CLARA-SAL: a global 28 yr timeseries of Earth's black-sky surface albedo

Riihelä, Aku; Manninen, Terhikki; Laine, Vesa; Andersson, Kristin; Kaspar, Frank

doi:10.5194/acp-13-3743-2013

Cited by 88 publications

(79 citation statements)

References 63 publications

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“…In an attempt to eliminate errors in surface albedo, in the experiments ALB1 and ALB1_NDG the model's albedo field over continents was replaced by prescribed surface albedos based on observations. Monthly mean albedos in the CLARA-SAL data set derived from AVHRR satellite data (Riihelä et al, 2013) were applied. Since this data set starts from year 1982, for years 1978-1981 the average annual cycle of CLARA-SAL albedo for years 1982-2006 was employed.…”

Section: Alb1 and Alb1_ndgmentioning

confidence: 99%

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: Alb1 and Alb1_ndgmentioning

confidence: 99%

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

et al. 2014

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“…Thus, even the simplest parameterizations can give equally good or bad results as the most complex ones (Essery et al, 2012;Brun et al, 2008). Recent advances in the remote sensing retrieval techniques of surface albedo over the Arctic allowed for the collection of a 28-year time series of albedo estimations in all sky conditions (Riihelä et al, 2013), offering a valuable reference data set to analyse spatial and temporal albedo variability.…”

Section: Snow and Ice Albedo: Observations And Parameterizationsmentioning

confidence: 99%

Advances in understanding and parameterization of small-scale physical processes in the marine Arctic climate system: a review

et al. 2014

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Abstract. The Arctic climate system includes numerous highly interactive small-scale physical processes in the atmosphere, sea ice, and ocean. During and since the International Polar Year 2007-2009, significant advances have been made in understanding these processes. Here, these recent advances are reviewed, synthesized, and discussed. In atmospheric physics, the primary advances have been in cloud physics, radiative transfer, mesoscale cyclones, coastal, and fjordic processes as well as in boundary layer processes and surface fluxes. In sea ice and its snow cover, advances have been made in understanding of the surface albedo and its relationships with snow properties, the internal structure of sea ice, the heat and salt transfer in ice, the formation of superimposed ice and snow ice, and the small-scale dynamics of sea ice. For the ocean, significant advances have been related to exchange processes at the ice-ocean interface, diapycnal mixing, double-diffusive convection, tidal currents and diurnal resonance. Despite this recent progress, some of these small-scale physical processes are still not sufficiently understood: these include wave-turbulence interactions in the atmosphere and ocean, the exchange of heat and salt at the ice-ocean interface, and the mechanical weakening of sea ice. Many other processes are reasonably well understood as stand-alone processes but the challenge is to understand their interactions with and impacts and feedbacks on other processes. Uncertainty in the parameterization of small-scale processes continues to be among the greatest challenges facing climate modelling, particularly in high latitudes. Further improvements in parameterization require new year-round field campaigns on the Arctic sea ice, closely combined with satellite remote sensing studies and numerical model experiments.

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“…We have compared the model's albedo to the CLARA-A2 dataset (Karlsson et al, 2017), which contains a global dataset of surface radiation products based on the measurements of the Advanced Very High Resolution Radiometer (AVHRR) on-10 board the polar orbiting National Oceanic and Atmospheric Administration (NOAA) and MetOP satellites (Schulz et al, 2009;Karlsson et al, 2012;Riihelä et al, 2013). Since the satellite dataset does not cover mid-winter months due to too low solar elevations, we have compared the monthly mean albedos for March and April.…”

Section: Snow Analysismentioning

confidence: 99%

The regional climate model REMO (v2015) coupled with the 1-D freshwater model FLake (v1): Fenno-Scandinavian climate and lakes

Pietikäinen¹,

Markkanen²,

Sieck³

et al. 2017

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Abstract. The regional climate model REMO was coupled with the FLake lake model to include an interactive treatment of lakes. Using this new version, the Fenno-Scandinavian climate and lake characteristics were studied in a set of 35-year hindcast simulations. Additionally, sensitivity tests related to the parameterization of snow albedo were conducted. Our results show that overall the new model version improves the representation of the Fenno-Scandinavian climate in terms of 2-m temperature and precipitation, but the downside is that an existing wintertime cold bias in the model is enhanced. The lake surface water 5 temperature, ice depth and ice season length were analyzed in detail for ten Finnish, four Swedish, two Russian and one Estonian lakes. The results show that the model can reproduce these characteristic with reasonably high accuracy. The cold bias during winter causes e.g. overestimation of ice layer thickness at several of the studied lakes, but overall the values from the model are realistic and represent well the lake physics in a long-term simulation. We also analyzed the snow depth on ice from ten Finnish lakes and vertical temperature profiles from five Finnish lakes and the model results are in realistic.

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CLARA-SAL: a global 28 yr timeseries of Earth's black-sky surface albedo

Cited by 88 publications

References 63 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

Advances in understanding and parameterization of small-scale physical processes in the marine Arctic climate system: a review

The regional climate model REMO (v2015) coupled with the 1-D freshwater model FLake (v1): Fenno-Scandinavian climate and lakes

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