Forecasting snow avalanche days from meteorological data using classification trees, Grasdalen, Western Norway.

Avalanches pose one of the most serious problems to infrastructure and people in the mountains in Norway. Processes leading to avalanche release are deterministic but the time and place of avalanche release is notoriously difficult to predict. Statistical approaches using meteorological parameters to predict the probability of natural avalanche release provide an alternative to deterministic prediction. We used classification trees to predict days with and without avalanches in the valley of Grasdalen in Western Norway based on meteorological parameters. A database with avalanche observations from almost 30 years was spatially and temporally coupled to grids of wind, precipitation and temperature. The grids were used because they provided more temporally consistent datasets than measurements from a local weather station. Avalanches were observed on 254 days and the same number of non-avalanche days was randomly selected. The optimal classification trees gave misclassification rates of 15% for all avalanche days, 18% for days with dry avalanches and 13% for days with wet avalanches. The most important meteorological parameters for the classification were the five-, one- and three-day sum of precipitation. Then followed wind speed, either measured as the maximum or mean over five days, three days or one day. Finally, daily temperature was important for the classification both alone and through a degree day parameter. Based on realistic scenarios for precipitation and temperature, our results imply that avalanche frequency will increase in the future. Further studies are needed to quantify this increase

Changes in winter warming events in the Nordic Arctic Region

In recent years extreme winter warming events have been reported in arctic areas. These events are characterized as extraordinarily warm weather episodes, occasionally combined with intense rainfall, causing ecological disturbance and challenges for arctic societies and infrastructure. Ground-ice formation due to winter rain or melting prevents ungulates from grazing, leads to vegetation browning, and impacts soil temperatures. The authors analyze changes in frequency and intensity of winter warming events in the Nordic arctic region—northern Norway, Sweden, and Finland, including the arctic islands Svalbard and Jan Mayen. This study identifies events in the longest available records of daily temperature and precipitation, as well as in future climate scenarios, and performs analyses of long-term trends for climate indices aimed to capture these individual events. Results show high frequencies of warm weather events during the 1920s–30s and the past 15 years (2000–14), causing weak positive trends over the past 90 years (1924–2014). In contrast, strong positive trends in occurrence and intensity for all climate indices are found for the past 50 years with, for example, increased rates for number of melt days of up to 9.2 days decade−1 for the arctic islands and 3–7 days decade−1 for the arctic mainland. Regional projections for the twenty-first century indicate a significant enhancement of the frequency and intensity of winter warming events. For northern Scandinavia, the simulations indicate a doubling in the number of warming events, compared to 1985–2014, while the projected frequencies for the arctic islands are up to 3 times higher

Measured and modeled historical precipitation trends for Svalbard

Abstract Precipitation plays an important role in the Arctic hydrological cycle, affecting different areas like the surface energy budget and the mass balance of glaciers. Thus, accurate measurements of precipitation are crucial for physical process studies, but gauge measurements in the Arctic are sparse and subject to relocations and several gauge issues. From Svalbard, we analyze precipitation trends at six weather stations for the last 50–100 years by combining different observation series and adjusting for inhomogeneities. For the past 50 years, the measured annual precipitation has increased by 30%–45%. However, precipitation measurements in the cold and windy climate are strongly influenced by gauge undercatch. Correcting for undercatch reduces the trend values by 10% points, since the fraction of solid precipitation has decreased and undercatch is larger for solid precipitation. Thus, precipitation corrected for undercatch should be used to study “true” precipitation trends in the Arctic. Precipitation over Svalbard has been modeled by downscaling reanalysis data to a spatial resolution of 1 km. In general, the modeled annual precipitation is higher (13%–175%) than the measured values and mainly higher than the precipitation corrected for undercatch. Although the model resolves orographic effects on a regional scale, the downscaling is not able to reproduce local orographic enhancement for onshore winds, nor local effects of rain shadow. The downscaled dataset explains approximately 60% of the interannual precipitation variability. The model-based trends during 1979–2018 are positive, but weaker (~4% decade −1 ) than the observed (~8% decade −1 ) trends

Forcasting snow avalanche days from meteorological data using classification trees; Grasdalen, Western Norway

Avalanches pose one of the most serious problems to infrastructure and people in the mountains in Norway. Processes leading to avalanche release are deterministic but the time and place of avalanche release is notoriously difficult to predict. Statistical approaches using meteorological parameters to predict the probability of natural avalanche release provide an alternative to deterministic prediction. We used classification trees to predict days with and without avalanches in the valley of Grasdalen in Western Norway based on meteorological parameters. A database with avalanche observations from almost 30 years was spatially and temporally coupled to grids of wind, precipitation and temperature. The grids were used because they provided more temporally consistent datasets than measurements from a local weather station. Avalanches were observed on 254 days and the same number of non-avalanche days was randomly selected. The optimal classification trees gave misclassification rates of 15% for all avalanche days, 18% for days with dry avalanches and 13% for days with wet avalanches. The most important meteorological parameters for the classification were the five-, one- and three-day sum of precipitation. Then followed wind speed, either measured as the maximum or mean over five days, three days or one day. Finally, daily temperature was important for the classification both alone and through a degree day parameter. Based on realistic scenarios for precipitation and temperature, our results imply that avalanche frequency will increase in the future. Further studies are needed to quantify this increase

Record-low primary productivity and highplant damage in the Nordic Arctic Region in2012 caused by multiple weather events andpest outbreaks

The release of cold temperature constraints on photosynthesis has led to increased productivity (greening) in significant parts (32–39%) of the Arctic, but much of the Arctic shows stable (57–64%) or reduced productivity (browning, <4%). Summer drought and wildfires are the bestdocumented drivers causing browning of continental areas, but factors dampening the greening effect of more maritime regions have remained elusive. Here we show how multiple anomalous weather events severely affected the terrestrial productivity during one water year (October 2011–September 2012) in a maritime region north of the Arctic Circle, the Nordic Arctic Region, and contributed to the lowest mean vegetation greenness (normalized difference vegetation index) recorded this century. Procedures for field data sampling were designed during or shortly after the events in order to assess both the variability in effects and the maximum effects of the stressors. Outbreaks of insect and fungal pests also contributed to low greenness. Vegetation greenness in 2012 was 6.8% lower than the 2000–11 average and 58% lower in the worst affected areas that were under multiple stressors. These results indicate the importance of events (some being mostly neglected in climate change effect studies and monitoring) for primary productivity in a high-latitude maritime region, and highlight the importance of monitoring plant damage in the field and including frequencies of stress events in models of carbon economy and ecosystem change in the Arctic. Fourteen weather events and anomalies and 32 hypothesized impacts on plant productivity are summarized as an aid for directing future research. anomalous weather events, disturbance, extreme events, NDVI, long-term monitoring series, pathogens, plant stres

Review of Snow Data Assimilation Methods for Hydrological, Land Surface, Meteorological and Climate Models: Results from a COST HarmoSnow Survey

The European Cooperation in Science and Technology (COST) Action ES1404 &#8220;HarmoSnow&#8222;, entitled, &#8220;A European network for a harmonized monitoring of snow for the benefit of climate change scenarios, hydrology and numerical weather prediction&#8222; (2014-2018) aims to coordinate efforts in Europe to harmonize approaches to validation, and methodologies of snow measurement practices, instrumentation, algorithms and data assimilation (DA) techniques. One of the key objectives of the action was &#8220;Advance the application of snow DA in numerical weather prediction (NWP) and hydrological models and show its benefit for weather and hydrological forecasting as well as other applications.&#8222; This paper reviews approaches used for assimilation of snow measurements such as remotely sensed and in situ observations into hydrological, land surface, meteorological and climate models based on a COST HarmoSnow survey exploring the common practices on the use of snow observation data in different modeling environments. The aim is to assess the current situation and understand the diversity of usage of snow observations in DA, forcing, monitoring, validation, or verification within NWP, hydrology, snow and climate models. Based on the responses from the community to the questionnaire and on literature review the status and requirements for the future evolution of conventional snow observations from national networks and satellite products, for data assimilation and model validation are derived and suggestions are formulated towards standardized and improved usage of snow observation data in snow DA. Results of the conducted survey showed that there is a fit between the snow macro-physical variables required for snow DA and those provided by the measurement networks, instruments, and techniques. Data availability and resources to integrate the data in the model environment are identified as the current barriers and limitations for the use of new or upcoming snow data sources. Broadening resources to integrate enhanced snow data would promote the future plans to make use of them in all model environments

Photochemical Tuning of Tris-Bidentate Acridine- and Phenazine-Based Ir(III) Complexes

International audienc

Changing Arctic Snow Cover: A Review of Recent Developments and Assessment of Future Needs for Observations, Modelling, and Impacts

Snow is a critically important and rapidly changing feature of the Arctic. However, snow-cover and snowpack conditions change through time pose challenges for measuring and prediction of snow. Plausible scenarios of how Arctic snow cover will respond to changing Arctic climate are important for impact assessments and adaptation strategies. Although much progress has been made in understanding and predicting snow-cover changes and their multiple consequences, many uncertainties remain. In this paper, we review advances in snow monitoring and modelling, and the impact of snow changes on ecosystems and society in Arctic regions. Interdisciplinary activities are required to resolve the current limitations on measuring and modelling snow characteristics through the cold season and at different spatial scales to assure human well-being, economic stability, and improve the ability to predict manage and adapt to natural hazards in the Arctic region