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

Show full item record



Permalink

http://hdl.handle.net/10138/346061

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

Title: High-resolution analysis of observed thermal growing season variability over northern Europe
Author: Aalto, Juha; Pirinen, Pentti; Kauppi, Pekka E.; Rantanen, Mika; Lussana, Cristian; Lyytikainen-Saarenmaa, Paivi; Gregow, Hilppa
Contributor organization: Department of Geosciences and Geography
Department of Forest Sciences
Forest Health Group
Date: 2022-03
Language: eng
Number of pages: 17
Belongs to series: Climate dynamics : observational, theoretical and computational research on the climate system
ISSN: 0930-7575
DOI: https://doi.org/10.1007/s00382-021-05970-y
URI: http://hdl.handle.net/10138/346061
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.
Subject: 114 Physical sciences
1171 Geosciences
Peer reviewed: Yes
Rights: cc_by
Usage restriction: openAccess
Self-archived version: publishedVersion


Files in this item

Total number of downloads: Loading...

Files Size Format View
Aalto2022_Artic ... olutionAnalysisOfObser.pdf 14.98Mb PDF View/Open

This item appears in the following Collection(s)

Show full item record