Global climate change impacts can already be tracked in many physical and biological systems; in particular, terrestrial ecosystems provide a consistent picture of observed changes. One of the preferred indicators is phenology, the science of natural recurring events, as their recorded dates provide a high-temporal resolution of ongoing changes. Thus, numerous analyses have demonstrated an earlier onset of spring events for mid and higher latitudes and a lengthening of the growing season. However, published single-site or single-species studies are particularly open to suspicion of being biased towards predominantly reporting climate change-induced impacts. No comprehensive study or meta-analysis has so far examined the possible lack of evidence for changes or shifts at sites where no temperature change is observed. We used an enormous systematic phenological network data set of more than 125 000 observational series of 542 plant and 19 animal species in 21 European countries . Our results showed that 78% of all leafing, flowering and fruiting records advanced (30% significantly) and only 3% were significantly delayed, whereas the signal of leaf colouring/fall is ambiguous. We conclude that previously published results of phenological changes were not biased by reporting or publication predisposition: the average advance of spring/summer was 2.5 days decade À1 in Europe. Our analysis of 254 mean national time series undoubtedly demonstrates that species' phenology is responsive to temperature of the preceding

Long‐term time series of key climate variables with a relevant spatiotemporal resolution are essential for environmental science. Moreover, such spatially continuous data, based on weather observations, are commonly used in, e.g., downscaling and bias correcting of climate model simulations. Here we conducted a comprehensive spatial interpolation scheme where seven climate variables (daily mean, maximum, and minimum surface air temperatures, daily precipitation sum, relative humidity, sea level air pressure, and snow depth) were interpolated over Finland at the spatial resolution of 10 × 10 km2. More precisely, (1) we produced daily gridded time series (FMI_ClimGrid) of the variables covering the period of 1961–2010, with a special focus on evaluation and permutation‐based uncertainty estimates, and (2) we investigated temporal trends in the climate variables based on the gridded data. National climate station observations were supplemented by records from the surrounding countries, and kriging interpolation was applied to account for topography and water bodies. For daily precipitation sum and snow depth, a two‐stage interpolation with a binary classifier was deployed for an accurate delineation of areas with no precipitation or snow. A robust cross‐validation indicated a good agreement between the observed and interpolated values especially for the temperature variables and air pressure, although the effect of seasons was evident. Permutation‐based analysis suggested increased uncertainty toward northern areas, thus identifying regions with suboptimal station density. Finally, several variables had a statistically significant trend indicating a clear but locally varying signal of climate change during the last five decades.

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Boreal forests are sensitive to climatic warming, because low temperatures hold back ecosystem processes, such as the mobilization of nitrogen in soils. A greening of the boreal landscape has been observed using remote sensing, and the seasonal amplitude of CO2 in the northern hemisphere has increased, indicating warming effects on ecosystem productivity. However, field observations on responses of ecosystem productivity have been lacking on a large sub-biome scale. Here we report a significant increase in the annual growth of boreal forests in Finland in response to climatic warming, especially since 1990. This finding is obtained by linking meteorological records and forest inventory data on an area between 60° and 70° northern latitude. An additional increase in growth has occurred in response to changes in other drivers, such as forest management, nitrogen deposition and/or CO2 concentration. A similar warming impact can be expected in the entire boreal zone, where warming takes place. Given the large size of the boreal biome – more than ten million km2– important climate feedbacks are at stake, such as the future carbon balance, transpiration and albedo.

ABSTRACT:The durations of the thermal seasons and the growing season till the end of this century are inferred from projected monthly mean temperatures, separately for the SRES A2 and B1 scenarios. For the baseline period 1971-2000, we use a high-resolution observational data set covering Finland, and an average of the temperature responses simulated by 19 global climate models (GCMs) is added to the observed temperatures to obtain projections for the future. Daily climatological temperatures, needed for the determination of the onset and end dates of the seasons and the effective temperature sum, are derived from the monthly means employing a Fourier algorithm that can reproduce monthly mean temperatures perfectly.Under baseline conditions, there are four thermal seasons everywhere in Finland apart from the elevated area in northwestern Lapland. Under the A2 scenario, thermal winter will disappear in the South-western part of the country by the period 2070-2099. Elsewhere winter shortens by 2-4 months. Summer lengthens by slightly over 1 month. Intermediate seasons become longer everywhere except in northernmost Lapland. The thermal growing season lengthens in inland areas by 40-50 days, on the south-western coast even more. The effective temperature sum doubles in the north and increases 1.5-fold in the south. Conditions in Lapland would thus resemble those currently prevailing in southern Finland. Under the B1 scenario the change is smaller, especially in the second half of the century.The robustness of the findings was assessed by considering the differences between the temperature change projections of the various models. The uncertainty in the onset and termination dates was typically of the order of ±2 weeks. Regional downscaling based on regional climate model (RCM) data did not alter the main conclusions.