Calculating of snow cover characteristics on a plain territory using the model SPONSOR and data of reanalyses (by the example of Moscow region)

The technique for calculating the snow cover characteristics (a water equivalent and a snow cover thickness) with high spatial and time resolution on spacious plains is proposed. The model SPONSOR of local heat- and moisture exchange (Land-Surface Model, LSM) and data of reanalyses NCEP/DOE and ECMWF ERA-Interim were used for calculations. The above characteristics of the snow cover on the test area of the Moscow region were calculated using this method over the period 1979–1996. The results were compared with actual data of the snow gauge stations and with data on snow cover, derived directly from reanalysis. The data from the NCEP/DOE reanalysis did not show satisfactory agreement with data of the observations for both the water equivalent and the thickness (Fig.  1,  б and Fig. 2, б): deviations reached 60–70%. Monthly mean values of snow water equivalent from the ERA-Interim reanalysis were in a good agreement with the observations, but the snow thicknesses were reproduced much worse. At the same time, using the LSM SPONSOR with input meteorological data from the reanalyses allowed obtaining the snow cover characteristics which were in a good agreement with data of the observations for both the monthly means and individual daily values. The correlation coefficients with the data of snow gauge surveys increased, on the average, up to 0.83–0.89 for the water equivalent, and up to 0.85–0.91 for the snow depth (see the Тable in the text). Especially good results were obtained when meteorological data from the ERA-Interim reanalysis were used together with the LSM SPONSOR (Fig. 1, д and Fig. 2, д). It allows us to conclude that meteorological data from the ERA-Interim reanalysis together with data of regular observational network can be used as an additional source of information for calculations of the snow characteristics. This conclusion is especially important for areas with sparse network of regular observations

Расчёт характеристик снежного покрова равнинных территорий с использованием модели локального тепловлагообмена SPONSOR и данных реанализа на примере Московской области

The technique for calculating the snow cover characteristics (a water equivalent and a snow cover thickness) with high spatial and time resolution on spacious plains is proposed. The model SPONSOR of local heat- and moisture exchange (Land-Surface Model, LSM) and data of reanalyses NCEP/DOE and ECMWF ERA-Interim were used for calculations. The above characteristics of the snow cover on the test area of the Moscow region were calculated using this method over the period 1979–1996. The results were compared with actual data of the snow gauge stations and with data on snow cover, derived directly from reanalysis. The data from the NCEP/DOE reanalysis did not show satisfactory agreement with data of the observations for both the water equivalent and the thickness (Fig.  1,  б and Fig. 2, б): deviations reached 60–70%. Monthly mean values of snow water equivalent from the ERA-Interim reanalysis were in a good agreement with the observations, but the snow thicknesses were reproduced much worse. At the same time, using the LSM SPONSOR with input meteorological data from the reanalyses allowed obtaining the snow cover characteristics which were in a good agreement with data of the observations for both the monthly means and individual daily values. The correlation coefficients with the data of snow gauge surveys increased, on the average, up to 0.83–0.89 for the water equivalent, and up to 0.85–0.91 for the snow depth (see the Тable in the text). Especially good results were obtained when meteorological data from the ERA-Interim reanalysis were used together with the LSM SPONSOR (Fig. 1, д and Fig. 2, д). It allows us to conclude that meteorological data from the ERA-Interim reanalysis together with data of regular observational network can be used as an additional source of information for calculations of the snow characteristics. This conclusion is especially important for areas with sparse network of regular observations.Предложена методика расчёта характеристик снежного покрова с высоким пространственным и временным разрешением с использованием модели локального тепловлагообмена (Land-Surface Model, LSM) SPONSOR и метеоданных реанализов NCEP/DOE и ECMWF ERA-Interim. Выполнены расчёты для тестового региона Московской области за период 1979–1996 гг. и проведено сравнение с данными наблюдений и реанализа. Данные о снежном покрове из реанализа существенно отличаются от данных наблюдений. Использование модели SPONSOR с входными метеоданными, взятыми из реанализа ECMWF ERA-Interim, позволяет получить характеристики снежного покрова с высоким пространственным и временным разрешением, которые хорошо согласуются с данными наблюдений

Численное моделирование снежного покрова на о. Гукера (архипелаг Земля Франца-Иосифа)

Results obtained by simulating snow characteristics with a numerical model of surface heat and moisture exchange SPONSOR are presented. The numerical experiments are carried out for Franz Josef Land with typical Arctic climate conditions. The blizzard evaporation parameter is shown to have great influence on snow depth on territories with high wind speed. This parameter significantly improves the simulation quality of the numerical model. Some discrepancies between evaluated and observed snow depth values can be explained by inaccuracies in precipitation measurements (at least in certain cases) and errors in calculations of incoming radiation, mostly due to low accuracy in the cloudiness observations.Рассмотрены результаты численного воспроизведения характеристик снежного покрова с помощью модели тепловлагообмена SPONSOR. Эксперименты проводились для Земли Франца-Иосифа с типичным арктическим климатом. Установлено, что на территориях, где наблюдаются высокие скорости ветра, толщина снежного покрова в значительной степени связана с величиной метелевого испарения. Учёт этого параметра заметно улучшает качество расчётов численной модели. Некоторые расхождения между рассчитанными и реальными значениями толщины снежного покрова можно объяснить неточностями в измерениях осадков и погрешностями вычислений приходящей солнечной радиации. Последнее, в основном, объясняется невысокой точностью наблюдений за облачностью

Определение снегозапасов Западной Сибири по расчётам на модели локального тепловлагообмена SPONSOR с использованием данных реанализа

Obtaining of reliable information about the characteristics of snow cover with high spatial and temporal resolution for large areas of Northern Eurasia, with rare or absent network of ground-based observations stations is an important and urgent task. Currently estimation of the value of the snow water equivalent (SWE) and the snow depth have a large degree of uncertainty, especially if we are moving from data at the point of observation stations to distributed space values. In this article, the simulations of SWE and the snow depth using Land-Surface Model (LSM) SPONSOR with input meteorological data taken from the ECMWF ERAInterim reanalysis was performed for Western Siberia for the period from 1979 to 2013. Fields of SWE and of the snow depth with high spatial and temporal resolution corresponding to the resolution of meteorological data of the ECMWF ERA-Interim reanalysis (time step of 6 hours, the grid resolution of 0.75° × 0.75° in latitude and longitude) were obtained. For the entire period SWE data were compared with observations, as simulated using the model and taken directly from the reanalysis ERA-Interim at points corresponding of observation stations. Also comparison of observations and satellite data of SWE for points of observation stations was performed. Correlation coefficients between observations and model and satellite data for SWE and the snow depth were calculated for the period from 1979 to 2013. These correlation coefficients between observations and results of simulations using LSM SPONSOR for SWE, and especially for the snow depth are the best of all methods. Maps with high spatial resolution for SWE, obtained by different methods, were constructed for February averaged. Comparing of constructed maps shows significant uncertainty of the SWE fields, besides field’s distortions are not evenly distributed across the region. It appears that no one of these methods currently can be used as a reference (unique) to determine SWE in the absence of data of ground-based observations. Overall, model simulations using LSM SPONSOR somewhat overstate SWE, however, this overestimation is not more than 10–20% for most part of the territory, except in the South. Model data are reasonably well reproduce SWE for Central, Eastern and, most probably, for Northern parts of the region, differing from a real at 10–15%. Data from used satellite archive a few underestimate of SWE. SWE data taken directly from the reanalysis ERA-Interim, give large distortions of the SWE field: these values for Northern parts of the region, are likely greatly underestimated, and for Western and Eastern parts of the region – inflated. It is shown that in general, the method of simulation of snow cover characteristics using LSM SPONSOR with input data taken from the ECMWF ERA-Interim reanalysis gives good results for the region of Western Siberia.Для территории Западной Сибири за период с 1979 по 2013 г. проведены расчёты снегозапасов и толщины снежного покрова с помощью модели локального тепловлагообмена SPONSOR с входными метеоданными, взятыми из реанализа ECMWF ERA-Interim. Показано, что коэффициенты корреляции между данными наблюдений и результатами численных расчётов на модели SPONSOR – наилучшие из всех методов. С помощью модели SPONSOR достаточно хорошо воспроизводятся данные снегозапасов по центральной, восточной и, наиболее вероятно, северной частям Западной Сибири

Методика оценки лавинного питания (на примере трёх ледников Тянь-Шаня)

The contribution of snow avalanches to the seasonal snow accumulation on a glacier is among the least studied components of the glacier’s mass balance. The methods for the numerical assessment of avalanche accumulation are still under development, which is related to poor avalanche data availability and difficulties in obtaining such data on most of mountain glaciers. We propose a possible methodology for the numerical assessment of snow avalanche contribution to snow accumulation at mountain glaciers based on DEM and weather data analysis using GIS and numerical modeling of snow avalanches. The developed methodology consists of the following steps: terrain analysis; weather data analysis; snow avalanche volume assessment during an analyzed balance year; numerical simulation of snow avalanches using RAMMS; evaluation of snow avalanches contribution into a glacier accumulation. The proposed methodology was tested on three glaciers located in the Inner Tien Shan: Batysh Sook, № 354 and Karabatkak during the 2015/16 balance year. To evaluate snow avalanche contribution to the seasonal accumulation, we reconstructed avalanche release zones that were most probably active during the 2015/16 balance year and corresponding snow fracture height in each of these zones. The numerical simulations of most probable released snow avalanches during the winter period 2015/16 using avalanche dynamics software RAMMS were performed and compared with the field observations and UAV orthophoto image from July 2016. The outlines of avalanches deposits were realistically reproduced by RAMMS according to the results of field observation. The estimated share of snow avalanche contribution to the accumulation on the research glaciers during the 2015/16 balance year turned out to be: Batysh Sook – 7,4±2,5%; № 354 – 2,2±0,7%; Karabatkak – 10,8±3,6% of the total accumulation. The next step would be to test the proposed methodology based on the data and regional dependences from the Inner Tien Shan in other mountainous regions. This methodology is applicable in the regions where DEMs, regular meteorological observations as well as data on the regional avalanche formation factors are available.Предложена новая методика количественной оценки лавинного питания ледников, основанная на анализе рельефа и данных метеорологических наблюдений с использованием методов геоинформационного картографирования и математического моделирования. Рассмотрены результаты её применения на трёх ледниках Тянь-Шаня: Западный Суёк, № 354, Карабаткак

Northern Eurasia Future Initiative (NEFI): facing the challenges and pathways of global change in the 21st century

During the past several decades, the Earth system has changed significantly, especially across Northern Eurasia. Changes in the socio-economic conditions of the larger countries in the region have also resulted in a variety of regional environmental changes that can have global consequences. The Northern Eurasia Future Initiative (NEFI) has been designed as an essential continuation of the Northern Eurasia Earth Science Partnership Initiative (NEESPI), which was launched in 2004. NEESPI sought to elucidate all aspects of ongoing environmental change, to inform societies and, thus, to better prepare societies for future developments. A key principle of NEFI is that these developments must now be secured through science-based strategies co-designed with regional decision makers to lead their societies to prosperity in the face of environmental and institutional challenges. NEESPI scientific research, data, and models have created a solid knowledge base to support the NEFI program. This paper presents the NEFI research vision consensus based on that knowledge. It provides the reader with samples of recent accomplishments in regional studies and formulates new NEFI science questions. To address these questions, nine research foci are identified and their selections are briefly justified. These foci include: warming of the Arctic; changing frequency, pattern, and intensity of extreme and inclement environmental conditions; retreat of the cryosphere; changes in terrestrial water cycles; changes in the biosphere; pressures on land-use; changes in infrastructure; societal actions in response to environmental change; and quantification of Northern Eurasia's role in the global Earth system. Powerful feedbacks between the Earth and human systems in Northern Eurasia (e.g., mega-fires, droughts, depletion of the cryosphere essential for water supply, retreat of sea ice) result from past and current human activities (e.g., large scale water withdrawals, land use and governance change) and potentially restrict or provide new opportunities for future human activities. Therefore, we propose that Integrated Assessment Models are needed as the final stage of global change assessment. The overarching goal of this NEFI modeling effort will enable evaluation of economic decisions in response to changing environmental conditions and justification of mitigation and adaptation efforts

Numerical modeling of a snow cover on Hooker Island (Franz Josef Land archipelago)

Results obtained by simulating snow characteristics with a numerical model of surface heat and moisture exchange SPONSOR are presented. The numerical experiments are carried out for Franz Josef Land with typical Arctic climate conditions. The blizzard evaporation parameter is shown to have great influence on snow depth on territories with high wind speed. This parameter significantly improves the simulation quality of the numerical model. Some discrepancies between evaluated and observed snow depth values can be explained by inaccuracies in precipitation measurements (at least in certain cases) and errors in calculations of incoming radiation, mostly due to low accuracy in the cloudiness observations

Evaluation of snow storage in Western Siberia based on the land-surface model SPONSOR simulation using reanalysis data

Obtaining of reliable information about the characteristics of snow cover with high spatial and temporal resolution for large areas of Northern Eurasia, with rare or absent network of ground-based observations stations is an important and urgent task. Currently estimation of the value of the snow water equivalent (SWE) and the snow depth have a large degree of uncertainty, especially if we are moving from data at the point of observation stations to distributed space values. In this article, the simulations of SWE and the snow depth using Land-Surface Model (LSM) SPONSOR with input meteorological data taken from the ECMWF ERAInterim reanalysis was performed for Western Siberia for the period from 1979 to 2013. Fields of SWE and of the snow depth with high spatial and temporal resolution corresponding to the resolution of meteorological data of the ECMWF ERA-Interim reanalysis (time step of 6 hours, the grid resolution of 0.75° × 0.75° in latitude and longitude) were obtained. For the entire period SWE data were compared with observations, as simulated using the model and taken directly from the reanalysis ERA-Interim at points corresponding of observation stations. Also comparison of observations and satellite data of SWE for points of observation stations was performed. Correlation coefficients between observations and model and satellite data for SWE and the snow depth were calculated for the period from 1979 to 2013. These correlation coefficients between observations and results of simulations using LSM SPONSOR for SWE, and especially for the snow depth are the best of all methods. Maps with high spatial resolution for SWE, obtained by different methods, were constructed for February averaged. Comparing of constructed maps shows significant uncertainty of the SWE fields, besides field’s distortions are not evenly distributed across the region. It appears that no one of these methods currently can be used as a reference (unique) to determine SWE in the absence of data of ground-based observations. Overall, model simulations using LSM SPONSOR somewhat overstate SWE, however, this overestimation is not more than 10–20% for most part of the territory, except in the South. Model data are reasonably well reproduce SWE for Central, Eastern and, most probably, for Northern parts of the region, differing from a real at 10–15%. Data from used satellite archive a few underestimate of SWE. SWE data taken directly from the reanalysis ERA-Interim, give large distortions of the SWE field: these values for Northern parts of the region, are likely greatly underestimated, and for Western and Eastern parts of the region – inflated. It is shown that in general, the method of simulation of snow cover characteristics using LSM SPONSOR with input data taken from the ECMWF ERA-Interim reanalysis gives good results for the region of Western Siberia