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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.

Presentation held at the The Norwegian Water Resources and Energy Directorate (NVE) on the latest version (ver.20.05) of seNorge_2018 published by the Norwegian Meteorological Institute (MET Norway). See also https://github.com/metno/seNorge_docs/wiki/seNorge_2018

seNorge_2018 is the newest member of the seNorge family of observational datasets that have been produced on a daily basis at the Norwegian Meteorological Institute (MET Norway) since 2002. The dataset includes: daily total precipitation; daily mean, minimum and maximum temperature for the Norwegian mainland covering the time period from 1957 to the present day. The fields are presented on a regular grid with 1 km of grid spacing. seNorge data are used for several applications in climatology, hydrology and meteorology and this version is the first to provide minimum and maximum daily temperatures.seNorge_2018 is based on in-situ observations from the MET Norway’s climate archive and the ECA&Ddataset. The data are quality controlled both through automatic checks and by MET Norway staff. The gauge observations are adjusted for wind-induced undercatch, which is quite important in Norway.This presentation focuses on the spatial interpolation methods for both temperature and precipitation. The two methods follow a similar approach, where the fields are first estimated over larger scales, subsequently we move to local scales. At the end, we aim at delivering the best (i.e. highest effective resolution) product for each day given its data density.For temperature, the larger scales are represented through the blending of several local vertical temperature profiles. Then, an Optimal Interpolation (OI) scheme adjust for the local scales. The previous seNorge version was similar, though seNorge_2018 features minor modifications (e.g., land-area fraction is in the spatial structure function) and an improved parameter optimization procedure. The results show significant differences from the previous version, especially along the coastline and in data-sparse regions. Minimum daily temperature is the most challenging temperature variable to represent.For precipitation, a successive correction algorithm has been implemented, which iterates OI over a sequence of decreasing spatial scales. With respect to previous versions, the innovative parts are: (i) the interpolation is performed over transformed data so as to better comply with the assumption of normality; (ii) a gridded adjustment factor is added to improve the interpolation performances in data-sparse regions. This factor is derived by processing a decade of precipitation data from a high-resolution numerical model.seNorge_2018 performances are evaluated through cross-validation and also by comparing it with other datasets.The dataset is available for public download at:http://thredds.met.no/thredds/catalog/senorge/seNorge_2018/catalog.html

12th EUMETNET Data Management Workshop, 6-8 Novemebr 2019, De Bilt (The Netherlands)MET Norway is using amateur weather observations and remote sensing data to post-process numerical model output so to deliver better automatic forecasts for temperature and precipitation on Yr.no. The temperature post-processing is operational since March 2018. The precipitation post-processed datasets are currently shared with hydropower companies, which are assessing their potential for hydrological applications. MET Norway has demonstrated that third party (or non-conventional) data can be used to improve the weather forecasts.Our contribution to the workshop begins describing the lessons learned so far in the use of a massive amount of non-conventional observations for monitoring the weather. Depending on the geographical area considered, the number of amateur observations can be up to two orders of magnitude larger than the number of conventional observations. New challenges must be addressed to effectively take advantage of non-conventional data. In particular, we will discuss the issues related to: data archiving; quality control and spatial interpolation.Public institutions have no control at all on third party data, as a consequence this data often lacks proper metadata. Furthermore, non-conventional stations can be easily relocated from one place to another. This poses a challenge on the data model used to store observations in climatological archives. We will present a possible solution, which is currently under development at MET Norway.Quality control is a key-component of any system that aims at making use of observations. For non-conventional data, the expected frequency of gross measurement errors is much larger than for observational networks managed by national MET services. Non-ideal sensor placements may cause significant representativity issues in the observation errors. On the other hand, the redundancy of non-conventional data allows the quality control to be extremely reliable in data-dense regions. MET Norway is developing a software for the data quality control tailored on the use of non-conventional data in spatial interpolation.Observational networks are characterized by an inhomogeneous spatial distribution of stations. Third party data enhances dramatically the differences in station coverage between urban and remote regions. State-of-the-art spatial interpolation methods often use a fixed spatial structure function to describe the correlation between observation errors over the domain. In this context, a fixed structure function must be optimized so to achieve reasonable results even for data sparse regions. The optimization would penalize the fine-scale representation of meteorological fields in data dense regions. We will present a possible way to adapt the spatial structure function to the local station density so to exploit the potential of the observational network.

Reanalysis datasets are gridded climate datasets obtained through numerical models in combination with observed data. Those datasets represent the recent climate and they are available with grid spacings of a few tenths of kilometers on a global scale. Traditionally, in-situ observations of near-surface temperature and precipitation are used to create observational gridded datasets with grid spacing of few kilometers on a regional level. This is the case at the Norwegian meteorological institute (MET Norway), where observational gridded datasets of daily-aggregated temperature and precipitation have been produced routinely over Scandinavia covering the time period 1957-2019.This contribution focuses on the ongoing work aiming at comparing climate indices derived from reanalyses against those derived from observational gridded datasets over the complex terrain of the Scandinavian peninsula. The newest global reanalysis dataset ERA5 produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) is considered. Two sets of indices that follow the definitions recommended by the CCl/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices (ETCCDI) are currently under production for: ERA5 and MET Norway’s observational datasets. Since we are considering two independent data sources, the comparison of their climate indices is of particular interest. The outcome of our investigation will be used for e.g. climate change assessments.

International audience; Permafrost can become a crucial factor of steep slope stability, and knowing the distribution of permafrost in steep terrain is a prerequisite when evaluating slope stability in such areas. However, permafrost distribution in such terrain is poorly known. In this study, we present a very first effort in modelling steep slopes permafrost at the nation-scale of Norway. This study is based on a multi-years dataset of 26 rock-surface temperature measurement points covering various aspects, elevations and latitudes. These measurements allow for statistical modelling of rock surface temperature that is then used to map permafrost probability. In this presentation, we present the measurement settings, the rock surface temperature data, the modelling approach and first results of the permafrost mapping.