High-resolution analysis of observed thermal growing season variability over northern Europe

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dc.contributor.author Aalto, Juha
dc.contributor.author Pirinen, Pentti
dc.contributor.author Kauppi, Pekka E.
dc.contributor.author Rantanen, Mika
dc.contributor.author Lussana, Cristian
dc.contributor.author Lyytikainen-Saarenmaa, Paivi
dc.contributor.author Gregow, Hilppa
dc.date.accessioned 2022-07-11T05:51:04Z
dc.date.available 2022-07-11T05:51:04Z
dc.date.issued 2022-03
dc.identifier.citation Aalto , J , Pirinen , P , Kauppi , P E , Rantanen , M , Lussana , C , Lyytikainen-Saarenmaa , P & Gregow , H 2022 , ' High-resolution analysis of observed thermal growing season variability over northern Europe ' , Climate dynamics : observational, theoretical and computational research on the climate system , vol. 58 , pp. 1477-1493 . https://doi.org/10.1007/s00382-021-05970-y
dc.identifier.other PURE: 169472577
dc.identifier.other PURE UUID: c96031c7-198a-4970-8c63-b41f99502e22
dc.identifier.other WOS: 000701008500001
dc.identifier.other ORCID: /0000-0003-1884-3084/work/115902756
dc.identifier.uri http://hdl.handle.net/10138/346061
dc.description.abstract Strong historical and predicted future warming over high-latitudes prompt significant effects on agricultural and forest ecosystems. Thus, there is an urgent need for spatially-detailed information of current thermal growing season (GS) conditions and their past changes. Here, we deployed a large network of weather stations, high-resolution geospatial environmental data and semi-parametric regression to model the spatial variation in multiple GS variables (i.e. beginning, end, length, degree day sum [GDDS, base temperature + 5 degrees C]) and their intra-annual variability and temporal trends in respect to geographical location, topography, water and forest cover, and urban land use variables over northern Europe. Our analyses revealed substantial spatial variability in average GS conditions (1990-2019) and consistent temporal trends (1950-2019). We showed that there have been significant changes in thermal GS towards earlier beginnings (on average 15 days over the study period), increased length (23 days) and GDDS (287 degrees C days). By using a spatial interpolation of weather station data to a regular grid we predicted current GS conditions at high resolution (100 m x 100 m) and with high accuracy (correlation >= 0.92 between observed and predicted mean GS values), whereas spatial variation in temporal trends and interannual variability were more demanding to predict. The spatial variation in GS variables was mostly driven by latitudinal and elevational gradients, albeit they were constrained by local scale variables. The proximity of sea and lakes, and high forest cover suppressed temporal trends and inter-annual variability potentially indicating local climate buffering. The produced high-resolution datasets showcased the diversity in thermal GS conditions and impacts of climate change over northern Europe. They are valuable in various forest management and ecosystem applications, and in adaptation to climate change. en
dc.format.extent 17
dc.language.iso eng
dc.relation.ispartof Climate dynamics : observational, theoretical and computational research on the climate system
dc.rights cc_by
dc.rights.uri info:eu-repo/semantics/openAccess
dc.subject 114 Physical sciences
dc.subject 1171 Geosciences
dc.title High-resolution analysis of observed thermal growing season variability over northern Europe en
dc.type Article
dc.contributor.organization Department of Geosciences and Geography
dc.contributor.organization Department of Forest Sciences
dc.contributor.organization Forest Health Group
dc.description.reviewstatus Peer reviewed
dc.relation.doi https://doi.org/10.1007/s00382-021-05970-y
dc.relation.issn 0930-7575
dc.rights.accesslevel openAccess
dc.type.version publishedVersion

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