Tykkylumen alueellinen esiintyminen Suomessa kahden laskentamenetelmän perusteella

TutkimusselosteSeloste julkaisusta: Lehtonen, I., Hoppula, P., Pirinen, P. & Gregow, H. 2014. Modelling crown snow loads in Finland: a comparison of two methods. Silva Fennica 48(3), article id 1120

Large Impacts of Climatic Warming on Growth of Boreal Forests since 1960

Peer reviewe

Integration of questionnaire-based risk factors improves polygenic risk scores for human coronary heart disease and type 2 diabetes

Max Tamlander et al. combine polygenic risk scores and clinical assessments to improve prediction of coronary artery disease and type 2 diabetes in European cohorts. Taken together, their results provide a useful method for preliminary cardiometabolic risk assessment in patients. Large-scale biobank initiatives and commercial repositories store genomic data collected from millions of individuals, and tools to leverage the rapidly growing pool of health and genomic data in disease prevention are needed. Here, we describe the derivation and validation of genomics-enhanced risk tools for two common cardiometabolic diseases, coronary heart disease and type 2 diabetes. Data used for our analyses include the FinnGen study (N = 309,154) and the UK Biobank project (N = 343,672). The risk tools integrate contemporary genome-wide polygenic risk scores with simple questionnaire-based risk factors, including demographic, lifestyle, medication, and comorbidity data, enabling risk calculation across resources where genome data is available. Compared to routinely used clinical risk scores for coronary heart disease and type 2 diabetes prevention, the risk tools show at least equivalent risk discrimination, improved risk reclassification (overall net reclassification improvements ranging from 3.7 [95% CI 2.8-4.6] up to 6.2 [4.6-7.8]), and capacity to be improved even further with standard lipid and blood pressure measurements. Without the need for blood tests or evaluation by a health professional, the risk tools provide a powerful yet simple method for preliminary cardiometabolic risk assessment for individuals with genome data available.Peer reviewe

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

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.Peer reviewe

Development of climate change scenarios for Latvia for the period until the year 2100

This report examines climatic changes projected for Latvia during the 21st century. Climate projections are based on a wide ensemble of state-of-the-art CMIP5 global climate models; that set of models was utilized in compiling the 5th Assessment Report of the Intergovernmental Panel on Climate Change. Projections have been elaborated separately for three greenhouse gas cenarios, the RCP2.6 scenario representing small, RCP4.5 medium and RCP8.5 large emissions. By the late 21st century, the following changes (expressed relative to the mean of the period 1971-2000) are projected: • In winter, mean temperatures are projected to increase by 1-4 °C under RCP2.6, 2-6 °C under RCP4.5 and 4-9 °C under RCP8.5. In summer, anticipated warming is weaker: 1-3 °C under RCP2.6, 1-4 °C under RCP4.5 and 2-7 °C under RCP8.5. In winter, warming appears to be somewhat larger in the eastern part of the country while in summer the geographical differences are small. • Diurnal temperature range would diminish in winter by 0-50 % and incident solar radiation by 0-30 %. In summer, changes in these quantities are most likely positive but fairly small. • Mean winter precipitation increases by 0-20 % under RCP2.6, 0-30 % under RCP4.5 and 10-50 % under RCP8.5. In summer, the sign of change is uncertain, but in southern Latvia it is somewhat more likely that precipitation decreases slightly rather than increases. • Ice days (with a maximum temperature below zero) become substantially less frequent while the count of summer days (maximum temperature above 25 °C) increases. • Thermal growing season would lengthen by up to two months and the degree day sum would nearly double (under RCP8.5). • According to the best estimate (multi-model mean), wind speeds would remain nearly unchanged throughout the year. Even so, scatter among the individual model projections is large, and in winter even changes larger than ± 20% are possible. When studying projections for a less distant future, the sign of change is the same as what is projected for the late 21st century but the magnitude is smaller. The above uncertainty intervals of projected changes reflect mainly the inter-model differences but the contribution of natural unforced variability has also been taken into account. In the course of the project, several data files have been delivered into Latvia to be used for additional analyses. These files include, for instance, the time series of 30-year running monthly mean changes and bias-corrected daily model output, both given for five climate variables and represented on a 10 x 10 km grid

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

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.Peer reviewe