Browsing by Subject "1171 Geosciences"

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  • Mäkelä, Samppa (2018)
    The objective of this study is to develop a method of appraising rock aggregate resources, using open data and open source tools. The availability of aggregates in Finland is mostly determined by competing land use and restrictions on extraction. Therefore, it is important to determine the extent of available resources, especially near areas of high demand. The study area consists of the 14 municipalities in the Helsinki metropolitan area, a total of 3841 km(2). The data used are open access, provided by the Geological Survey of Finland and the National Land Survey. These are combined in a GIS to identify locations where extraction of aggregates is possible. Geology, limitations and the highest and lowest point of possible extraction are determined. These are used to estimate the available resources and locate the economically feasible sites. Data used include a digital elevation model and layers on geology and land use. The results show that competing land use has sterilized most aggregate locations in the area. Remaining locations are concentrated on the edges. However, some potential sites remain. Field evaluations and comparison to previous studies show that the method has potential in evaluating remaining resources and directing further study for prospective production areas. The model is fast in coarsely determining aggregate volume. It is highly suitable for focusing expert fieldwork. Land use in the area continues to sterilize new locations. To avoid economic and ecological damage, a plan should be implemented for securing this resource. This may include the reserving of locations, reducing use, checking legislation on production and recycling used aggregates.
  • Väkevä, Lauri Sakari Oleksi; Koivisto, Emilia Anna-Liisa; Hillers, Gregor; Chamarczuk, Michal; Malinowski, Michal (Institute of Seismology, University of Helsinki, 2018)
  • Kauti, Tuomas; Skyttä, Pietari; Koivisto, Emilia; Savolainen, Mikko (Luleå University of Technology, 2019)
  • Qi, Lu; Vogel, Alexander L.; Esmaeilirad, Sepideh; Cao, Liming; Zheng, Jing; Jaffrezo, Jean-Luc; Fermo, Paola; Kasper-Giebl, Anne; Dällenbach, Kaspar; Chen, Mindong; Ge, Xinlei; Baltensperger, Urs; Prevot, Andre S. H.; Slowik, Jay G. (2020)
    The aerosol mass spectrometer (AMS), combined with statistical methods such as positive matrix factorization (PMF), has greatly advanced the quantification of primary organic aerosol (POA) sources and total secondary organic aerosol (SOA) mass. However, the use of thermal vaporization and electron ionization yields extensive thermal decomposition and ionization-induced fragmentation, which limit chemical information needed for SOA source apportionment. The recently developed extractive electrospray ionization time-of-flight mass spectrometer (EESI-TOF) provides mass spectra of the organic aerosol fraction with a linear response to mass and no thermal decomposition or ionization-induced fragmentation. However, the costs and operational requirements of online instruments make their use impractical for long-term or spatially dense monitoring applications. This challenge was overcome for AMS measurements by measuring re-nebulized water extracts from ambient filter samples. Here, we apply the same strategy for EESI-TOF measurements of 1 year of 24 h filter samples collected approximately every fourth day throughout 2013 at an urban site. The nebulized water extracts were measured simultaneously with an AMS. The application of positive matrix factorization (PMF) to EESI-TOF spectra resolved seven factors, which describe water-soluble OA: less and more aged biomass burning aerosol (LABB(EESI) and MABB(EESI), respectively), cigarette-smoke-related organic aerosol, primary biological organic aerosol, biogenic secondary organic aerosol, and a summer mixed oxygenated organic aerosol factor. Seasonal trends and relative contributions of the EESI-TOF OA sources were compared with AMS source apportionment factors, measured water-soluble ions, cellulose, and meteorological data. Cluster analysis was utilized to identify key factor-specific ions based on PMF. Both LABB and MABB contribute strongly during winter. LABB is distinguished by very high signals from C6H10O5 (levoglucosan and isomers) and C8H12O6, whereas MABB is characterized by a large number of CxHyOz and CxHyOzN species of two distinct populations: one with low H : C and high O : C and the other with high H : C and low O : C. Two oxygenated summertime SOA sources were attributed to terpene-derived biogenic SOA, a major summertime aerosol source in central Europe. Furthermore, a primary biological organic aerosol factor was identified, which was dominated by plant-derived fatty acids and correlated with free cellulose. The cigarette-smoke-related factor contained a high contribution of nicotine and high abundance of organic nitrate ions with low m/z.
  • Arppe, Laura; Kurki, Eija; Wooller, Matthew J.; Luoto, Tomi P.; Zajaczkowski, Marek; Ojala, Antti E. K. (2017)
    The oxygen isotope composition of chironomid head capsules in a sediment core spanning the past 5500 years from Lake Svartvatnet in southern Spitsbergen was used to reconstruct the oxygen isotope composition of lake water (O-18(lw)) and local precipitation. The O-18(lw) values display shifts from the baseline variability consistent with the timing of recognized historical climatic episodes, such as the Roman Warm Period, the Dark Ages Cold Period and the Little Ice Age'. The highest values of the record, ca. 3 parts per thousand above modern O-18(lw) values, occur at ca. 1900-1800 cal. yr BP. Three negative excursions increasing in intensity toward the present, at 3400-3200, 1250-1100, and 350-50 cal. yr BP, are tentatively linked to roughly synchronous episodes of increased glacier activity and general cold spells around the northern North Atlantic. Their manifestation in the Svartvatnet O-18(lw) record not only testify to the sensitivity and potential of high Arctic lacustrine O-18(chir) records in tracking terrestrial climate evolution but also highlight nonlinear dynamics within the northern North Atlantic hydroclimatic system. The Little Ice Age' period at 350-50 cal. yr BP displays a remarkable 8-9 parts per thousand drop in O-18(lw) values, construed to predominantly represent significantly decreased winter temperatures during a period of increased seasonal differences and extended sea ice cover inducing changes in moisture source regions.
  • Gibbons, S. J.; Kvaerna, Tormod; Tiira, T.; Kozlovskaya, Elena (2020)
    'Precision seismology' encompasses a set of methods which use differential measurements of time-delays to estimate the relative locations of earthquakes and explosions. Delay-times estimated from signal correlations often allow far more accurate estimates of one event location relative to another than is possible using classical hypocentre determination techniques. Many different algorithms and software implementations have been developed and different assumptions and procedures can often result in significant variability between different relative event location estimates. We present a Ground Truth (GT) dataset of 55 military surface explosions in northern Finland in 2007 that all took place within 300 m of each other. The explosions were recorded with a high signal-to-noise ratio to distances of about 2 degrees, and the exceptional waveform similarity between the signals from the different explosions allows for accurate correlation-based time-delay measurements. With exact coordinates for the explosions, we are able to assess the fidelity of relative location estimates made using any location algorithm or implementation. Applying double-difference calculations using two different 1-D velocity models for the region results in hypocentre-to-hypocentre distances which are too short and it is clear that the wavefield leaving the source region is more complicated than predicted by the models. Using the GT event coordinates, we are able to measure the slowness vectors associated with each outgoing ray from the source region. We demonstrate that, had such corrections been available, a significant improvement in the relative location estimates would have resulted. In practice we would of course need to solve for event hypocentres and slowness corrections simultaneously, and significant work will be needed to upgrade relative location algorithms to accommodate uncertainty in the form of the outgoing wavefield. We present this data set, together with GT coordinates, raw waveforms for all events on six regional stations, and tables of time-delay measurements, as a reference benchmark by which relative location algorithms and software can be evaluated.
  • Brugnara, Y.; Auchmann, R.; Broennimann, S.; Allan, R. J.; Auer, I.; Barriendos, M.; Bergstrom, H.; Bhend, J.; Brazdil, R.; Compo, G. P.; Cornes, R. C.; Dominguez-Castro, F.; van Engelen, A. F. V.; Filipiak, J.; Holopainen, J.; Jourdain, S.; Kunz, M.; Luterbacher, J.; Maugeri, M.; Mercalli, L.; Moberg, A.; Mock, C. J.; Pichard, G.; Reznckova, L.; van der Schrier, G.; Slonosky, V.; Ustrnul, Z.; Valente, M. A.; Wypych, A.; Yin, X. (2015)
    The eruption of Mount Tambora (Indonesia) in April 1815 is the largest documented volcanic eruption in history. It is associated with a large global cooling during the following year, felt particularly in parts of Europe and North America, where the year 1816 became known as the "year without a summer". This paper describes an effort made to collect surface meteorological observations from the early instrumental period, with a focus on the years of and immediately following the eruption (1815-1817). Although the collection aimed in particular at pressure observations, correspondent temperature observations were also recovered. Some of the series had already been described in the literature, but a large part of the data, recently digitised from original weather diaries and contemporary magazines and newspapers, is presented here for the first time. The collection puts together more than 50 sub-daily series from land observatories in Europe and North America and from ships in the tropics. The pressure observations have been corrected for temperature and gravity and reduced to mean sea level. Moreover, an additional statistical correction was applied to take into account common error sources in mercury barometers. To assess the reliability of the corrected data set, the variance in the pressure observations is compared with modern climatologies, and single observations are used for synoptic analyses of three case studies in Europe. All raw observations will be made available to the scientific community in the International Surface Pressure Databank.
  • Oswald, R.; Ermel, M.; Hens, K.; Novelli, A.; Ouwersloot, H. G.; Paasonen, Pauli; Petäjä, Tuukka; Sipilä, Mikko; Keronen, Petri; Bäck, Jaana; Konigstedt, R.; Beygi, Z. Hosaynali; Fischer, H.; Bohn, B.; Kubistin, D.; Harder, H.; Martinez, M.; Williams, J.; Hoffmann, T.; Trebs, I.; Soergel, M. (2015)
  • Peltoniemi, J. I.; Gritsevich, M.; Markkanen, J.; Hakala, T.; Suomalainen, J.; Zubko, N.; Wilkman, O.; Muinonen, Karri (2020)
  • Silva, S. J.; Heald, C. L.; Ravela, S.; Mammarella, I.; Munger, J. William (2019)
    The loss of ozone to terrestrial and aquatic systems, known as dry deposition, is a highly uncertain process governed by turbulent transport, interfacial chemistry, and plant physiology. We demonstrate the value of using Deep Neural Networks (DNN) in predicting ozone dry deposition velocities. We find that a feedforward DNN trained on observations from a coniferous forest site (Hyytiala, Finland) can predict hourly ozone dry deposition velocities at a mixed forest site (Harvard Forest, Massachusetts) more accurately than modern theoretical models, with a reduction in the normalized mean bias (0.05 versus similar to 0.1). The same DNN model, when driven by assimilated meteorology at 2 degrees x 2.5 degrees spatial resolution, outperforms the Wesely scheme as implemented in the GEOS-Chem model. With more available training data from other climate and ecological zones, this methodology could yield a generalizable DNN suitable for global models. Plain Language Summary Ozone in the lower atmosphere is a toxic pollutant and greenhouse gas. In this work, we use a machine learning technique known as deep learning, to simulate the loss of ozone to Earth's surface. We show that our deep learning simulation of this loss process outperforms existing traditional models and demonstrate the opportunity for using machine learning to improve our understanding of the chemical composition of the atmosphere.
  • Laj, Paolo; Bigi, Alessandro; Rose, Clemence; Andrews, Elisabeth; Myhre, Cathrine Lund; Coen, Martine Collaud; Lin, Yong; Wiedensohler, Alfred; Schulz, Michael; Ogren, John A.; Fiebig, Markus; Gliss, Jonas; Mortier, Augustin; Pandolfi, Marco; Petäjä, Tuukka; Kim, Sang-Woo; Aas, Wenche; Putaud, Jean-Philippe; Mayol-Bracero, Olga; Keywood, Melita; Labrador, Lorenzo; Aalto, Pasi; Ahlberg, Erik; Alados Arboledas, Lucas; Alastuey, Andres; Andrade, Marcos; Artinano, Begona; Ausmeel, Stina; Arsov, Todor; Asmi, Eija; Backman, John; Baltensperger, Urs; Bastian, Susanne; Bath, Olaf; Beukes, Johan Paul; Brem, Benjamin T.; Bukowiecki, Nicolas; Conil, Sebastien; Couret, Cedric; Day, Derek; Dayantolis, Wan; Degorska, Anna; Eleftheriadis, Konstantinos; Fetfatzis, Prodromos; Favez, Olivier; Flentje, Harald; Gini, Maria I.; Gregoric, Asta; Gysel-Beer, Martin; Hallar, A. Gannet; Hand, Jenny; Hoffer, Andras; Hueglin, Christoph; Hooda, Rakesh K.; Hyvärinen, Antti; Kalapov, Ivo; Kalivitis, Nikos; Kasper-Giebl, Anne; Kim, Jeong Eun; Kouvarakis, Giorgos; Kranjc, Irena; Krejci, Radovan; Kulmala, Markku; Labuschagne, Casper; Lee, Hae-Jung; Lihavainen, Heikki; Lin, Neng-Huei; Loeschau, Gunter; Luoma, Krista; Marinoni, Angela; Dos Santos, Sebastiao Martins; Meinhardt, Frank; Merkel, Maik; Metzger, Jean-Marc; Mihalopoulos, Nikolaos; Nhat Anh Nguyen,; Ondracek, Jakub; Perez, Noemi; Perrone, Maria Rita; Petit, Jean-Eudes; Picard, David; Pichon, Jean-Marc; Pont, Veronique; Prats, Natalia; Prenni, Anthony; Reisen, Fabienne; Romano, Salvatore; Sellegri, Karine; Sharma, Sangeeta; Schauer, Gerhard; Sheridan, Patrick; Sherman, James Patrick; Schuetze, Maik; Schwerin, Andreas; Sohmer, Ralf; Sorribas, Mar; Steinbacher, Martin; Sun, Junying; Titos, Gloria; Toczko, Barbara; Tuch, Thomas; Tulet, Pierre; Tunved, Peter; Vakkari, Ville; Velarde, Fernando; Velasquez, Patricio; Villani, Paolo; Vratolis, Sterios; Wang, Sheng-Hsiang; Weinhold, Kay; Weller, Rolf; Yela, Margarita; Yus-Diez, Jesus; Zdimal, Vladimir; Zieger, Paul; Zikova, Nadezda (2020)
    Aerosol particles are essential constituents of the Earth's atmosphere, impacting the earth radiation balance directly by scattering and absorbing solar radiation, and indirectly by acting as cloud condensation nuclei. In contrast to most greenhouse gases, aerosol particles have short atmospheric residence times, resulting in a highly heterogeneous distribution in space and time. There is a clear need to document this variability at regional scale through observations involving, in particular, the in situ near-surface segment of the atmospheric observation system. This paper will provide the widest effort so far to document variability of climate-relevant in situ aerosol properties (namely wavelength dependent particle light scattering and absorption coefficients, particle number concentration and particle number size distribution) from all sites connected to the Global Atmosphere Watch network. High-quality data from almost 90 stations worldwide have been collected and controlled for quality and are reported for a reference year in 2017, providing a very extended and robust view of the variability of these variables worldwide. The range of variability observed worldwide for light scattering and absorption coefficients, single-scattering albedo, and particle number concentration are presented together with preliminary information on their long-term trends and comparison with model simulation for the different stations. The scope of the present paper is also to provide the necessary suite of information, including data provision procedures, quality control and analysis, data policy, and usage of the ground-based aerosol measurement network. It delivers to users of the World Data Centre on Aerosol, the required confidence in data products in the form of a fully characterized value chain, including uncertainty estimation and requirements for contributing to the global climate monitoring system.
  • Kaufman, Darrell S.; McKay, Nicholas P.; Routson, Cody; Erb, Michael; Davis, Basil A. S.; Heiri, Oliver; Jaccard, Samuel; Tierney, Jessica; Dätwyler, Christoph; Axford, Yarrow; Brussel, Thomas; Cartapanis, Olivier; Chase, Brian M.; Dawson, Andria; de Vernal, Anne; Engels, Stefan; Jonkers, Lukas; Marsicek, Jeremiah; Moffa-Sánchez, Paola; Morrill, Carrie; Orsi, Anais; Rehfeld, Kira; Saunders, Krystyna; Sommer, Philipp S.; Thomas, Elizabeth K.; Tonello, Marcela S.; Toth, Monika; Vachula, Richard; Andreev, Andrei A; Bertrand, Sebastien; Biskaborn, Boris; Bringue, Manuel; Brooks, Stephen J.; Caniupán, Magaly; Chevalier, Manuel; Cwynar, Les C.; Emile-Geay, Julien; Fegyveresi, John; Feurdean, Angelica; Finsinger, Walter; Fortin, Marie-Claude; Foster, Louise; Fox, Mathew; Gajewski, Konrad; Grosjean, Martin; Hausmann, Sonja; Heinrichs, Markus; Holmes, Naomi; Ilyashuk, Boris; Ilyashuk, Elena; Juggins, Steve; Khider, Deborah; Koinig, Karin A.; Langdon, Peter; Larocque-Tobler, Isabelle; Li, Jianyong; Lotter, Andre F.; Luoto, Tomi P.; Mackay, Anson W.; Magyari, Eniko; Malevich, Steven; Mark, Bryan; Massaferro, Julieta; Montade, Vincent; Nazarova, Larisa; Novenko, Elena Y; Pařil, Petr; Pearson, Emma J.; Peros, Matthew; Pienitz, Reinhard; Plociennik, Mateusz; Porinchu, David F.; Potito, Aaron; Rees, Andrew B. H.; Reinemann, Scott; Roberts, Stephen J.; Rolland, Nicolas; Salonen, J. Sakari; Self, Angela E.; Seppä, Heikki; Shala, Shyhrete; St-Jacques, Jeannine-Marie; Stenni, Barbara; Syrykh, Liudmila; Tarrats, Pol; Taylor, Karen; van den Bos, Valerie; Velle, Gaute; Wahl, Eugene; Walker, Ian; Wilmhurst, Janet; Zhang, Enlou; Zhilich, Snezhana (2020)
  • Fülöp, Ludovic; Jussila, Vilho; Aapasuo, Riina Maria; Vuorinen, Tommi Antton Tapani; Mäntyniemi, Päivi Birgitta (2020)
    We propose a ground-motion prediction equation (GMPE) for probabilistic seismic hazard analysis of nuclear installations in Finland. We collected and archived the acceleration recordings of 77 earthquakes from seismic stations on very hard rock (VHR, i.e., the shear-wave velocity in the upper 30 m of the geological profile = 2800 m/s according to the definition used in the nuclear industry) in Finland and Sweden since 2006 and computed the corresponding response spectra important for engineering evaluation. We augmented the narrow magnitude range of the local data by a subset of VHR recordings of 33 earthquakes from the Next Generation Attenuation for Central and Eastern North America (CENA) (NGA-East) database, mainly from eastern Canada. We adapted the backbone curves of the G16 equation proposed by Graizer (2016) for CENA. After the calibration, we evaluated the accuracy of the median prediction and the random error. We conclude that the GMPE developed can be used for predicting ground motions in Fennoscandia. Because of compatibility with the original G16 backbone curve and comparisons with the NGA-East GMPEs, we estimate that the formulation proposed is valid on VHR over the range of 2
  • Warnock, Jonathan; Andren, Elinor; Juggins, Steve; Lewis, Jonathan; Ryves, David B.; Andren, Thomas; Weckstrom, Kaarina (2020)
    The large‐scale shifts in the salinity of the Baltic Sea over the Holocene are well understood and have been comprehensively documented using sedimentary proxy records. More recent work has focused on understanding how past salinity fluctuations have affected other ecological parameters (e.g. primary productivity, nutrient content) of the Baltic basin, and salinity changes over key events and over short time scales are still not well understood. The International Ocean Drilling Program Expedition 347 cored the Baltic basin in order to collect basin‐wide environmental records through a glacial–interglacial cycle. Site M0059 is located in the Little Belt between the Baltic Sea and the Atlantic Ocean. A composite splice section from Site M0059 was analysed at a decadal resolution to study changes in salinity, nutrient conditions and other surface water column parameters based on changes in diatom assemblages and on quantitative diatom‐based salinity inferences. A mesotrophic slightly brackish assemblage is seen in the lowermost analysed depths, corresponding to 7800–7500 cal. a BP . An increase in salinity and nutrient content of the water column leads into a meso‐eutrophic brackish phase. The observed salinity increase is rapid, lasting from 7500 to 7150 cal. a BP . Subsequently, the Little Belt becomes oligotrophic and is dominated by tychopelagic diatoms from c . 7100 to c . 3900 cal. a BP . This interval contains some of the highest salinities observed followed by diatom assemblages similar to those of the Northern Atlantic Ocean, composed primarily of cosmopolitan open ocean marine diatoms. A return to tychopelagic productivity is seen from 3850 to 980 cal. a BP . Anthropogenic eutrophication is detected in the last 300 years of the record, which intensifies in the uppermost sediments. These results represent the first decadally resolved record in the region and provide new insight into the transition to a brackish basin and subsequent ecological development.
  • Firozjaei, Mohammad Karimi; Sedighi, Amir; Firozjaei, Hamzeh Karimi; Kiavarz, Majid; Homaee, Mehdi; Arsanjani, Jamal Jokar; Makki, Mohsen; Naimi, Babak; Alavipanah, Seyed Kazem (2021)
    Mining activities and associated actions cause land-use/land-cover (LULC) changes across the world. The objective of this study were to evaluate the historical impacts of mining activities on surface biophysical characteristics, and for the first time, to predict the future changes in pattern of vegetation cover and land surface temperature (LST). In terms of the utilized data, satellite images of Landsat, and meteorological data of Sungun mine in Iran, Athabasca oil sands in Canada, Singrauli coalfield in India and Hambach mine in Germany, were used over the period of 1989-2019. In the first step, the spectral bands of Landsat images were employed to extract historical LULC changes in the study areas based on the homogeneity distance classification algorithm (HDCA). Thereafter, a CA-Markov model was used to predict the future of LULC changes based on the historical changes. In addition, LST and vegetation cover maps were calculated using the single channel algorithm, and the normalized difference vegetation index (NDVI), respectively. In the second step, the trends of LST and NDVI variations in different LULC change types and over different time periods were investigated. Finally, a CA-Markov model was used to predict the LST and NDVI maps and the trend of their variations in future. The results indicated that the forest and green space cover was reduced from 9.95 in 1989 to 5.9 Km(2) in 2019 for Sungun mine, from 42.14 in 1999 to 33.09 Km(2) in 2019 for Athabasca oil sands, from 231.46 in 1996 to 263.95 Km(2) in 2016 for Singrauli coalfield, and from 180.38 in 1989 to 133.99 Km(2) in 2017 for Hambach mine, as a result of expansion and development of of mineral activities. Our findings about Sungun revealed that the areal coverage of forest and green space will decrease to 15% of the total study area by 2039, resulting in reduction of the mean NDVI by almost 0.06 and increase of mean standardized LST from 0.52 in 2019 to 0.61 in 2039. our results further indicate that for Athabasca oil sands (Singrauli coalfield, Hambach mine), the mean values of standardized LST and NDVI will change from 0.5 (0.44 and 0.4) and 0.38 (0.38, 0.35) in 2019 (2016, 2017) to 0.57 (0.5, 0.47) and 0.33 (0.32, 0.28), in 2039 (2036, 2035), respectively. This can be mainly attributed to the increasing mining activities in the past as well as future years. The discussion and conclusions presented in this study can be of interest to local planners, policy makers, and environmentalists in order to observe the damages brought to the environment and the society in a larger picture.
  • Oksanen, Otto; Zliobaite, Indre; Saarinen, Juha; Lawing, A. Michelle; Fortelius, Mikael (2019)
    Aim The links between geo- and biodiversity, postulated by Humboldt, can now be made quantitative. Species are adapted to their environments and interact with their environments by having pertinent functional traits. We aim to improve global ecometric models using functional traits for estimating palaeoclimate and apply models to Pleistocene fauna for palaeoclimate interpretation. Location Global at present day, Pleistocene of Europe for fossil data analysis. Taxa Artiodactyla, Perissodactyla, Proboscidea and Primates. Methods We quantify functional traits of large mammal communities and develop statistical models linking trait distributions to local climate at present day. We apply these models to the fossil record, survey functional traits, and quantitatively estimate climates of the past. This approach to analysing functional relationships between faunal communities and their environments is called ecometrics. Results and main conclusions Here, we present new global ecometric models for estimating mean annual and minimum temperature from dental traits of present day mammalian communities. We also present refined models for predicting net primary productivity. Using dental ecometric models, we produce palaeoclimate estimates for 50 Pleistocene fossil localities in Europe and show that the estimates are consistent with trends derived from other proxies, especially for minimum temperatures, which we hypothesize to be ecologically limiting. Our new temperature models allow us to trace the distribution of freezing and non-freezing ecosystems in the recent past, opening new perspectives on the evolution of cold-adaptive biota as the Pleistocene cooling progressed.
  • Howett, Peter James; Salonen, Veli-Pekka; Hyttinen, Outi Sanna Maria; Korkka-Niemi, Kirsti Inkeri; Moreau, Julien (2015)
  • Uotila, Petteri; Siponen, Joula; Rinne, Eero; Tietsche, Steffen (2021)
    Decadal changes in sea-ice thickness are one of the most visible signs of climate variability and change. To gain a comprehensive understanding of mechanisms involved, long time series, preferably with good uncertainty estimates, are needed. Importantly, the development of accurate predictions of sea ice in the Arctic requires good observational products. To assist this, a new sea-ice thickness product by ESA Climate Change Initiative (CCI) is compared to a set of five ocean reanalysis (ECCO-V4r4, GLORYS12V1, ORAS5 and PIOMAS). The CCI product is based on two satellite altimetry missions, CryoSat-2 and ENVISAT, which are combined to the longest continuous satellite altimetry time series of Arctic-wide sea-ice thickness, 2002–2017. The CCI product performs well in the validation of the reanalyses: overall root-mean-square difference (RMSD) between monthly sea-ice thickness from CCI and the reanalyses ranges from 0.4–1.2 m. The differences are a sum of reanalysis biases, such as incorrect physics or forcing, as well as uncertainties in satellite altimetry, such as the snow climatology used in the thickness retrieval. The CCI and reanalysis basin-scale sea-ice volumes have a good match in terms of year-to-year variability and long-term trends but rather different monthly mean climatologies. These findings provide a rationale to construct a multi-decadal sea-ice volume time series for the Arctic Ocean and its sub-basins from 1990–2019 by adjusting the ocean reanalyses ensemble toward CCI observations. Such a time series, including its uncertainty estimate, provides new insights to the evolution of the Arctic sea-ice volume during the past 30 years.
  • Tang, Zhipeng; Adhikari, Hari; Pellikka, Petri; Heiskanen, Janne (2021)
    Combined with increasing computing ability, the free and open access to Landsat archive has enabled the changes on the Earth’s surface to be monitored for almost 50 years. However, due to missing observations that result from clouds, cloud shadows, and scan line corrector failure, the Landsat data record is neither a continuous nor consistent time series. We present a new gap-filling method, Missing Observation Prediction based on Spectral-Temporal Metrics (MOPSTM), which uses spectral-temporal metrics computed from Landsat one-year time series and the k-Nearest Neighbor (k-NN) regression. Herein, we demonstrate the performance of MOPSTM by using five, nearly cloud-free, full scene Landsat images from Kenya, Finland, Germany, the USA, and China. Cloud masks from the images with extensive cloud cover were used to simulate large-area gaps, with the highest value we tested being 92% of missing data. The gap-filling accuracy was assessed quantitatively considering all five sites and different land use/land cover types, and the MOPSTM algorithm performed better than the spectral angle-mapper based spatiotemporal similarity (SAMSTS) gap-filling algorithm. The mean RMSE values of MOPSTM were 0.010, 0.012, 0.025, 0.012, and 0.018 for the five sites, while those of SAMSTS were 0.011, 0.017, 0.038, 0.014, and 0.023, respectively. Furthermore, MOPSTM had mean coefficient of determination (R2) values of 0.90, 0.86, 0.78, 0.92, and 0.89, which were higher than those for SAMSTS (0.84, 0.75, 0.55, 0.89, and 0.83). The performance of MOPSTM was not considerably affected by image gap sizes as images ranging from gap sizes of 51% of the image all the way to 92% of the image yielded similar gap-filling accuracy. Also, MOPSTM does not require local parametertuning except for the k values in the k-NN regression, and it can make a gap-free image from any acquisition date. MOPSTM provides a new spectral-temporal approach to generate the gap-free imagery for typical Landsat applications, such as land use, land cover, and forest monitoring.