On the persistent spread in snow-albedo feedback

Qu, Xiaoshu; Hall, Alex

doi:10.1007/s00382-013-1774-0

Cited by 208 publications

(283 citation statements)

References 41 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: 94%

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: 94%

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

et al. 2014

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“…Crook and Forster [8] show the extratropical surface cryospheric feedback in CMIP3 models to be considerably higher for observations (3.1 ± 1.3 W/m 2 /K) than models (0.4-1.2 W/m 2 /K), despite their exhibiting comparable seasonal sensitivities. Colman [7] also finds a lack of correlation between surface albedo feedback at climate change and other timescales despite significant correlations between climate change, seasonal, and interannual timescales for NH snow cover, a relationship also evident in CMIP5 models [27].…”

Section: Cryospheric and Water Vapor Ecsmentioning

confidence: 96%

Recent Progress in Constraining Climate Sensitivity With Model Ensembles

Fasullo

Sanderson

Trenberth

2015

Curr Clim Change Rep

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Recently available model ensembles have created an unprecedented opportunity for exploring and narrowing uncertainty in one of climate's benchmark indices, equilibrium climate sensitivity. A range of novel approaches for constraining the raw sensitivity estimates from these ensembles with observations has also been proposed, applied, and explored in a diversity of contexts. Through subsequent analysis, an increased understanding of the relative merits and limitations of these methods has been gained and their refinement and optimal implementation continue to be actively studied and debated with the hopes of reducing uncertainty in one of climate science's most persistent and elusive measures.

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“…Intermodel spread herein has previously been attributed to different treatment of vegetation masking effects (Hall and Qu, 2006;Qu and Hall, 2014). They found that a higher the snow-covered albedo yields a stronger surface albedo feedback, hence a stronger climate response.…”

Section: B What Is the Role Of Downwelling Longwave Radiation In Thementioning

confidence: 84%

“…A relatively large contrast in albedo between snow-covered and snow-free surfaces yields a stronger surface albedo feedback, because the net solar radiation increase will be larger if the albedo contrast is more pronounced. The surface albedo of snowcovered surfaces is therefore also a good predictor of Arctic warming (Hall and Qu, 2006;Qu and Hall, 2014. Note that both the albedo of the continents and sea ice surface are good predictors for Arctic warming ( Supplementary Fig.…”

Section: Chaptermentioning

confidence: 99%

“…CMIP5). An example of this approach concerns the link between seasonal and long-term changes in the albedo feedback strength over northern hemispheric land masses (Hall and Qu, 2006;Qu and Hall, 2014). The lapserate feedback is more difficult to evaluate, as it involves various atmospheric processes that affect the atmospheric lapse-rate differently (Pithan and Mauritsen, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Understanding the predictability of the Arctic climate

Krikken

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On the persistent spread in snow-albedo feedback

Cited by 208 publications

References 41 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

Recent Progress in Constraining Climate Sensitivity With Model Ensembles

Understanding the predictability of the Arctic climate

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