Using models and satellite observations to evaluate the strength of snow albedo feedback

Fletcher, Christopher G.; Hong-yan, Zhao; Kushner, Paul J.; Fernandes, Richard

doi:10.1029/2012jd017724

Cited by 54 publications

(87 citation statements)

References 25 publications

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“…Multi-model intercomparisons have also demonstrated that the strength of SAF varies substantially among both CMIP3 (Hall and Qu, 2006;Qu and Hall, 2007;Fletcher et al, 2012) and CMIP5 models (Qu and Hall, 2014). There is a strong correspondence between the SAF evaluated based on transient climate change experiments and based on the seasonal cycle.…”

Section: P Räisänen Et Al: Snow-off Timing In Echam5mentioning

confidence: 95%

“…There is a strong correspondence between the SAF evaluated based on transient climate change experiments and based on the seasonal cycle. Model results for the seasonal SAF fall on both sides of the corresponding observational estimates (Hall and Qu, 2006;Fletcher et al, 2012;Qu and Hall, 2014). The simulated SAF is strongly influenced by the climatological surface albedo of snow-covered land, which shows a surprisingly large spread even among the CMIP5 models.…”

Section: P Räisänen Et Al: Snow-off Timing In Echam5mentioning

confidence: 99%

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Evaluation of North Eurasian snow-off dates in the ECHAM5.4 atmospheric general circulation model

et al. 2014

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Abstract. The timing of springtime end of snowmelt (snowoff date) in northern Eurasia in version 5.4 of the ECHAM5 atmospheric general circulation model (GCM) is evaluated through comparison with a snow-off date data set based on space-borne microwave radiometer measurements and with Russian snow course data. ECHAM5 reproduces well the observed gross geographical pattern of snow-off dates, with earliest snow-off (in March) in the Baltic region and latest snow-off (in June) in the Taymyr Peninsula and in northeastern parts of the Russian Far East. The primary biases are (1) a delayed snow-off in southeastern Siberia (associated with too low springtime temperature and too high surface albedo, in part due to insufficient shielding by canopy); and (2) an early bias in the western and northern parts of northern Eurasia. Several sensitivity experiments were conducted, where biases in simulated atmospheric circulation were corrected through nudging and/or the treatment of surface albedo was modified. While this alleviated some of the model biases in snow-off dates, 2 m temperature and surface albedo, especially the early bias in snow-off in the western parts of northern Eurasia proved very robust and was actually larger in the nudged runs.A key issue underlying the snow-off biases in ECHAM5 is that snowmelt occurs at too low temperatures. Very likely, this is related to the treatment of the surface energy budget. On one hand, the surface temperature T s is not computed separately for the snow-covered and snow-free parts of the grid cells, which prevents T s from rising above 0 • C before all snow has vanished. Consequently, too much of the surface net radiation is consumed in melting snow and too little in heating the air. On the other hand, ECHAM5 does not include a canopy layer. Thus, while the albedo reduction due to canopy is accounted for, the shielding of snow on ground by the overlying canopy is not considered, which leaves too much solar radiation available for melting snow.

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Section: P Räisänen Et Al: Snow-off Timing In Echam5mentioning

confidence: 95%

Section: P Räisänen Et Al: Snow-off Timing In Echam5mentioning

confidence: 99%

Evaluation of North Eurasian snow-off dates in the ECHAM5.4 atmospheric general circulation model

et al. 2014

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“…Groisman et al, 1994;Déry and Brown, 2007). (ii) Hall and Qu (2006) and Fletcher et al (2012) have shown this feedback to be correctly represented only in a minority of the CMIP3 (Coupled Model Intercomparison Project -Phase 3: http://www-pcmdi.llnl.gov/ ipcc/about ipcc.php) models. Due to its low heat conductivity, snow also effectively insulates the underlying soil, with important effects on deep soil temperatures and permafrost extent (Zhang, 2005;Lawrence and Slater, 2010;Gouttevin et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

An analysis of present and future seasonal Northern Hemisphere land snow cover simulated by CMIP5 coupled climate models

2013

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Abstract. The 20th century seasonal Northern Hemisphere (NH) land snow cover as simulated by available CMIP5 model output is compared to observations. On average, the models reproduce the observed snow cover extent very well, but the significant trend towards a reduced spring snow cover extent over the 1979-2005 period is underestimated (observed: (−3.4 ± 1.1) % per decade; simulated: (−1.0 ± 0.3) % per decade). We show that this is linked to the simulated Northern Hemisphere extratropical spring land warming trend over the same period, which is also underestimated, although the models, on average, correctly capture the observed global warming trend. There is a good linear correlation between the extent of hemispheric seasonal spring snow cover and boreal large-scale spring surface air temperature in the models, supported by available observations. This relationship also persists in the future and is independent of the particular anthropogenic climate forcing scenario. Similarly, the simulated linear relationship between the hemispheric seasonal spring snow cover extent and global mean annual mean surface air temperature is stable in time. However, the slope of this relationship is underestimated at present (observed: (−11.8 ± 2.7) % • C −1 ; simulated: (−5.1 ± 3.0) % • C −1 ) because the trend towards lower snow cover extent is underestimated, while the recent global warming trend is correctly represented.

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“…Variations in their seasonal cycle have a major impact on the annual carbon balance [14,15] and vegetation growth [16]. Snow qualities, such as density, influence the energy budget through albedo feedbacks [17], and control thermal insulation of the soil [18], which in turn affects river run-off in the northern hemisphere [19][20][21] and mountainous [22] regions. Beyond the possibility of estimating snow liquid water using the sensitivity of L-band brightness temperatures to the snow moisture investigated in [41].…”

Section: Introductionmentioning

confidence: 99%

Snow Density and Ground Permittivity Retrieved from L-Band Radiometry: Melting Effects

Schwank

Naderpour

2018

Remote Sensing

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Ground permittivity and snow density retrievals for the "snow-free period", "cold winter period", and "early spring period" are performed using the experimental L-band radiometry data from the winter 2016/2017 campaign at the Davos-Laret Remote Sensing Field Laboratory. The performance of the single-angle and multi-angle two-parameter retrieval algorithms employed during each of the aforementioned three periods is assessed using in-situ measured ground permittivity and snow density. Additionally, a synthetic sensitivity analysis is conducted that studies melting effects on the retrievals in the form of two types of "geophysical noise" (snow liquid water and footprint-dependent ground permittivity). Experimental and synthetic analyses show that both types of investigated "geophysical noise" noticeably disturb the retrievals and result in an increased correlation between them. The strength of this correlation is successfully used as a quality-indicator flag for the purpose of filtering out highly correlated ground permittivity and snow density retrievals. It is demonstrated that this filtering significantly improves the accuracy of both ground permittivity and snow density retrievals compared to corresponding reference in-situ data. Experimental and synthetic retrievals are performed in retrieval modes RM = "H", "V", and "HV", where brightness temperatures from polarizations p = H, p = V, or both p = H and V are used, respectively, in the retrieval procedure. Our analysis shows that retrievals for RM = "V" are predominantly least prone to the investigated "geophysical noise". The presented experimental results indicate that retrievals match in-situ observations best for the "snow-free period" and the "cold winter period" when "geophysical noise" is at minimum.

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Using models and satellite observations to evaluate the strength of snow albedo feedback

Cited by 54 publications

References 25 publications

Evaluation of North Eurasian snow-off dates in the ECHAM5.4 atmospheric general circulation model

Evaluation of North Eurasian snow-off dates in the ECHAM5.4 atmospheric general circulation model

An analysis of present and future seasonal Northern Hemisphere land snow cover simulated by CMIP5 coupled climate models

Snow Density and Ground Permittivity Retrieved from L-Band Radiometry: Melting Effects

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