Reducing Snow-Climate Uncertainty in Earth System modelling (ReSCUES)

Lead Research Organisation: University of Edinburgh
Department Name: Sch of Geosciences


Snow is a material with remarkable physical properties that profoundly alters the characteristics of the Earth's surface where it lies. Because snow has a high albedo (the fraction of solar radiation that it reflects rather than absorbs) and a high latent heat of fusion (the energy required to melt it), it delays the warming of the atmosphere and the ground in spring each year. Satellite measurements of Northern Hemisphere snow cover have now been available for 50 years, and a strong decreasing trend correlated with warming has been observed in spring over that period. Less snow accumulates in a warmer climate and melts sooner, increasing the absorption of solar radiation and reinforcing the warming (a strong positive feedback). Snow conducts heat poorly because it contains trapped air and so insulates the ground from cold temperatures in winter; this controls soil freezing and provides protection for short plants, small animals and soil microbes living in snowy regions, with important and complex impacts on the global carbon cycle. For all of these reasons, it is important that climate models should be able to predict snow cover accurately. Unfortunately, the latest climate models still differ greatly in their simulations of how snow cover varies from year to year in the current climate and how it will change in the future. There are many potential sources for this uncertainty, including errors in snowfall and temperature patterns predicted by models, multiple processes that control the rate of snowmelt but may be poorly represented in models, and uncertainty in setting optimal values for model parameters. It has proven very difficult to disentangle these sources of uncertainty and to determine how they can be reduced. In this project, we will use a new modelling system in which a single meteorological variable, model process or parameter value can be varied at a time, allowing highly controlled experiments to precisely determine how they influence simulations. Direct measurements of snow properties at research sites and satellite measurements of snow cover and albedo across the Northern Hemisphere will be used to identify the best simulations. Because snow melts both as the weather warms in spring and as the climate warms, improving the ability of models to simulate the current seasonal cycle and past trends can be expected to improve projections of future conditions, provided that the improvements are obtained for sound physical reasons. Improved predictions and better understanding of the sensitivity of snow to climate change will contribute to reviews of climate science by the Intergovernmental Panel on Climate Change which are essential resources for policymakers. Another important feature of snow is that it stores precipitation that falls in the mountains over winter and releases it in warmer times of year when human demand for water is higher. Many parts of the world are provided with water and threatened by floods from melting snow in upstream mountain regions. Even if the total amount of precipitation does not change in a warming climate, a shift to more falling as rain rather than snow will lead to river flows peaking earlier in the year, requiring major changes in the management of water resources. Global climate models, which cannot resolve processes occurring on scales smaller than a few hundred kilometres, are not adequate tools for informing water management decisions, but national weather services are now beginning to run forecasts for limited areas and short periods with kilometre-scale resolutions. We will use high-resolution meteorological data and the same modelling methods that we applied on the hemispheric scale to make and test predictions for snowmelt in well-instrumented areas of the French and Swiss Alps. Methods developed will be incorporated in a "downscaling toolkit" which will be made available to researchers and water managers by the International Network for Alpine Research Catchment Hydrology.

Planned Impact

Policymakers will benefit from improved projections of how snow cover will change in response to climate change, allowing better-informed choices about adaptation and mitigation options.

Water managers will benefit from improved forecasting of timing and rate of snowmelt in mountain headwaters.

Schools will benefit from teaching materials on snow and climate aligned with curricula in STEM subjects

The general public will benefit from raised awareness and understanding of impacts that arise from changes in climate and snow cover.
Description It appears that the best models for predicting accumulation and melt of snow on the ground are now limited more by input data quality than model physics, but representations of snow in Earth System models have not attained the level of specialist snow physics models. Prominent errors are related to snow albedo - either too low due to shallow snow covering too small a fraction of the ground or too high for deep snow. Many models show a cold bias for soils under snow, with implications for the prediction of permafrost and seasonal ground freezing. Land surface models driven with observed meteorology over recent decades have much lower uncertainty in snow cover trends than when coupled in Earth System Models. A surprising result is that many of the problems identified by close scrutiny of model simulations could be traced to human errors.
Exploitation Route Preliminary results were shown in an invited presentation at the American Geophysical Union meeting in December 2017. Five papers have now been published and two more are in preparation. Public engagement activities were held during the Edinburgh International Science Festival in April 2018, the Midlothian Science Festival in October 2018 and with ASCUS Art & Science in September 2019.
Sectors Education,Leisure Activities, including Sports, Recreation and Tourism,Transport

Description Working with an experienced teacher, we have developed a game for schools which involves consideration of diverse pieces of information on snow, climate, water resources and impacts on the skiing industry to develop critical thinking skills. Building on this, we delivered a workshop at the University of Edinburgh as part of a STEM Learning & Teaching Day for the university's initiative in widening participation. Data generated for the project were used in a workshop with artists on visualizing snow data at Summerhall, Edinburgh
First Year Of Impact 2018
Sector Education
Impact Types Cultural,Societal

Title ESM-SnowMIP meteorological and evaluation datasets at ten reference sites (in situ and bias corrected reanalysis data), supplement to: Menard, Cecile; Essery, Richard; Barr, Alan; Bartlett, Paul; Derry, Jeff; Dumont, Marie; Fierz, Charles; Kim, Hyungjun; Kontu, Anna; Lejeune, Yves; Marks, Danny; Niwano, Masashi; Raleigh, Mark; Wang, Libo; Wever, Nander (2019): Meteorological and evaluation datasets for snow modelling at 10 reference sites: description of in situ and bias-corrected reanalysis da 
Description In situ meteorological forcing and evaluation data, and bias-corrected reanalysis forcing data for cold regions modelling at ten sites: one maritime (Sapporo, Japan), one arctic (Sodankylä, Finland), three boreal (Old Aspen, Old Jack Pine and Old Black Spruce, Saskatchewan, Canada) and five mid-latitude alpine (Col de Porte, France; Reynolds Mountain East, Idaho, USA, Senator Beck and Swamp Angel, Colorado, USA; Weissfluhjoch, Switzerland). The long-term datasets are the reference sites chosen for evaluating models participating in the Earth System Model-Snow Model Intercomparison Project (ESM-SnowMIP). Periods covered by the in situ data vary between seven and twenty years of hourly meteorological data, with evaluation data (snow depth, snow water equivalent, albedo, soil temperature and surface temperature) available at varying temporal intervals. 30-year (1980-2010) time-series have been extracted from a global gridded surface meteorology dataset (Global Soil Wetness Project Phase 3) for the grid cells containing the reference sites, interpolated to one-hour timesteps and bias corrected. 
Type Of Material Database/Collection of data 
Year Produced 2019 
Provided To Others? Yes  
Description Midlothian Science Festival 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach Regional
Primary Audience Public/other audiences
Results and Impact Display at Penicuick Library on 15 October 2018 as part of the Midlothian Science Festival, visited by 240 parents and children, leading to discussions of climate research in the Arctic. This experience will inform training for NERC E3 DTP students on public outreach.
Year(s) Of Engagement Activity 2018
Description STEM Learning & Teaching Day 
Form Of Engagement Activity Participation in an open day or visit at my research institution
Part Of Official Scheme? No
Geographic Reach Regional
Primary Audience Schools
Results and Impact STEM workshop on snow and mountain meteorology with 46 pupils from two schools in SIMD20 areas of Edinburgh, on behalf of the University of Edinburgh School of GeoSciences working group on widening participation.
Year(s) Of Engagement Activity 2018
Description Thinking Detectives Game: The Alps and Climate Change developed for schools 
Form Of Engagement Activity Engagement focused website, blog or social media channel
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Schools
Results and Impact A board game was created for school pupils in the 11-14 age range using discussion of climate change in the Alps to develop higher order thinking skills. Revisions were made after trialling with pupils at Boroughmuir High School in Edinburgh. The game was posted on Tes in August 2017 and has been downloaded 132 times since then.
Year(s) Of Engagement Activity 2017,2018