Snow water equivalent in the Alps as seen by gridded data sets, CMIP5 and CORDEX climate models

Abstract. The estimate of the current and future conditions of snow resources in mountain areas would require reliable, kilometre-resolution, regional-observation-based gridded data sets and climate models capable of properly representing snow processes and snow–climate interactions. At the moment, the development of such tools is hampered by the sparseness of station-based reference observations. In past decades passive microwave remote sensing and reanalysis products have mainly been used to infer information on the snow water equivalent distribution. However, the investigation has usually been limited to flat terrains as the reliability of these products in mountain areas is poorly characterized.This work considers the available snow water equivalent data sets from remote sensing and from reanalyses for the greater Alpine region (GAR), and explores their ability to provide a coherent view of the snow water equivalent distribution and climatology in this area. Further we analyse the simulations from the latest-generation regional and global climate models (RCMs, GCMs), participating in the Coordinated Regional Climate Downscaling Experiment over the European domain (EURO-CORDEX) and in the Fifth Coupled Model Intercomparison Project (CMIP5) respectively. We evaluate their reliability in reproducing the main drivers of snow processes – near-surface air temperature and precipitation – against the observational data set EOBS, and compare the snow water equivalent climatology with the remote sensing and reanalysis data sets previously considered. We critically discuss the model limitations in the historical period and we explore their potential in providing reliable future projections.The results of the analysis show that the time-averaged spatial distribution of snow water equivalent and the amplitude of its annual cycle are reproduced quite differently by the different remote sensing and reanalysis data sets, which in fact exhibit a large spread around the ensemble mean. We find that GCMs at spatial resolutions equal to or finer than 1.25° longitude are in closer agreement with the ensemble mean of satellite and reanalysis products in terms of root mean square error and standard deviation than lower-resolution GCMs. The set of regional climate models from the EURO-CORDEX ensemble provides estimates of snow water equivalent at 0.11° resolution that are locally much larger than those indicated by the gridded data sets, and only in a few cases are these differences smoothed out when snow water equivalent is spatially averaged over the entire Alpine domain. ERA-Interim-driven RCM simulations show an annual snow cycle that is comparable in amplitude to those provided by the reference data sets, while GCM-driven RCMs present a large positive bias. RCMs and higher-resolution GCM simulations are used to provide an estimate of the snow reduction expected by the mid-21st century (RCP 8.5 scenario) compared to the historical climatology, with the main purpose of highlighting the limits of our current knowledge and the need for developing more reliable snow simulations.

Download Full-text

Snow water equivalent in the Alps as seen by gridded datasets, CMIP5 and CORDEX climate models

10.5194/tc-2016-280 ◽

2017 ◽

Cited By ~ 1

Author(s):

Silvia Terzago ◽

Jost von Hardenberg ◽

Elisa Palazzi ◽

Antonello Provenzale

Keyword(s):

Remote Sensing ◽

Annual Cycle ◽

Climate Models ◽

Snow Water Equivalent ◽

Regional Climate ◽

Microwave Remote Sensing ◽

Global Climate Models ◽

Mountain Areas ◽

Ensemble Mean ◽

Snow Water

Abstract. The estimate of the current and future conditions of snow resources in mountain areas depends on the availability of reliable, high resolution, regional observation-based gridded datasets and of climate models capable of properly representing snow processes and snow-climate interactions. Owing to the sparseness of station-based reference observations, in past decades mainly passive microwave remote sensing and reanalysis products have been used to infer information on the snow water equivalent distribution. However, the investigation has usually been limited to flat terrains as the reliability of these products in mountain areas is poorly characterized. This work considers the available snow water equivalent datasets from remote sensing and from reanalyses for the Greater Alpine Region (GAR), and explores their ability to provide a coherent view of the snow water equivalent distribution and climatology in this area. Further we analyze the simulations from the regional and global climate models (RCMs, GCMs) participating in the Coordinated Regional Climate Downscaling Experiment over the European domain (EURO-CORDEX) and in the latest Coupled Model Intercomparison Project (CMIP5) respectively. We evaluate their reliability in reproducing snow water equivalent against the remote sensing and reanalysis datasets previously considered. The results of the analysis show that the distribution of snow water equivalent and the amplitude of its annual cycle are reproduced quite differently by the different remote sensing and renalysis datasets, which in fact exhibit a large spread around the ensemble mean. We find that GCMs at spatial resolutions finer than 1.25° longitude are in closer agreement with the ensemble mean of satellite and reanalysis products in terms of RMSE and standard deviation than lower resolution GCMs. The set of regional climate models from the EURO-CORDEX ensemble provides estimates of snow water equivalent that are locally much larger than those indicated by the gridded datasets but these differences are smoothed out when snow water equivalent is spatially averaged over the Alpine domain. ERA-Interim driven RCM simulations show a snow annual cycle comparable in amplitude to those provided by the reference datasets while GCM-driven RCMs present a large positive bias. The snow reduction expected by mid-21st century in the RCP 8.5 scenario is weaker in higher-resolution RCM simulations than in GCM runs.

Download Full-text

Implications of model selection: a comparison of publicly available, conterminous US-extent hydrologic component estimates

Hydrology and Earth System Sciences ◽

10.5194/hess-25-1529-2021 ◽

2021 ◽

Vol 25 (3) ◽

pp. 1529-1568

Author(s):

Samuel Saxe ◽

William Farmer ◽

Jessica Driscoll ◽

Terri S. Hogue

Keyword(s):

United States ◽

Remote Sensing ◽

Snow Water Equivalent ◽

Reanalysis Data ◽

Data Sets ◽

Hydrologic Models ◽

Study Results ◽

Hydrologic System ◽

Snow Water ◽

Multimodel Ensembles

Abstract. Spatiotemporally continuous estimates of the hydrologic cycle are often generated through hydrologic modeling, reanalysis, or remote sensing (RS) methods and are commonly applied as a supplement to, or a substitute for, in situ measurements when observational data are sparse or unavailable. This study compares estimates of precipitation (P), actual evapotranspiration (ET), runoff (R), snow water equivalent (SWE), and soil moisture (SM) from 87 unique data sets generated by 47 hydrologic models, reanalysis data sets, and remote sensing products across the conterminous United States (CONUS). Uncertainty between hydrologic component estimates was shown to be high in the western CONUS, with median uncertainty (measured as the coefficient of variation) ranging from 11 % to 21 % for P, 14 % to 26 % for ET, 28 % to 82 % for R, 76 % to 84 % for SWE, and 36 % to 96 % for SM. Uncertainty between estimates was lower in the eastern CONUS, with medians ranging from 5 % to 14 % for P, 13 % to 22 % for ET, 28 % to 82 % for R, 53 % to 63 % for SWE, and 42 % to 83 % for SM. Interannual trends in estimates from 1982 to 2010 show common disagreement in R, SWE, and SM. Correlating fluxes and stores against remote-sensing-derived products show poor overall correlation in the western CONUS for ET and SM estimates. Study results show that disagreement between estimates can be substantial, sometimes exceeding the magnitude of the measurements themselves. The authors conclude that multimodel ensembles are not only useful but are in fact a necessity for accurately representing uncertainty in research results. Spatial biases of model disagreement values in the western United States show that targeted research efforts in arid and semiarid water-limited regions are warranted, with the greatest emphasis on storage and runoff components, to better describe complexities of the terrestrial hydrologic system and reconcile model disagreement.

Download Full-text

A quantitative method to decompose SWE differences between regional climate models and reanalysis datasets

Scientific Reports ◽

10.1038/s41598-019-52880-5 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 2

Author(s):

Yun Xu ◽

Andrew Jones ◽

Alan Rhoades

Keyword(s):

Sierra Nevada ◽

Climate Models ◽

Snow Water Equivalent ◽

Driving Forces ◽

Regional Climate ◽

Model Development ◽

Regional Climate Models ◽

The North ◽

Snow Water ◽

Regional Downscaling

Abstract The simulation of snow water equivalent (SWE) remains difficult for regional climate models. Accurate SWE simulation depends on complex interacting climate processes such as the intensity and distribution of precipitation, rain-snow partitioning, and radiative fluxes. To identify the driving forces behind SWE difference between model and reanalysis datasets, and guide model improvement, we design a framework to quantitatively decompose the SWE difference contributed from precipitation distribution and magnitude, ablation, temperature and topography biases in regional climate models. We apply this framework within the California Sierra Nevada to four regional climate models from the North American Coordinated Regional Downscaling Experiment (NA-CORDEX) run at three spatial resolutions. Models generally predict less SWE compared to Landsat-Era Sierra Nevada Snow Reanalysis (SNSR) dataset. Unresolved topography associated with model resolution contribute to dry and warm biases in models. Refining resolution from 0.44° to 0.11° improves SWE simulation by 35%. To varying degrees across models, additional difference arises from spatial and elevational distribution of precipitation, cold biases revealed by topographic correction, uncertainties in the rain-snow partitioning threshold, and high ablation biases. This work reveals both positive and negative contributions to snow bias in climate models and provides guidance for future model development to enhance SWE simulation.

Download Full-text

The Swiss snow climatology as seen by reanalyses and further gridded datasets

10.5194/ems2021-282 ◽

2021 ◽

Author(s):

Monika Goeldi ◽

Stefanie Gubler ◽

Christian Steger ◽

Simon C. Scherrer ◽

Sven Kotlarski

Keyword(s):

Remote Sensing ◽

Snow Cover ◽

Snow Water Equivalent ◽

Spatiotemporal Variability ◽

Data Sets ◽

Data Set ◽

Climate Services ◽

Snow Cover Extent ◽

Snow Water

Snow cover is a key component of alpine environments and knowledge of its spatiotemporal variability, including long-term trends, is vital for a range of dependent systems like winter tourism, hydropower production, etc. Snow cover retreat during the past decades is considered as an important and illustrative indicator of ongoing climate change. As such, the monitoring of surface snow cover and the projection of its future changes play a key role for climate services in alpine regions.In Switzerland, a spatially and temporally consistent snow cover climatology that can serve as a reference for both climate monitoring and for future snow cover projections is currently missing. To assess the value and the potential of currently available long term spatial snow data we compare a range of different gridded snow water equivalent (SWE) datasets for the area of Switzerland, including three reanalysis-based products (COSMO-REA6, ERA5, ERA5-Land). The gridded data sets have a horizontal resolution between 1 and 30 km. The performance of the data sets is assessed by comparing them against three reference data sets with different characteristics (station data, a high-resolution 1km snow model that assimilates snow observations, and an optical remote sensing data set). Four different snow indicators are considered (mean SWE, number of snow days, date of maximum SWE, and snow cover extent) in nine different regions of Switzerland and six elevation classes.The results reveal high temporal correlations between the individual datasets and, in general, a good performance regarding both countrywide and regional estimates of mean SWE. In individual regions, however, larger biases appear. All data sets qualitatively agree on a decreasing trend of mean SWE during the previous decades particularly at low elevations, but substantial differences can exist. Furthermore, all data sets overestimate the snow cover fraction as provided by the remote sensing reference. In general, reanalysis products capture the general characteristics of the Swiss snow climatology but indicate some distinctive deviations &#8211; e.g. like a systematic under- respectively overestimation of the mean snow water equivalent.

Download Full-text

Future Snow Water Equivalent and Snowmelt Extremes from NA-CORDEX Ensembles

10.5194/egusphere-egu2020-5955 ◽

2020 ◽

Author(s):

Eunsang Cho ◽

Rachel R. McCrary ◽

Jennifer M. Jacobs

Keyword(s):

21St Century ◽

Extreme Events ◽

Climate Models ◽

Snow Water Equivalent ◽

Regional Climate ◽

Regional Climate Models ◽

Natural Environments ◽

The North ◽

Snow Water ◽

Late 21St Century

Snowpack and snowmelt driven extreme events can have large societal and economic consequences. Extreme snow can damage infrastructure and buildings. Snow meltwater is a dominant driver of severe spring flooding in the north-central and -eastern U.S. and southern Canada with impacts to the built and natural environments. However, the currently there is very limited guidance regarding the magnitude of &#8220;future&#8221; snow-driven extremes in a changing climate as needed to plan, design, and manage potentially vulnerable infrastructure and ecosystems. Regional climate models (RCMs) are commonly used to study and quantify regional climate changes, even though the ability of these models to accurately represent snow varies. In this study, trends and designs of extreme 25- and 100-year snowpack (snow water equivalent; SWE) and snowmelt events are estimated in the&#160;mid&#160;and&#160;late 21st century&#160;using the North America - Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) ensemble of RCMs under Representative Con-centration Pathways 8.5 (RCP 8.5). This study aims to answer the following three research questions:<ol><li>How much will snow-driven extreme events be changed in the&#160;mid&#160;and&#160;late 21st century?</li> <li>Which regions have the largest differences among models?</li> <li>Which RCM models are the source of these regional uncertainties?</li> </ol>

Download Full-text

Cool season precipitation projections for California and the Western United States in NA-CORDEX models

Climate Dynamics ◽

10.1007/s00382-021-05632-z ◽

2021 ◽

Author(s):

Kelly Mahoney ◽

James D. Scott ◽

Michael Alexander ◽

Rachel McCrary ◽

Mimi Hughes ◽

...

Keyword(s):

United States ◽

Climate Models ◽

Snow Water Equivalent ◽

Regional Climate ◽

Regional Climate Models ◽

Western United States ◽

Monthly Precipitation ◽

Wet Season ◽

Cool Season ◽

Projected Change

AbstractUnderstanding future precipitation changes is critical for water supply and flood risk applications in the western United States. The North American COordinated Regional Downscaling EXperiment (NA-CORDEX) matrix of global and regional climate models at multiple resolutions (~ 50-km and 25-km grid spacings) is used to evaluate mean monthly precipitation, extreme daily precipitation, and snow water equivalent (SWE) over the western United States, with a sub-regional focus on California. Results indicate significant model spread in mean monthly precipitation in several key water-sensitive areas in both historical and future projections, but suggest model agreement on increasing daily extreme precipitation magnitudes, decreasing seasonal snowpack, and a shortening of the wet season in California in particular. While the beginning and end of the California cool season are projected to dry according to most models, the core of the cool season (December, January, February) shows an overall wetter projected change pattern. Daily cool-season precipitation extremes generally increase for most models, particularly in California in the mid-winter months. Finally, a marked projected decrease in future seasonal SWE is found across all models, accompanied by earlier dates of maximum seasonal SWE, and thus a shortening of the period of snow cover as well. Results are discussed in the context of how the diverse model membership and variable resolutions offered by the NA-CORDEX ensemble can be best leveraged by stakeholders faced with future water planning challenges.

Download Full-text

Exploring the use of gpr and satellite-based snow data for snowmelt runoff predictions

10.5194/egusphere-egu21-14972 ◽

2021 ◽

Author(s):

Ilaria Clemenzi ◽

David Gustafsson ◽

Jie Zhang ◽

Björn Norell ◽

Wolf Marchand ◽

...

Keyword(s):

Remote Sensing ◽

Spatial Distribution ◽

Data Assimilation ◽

Snow Water Equivalent ◽

Distribution Model ◽

Accurate Estimation ◽

Snowmelt Runoff ◽

Snow Water ◽

Snow Distribution ◽

The Impact

Snow in the mountains is the result of the interplay between meteorological conditions, e.g., precipitation, wind and solar radiation, and landscape features, e.g., vegetation and topography. For this reason, it is highly variable in time and space. It represents an important water storage for several sectors of the society including tourism, ecology and hydropower. The estimation of the amount of snow stored in winter and available in the form of snowmelt runoff can be strategic for their sustainability. In the hydropower sector, for example, the occurrence of higher snow and snowmelt runoff volumes at the end of the spring and in the early summer compared to the estimated one can substantially impact reservoir regulation with energy and economical losses. An accurate estimation of the snow volumes and their spatial and temporal distribution is thus essential for spring flood runoff prediction. Despite the increasing effort in the development of new acquisition techniques, the availability of extensive and representative snow and density measurements for snow water equivalent estimations is still limited. Hydrological models in combination with data assimilation of ground or remote sensing observations is a way to overcome these limitations. However, the impact of using different types of snow observations on snowmelt runoff predictions is, little understood. In this study we investigated the potential of assimilating in situ and remote sensing snow observations to improve snow water equivalent estimates and snowmelt runoff predictions. We modelled the seasonal snow water equivalent distribution in the Lake &#214;veruman catchment, Northern Sweden, which is used for hydropower production. Simulations were performed using the semi-distributed hydrological model HYPE for the snow seasons 2017-2020. For this purpose, a snowfall distribution model based on wind-shelter factors was included to represent snow spatial distribution within model units. The units consist of 2.5x2.5 km2 grid cells, which were further divided into hydrological response units based on elevation, vegetation and aspect. The impact on the estimation of the total catchment mean snow water equivalent and snowmelt runoff volume were evaluated using for data assimilation, gpr-based snow water equivalent data acquired along survey lines in the catchment in the early spring of the four years, snow water equivalent data obtained by a machine learning algorithm and satellite-based fractional snow cover data. Results show that the wind-shelter based snow distribution model was able to represent a similar spatial distribution as the gpr survey lines, when assessed on the catchment level. Deviations in the model performance within and between specific gpr survey lines indicate issues with the spatial distribution of input precipitation, and/or need to include explicit representation of snow drift between model units. The explicit snow distribution model also improved runoff simulations, and the ability of the model to improve forecast through data assimilation.

Download Full-text

Testing snow water equivalent retrieval algorithms for passive microwave remote sensing in an alpine watershed of western Canada

Canadian Journal of Remote Sensing ◽

10.5589/m10-009 ◽

2010 ◽

Vol 36 (sup1) ◽

pp. S74-S86 ◽

Cited By ~ 32

Author(s):

Jinjun Tong ◽

Stephen J Déry ◽

Peter L Jackson ◽

Chris Derksen

Keyword(s):

Remote Sensing ◽

Snow Water Equivalent ◽

Microwave Remote Sensing ◽

Passive Microwave ◽

Western Canada ◽

Passive Microwave Remote Sensing ◽

Retrieval Algorithms ◽

Snow Water ◽

Alpine Watershed

Download Full-text

Using high-resolution regional climate models to estimate return levels of daily extreme precipitation over Bavaria

Natural Hazards and Earth System Science ◽

10.5194/nhess-21-3573-2021 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3573-3598

Author(s):

Benjamin Poschlod

Keyword(s):

High Resolution ◽

Climate Models ◽

Regional Climate ◽

Daily Rainfall ◽

Regional Climate Models ◽

Reanalysis Data ◽

Extreme Value ◽

Return Level ◽

Gev Distribution ◽

Return Levels

Abstract. Extreme daily rainfall is an important trigger for floods in Bavaria. The dimensioning of water management structures as well as building codes is based on observational rainfall return levels. In this study, three high-resolution regional climate models (RCMs) are employed to produce 10- and 100-year daily rainfall return levels and their performance is evaluated by comparison to observational return levels. The study area is governed by different types of precipitation (stratiform, orographic, convectional) and a complex terrain, with convective precipitation also contributing to daily rainfall levels. The Canadian Regional Climate Model version 5 (CRCM5) at a 12 km spatial resolution and the Weather and Forecasting Research (WRF) model at a 5 km resolution both driven by ERA-Interim reanalysis data use parametrization schemes to simulate convection. WRF at a 1.5 km resolution driven by ERA5 reanalysis data explicitly resolves convectional processes. Applying the generalized extreme value (GEV) distribution, the CRCM5 setup can reproduce the observational 10-year return levels with an areal average bias of +6.6 % and a spatial Spearman rank correlation of ρ=0.72. The higher-resolution 5 km WRF setup is found to improve the performance in terms of bias (+4.7 %) and spatial correlation (ρ=0.82). However, the finer topographic details of the WRF-ERA5 return levels cannot be evaluated with the observation data because their spatial resolution is too low. Hence, this comparison shows no further improvement in the spatial correlation (ρ=0.82) but a small improvement in the bias (2.7 %) compared to the 5 km resolution setup. Uncertainties due to extreme value theory are explored by employing three further approaches. Applied to the WRF-ERA5 data, the GEV distributions with a fixed shape parameter (bias is +2.5 %; ρ=0.79) and the generalized Pareto (GP) distributions (bias is +2.9 %; ρ=0.81) show almost equivalent results for the 10-year return period, whereas the metastatistical extreme value (MEV) distribution leads to a slight underestimation (bias is −7.8 %; ρ=0.84). For the 100-year return level, however, the MEV distribution (bias is +2.7 %; ρ=0.73) outperforms the GEV distribution (bias is +13.3 %; ρ=0.66), the GEV distribution with fixed shape parameter (bias is +12.9 %; ρ=0.70), and the GP distribution (bias is +11.9 %; ρ=0.63). Hence, for applications where the return period is extrapolated, the MEV framework is recommended. From these results, it follows that high-resolution regional climate models are suitable for generating spatially homogeneous rainfall return level products. In regions with a sparse rain gauge density or low spatial representativeness of the stations due to complex topography, RCMs can support the observational data. Further, RCMs driven by global climate models with emission scenarios can project climate-change-induced alterations in rainfall return levels at regional to local scales. This can allow adjustment of structural design and, therefore, adaption to future precipitation conditions.

Download Full-text

Snow as a source of predictability in seasonal forecasts?

10.5194/egusphere-egu21-7331 ◽

2021 ◽

Author(s):

Danny Risto ◽

Bodo Ahrens ◽

Kristina Fröhlich

Keyword(s):

Latent Heat ◽

Land Surface ◽

Snow Water Equivalent ◽

Reanalysis Data ◽

Heat Fluxes ◽

Seasonal Forecasting ◽

Seasonal Forecasts ◽

Predictive Skill ◽

Temperature Forecast ◽

Snow Water

Besides the ocean, the land surface is a crucial component for predictability at (sub-)seasonal time scales. While the prediction of 2m temperature up to several months is possible for some maritime regions, continental regions lack predictive skill. Improved representation of the land surface in seasonal forecasting systems could help to close this gap. Snow cover fraction and snow water equivalent (SWE) are essential properties of the land surface. A snow-covered land surface leads to local temperature decreases in the overlying air (snow-albedo effect and high emissivity) and melting snow cools the surface air and contributes to soil moisture. First, we analyse the dynamical relationships between snow, 2m temperature and sensible/latent heat fluxes in reanalysis data in the northern hemisphere. Then we investigate whether these relationships are also present in operational seasonal forecast models provided by Copernicus Climate Change Service (C3S). First results show that the quality of the 2m temperature forecast over continental regions drops sharply after the first forecasted month, whereas anomalies in snow water equivalent can be predicted up to several months. Forecasted anomalies in sensible and latent heat fluxes of continental land surfaces show predictive skill during winter and spring only locally in some places, which reduces potential interactions between snow/land surface and the atmosphere in the models. The goal of this ongoing work is to assess the importance of snow initialisation and parameterisation for seasonal forecasting.

Download Full-text