2016
DOI: 10.1002/2015jd024651
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New gridded daily climatology of Finland: Permutation‐based uncertainty estimates and temporal trends in climate

Abstract: 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, a… Show more

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Cited by 143 publications
(144 citation statements)
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“…The warming has not been even. For example, between the 1940s and the 1960s the climate did not warm (Mikkonen et al 2015), but from the end of the 1960s onwards the mean daily temperatures have warmed on average by 0.3 °C per decade (Aalto et al 2016).…”
Section: The Life Cycles Of Pest Insects In Relation To Recent Climatmentioning
confidence: 99%
“…The warming has not been even. For example, between the 1940s and the 1960s the climate did not warm (Mikkonen et al 2015), but from the end of the 1960s onwards the mean daily temperatures have warmed on average by 0.3 °C per decade (Aalto et al 2016).…”
Section: The Life Cycles Of Pest Insects In Relation To Recent Climatmentioning
confidence: 99%
“…The simulation results were analysed for the near-future period and for the far-future period 2070-2099 as compared to the baseline period 1981-2010. In addition, over the baseline period 25 the soil temperature and snow depth models were ran also by using the observational Finnish gridded climate data (Aalto et al, 2016). We modelled the number of days with good bearing capacity in the forest harvesting point of view.…”
Section: Simulation Of Soil Frost and Snow Depth For Different Forestmentioning
confidence: 99%
“…Daily mean temperatures used in the model optimization were extracted from a gridded data set covering Finland (Aalto et al, 2016).…”
Section: Parametrization Of Soil Temperature Modelmentioning
confidence: 99%
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“…In our work, OI has been used as a spatial interpolation technique and the background field has been estimated from the in situ observations instead of being observation-independent information derived from numerical atmospheric models or climatology, as for the "classical" OI. Bayesian spatial interpolation schemes have been applied to precipitation in the past (Todini, 2001;Schiemann et al, 2010b;Lussana et al, 2009;Aalto et al, 2016). However, the absence of an independent background motivated us to adopt an approach inspired by the successivecorrection methods (Barnes, 1964) in the form proposed by Bratseth (1986).…”
Section: Introductionmentioning
confidence: 99%