes unstable fluctuations at the end, for soil flux calculations, only 5 minutes of the linear trend of N2O concentration change has been used for soil flux calculations. After the quality checking 105,830 flux values (98.7% of total possible) of soil N2O fluxes could be used during the whole study period. 3 Stem flux measurements The tree stem fluxes were measured manually with frequency 1-2 times per week from September 2017 until December 2018. Twelve representative mature grey alder trees were selected for stem flux measurements and equipped with static closed tree stem chamber systems for stem flux measurements (Machacova et al 2016). Soil fluxes were investigated close to each selected tree. The tree chambers were installed in June 2017 in following order: at the bottom part of the tree stem (approximately 10 cm above the soil) and at 80 and 170 cm above the ground. The rectangular shape stem chambers were made of transparent plastic containers, including removable airtight lids (Lock & Lock Co Ltd, Seoul, Republic of Korea). For chamber preparation see Schindler et al. (2020). Two chambers per profile were set randomly across 180° and interconnected with tubes into one system (total volume of 0.00119 m³) covering 0.0108 m² of stem surface. A pump (model 1410VD, 12 V; Thomas GmbH, Fürstenfeldbruck, Germany) was used to homogenize the gas concentration prior to sampling. Chamber systems remained open between each sampling campaign. During 60 measurement campaigns, four gas samples (each 25 ml) were collected from each chamber system via septum in a 60 min interval: 0/60/120/180 min sequence (sampling time between 12:00 and 16:00) and stored in pre-evacuated (0.3 bar) 12 ml coated gas-tight vials (LabCo International, Ceregidion, UK). The gas samples were analysed in the laboratory at University of Tartu within a week using gas chromatograph (GC-2014; Shimadzu, Kyoto, Japan) equipped with an electron capture detector for detection of N2O and a flame ionization detector for CH4. The gas samples were injected automatically using Loftfield autosampler (Loftfield Analytics, Göttingen, Germany). For gas-chromatographical settings see Soosaar et al. (2011). 4 Soil and stem flux calculation Fluxes were quantified on a linear approach according to change of CH4 and N2O concentrations in the chamber headspace over time, using the equation according to Livingston & Hutchison (1995). Stem fluxes were quantified on a linear approach according to change of N2O concentrations in the chamber headspace over time. A data quality control was applied based on R2 values of linear fit for CO2 measurements. When the R2 value for CO2 efflux was above 0.9, the conditions inside the chamber were applicable, and the calculations for N2O gases were also accepted in spite of their R2 values. To compare the contribution of soil and stems, the stem fluxes were upscaled to hectare of ground area based on average stem diameter, tree height, stem surface area, tree density, and stand basal area estimated for each period. A cylindric shape of tree stem was assumed. To estimate average stem emissions per tree, fitted regression curves for different periods were made between the stem emissions and height of the measurements as previously done by Schindler et al. (2020). 5 Eddy covariance instrumentation Eddy-covariance system was installed on a 21 m height scaffolding tower. Fast 3-D sonic anemometer Gill HS-50 (Gill Instruments Ltd., Lymington, Hampshire, UK) was used to obtain 3 wind components. CO2 fluxes were measured using the Li-Cor 7200 analyser (Li-Cor Inc., Lincoln, NE, USA). Air was sampled synchronously with the 30 m teflon inlet tube and analyzed by a quantum cascade laser absorption spectrometer (QCLAS) (Aerodyne Research Inc., Billerica, MA, USA) for N2O concentrations. The Aerodyne QCLAS was installed in the heated and ventilated cottage near the tower base. A high-capacity free scroll vacuum pump (Agilent, Santa Clara, CA, USA) guaranteed air flow rate 15 L min-1 between the tower and gas analyzer during the measurements. Air was filtered for dust and condense water. All measurements were done at 10Hz and the gas-analyzer reported concentrations per dry air (mixing ratios). 6 Eddy-covariance flux calculation and data quality control The fluxes of N2O were calculated using the EddyPro software (v.6.0-7.0, Li-Cor) as a covariance of the gas mixing ratio with the vertical wind component over 30-minute periods. Despiking of the raw data was performed following Mauder (2013). Anemometer tilt was corrected with the double axis rotation. Linear detrending was chosen over block averaging to minimize the influence of a possible fluctuations of a gas analyser. Time lags were detected using covariance maximisation in a given time window (5±2s was chosen based on the tube length and flow rate). While WPL-correction is typically performed for the closed-path systems, we did not apply it as water correction was already performed by the Aerodyne and the software reported mixing ratios. Both low and high frequency spectral corrections were applied using fully analytic corrections (Moncrieff et al. 1997, 2004). Calculated fluxes were filtered out in case they were coming from the half-hour averaging periods with at least one of the following criteria: more than 1000 spikes, half-hourly averaged mixing ratio out of range (300-350 ppb), quality control (QC) flags higher than 7 (Foken et al, 2004). Footprint area was estimated using Kljun et al (2015) implemented in TOVI software (Li-Cor Inc.). Footprint allocation tool was implemented to flag the non-forested areas within the 90% cumulative footprint and fluxes appointed to these areas were removed from the further analysis. Storage fluxes were estimated using point concentration measurements from the eddy system, assuming the uniform change within the air column under the tower during every 30 min period (calculated in EddyPro software). In the absence of a better estimate or profile measurements, these estimates were used to correct for storage change. Total flux values that were higher than eight times the standard deviation were additionally filtered out (following Wang et al., 2013). Overall, the quality control procedures resulted in 61% data coverage. While friction velocity (u*) threshold is used to filter eddy fluxes of CO2 (Papale et al. 2006), visual inspection of the friction velocity influence on N2O fluxes demonstrated no effect. Thus, we decided not to apply it, taking into account that 1-9 QC flag system already marks the times when the turbulence is not sufficient. To obtain the continuous time-series and to enable the comparison to chamber estimates over hourly time scales, gap-filling of N2O fluxes was performed using marginal distribution sampling method implemented in ReddyProcWeb online tool (https://www.bgc-jena.mpg.de/bgi/index.php/Services/REddyProcWeb) (described in detail in Wutzler et al 2018). MATLAB (ver. 2018a-b, Mathworks Inc., Natick, MA, USA) was used for all the eddy fluxes data analysis. 7 Ancillary measurements Air temperature and relative humidity were measured within the canopy at 10m height using the HC2A-S3 - Standard Meteo Probe / RS24T (Rotronic AG, Bassersdorf, Switzerland) and Campbell CR100 data logger (Campbell Scientific Inc., Logan, UT, USA). Based on these data, dew point depression was calculated to characterise chance of fog formation within the canopy. The incoming solar radiation data were obtained from the SMEAR Estonia station located at 2 km from the study site (Noe et al 201587) using the Delta-T-SPN-1 sunshine pyranometer (Delta-T Devices Ltd., Cambridge, UK). The cloudiness ratio was calculated based on radiation data. Near-ground air temperature, soil temperature (Campbell Scientific Inc.) and soil water content sensors (ML3 ThetaProbe, Delta-T Devices, Burwell, Cambridge, UK) were installed directly on the ground and 0-10 cm soil depth close to the studied tree spots. During six campaigns from August to November 2017 composite topsoil samples were taken with a soil corer from a depth of 0-10 cm for physical and chemical analysis using standard methods (APHA-AWWA-WEF, 2005).

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    Authors: Asmala, Eero; Österholm, Peter; Virtasalo, Joonas J;

    Water samples were collected from the Laihianjoki and Sulvanjoki rivers in April 2021, using a bucket and transferred to 30 L acid-washed containers. The sampling was conducted during springtime, a few weeks after the peak flow caused by snow smelt. The collected river waters were immediately analyzed for pH, temperature, electrical conductivity, and salinity using a WTW Multiline P4 meter. The samples were then transported to the Tvärminne field station of the University of Helsinki, where laboratory experiments began within 24 hours. A large-volume bucket experiment was conducted to study the dynamics of suspended particle size distribution and associated nutrients during the transition from fresh to brackish water conditions. Artificial seawater with salinity of 66.8 g kg-1 was added to river water in an acid-washed basin, while changes in water chemistry and particle size distribution were continuously monitored using a multiparameter water quality sonde and a laser-diffraction particle size analyzer. Additionally, small-volume jar experiments were performed to collect particle samples under controlled conditions using a flocculator apparatus. The collected material was analyzed, and the filtrate was used for further analysis of colored dissolved organic matter. Triplicate samples from flocculator experiments were acidified and analyzed for metal concentrations using an inductively coupled plasma optical emission spectrometer (ICP-OES) and an inductively coupled plasma mass spectrometer (ICP-MS). Filtrate samples were measured for colored dissolved organic matter (CDOM) absorbance and fluorescence using a spectrophotometer and a spectrofluorometer, respectively. Particulate organic carbon (POC) and particulate organic-bound metals were analyzed in filter retentate samples using a mass spectrometer and ICP-MS. Total suspended matter (TSM) was determined by filtering and weighing samples. The apparent particle density was calculated by dividing TSM by volumetric concentrations measured by a laser-diffraction particle size analyzer. However, the density results are biased as particles larger than the detection limit of the instrument are not included in the analysis.

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    Authors: Bohm, Katja; Kaakinen, Anu; Stevens, Thomas; Lahaye, Yann; +5 Authors

    The data were collected for a joint detrital zircon and detrital rutile provenance study of the late Neogene aeolian Baode Red Clay, located on the northern part of the Chinese Loess Plateau. The data consist of detrital zircon U-Pb ages of the 4.04–2.64 Ma Baode Red Clay (four samples from the Pliocene Jingle Formation and one sample from the 2.64 Ma Transitional Unit), and detrital rutile trace element geochemistry of the 6.91–2.64 Ma Baode Red Clay (three samples from the Miocene Baode Formation, five samples from the Pliocene Jingle Formation, and one sample from the Transitional Unit) and 14 potential sedimentary source areas in Central-East Asia. The data were collected using Nu Plasma AttoM single collector ICP-MS (Nu Instruments Ltd., Wrexham, UK) connected to an Analyte Excite 193 ArF laser ablation system (Photon Machines, San Diego, USA) at the Geological Survey of Finland. The rutiles were analysed for Li, Mg, Al, Si, P, Ca, Sc, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Rb, Sr, Y, Zr, Nb, Mo, Sn, Sb, Ba, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, Hf, Ta, W, Pb, Th, and U. The grain size fractions of the analysed grains were mostly 30–90 μm for the Red Clay zircons and rutiles, and 20–500 μm for the potential source area rutiles.

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    Authors: Spilling, Kristian; Piiparinen, Jonna; Achterberg, Eric Pieter; Arístegui, Javier; +9 Authors

    The data is from a mesocosm experiment set up outside Lima, Peru to study the influence of upwelling of oxygen minimum zone (OMZ) water. The mesocosm bags were 2 m in diameter and extended from the surface down to 19 m depth, where the last 2 m was a conical sediment trap. Eight mesocosm bags were used and they were moored at 12.0555°S; 77.2348°W just north of Isla San Lorenzo where the water depth is ~30 m. The experiment was started 25 February 2017 by closing the mesocosm bags and were run for 50 days. Two treatments were used (water with different OMZ signature), each with four replicates. Water (100 m3) from the OMZ was collected from two locations and depths. The first was collected from 12.028323°S; 77.223603°W from 30 m depth, and the second one from 12.044333°S; 77.377583°W from 70 m depth. The original aim was to collect severe and moderate OMZ signature water (differing in e.g. nitrate concentrations) from the first and second site, respectively. This assumption was based on long-term monitoring data, however, the chemical properties (e.g. nitrate concentration) was more similar in these water masses than anticipated, rather reflecting low and very low OMZ signatures from site 1 and 2 respectively. To have a baseline of measured variables, the mesocosms where closed and environmental and biological variables were determined over 10 days. After this period, the OMZ water was added to the mesocosms in two steps on day 11 and 12 after the enclosure of the mesocosms. As the mesocosms contain a specific volume (~54 m3), the process of adding the OMZ water started with first removing water from the mesocosms. The water removed (~20 m3) was pumped out from 11-12 m depth. A similar volume of OMZ water, from both collection sites, was then pumped into four replicate mesocosms each. The OMZ water was pumped into the mesocosms moving the input hose between 14-17 m depth. The water collected at 30 m depth was pumped into mesocosms M1, M4, M5 and M8 having a low OMZ signature and water from 70 m depth into mesocosms M2, M3, M6 and M7 having a very low OMZ signature. Due a halocline at 12 m depth (see below), the added OMZ water was not immediately mixed throughout the mesocosm bag. Sampling took place every second day over a period of 50 days, and all variables were taken with an integrated water sampler (HydroBios, IWS) pre-programed to fill from 0 – 10 m depth and all samples consisted of this integrated samples from the upper 10 m. The samples were stored dark in cool boxes and brought back to the laboratory and processed right away. Sampling took place in the morning, and the samples were usually back in the laboratory around noon. Measured variables included inorganic nutrients, dissolved organic nutrients, extracellular enzyme activity: leucine aminopeptidase (LAP) and alkaline phosphatase, and the phytoplankton and bacterial community composition.

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    Authors: Voigt, Carolina; Chevrier-Dion, Charles; Marquis, Charlotte; Nesic, Zoran; +7 Authors

    This dataset includes two data tables of methane (CH4) fluxes measured in Arctic uplands. Dataset 1 contains CH4 fluxes measured at high temporal resolution (hourly fluxes) collected over two snow-free seasons (June–August; 2019, 2021) at Trail Valley Creek, an Arctic tundra site in the Western Canadian Arctic. Fluxes were measured with automated chambers installed in replication of six at three individual landcover vegetation units (Lichen, Shrub, Tussock) within dwarf-shrub dominated tundra. Site meteorological data are provided with the flux data at hourly resolution. Dataset 2 includes campaign-based, manual chamber measurements at sites displaying net CH4 uptake. These manual measurements were conducted during the growing season at typical, well-drained upland sites, which included, besides Trail Valley Creek, three additional sites in the Canadian and European Arctic (Havikpak Creek, Scotty Creek, Kilpisjärvi). Besides CH4 flux observations, dataset 2 contains measured greenhouse gas concentration profiles of CH4, carbon dioxide (CO2) and nitrous oxide (N2O) at 2 cm, 5 cm, 10 cm, and 20 cm soil depths, as well as site meteorological data. While wetlands are known CH4 emitters, drier arctic and boreal uplands may act as sinks of atmospheric CH4. The scope of the study and this dataset is to improve the spatial and temporal coverage of low CH4 emitting and sites displaying net CH4 uptake across the Arctic. Both datasets are meant as supplement to the published study, where further, detailed information on site conditions and methodology can be found.

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    Authors: A. Rutgersson; A. Rutgersson; E. Kjellström; E. Kjellström; +16 Authors

    A natural hazard is a naturally occurring extreme event that has a negative effect on people and society or the environment. Natural hazards may have severe implications for human life and can potentially generate economic losses and damage ecosystems. A better understanding of their major causes, probability of occurrence, and consequences enables society to be better prepared to save human lives as well as to invest in adaptation options. Natural hazards related to climate change are identified as one of the Grand Challenges in the Baltic Sea region. Here, we summarize existing knowledge about extreme events in the Baltic Sea region with a focus on the past 200 years as well as on future climate scenarios. The events considered here are the major hydro-meteorological events in the region and include wind storms, extreme waves, high and low sea levels, ice ridging, heavy precipitation, sea-effect snowfall, river floods, heat waves, ice seasons, and drought. We also address some ecological extremes and the implications of extreme events for society (phytoplankton blooms, forest fires, coastal flooding, offshore infrastructure, and shipping). Significant knowledge gaps are identified, including the response of large-scale atmospheric circulation to climate change and also concerning specific events, for example, the occurrence of marine heat waves and small-scale variability in precipitation. Suggestions for future research include the further development of high-resolution Earth system models and the potential use of methodologies for data analysis (statistical methods and machine learning). With respect to the expected impacts of climate change, changes are expected for sea level, extreme precipitation, heat waves and phytoplankton blooms (increase), and cold spells and severe ice winters (decrease). For some extremes (drying, river flooding, and extreme waves), the change depends on the area and time period studied.

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    Earth System Dynamics
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      Earth System Dynamics
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    Authors: Virkkala​​​​​​​, A.; Natali, S.; Rogers, B.; Watts, J.; +60 Authors

    Past efforts to synthesize and quantify the magnitude and change in carbon dioxide (CO2) fluxes in terrestrial ecosystems across the rapidly warming Arctic–boreal zone (ABZ) have provided valuable information but were limited in their geographical and temporal coverage. Furthermore, these efforts have been based on data aggregated over varying time periods, often with only minimal site ancillary data, thus limiting their potential to be used in large-scale carbon budget assessments. To bridge these gaps, we developed a standardized monthly database of Arctic–boreal CO2 fluxes (ABCflux) that aggregates in situ measurements of terrestrial net ecosystem CO2 exchange and its derived partitioned component fluxes: gross primary productivity and ecosystem respiration. The data span from 1989 to 2020 with over 70 supporting variables that describe key site conditions (e.g., vegetation and disturbance type), micrometeorological and environmental measurements (e.g., air and soil temperatures), and flux measurement techniques. Here, we describe these variables, the spatial and temporal distribution of observations, the main strengths and limitations of the database, and the potential research opportunities it enables. In total, ABCflux includes 244 sites and 6309 monthly observations; 136 sites and 2217 monthly observations represent tundra, and 108 sites and 4092 observations represent the boreal biome. The database includes fluxes estimated with chamber (19 % of the monthly observations), snow diffusion (3 %) and eddy covariance (78 %) techniques. The largest number of observations were collected during the climatological summer (June–August; 32 %), and fewer observations were available for autumn (September–October; 25 %), winter (December–February; 18 %), and spring (March–May; 25 %). ABCflux can be used in a wide array of empirical, remote sensing and modeling studies to improve understanding of the regional and temporal variability in CO2 fluxes and to better estimate the terrestrial ABZ CO2 budget. ABCflux is openly and freely available online (Virkkala et al., 2021b, https://doi.org/10.3334/ORNLDAAC/1934).

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    Authors: Virkkala, A.-M. (Anna-Maria); Natali, S. M. (Susan M.); Rogers, B. M. (Brendan M.); Watts, J. D. (Jennifer D.); +60 Authors

    Abstract Past efforts to synthesize and quantify the magnitude and change in carbon dioxide (CO₂) fluxes in terrestrial ecosystems across the rapidly warming Arctic–boreal zone (ABZ) have provided valuable information but were limited in their geographical and temporal coverage. Furthermore, these efforts have been based on data aggregated over varying time periods, often with only minimal site ancillary data, thus limiting their potential to be used in large-scale carbon budget assessments. To bridge these gaps, we developed a standardized monthly database of Arctic–boreal CO₂ fluxes (ABCflux) that aggregates in situ measurements of terrestrial net ecosystem CO₂ exchange and its derived partitioned component fluxes: gross primary productivity and ecosystem respiration. The data span from 1989 to 2020 with over 70 supporting variables that describe key site conditions (e.g., vegetation and disturbance type), micrometeorological and environmental measurements (e.g., air and soil temperatures), and flux measurement techniques. Here, we describe these variables, the spatial and temporal distribution of observations, the main strengths and limitations of the database, and the potential research opportunities it enables. In total, ABCflux includes 244 sites and 6309 monthly observations; 136 sites and 2217 monthly observations represent tundra, and 108 sites and 4092 observations represent the boreal biome. The database includes fluxes estimated with chamber (19 % of the monthly observations), snow diffusion (3 %) and eddy covariance (78 %) techniques. The largest number of observations were collected during the climatological summer (June–August; 32 %), and fewer observations were available for autumn (September–October; 25 %), winter (December–February; 18 %), and spring (March–May; 25 %). ABCflux can be used in a wide array of empirical, remote sensing and modeling studies to improve understanding of the regional and temporal variability in CO₂ fluxes and to better estimate the terrestrial ABZ CO₂ budget.

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    Authors: J. Schneider; K. Höhler; P. Heikkilä; J. Keskinen; +21 Authors

    Ice-nucleating particles (INPs) trigger the formation of cloud ice crystals in the atmosphere. Therefore, they strongly influence cloud microphysical and optical properties and precipitation and the life cycle of clouds. Improving weather forecasting and climate projection requires an appropriate formulation of atmospheric INP concentrations. This remains challenging as the global INP distribution and variability depend on a variety of aerosol types and sources, and neither their short-term variability nor their long-term seasonal cycles are well covered by continuous measurements. Here, we provide the first year-long set of observations with a pronounced INP seasonal cycle in a boreal forest environment. Besides the observed seasonal cycle in INP concentrations with a minimum in wintertime and maxima in early and late summer, we also provide indications for a seasonal variation in the prevalent INP type. We show that the seasonal dependency of INP concentrations and prevalent INP types is most likely driven by the abundance of biogenic aerosol. As current parameterizations do not reproduce this variability, we suggest a new mechanistic description for boreal forest environments which considers the seasonal variation in INP concentrations. For this, we use the ambient air temperature measured close to the ground at 4.2 m height as a proxy for the season, which appears to affect the source strength of biogenic emissions and, thus, the INP abundance over the boreal forest. Furthermore, we provide new INP parameterizations based on the Ice Nucleation Active Surface Site (INAS) approach, which specifically describes the ice nucleation activity of boreal aerosols particles prevalent in different seasons. Our results characterize the boreal forest as an important but variable INP source and provide new perspectives to describe these new findings in atmospheric models.

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    Authors: Laiho, Raija; Lampela, Maija; Minkkinen, Kari; Straková, Petra; +5 Authors

    We estimated fine-root biomass (FRB) and production (FRP) and their depth distribution and plant functional type (PFT) composition in four forested boreal peatland site types that varied in soil nutrient and water-table level regimes, ground vegetation and tree stand characteristics. Two were pine-dominated nutrient-poor sites (dwarf-shrub pine bog, tall-sedge pine fen) and two spruce-dominated nutrient-rich sites (Vaccinium myrtillus spruce swamp, herb-rich hardwood-spruce swamp). Measurements were done in two sites per site type: one undrained site and one site that had been drained for forestry. In each of the eight sites, we established three measurement plots. FRB was estimated by separating and visually identifying roots from soil cores extending down to 50-cm depth. The cores were taken in late August, 2016. FRP was estimated using ingrowth cores covering the same depth, and the separated roots were identified using Fourier transform infrared spectroscopy (FTIR). The ingrowth cores were incubated for two years, starting in November 2015 and ending in November 2017. Tree-stand basal area and stem volume per species, and projection cover of ground vegetation per species were determined in summer 2018. We monitored the soil water-table level and soil temperatures in 5 and 30 cm depths with dataloggers. Soil pH, bulk density, and carbon, nitrogen, phosphorus, potassium, calcium, magnesium, iron, manganese, boron, zinc, and copper concentrations were measured from peat cores extending down to 50-cm depth and taken simultaneously with the FRB cores. FRB, FRP and peat properties are presented for 10-cm depth segments. FRB, FRP and peat properties are presented for 10-cm depth segments. Peat cores were taken with a box-shaped 65 mm x 37 mm peat corer, except in the wet TP site where a 60 mm x 60 mm corer was used.

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    Authors: Mander, Ülo; Krasnova, Alisa; Escuer-Gatius, Jordi; Espenberg, Mikk; +10 Authors

    1 Study site and set-up The studied hemiboreal riparian forest is a 40-year old Filipendula type grey alder (Alnus incana (L.) Moench) forest stand grown on a former agricultural agricultural land. It is situated in the Agali Village (58o17' N; 27o17' E) in eastern Estonia within the Lake Peipsi Lowland (Varep 1964). The area is characterized by a flat relief with an average elevation of 32m a.s.l., formed from the bottom of former periglacial lake systems, it is slightly inclined (1%) towards a tributary of the Kalli River. The soil is Gleyic Luvisol. The thickness of the humus layer was 15-20 cm. The content of total carbon (TC), total nitrogen (TN), nitrate (NO3- -N), ammonia NH4+-N, Ca and Mg per dry matter in 10cm topsoil was 3.8 and 0.33 %, and 2.42, 2.89, 1487 and 283 mg kg-1, respectively, which was correspondingly 6.3, 8.3, 4.4, 3.6, 2.3, and 2.0 times more than those in 20cm deep zone. The long-term average annual precipitation of the region is 650 mm, and the average temperature is 17.0 °C in July and -6.7 °C in January. The duration of the growing season is typically 175-180 days from mid-April to October (Kupper et al. 2011). The mean height of the forest stand is 17.5 m, the mean stem diameter at breast height 15.6 cm and the growing stock 245 m3 ha−1 (based on Uri et al 2014 and Becker et al 2015). In the forest floor, the following herbs dominate: Filipendula ulmaria (L.) Maxim., Aegopodium podagraria L., Cirsium oleraceum (L.) Scop., Geum rivale L., Crepis paludosa (L.) Moench,), shrubs (Rubus idaeus L., Frangula alnus L., Daphne mezereum L.) and young trees (A. incana, Prunus padus (L.)) dominate. In moss-layer Climacium dendroides (Hedw.) F. Weber & D. Mohr, Plagiomnium spp and Rhytidiadelphus triquetrus (Hedw.) Warnst. 2 Soil flux measurements Soil fluxes were measured using 12 automatic dynamic chambers located close to each studied tree and installed in June 2017. The chambers were made from polymethyl methacrylate (Plexiglas) covered with non-transparent plastic film. Each soil chamber (volume of 0.032 m³) covered a 0.16 m² soil surface. To avoid stratification of gas inside the chamber, air with a constant flow rate of 1.8 L min-1 was circulated within a closed loop between the chamber and gas analyzer unit during the measurements by a diaphragm pump. The air sample was taken from the top of the chamber headspace and pumped back by distributing it to each side of the chamber. For the measurements, the soil chambers were closed automatically for 9 minutes each. Flushing time of the whole system with ambient air between measurement periods was 1 minute. Thus, there were approximately 12 measurements per chamber per day. A Picarro G2508 (Picarro Inc., Santa Clara, CA, USA) gas analyzer using cavity ring-down spectroscopy (CRDS) technology was used to monitor N2O gas concentrations in the frequency of approximately 1.17 measurements per second. The chambers were connected to the gas analyzer using a multiplexer. Since the 9 minutes of closing each soil chamber for measurements consisted of two minutes for stabilization the trend in the beginning and about two minutes unstable fluctuations at the end, for soil flux calculations, only 5 minutes of the linear trend of N2O concentration change has been used for soil flux calculations. After the quality checking 105,830 flux values (98.7% of total possible) of soil N2O fluxes could be used during the whole study period. 3 Stem flux measurements The tree stem fluxes were measured manually with frequency 1-2 times per week from September 2017 until December 2018. Twelve representative mature grey alder trees were selected for stem flux measurements and equipped with static closed tree stem chamber systems for stem flux measurements (Machacova et al 2016). Soil fluxes were investigated close to each selected tree. The tree chambers were installed in June 2017 in following order: at the bottom part of the tree stem (approximately 10 cm above the soil) and at 80 and 170 cm above the ground. The rectangular shape stem chambers were made of transparent plastic containers, including removable airtight lids (Lock & Lock Co Ltd, Seoul, Republic of Korea). For chamber preparation see Schindler et al. (2020). Two chambers per profile were set randomly across 180° and interconnected with tubes into one system (total volume of 0.00119 m³) covering 0.0108 m² of stem surface. A pump (model 1410VD, 12 V; Thomas GmbH, Fürstenfeldbruck, Germany) was used to homogenize the gas concentration prior to sampling. Chamber systems remained open between each sampling campaign. During 60 measurement campaigns, four gas samples (each 25 ml) were collected from each chamber system via septum in a 60 min interval: 0/60/120/180 min sequence (sampling time between 12:00 and 16:00) and stored in pre-evacuated (0.3 bar) 12 ml coated gas-tight vials (LabCo International, Ceregidion, UK). The gas samples were analysed in the laboratory at University of Tartu within a week using gas chromatograph (GC-2014; Shimadzu, Kyoto, Japan) equipped with an electron capture detector for detection of N2O and a flame ionization detector for CH4. The gas samples were injected automatically using Loftfield autosampler (Loftfield Analytics, Göttingen, Germany). For gas-chromatographical settings see Soosaar et al. (2011). 4 Soil and stem flux calculation Fluxes were quantified on a linear approach according to change of CH4 and N2O concentrations in the chamber headspace over time, using the equation according to Livingston & Hutchison (1995). Stem fluxes were quantified on a linear approach according to change of N2O concentrations in the chamber headspace over time. A data quality control was applied based on R2 values of linear fit for CO2 measurements. When the R2 value for CO2 efflux was above 0.9, the conditions inside the chamber were applicable, and the calculations for N2O gases were also accepted in spite of their R2 values. To compare the contribution of soil and stems, the stem fluxes were upscaled to hectare of ground area based on average stem diameter, tree height, stem surface area, tree density, and stand basal area estimated for each period. A cylindric shape of tree stem was assumed. To estimate average stem emissions per tree, fitted regression curves for different periods were made between the stem emissions and height of the measurements as previously done by Schindler et al. (2020). 5 Eddy covariance instrumentation Eddy-covariance system was installed on a 21 m height scaffolding tower. Fast 3-D sonic anemometer Gill HS-50 (Gill Instruments Ltd., Lymington, Hampshire, UK) was used to obtain 3 wind components. CO2 fluxes were measured using the Li-Cor 7200 analyser (Li-Cor Inc., Lincoln, NE, USA). Air was sampled synchronously with the 30 m teflon inlet tube and analyzed by a quantum cascade laser absorption spectrometer (QCLAS) (Aerodyne Research Inc., Billerica, MA, USA) for N2O concentrations. The Aerodyne QCLAS was installed in the heated and ventilated cottage near the tower base. A high-capacity free scroll vacuum pump (Agilent, Santa Clara, CA, USA) guaranteed air flow rate 15 L min-1 between the tower and gas analyzer during the measurements. Air was filtered for dust and condense water. All measurements were done at 10Hz and the gas-analyzer reported concentrations per dry air (mixing ratios). 6 Eddy-covariance flux calculation and data quality control The fluxes of N2O were calculated using the EddyPro software (v.6.0-7.0, Li-Cor) as a covariance of the gas mixing ratio with the vertical wind component over 30-minute periods. Despiking of the raw data was performed following Mauder (2013). Anemometer tilt was corrected with the double axis rotation. Linear detrending was chosen over block averaging to minimize the influence of a possible fluctuations of a gas analyser. Time lags were detected using covariance maximisation in a given time window (5±2s was chosen based on the tube length and flow rate). While WPL-correction is typically performed for the closed-path systems, we did not apply it as water correction was already performed by the Aerodyne and the software reported mixing ratios. Both low and high frequency spectral corrections were applied using fully analytic corrections (Moncrieff et al. 1997, 2004). Calculated fluxes were filtered out in case they were coming from the half-hour averaging periods with at least one of the following criteria: more than 1000 spikes, half-hourly averaged mixing ratio out of range (300-350 ppb), quality control (QC) flags higher than 7 (Foken et al, 2004). Footprint area was estimated using Kljun et al (2015) implemented in TOVI software (Li-Cor Inc.). Footprint allocation tool was implemented to flag the non-forested areas within the 90% cumulative footprint and fluxes appointed to these areas were removed from the further analysis. Storage fluxes were estimated using point concentration measurements from the eddy system, assuming the uniform change within the air column under the tower during every 30 min period (calculated in EddyPro software). In the absence of a better estimate or profile measurements, these estimates were used to correct for storage change. Total flux values that were higher than eight times the standard deviation were additionally filtered out (following Wang et al., 2013). Overall, the quality control procedures resulted in 61% data coverage. While friction velocity (u*) threshold is used to filter eddy fluxes of CO2 (Papale et al. 2006), visual inspection of the friction velocity influence on N2O fluxes demonstrated no effect. Thus, we decided not to apply it, taking into account that 1-9 QC flag system already marks the times when the turbulence is not sufficient. To obtain the continuous time-series and to enable the comparison to chamber estimates over hourly time scales, gap-filling of N2O fluxes was performed using marginal distribution sampling method implemented in ReddyProcWeb online tool (https://www.bgc-jena.mpg.de/bgi/index.php/Services/REddyProcWeb) (described in detail in Wutzler et al 2018). MATLAB (ver. 2018a-b, Mathworks Inc., Natick, MA, USA) was used for all the eddy fluxes data analysis. 7 Ancillary measurements Air temperature and relative humidity were measured within the canopy at 10m height using the HC2A-S3 - Standard Meteo Probe / RS24T (Rotronic AG, Bassersdorf, Switzerland) and Campbell CR100 data logger (Campbell Scientific Inc., Logan, UT, USA). Based on these data, dew point depression was calculated to characterise chance of fog formation within the canopy. The incoming solar radiation data were obtained from the SMEAR Estonia station located at 2 km from the study site (Noe et al 201587) using the Delta-T-SPN-1 sunshine pyranometer (Delta-T Devices Ltd., Cambridge, UK). The cloudiness ratio was calculated based on radiation data. Near-ground air temperature, soil temperature (Campbell Scientific Inc.) and soil water content sensors (ML3 ThetaProbe, Delta-T Devices, Burwell, Cambridge, UK) were installed directly on the ground and 0-10 cm soil depth close to the studied tree spots. During six campaigns from August to November 2017 composite topsoil samples were taken with a soil corer from a depth of 0-10 cm for physical and chemical analysis using standard methods (APHA-AWWA-WEF, 2005).

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    Authors: Asmala, Eero; Österholm, Peter; Virtasalo, Joonas J;

    Water samples were collected from the Laihianjoki and Sulvanjoki rivers in April 2021, using a bucket and transferred to 30 L acid-washed containers. The sampling was conducted during springtime, a few weeks after the peak flow caused by snow smelt. The collected river waters were immediately analyzed for pH, temperature, electrical conductivity, and salinity using a WTW Multiline P4 meter. The samples were then transported to the Tvärminne field station of the University of Helsinki, where laboratory experiments began within 24 hours. A large-volume bucket experiment was conducted to study the dynamics of suspended particle size distribution and associated nutrients during the transition from fresh to brackish water conditions. Artificial seawater with salinity of 66.8 g kg-1 was added to river water in an acid-washed basin, while changes in water chemistry and particle size distribution were continuously monitored using a multiparameter water quality sonde and a laser-diffraction particle size analyzer. Additionally, small-volume jar experiments were performed to collect particle samples under controlled conditions using a flocculator apparatus. The collected material was analyzed, and the filtrate was used for further analysis of colored dissolved organic matter. Triplicate samples from flocculator experiments were acidified and analyzed for metal concentrations using an inductively coupled plasma optical emission spectrometer (ICP-OES) and an inductively coupled plasma mass spectrometer (ICP-MS). Filtrate samples were measured for colored dissolved organic matter (CDOM) absorbance and fluorescence using a spectrophotometer and a spectrofluorometer, respectively. Particulate organic carbon (POC) and particulate organic-bound metals were analyzed in filter retentate samples using a mass spectrometer and ICP-MS. Total suspended matter (TSM) was determined by filtering and weighing samples. The apparent particle density was calculated by dividing TSM by volumetric concentrations measured by a laser-diffraction particle size analyzer. However, the density results are biased as particles larger than the detection limit of the instrument are not included in the analysis.

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    Authors: Bohm, Katja; Kaakinen, Anu; Stevens, Thomas; Lahaye, Yann; +5 Authors

    The data were collected for a joint detrital zircon and detrital rutile provenance study of the late Neogene aeolian Baode Red Clay, located on the northern part of the Chinese Loess Plateau. The data consist of detrital zircon U-Pb ages of the 4.04–2.64 Ma Baode Red Clay (four samples from the Pliocene Jingle Formation and one sample from the 2.64 Ma Transitional Unit), and detrital rutile trace element geochemistry of the 6.91–2.64 Ma Baode Red Clay (three samples from the Miocene Baode Formation, five samples from the Pliocene Jingle Formation, and one sample from the Transitional Unit) and 14 potential sedimentary source areas in Central-East Asia. The data were collected using Nu Plasma AttoM single collector ICP-MS (Nu Instruments Ltd., Wrexham, UK) connected to an Analyte Excite 193 ArF laser ablation system (Photon Machines, San Diego, USA) at the Geological Survey of Finland. The rutiles were analysed for Li, Mg, Al, Si, P, Ca, Sc, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Rb, Sr, Y, Zr, Nb, Mo, Sn, Sb, Ba, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, Hf, Ta, W, Pb, Th, and U. The grain size fractions of the analysed grains were mostly 30–90 μm for the Red Clay zircons and rutiles, and 20–500 μm for the potential source area rutiles.

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    Authors: Spilling, Kristian; Piiparinen, Jonna; Achterberg, Eric Pieter; Arístegui, Javier; +9 Authors

    The data is from a mesocosm experiment set up outside Lima, Peru to study the influence of upwelling of oxygen minimum zone (OMZ) water. The mesocosm bags were 2 m in diameter and extended from the surface down to 19 m depth, where the last 2 m was a conical sediment trap. Eight mesocosm bags were used and they were moored at 12.0555°S; 77.2348°W just north of Isla San Lorenzo where the water depth is ~30 m. The experiment was started 25 February 2017 by closing the mesocosm bags and were run for 50 days. Two treatments were used (water with different OMZ signature), each with four replicates. Water (100 m3) from the OMZ was collected from two locations and depths. The first was collected from 12.028323°S; 77.223603°W from 30 m depth, and the second one from 12.044333°S; 77.377583°W from 70 m depth. The original aim was to collect severe and moderate OMZ signature water (differing in e.g. nitrate concentrations) from the first and second site, respectively. This assumption was based on long-term monitoring data, however, the chemical properties (e.g. nitrate concentration) was more similar in these water masses than anticipated, rather reflecting low and very low OMZ signatures from site 1 and 2 respectively. To have a baseline of measured variables, the mesocosms where closed and environmental and biological variables were determined over 10 days. After this period, the OMZ water was added to the mesocosms in two steps on day 11 and 12 after the enclosure of the mesocosms. As the mesocosms contain a specific volume (~54 m3), the process of adding the OMZ water started with first removing water from the mesocosms. The water removed (~20 m3) was pumped out from 11-12 m depth. A similar volume of OMZ water, from both collection sites, was then pumped into four replicate mesocosms each. The OMZ water was pumped into the mesocosms moving the input hose between 14-17 m depth. The water collected at 30 m depth was pumped into mesocosms M1, M4, M5 and M8 having a low OMZ signature and water from 70 m depth into mesocosms M2, M3, M6 and M7 having a very low OMZ signature. Due a halocline at 12 m depth (see below), the added OMZ water was not immediately mixed throughout the mesocosm bag. Sampling took place every second day over a period of 50 days, and all variables were taken with an integrated water sampler (HydroBios, IWS) pre-programed to fill from 0 – 10 m depth and all samples consisted of this integrated samples from the upper 10 m. The samples were stored dark in cool boxes and brought back to the laboratory and processed right away. Sampling took place in the morning, and the samples were usually back in the laboratory around noon. Measured variables included inorganic nutrients, dissolved organic nutrients, extracellular enzyme activity: leucine aminopeptidase (LAP) and alkaline phosphatase, and the phytoplankton and bacterial community composition.

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    Authors: Voigt, Carolina; Chevrier-Dion, Charles; Marquis, Charlotte; Nesic, Zoran; +7 Authors

    This dataset includes two data tables of methane (CH4) fluxes measured in Arctic uplands. Dataset 1 contains CH4 fluxes measured at high temporal resolution (hourly fluxes) collected over two snow-free seasons (June–August; 2019, 2021) at Trail Valley Creek, an Arctic tundra site in the Western Canadian Arctic. Fluxes were measured with automated chambers installed in replication of six at three individual landcover vegetation units (Lichen, Shrub, Tussock) within dwarf-shrub dominated tundra. Site meteorological data are provided with the flux data at hourly resolution. Dataset 2 includes campaign-based, manual chamber measurements at sites displaying net CH4 uptake. These manual measurements were conducted during the growing season at typical, well-drained upland sites, which included, besides Trail Valley Creek, three additional sites in the Canadian and European Arctic (Havikpak Creek, Scotty Creek, Kilpisjärvi). Besides CH4 flux observations, dataset 2 contains measured greenhouse gas concentration profiles of CH4, carbon dioxide (CO2) and nitrous oxide (N2O) at 2 cm, 5 cm, 10 cm, and 20 cm soil depths, as well as site meteorological data. While wetlands are known CH4 emitters, drier arctic and boreal uplands may act as sinks of atmospheric CH4. The scope of the study and this dataset is to improve the spatial and temporal coverage of low CH4 emitting and sites displaying net CH4 uptake across the Arctic. Both datasets are meant as supplement to the published study, where further, detailed information on site conditions and methodology can be found.

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    Authors: A. Rutgersson; A. Rutgersson; E. Kjellström; E. Kjellström; +16 Authors

    A natural hazard is a naturally occurring extreme event that has a negative effect on people and society or the environment. Natural hazards may have severe implications for human life and can potentially generate economic losses and damage ecosystems. A better understanding of their major causes, probability of occurrence, and consequences enables society to be better prepared to save human lives as well as to invest in adaptation options. Natural hazards related to climate change are identified as one of the Grand Challenges in the Baltic Sea region. Here, we summarize existing knowledge about extreme events in the Baltic Sea region with a focus on the past 200 years as well as on future climate scenarios. The events considered here are the major hydro-meteorological events in the region and include wind storms, extreme waves, high and low sea levels, ice ridging, heavy precipitation, sea-effect snowfall, river floods, heat waves, ice seasons, and drought. We also address some ecological extremes and the implications of extreme events for society (phytoplankton blooms, forest fires, coastal flooding, offshore infrastructure, and shipping). Significant knowledge gaps are identified, including the response of large-scale atmospheric circulation to climate change and also concerning specific events, for example, the occurrence of marine heat waves and small-scale variability in precipitation. Suggestions for future research include the further development of high-resolution Earth system models and the potential use of methodologies for data analysis (statistical methods and machine learning). With respect to the expected impacts of climate change, changes are expected for sea level, extreme precipitation, heat waves and phytoplankton blooms (increase), and cold spells and severe ice winters (decrease). For some extremes (drying, river flooding, and extreme waves), the change depends on the area and time period studied.

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    Earth System Dynamics
    Article . 2022
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      Earth System Dynamics
      Article . 2022
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    Authors: Virkkala​​​​​​​, A.; Natali, S.; Rogers, B.; Watts, J.; +60 Authors

    Past efforts to synthesize and quantify the magnitude and change in carbon dioxide (CO2) fluxes in terrestrial ecosystems across the rapidly warming Arctic–boreal zone (ABZ) have provided valuable information but were limited in their geographical and temporal coverage. Furthermore, these efforts have been based on data aggregated over varying time periods, often with only minimal site ancillary data, thus limiting their potential to be used in large-scale carbon budget assessments. To bridge these gaps, we developed a standardized monthly database of Arctic–boreal CO2 fluxes (ABCflux) that aggregates in situ measurements of terrestrial net ecosystem CO2 exchange and its derived partitioned component fluxes: gross primary productivity and ecosystem respiration. The data span from 1989 to 2020 with over 70 supporting variables that describe key site conditions (e.g., vegetation and disturbance type), micrometeorological and environmental measurements (e.g., air and soil temperatures), and flux measurement techniques. Here, we describe these variables, the spatial and temporal distribution of observations, the main strengths and limitations of the database, and the potential research opportunities it enables. In total, ABCflux includes 244 sites and 6309 monthly observations; 136 sites and 2217 monthly observations represent tundra, and 108 sites and 4092 observations represent the boreal biome. The database includes fluxes estimated with chamber (19 % of the monthly observations), snow diffusion (3 %) and eddy covariance (78 %) techniques. The largest number of observations were collected during the climatological summer (June–August; 32 %), and fewer observations were available for autumn (September–October; 25 %), winter (December–February; 18 %), and spring (March–May; 25 %). ABCflux can be used in a wide array of empirical, remote sensing and modeling studies to improve understanding of the regional and temporal variability in CO2 fluxes and to better estimate the terrestrial ABZ CO2 budget. ABCflux is openly and freely available online (Virkkala et al., 2021b, https://doi.org/10.3334/ORNLDAAC/1934).

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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ GFZ German Research ...arrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/