Mapping the annual cycle of temperature in Iceland

Björnsson, Halldór; Jónsson, Trausti; Gylfadóttir, Sigríður Sif; Ólason, Einar

doi:10.1127/0941-2948/2007/0175

Cited by 24 publications

(36 citation statements)

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“…When applied to temperature distributions, this method is found most reliable during the summer period (which was applied in this work). Bjornsson et al. (2007) investigated the accuracy of the maps created and estimated an error of ±1 °C spatially (not to be confused with a temporal trend of 1 °C, see below).…”

Section: Methodsmentioning

confidence: 99%

“…The method used to create the maps is 'residual kriging', which is often used in climatological studies (e.g. Phillips, Dolph & Marks, 1992;Ninyerola, Pons & Roure, 2000;Ustrnul & Czekierda, 2005;Ustrnul, 2006;Bjornsson et al, 2007;Alsamamra et al, 2009). It builds on the assumption that the distribution of temperature can be divided into one deterministic and one stochastic component, representing large-scale and small-scale features, respectively.…”

Section: Climate Analysesmentioning

confidence: 99%

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Climate‐driven range retraction of an Arctic freshwater crustacean

et al. 2012

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Summary 1. Species responses to global warming are predicted to be manifest as poleward and upward extension of species ranges, whereas cold‐adapted species experience range retractions. We report on recent range retraction of a freshwater crustacean, the Arctic fairy shrimp Branchinecta paludosa (Branchiopoda, Anostraca) from alpine ponds of southern Norway, a southernmost extension of its otherwise arctic range. 2. The species was mapped during two separate surveys, in 1970 and 2011. In 1970, it occurred in numerous ponds from the tree line at 900 m altitude to high alpine sites at 1500 m. Re‐sampling of the same ponds 41 years later revealed extinction of populations from ponds along the lowest 200 m of its altitudinal range. 3. Reconstruction of summer temperatures for the periods 1965–70 and 2005–10 revealed a thermal increase for the period, corresponding to a c. 200‐m upward shift of the local isotherms. More specifically, the number of warm summer days had doubled in the lowest 200 m of the region, which might be particularly detrimental for the species. 4. The extinctions at lower altitudes were not compensated for by corresponding upward colonisations. The range retraction of B. paludosa is possibly associated with oxygen stress and hypoxia, induced by increased water temperatures.

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Section: Methodsmentioning

confidence: 99%

Section: Climate Analysesmentioning

confidence: 99%

Climate‐driven range retraction of an Arctic freshwater crustacean

et al. 2012

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“…Finally, climatological studies such as Björnsson et al . () are strongly linked with monthly means and seasonal forecasting is also within the framework of months.…”

Section: Methodsmentioning

confidence: 99%

Persistence of observed air temperatures in Iceland

Degenhardt

Ólafsson

2018

Intl Journal of Climatology

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A large data set from 40 weather stations in Iceland is explored for persistence in monthly mean temperatures. There are great seasonal and regional variations in the persistence. Extremely high values of correlation (r > 0.8) of temperatures with subsequent months are found. These values are higher than reported elsewhere in the scientific literature. The highest values are found in coastal regions in the summer, while in the early winter there is overall little correlation. In general, there are two distinct maxima in the temperature correlations, one in late winter/early spring and one in the summer. In most seasons, there is greater correlation at the coast than inland. The high correlations are linked to snow melt, persistence in sea surface temperatures, weak winds and strong static stability. Remarkably low correlations reveal a negative feedback process: A warm May leads to less snow in inland regions, which favours a cold sea breeze in coastal regions in June. In regions of high correlation, the persistence is indeed useful for subseasonal forecasting of mean temperatures in late winter/spring and summer.

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“…Another, very frequently used variable is a distance from the sea (WHITE 1979;BJORNSSON et al 2007;BOI et al 2011;JOLY et al 2011) or distance from other major bodies of water (HOLDAWAY 1996;HIEBL et al 2009;TIETAVAINEN et al 2010). Distance from water bodies, like most of the variables capturing the impact of land use/land cover, is typical of regional or local scale.…”

Section: Introductionmentioning

confidence: 99%

“…Explanatory variables can also be further modified by application of various moving window filters (focal functions) to simulate the effect of the so-called source area (defined by the window size) on the air temperature distribution (AGNEW and PALUTIKOF 2000;JARVIS and STUART 2001a, b;SZYMANOWSKI et al 2013). Auxiliary variables can also be created as combinations of environmental factors using map algebra or grouping in principal components to reduce the dimension of the model (WHITE 1979;LENNON and TURNER 1995;BJORNSSON et al 2007;PERCEC TADIC 2010).…”

Section: Introductionmentioning

confidence: 99%

The Role of Auxiliary Variables in Deterministic and Deterministic-Stochastic Spatial Models of Air Temperature in Poland

Szymanowski

Kryza

2015

Pure Appl. Geophys.

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Abstract-Our study examines the role of auxiliary variables in the process of spatial modelling and mapping of climatological elements, with air temperature in Poland used as an example. The multivariable algorithms are the most frequently applied for spatialization of air temperature, and their results in many studies are proved to be better in comparison to those obtained by various onedimensional techniques. In most of the previous studies, two main strategies were used to perform multidimensional spatial interpolation of air temperature. First, it was accepted that all variables significantly correlated with air temperature should be incorporated into the model. Second, it was assumed that the more spatial variation of air temperature was deterministically explained, the better was the quality of spatial interpolation. The main goal of the paper was to examine both above-mentioned assumptions. The analysis was performed using data from 250 meteorological stations and for 69 air temperature cases aggregated on different levels: from daily means to 10-year annual mean. Two cases were considered for detailed analysis. The set of potential auxiliary variables covered 11 environmental predictors of air temperature. Another purpose of the study was to compare the results of interpolation given by various multivariable methods using the same set of explanatory variables. Two regression models: multiple linear (MLR) and geographically weighted (GWR) method, as well as their extensions to the regression-kriging form, MLRK and GWRK, respectively, were examined. Stepwise regression was used to select variables for the individual models and the crossvalidation method was used to validate the results with a special attention paid to statistically significant improvement of the model using the mean absolute error (MAE) criterion. The main results of this study led to rejection of both assumptions considered. Usually, including more than two or three of the most significantly correlated auxiliary variables does not improve the quality of the spatial model. The effects of introduction of certain variables into the model were not climatologically justified and were seen on maps as unexpected and undesired artefacts. The results confirm, in accordance with previous studies, that in the case of air temperature distribution, the spatial process is non-stationary; thus, the local GWR model performs better than the global MLR if they are specified using the same set of auxiliary variables. If only GWR residuals are autocorrelated, the geographically weighted regression-kriging (GWRK) model seems to be optimal for air temperature spatial interpolation.

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Mapping the annual cycle of temperature in Iceland

Cited by 24 publications

References 0 publications

Climate‐driven range retraction of an Arctic freshwater crustacean

Climate‐driven range retraction of an Arctic freshwater crustacean

Persistence of observed air temperatures in Iceland

The Role of Auxiliary Variables in Deterministic and Deterministic-Stochastic Spatial Models of Air Temperature in Poland

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