Recent air and ground temperature increases at Tarfala Research Station, Sweden

Long-term data records are essential to detect and understand environmental change, in particular in generally data-sparse high-latitude and high-altitude regions. Here, we analyse a 47-year air temperature record (1965-2011) at Tarfala Research Station (67° 54.7'N, 18° 36.7'E, 1135 m a.s.l.) in northern Sweden, and a nearby 11-year record of 100-m-deep ground temperature (2001-11; 1540 m a.s.l.). The air temperature record shows a mean annual air temperature of -3.5±0.9°C (±1 standard deviation s) and a linear warming trend of ±0.042°C yr-1. The warming trend shows large month-to-month variations with the largest trend in January followed by October. Also, the number of days with positive mean daily temperatures and positive degree-day sums has increased during the last two decades compared to the previous period. Temperature lapse rates derived from the mean daily Tarfala record and an air temperature record at the borehole site average 4.5°C km-1 and tend to be higher in summer than in winter. Mean summer air temperatures at Tarfala explain 76% of the variance of the summer glacier mass balance of nearby Storglacia¨ren. Consistent with the observed increase in Tarfala’s air temperature, the ground temperature record shows significant permafrost warming with the largest trend (0.047°C yr-1) found at 20 m depth.Keywords: Air temperature; climate change; permafrost; lapse rate; degree-days; NAO(Published: 15 July 2013)Citation: Polar Research 2013, 32, 19807, http://dx.doi.org/10.3402/polar.v32i0.1980

Snow cover maps from MODIS images at 250 m resolution, part 1: Algorithm description

A new algorithm for snow cover monitoring at 250 m resolution based on Moderate Resolution Imaging Spectroradiometer (MODIS) images is presented. In contrast to the 500 m resolution MODIS snow products of NASA (MOD10 and MYD10), the main goal was to maintain the resolution as high as possible to allow for a more accurate detection of snow covered area (SCA). This is especially important in mountainous regions characterized by extreme landscape heterogeneity, where maps at a resolution of 500 m could not provide the desired amount of spatial details. Therefore, the algorithm exploits only the 250 m resolution bands of MODIS in the red (B1) and infrared (B2) spectrum, as well as the Normalized Difference Vegetation Index (NDVI) for snow detection, while clouds are classified using also bands at 500 m and 1 km resolution. The algorithm is tailored to process MODIS data received in real-time through the EURAC receiving station close to Bolzano, Italy, but also standard MODIS products are supported. It is divided into three steps: first the data is preprocessed, including reprojection, calculation of physical reflectance values and masking of water bodies. In a second step, the actual classification of snow, snow in forested areas, and clouds takes place based on MODIS images both from Terra and Aqua satellites. In the third step, snow cover maps derived from images of both sensors of the same day are combined to reduce cloud coverage in the final SCA product. Four different quality indices are calculated to verify the reliability of input data, snow classification, cloud detection and viewing geometry. Using the data received through their own station, EURAC can provide SCA maps of central Europe to end users in near real-time. Validation of the algorithm is outlined in a companion paper and indicates good performance with accuracies ranging from 94% to around 82% compared to in situ snow depth measurements, and around 93% compared to SCA derived from Landsat ETM+ images

Snow cover maps from MODIS images at 250 m resolution, part 2: Validation

The performance of a new algorithm for binary snow cover monitoring based on Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images at 250 m resolution is validated using snow cover maps (SCA) based on Landsat 7 ETM+ images and in situ snow depth measurements from ground stations in selected test sites in Central Europe. The advantages of the proposed algorithm are the improved ground resolution of 250 m and the near real-time availability with respect to the 500 m standard National Aeronautics and Space Administration (NASA) MODIS snow products (MOD10 and MYD10). It allows a more accurate snow cover monitoring at a local scale, especially in mountainous areas characterized by large landscape heterogeneity. The near real-time delivery makes the product valuable as input for hydrological models, e.g., for flood forecast. A comparison to sixteen snow cover maps derived from Landsat ETM/ETM+ showed an overall accuracy of 88.1%, which increases to 93.6% in areas outside of forests. A comparison of the SCA derived from the proposed algorithm with standard MODIS products, MYD10 and MOD10, indicates an agreement of around 85.4% with major discrepancies in forested areas. The validation of MODIS snow cover maps with 148 in situ snow depth measurements shows an accuracy ranging from 94% to around 82%, where the lowest accuracies is found in very rugged terrain restricted to in situ stations along north facing slopes, which lie in shadow in winter during the early morning acquisition

Assessing the quality of a real-time Snow Cover Area product for hydrological applications

Using reliable observed data is important for performing real-time flood forecasts or hydrological simulation in order to calibrate parameters or to update model variables. Satellite snow products can be one of these data, since snow is a water reservoir with a high impact on the quality of discharge simulation. The satellite Snow Cover Area products are known to be of good quality and are regularly used for studies in the meteorological and hydrological fields. However, these products are reprocessed and thus are not representative of the quality of a real-time product that would be needed for operational applications. The assessment of a real-time Snow Cover Area daily product at 250 m-resolution (the EURAC MODIS SCA product, which is based on the MODIS sensor) by comparing it directly or indirectly to the classical NASA MODIS SCA daily product and to the simulated Snow Cover Area of the distributed hydrological model, LISFLOOD, is realized in this article at the pan-European scale. This real-time product showed an overall good performance compared with the classical product, and a good agreement with the LISFLOOD simulated snow over Europe. The study showed the impact of forest cover on the scores of the compared products, whereas altitude did not have an impact. Using quality flags that are provided with the EURAC product improved its performance by reducing the misclassification of clouds as snow

Assessing the quality of a real-time Snow Cover Area product for hydrological applications

Using reliable observed data is important for performing real-time flood forecasts or hydrological simulation in order to calibrate parameters or to update model variables. Satellite snow products can be one of these data, since snow is a water reservoir with a high impact on the quality of discharge simulation. The satellite Snow Cover Area products are known to be of good quality and are regularly used for studies in the meteorological and hydrological fields. However, these products are reprocessed and thus are not representative of the quality of a real-time product that would be needed for operational applications. The assessment of a real-time Snow Cover Area daily product at 250 m-resolution (the EURAC MODIS SCA product, which is based on the MODIS sensor) by comparing it directly or indirectly to the classical NASA MODIS SCA daily product and to the simulated Snow Cover Area of the distributed hydrological model, LISFLOOD, is realized in this article at the pan-European scale. This real-time product showed an overall good performance compared with the classical product, and a good agreement with the LISFLOOD simulated snow over Europe. The study showed the impact of forest cover on the scores of the compared products, whereas altitude did not have an impact. Using quality flags that are provided with the EURAC product improved its performance by reducing the misclassification of clouds as snow.JRC.H.1-Water Resource

Recent air and ground temperature increases at Tarfala Research Station, Sweden

Long-term data records are essential to detect and understand environmental change, in particular in generally data-sparse high-latitude and high-altitude regions. Here, we analyse a 47-year air temperature record (1965–2011) at Tarfala Research Station (67° 54.7′N, 18° 36.7′E, 1135 m a.s.l.) in northern Sweden, and a nearby 11-year record of 100-m-deep ground temperature (2001–11; 1540 m a.s.l.). The air temperature record shows a mean annual air temperature of −3.5±0.9°C (±1 standard deviation σ) and a linear warming trend of ±0.042°C yr−1. The warming trend shows large month-to-month variations with the largest trend in January followed by October. Also, the number of days with positive mean daily temperatures and positive degree-day sums has increased during the last two decades compared to the previous period. Temperature lapse rates derived from the mean daily Tarfala record and an air temperature record at the borehole site average 4.5°C km−1 and tend to be higher in summer than in winter. Mean summer air temperatures at Tarfala explain 76% of the variance of the summer glacier mass balance of nearby Storglaciären. Consistent with the observed increase in Tarfala's air temperature, the ground temperature record shows significant permafrost warming with the largest trend (0.047°C yr−1) found at 20 m depth