Snow model intercomparisons
– relevance to alpine research
Richard Essery, University of Edinburgh
INARCH Workshop, 23 October 2015
Evaluation, intercomparison and benchmarking
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Evaluation – comparing model outputs with observations
•
(Inter)comparison – comparing models with each other
Pomeroy definition: “A tedious and thankless task of dubious
scientific merit”
•
Benchmarking – comparing models with an a priori standard
Abramowitz, 2012. Towards a public, standardized, diagnostic
benchmarking system for land surface models. Geosci. Model
Dev., 5, 819-827
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Intercomparison projects with snow components
Global
Regional
Local
Coupled
Uncoupled
AMIP
CMIP
ESM-SnowMIP
GSWP
WaterMIP
ESM-SnowMIP
PILPS2e
Rhône-Agg
PILPS2d
SnowMIP
ESM-SnowMIP
http://www.climate-cryosphere.org/activities/targeted/esm-snowmip
Drive snow models by:
• full coupling with global Earth System Models (CMIP6)
• bias-corrected global reanalyses (GSWP3)
• in-situ meteorological data from well-instrumented reference sites
• global reanalyses downscaled from 0.5º grids to reference sites
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SnowMIP and SnowMIP2
● SnowMIP (Etchevers et al. 2004)
● SnowMIP2 (Essery et al. 2009)
Col de Porte
Goose Bay
Sleepers River
Weissfluhjoch
Alptal
BERMS
Fraser
Hitsujigaoka
Hyytiälä
(45.3ºN, 5.8ºE)
(53.3ºN, 60.4ºW)
(44.5ºN, 72.2ºW)
(46.8ºN, 9.8ºE)
(47.3ºN, 8.7ºE)
(53.6ºN, 104.4ºW)
(39.5ºN, 105.5ºW)
(42.6ºN, 141.2ºE)
(61.5ºN, 24.2ºE)
Only 1 or 2 winters at most sites
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ESM-SnowMIP reference sites
•
•
•
•
•
Bayelva, Svalbard
BERMS, Saskatchewan
Col de Porte, France *
Imnavait Creek, Alaska
Marmot Creek, Alberta *
• Reynolds Creek, Idaho *
• Sodankylä, Finland
• Swamp Angel, Colorado *
• Weissfluhjoch, Switzerland *
7 – 25 years at each site
* high-elevation sites
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Snow study sites and synoptic stations
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In situ met data and reanalyses
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In situ met data and reanalyses
Sodankylä 2009-2010
daily ‫ ݎ‬ൌ 0.989
‫ ݎ‬ൌ 0.996
‫ ݎ‬ൌ 0.719
‫ ݎ‬ൌ 0.628
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In situ met data and reanalyses
Col de Porte 2009-2010
daily ‫ ݎ‬ൌ 0.978
‫ ݎ‬ൌ 0.971
‫ ݎ‬ൌ 0.552
‫ ݎ‬ൌ 0.713
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Snow mass simulations with in situ driving
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Simulations driven with reanalyses
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Simulations with bias-corrected reanalyses
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Single variables replaced with reanalyses
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Temperature sensitivity and energy partition
Weissfluhjoch in situ
Weissfluhjoch reanalyses
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Conclusions
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snow accumulation and melt can be modelled well at (sheltered)
mountain sites with an energy balance model and good-quality in
situ meteorological data
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large-scale meteorological reanalyses tend to have positive
temperature and negative snowfall biases at high elevations
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adjusting reanalyses to site averages of driving variables (if known)
will improve simulations
•
total precipitation should be downscaled first and then partitioned
into rain and snow according to downscaled temperature
•
models differ in their sensitivities to temperature biases
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snow mass balance alone does not contain enough information to
constrain snow energy balance
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CMIP5 snow headlines
"models reproduced the observed snow cover extent very well, but the
significant trend towards reduced spring snow cover extent over the
1979-2005 period was underestimated"
Brutel-Vuilmet et al. (2012), doi:10.5194/tcd-6-3317-2012
“The observed decreases in June SCE were found to diverge markedly
from the rate of snow cover loss projected by CMIP5 climate model
simulations”
Derksen and Brown (2012), doi:10.1029/2012GL053387
"The spread in snow albedo feedback is very similar to that found in
CMIP3 models, and it accounts for much of the spread in the 21st
century warming of Northern Hemisphere land masses in the CMIP5
ensemble”
Qu and Hall (2013), doi:10.1007/s00382-013-1774-0
“Much of the disagreement in modeled mean soil temperatures can be
traced to the representation of thermal connection between the air and
land surface and, in particular, its mediation by snow in winter"
Koven et al. (2013), doi:10.1175/JCLI-D-12-00228.1
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GSWP-2, Rhône-Agg and WaterMIP snow headlines
“Most of the imbalances in both energy and water can be attributed to
shortcomings in the handling of snow”
“there is mediocre agreement among the models for most of the snowrelated variables, suggesting a potential area of continuing weakness”
Dirmeyer et al. (2009), doi:10.1175/BAMS-87-10-1381
“The need for a better treatment of snow-cover dependence on spatial
scale is warranted”
Boone et al. (2004), J. Climate, 17
“Significant simulation differences between land surface and global
hydrological models are found to be caused by the snow scheme
employed” Haddeland (2011), doi:10.1175/2011JHM1324.1
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PILPS and SnowMIP snow headlines
Mid-latitudes:
“early season ablation events are a significant source of model scatter”
Slater et al. (2001), J. Hydrometeorol., 2
High latitudes:
“The greatest among-model differences in energy and moisture fluxes in
these high-latitude environments occur during the spring snowmelt
period, reflecting different model parameterizations of snow processes”
Nijssen et al. (2003), doi:10.1016/S0921-8181(03)00004-3
“models generally predict the duration of snow cover quite well but
show broad ranges in their simulations of maximum snow accumulation,
particularly at warmer sites and during warmer winters, and differences
between open and forested plots are underestimated on average”
Essery et al. (2009), doi:10.1175/2009BAMS2629.1
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