Surface albedo feedback - observational constraints on climate models

Assessing Snow Albedo Feedback in Simulated Climate Change

Journal of Climate ◽

10.1175/jcli3750.1 ◽

2006 ◽

Vol 19 (11) ◽

pp. 2617-2630 ◽

Cited By ~ 83

Author(s):

Xin Qu ◽

Alex Hall

Keyword(s):

Climate Change ◽

Standard Deviation ◽

Northern Hemisphere ◽

Surface Albedo ◽

Climate Models ◽

Attenuation Effect ◽

Albedo Feedback ◽

Snow Albedo ◽

The Mean ◽

Planetary Albedo

Abstract In this paper, the two factors controlling Northern Hemisphere springtime snow albedo feedback in transient climate change are isolated and quantified based on scenario runs of 17 climate models used in the Intergovernmental Panel on Climate Change Fourth Assessment Report. The first factor is the dependence of planetary albedo on surface albedo, representing the atmosphere's attenuation effect on surface albedo anomalies. It is potentially a major source of divergence in simulations of snow albedo feedback because of large differences in simulated cloud fields in Northern Hemisphere land areas. To calculate the dependence, an analytical model governing planetary albedo was developed. Detailed validations of the analytical model for two of the simulations are shown, version 3 of the Community Climate System Model (CCSM3) and the Geophysical Fluid Dynamics Laboratory global coupled Climate Model 2.0 (CM2.0), demonstrating that it facilitates a highly accurate calculation of the dependence of planetary albedo on surface albedo given readily available simulation output. In all simulations it is found that surface albedo anomalies are attenuated by approximately half in Northern Hemisphere land areas as they are transformed into planetary albedo anomalies. The intermodel standard deviation in the dependence of planetary albedo on surface albedo is surprisingly small, less than 10% of the mean. Moreover, when an observational estimate of this factor is calculated by applying the same method to the satellite-based International Satellite Cloud Climatology Project (ISCCP) data, it is found that most simulations agree with ISCCP values to within about 10%, despite further disagreements between observed and simulated cloud fields. This suggests that even large relative errors in simulated cloud fields do not result in significant error in this factor, enhancing confidence in climate models. The second factor, related exclusively to surface processes, is the change in surface albedo associated with an anthropogenically induced temperature change in Northern Hemisphere land areas. It exhibits much more intermodel variability. The standard deviation is about ⅓ of the mean, with the largest value being approximately 3 times larger than the smallest. Therefore this factor is unquestionably the main source of the large divergence in simulations of snow albedo feedback. To reduce the divergence, attention should be focused on differing parameterizations of snow processes, rather than intermodel variations in the attenuation effect of the atmosphere on surface albedo anomalies.

Download Full-text

Assessing Prior Emergent Constraints on Surface Albedo Feedback in CMIP6

Journal of Climate ◽

10.1175/jcli-d-20-0703.1 ◽

2021 ◽

Vol 34 (10) ◽

pp. 3889-3905

Author(s):

Chad W. Thackeray ◽

Alex Hall ◽

Mark D. Zelinka ◽

Christopher G. Fletcher

Keyword(s):

Sea Ice ◽

Snow Cover ◽

Surface Albedo ◽

Climate Models ◽

Sea Ice Thickness ◽

Albedo Feedback ◽

Out Of Sample ◽

Ice Retreat ◽

Sea Ice Retreat ◽

Emergent Constraint

AbstractAn emergent constraint (EC) is a popular model evaluation technique, which offers the potential to reduce intermodel variability in projections of climate change. Two examples have previously been laid out for future surface albedo feedbacks (SAF) stemming from loss of Northern Hemisphere (NH) snow cover (SAFsnow) and sea ice (SAFice). These processes also have a modern-day analog that occurs each year as snow and sea ice retreat from their seasonal maxima, which is strongly correlated with future SAF across an ensemble of climate models. The newly released CMIP6 ensemble offers the chance to test prior constraints through out-of-sample verification, an important examination of EC robustness. Here, we show that the SAFsnow EC is equally strong in CMIP6 as it was in past generations, while the SAFice EC is also shown to exist in CMIP6, but with different, slightly weaker characteristics. We find that the CMIP6 mean NH SAF exhibits a global feedback of 0.25 ± 0.05 W m−2 K−1, or ~61% of the total global albedo feedback, largely in line with prior generations despite its increased climate sensitivity. The NH SAF can be broken down into similar contributions from snow and sea ice over the twenty-first century in CMIP6. Crucially, intermodel variability in seasonal SAFsnow and SAFice is largely unchanged from CMIP5 because of poor outlier simulations of snow cover, surface albedo, and sea ice thickness. These outliers act to mask the noted improvement from many models when it comes to SAFice, and to a lesser extent SAFsnow.

Download Full-text

The Effect of Atmospheric Transmissivity on Model and Observational Estimates of the Sea Ice Albedo Feedback

Journal of Climate ◽

10.1175/jcli-d-19-0674.1 ◽

2020 ◽

Vol 33 (13) ◽

pp. 5743-5765

Author(s):

Aaron Donohoe ◽

Ed Blanchard-Wrigglesworth ◽

Axel Schweiger ◽

Philip J. Rasch

Keyword(s):

Sea Ice ◽

Surface Albedo ◽

Climate Models ◽

Arctic Sea Ice ◽

The Arctic ◽

Surface Temperature Change ◽

Feedback Parameter ◽

Radiative Fluxes ◽

Albedo Feedback ◽

Concentration Changes

AbstractThe sea ice-albedo feedback (SIAF) is the product of the ice sensitivity (IS), that is, how much the surface albedo in sea ice regions changes as the planet warms, and the radiative sensitivity (RS), that is, how much the top-of-atmosphere radiation changes as the surface albedo changes. We demonstrate that the RS calculated from radiative kernels in climate models is reproduced from calculations using the “approximate partial radiative perturbation” method that uses the climatological radiative fluxes at the top of the atmosphere and the assumption that the atmosphere is isotropic to shortwave radiation. This method facilitates the comparison of RS from satellite-based estimates of climatological radiative fluxes with RS estimates across a full suite of coupled climate models and, thus, allows model evaluation of a quantity important in characterizing the climate impact of sea ice concentration changes. The satellite-based RS is within the model range of RS that differs by a factor of 2 across climate models in both the Arctic and Southern Ocean. Observed trends in Arctic sea ice are used to estimate IS, which, in conjunction with the satellite-based RS, yields an SIAF of 0.16 ± 0.04 W m−2 K−1. This Arctic SIAF estimate suggests a modest amplification of future global surface temperature change by approximately 14% relative to a climate system with no SIAF. We calculate the global albedo feedback in climate models using model-specific RS and IS and find a model mean feedback parameter of 0.37 W m−2 K−1, which is 40% larger than the IPCC AR5 estimate based on using RS calculated from radiative kernel calculations in a single climate model.

Download Full-text

Snow albedo feedback

Progress in Physical Geography Earth and Environment ◽

10.1177/0309133315620999 ◽

2016 ◽

Vol 40 (3) ◽

pp. 392-408 ◽

Cited By ~ 28

Author(s):

Chad W. Thackeray ◽

Christopher G. Fletcher

Keyword(s):

Surface Albedo ◽

Climate Models ◽

Regional Climate ◽

Regional Climate Change ◽

Global Scale ◽

Albedo Feedback ◽

Snow Albedo ◽

Small Influence ◽

Large Ensembles ◽

Substantial Progress

Over the past decade, substantial progress has been made in improving our understanding of surface albedo feedbacks, where changes in surface albedo from warming (cooling) can cause increases (decreases) in absorbed solar radiation, amplifying the initial warming (cooling). The goal of this review is to synthesize and assess recent research into the feedback caused by changing continental snow cover, or snow albedo feedback (SAF). Four main topics are evaluated: (i) the importance of SAF to the global energy budget, (ii) estimates of SAF from various data sources, (iii) factors influencing the spread in SAF, and (iv) outstanding issues related to our understanding of the physical processes that control SAF (and their uncertainties). SAF is found to exert a small influence on a global scale, with amplitude of ∼ 0.1 Wm−2 K−1, roughly 7% of the strength of water vapor feedback. However, SAF is an important driver of regional climate change over Northern Hemisphere (NH) extratropical land, where observation-based estimates show a peak feedback of around 1% decrease in surface albedo per degree of warming during spring. Viewed collectively, the current generation of climate models represent this process accurately, but several models still use outdated parameterizations of snow and surface albedo that contribute to biases that impact the simulation of SAF. This discussion serves to synthesize and evaluate previously published literature, while highlighting promising directions being taken at the forefront of research such as high resolution modeling and the use of large ensembles.

Download Full-text

Surface Albedo Feedback Estimates for the AR4 Climate Models

Journal of Climate ◽

10.1175/jcli3624.1 ◽

2006 ◽

Vol 19 (3) ◽

pp. 359-365 ◽

Cited By ~ 81

Author(s):

Michael Winton

Keyword(s):

Climate Change ◽

Standard Deviation ◽

Surface Albedo ◽

Climate Models ◽

Climate Model ◽

Intergovernmental Panel ◽

Albedo Feedback ◽

Mean Climate ◽

Carbon Dioxide Co2 ◽

The Impact

Abstract A technique for estimating surface albedo feedback (SAF) from standard monthly mean climate model diagnostics is applied to the 1% yr−1 carbon dioxide (CO2)-increase transient climate change integrations of 12 Intergovernmental Panel on Climate Change (IPCC) fourth assessment report (AR4) climate models. Over the 80-yr runs, the models produce a mean SAF at the surface of 0.3 W m−2 K−1 with a standard deviation of 0.09 W m−2 K−1. Relative to 2 × CO2 equilibrium run estimates from an earlier group of models, both the mean SAF and the standard deviation are reduced. Three-quarters of the model mean SAF comes from the Northern Hemisphere in roughly equal parts from the land and ocean areas. The remainder is due to Southern Hemisphere ocean areas. The SAF differences between the models are shown to stem mainly from the sensitivity of the surface albedo to surface temperature rather from the impact of a given surface albedo change on the shortwave budget.

Download Full-text

Comparison of surface albedo feedback in climate models and observations

Geophysical Research Letters ◽

10.1002/2014gl059280 ◽

2014 ◽

Vol 41 (5) ◽

pp. 1717-1723 ◽

Cited By ~ 21

Author(s):

J. A. Crook ◽

P. M. Forster

Keyword(s):

Surface Albedo ◽

Climate Models ◽

Albedo Feedback

Download Full-text

Global high-resolution land surface albedo monthly maps from NOAA/AVHRR for use in climate models

IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174) ◽

10.1109/igarss.1998.699558 ◽

1998 ◽

Author(s):

I. Csiszar ◽

G. Gutman

Keyword(s):

High Resolution ◽

Land Surface ◽

Surface Albedo ◽

Climate Models ◽

Noaa Avhrr

Download Full-text

Inferring past land use-induced changes in surface albedo from satellite observations: a useful tool to evaluate model simulations

Biogeosciences ◽

10.5194/bg-10-1501-2013 ◽

2013 ◽

Vol 10 (3) ◽

pp. 1501-1516 ◽

Cited By ~ 29

Author(s):

J. P. Boisier ◽

N. de Noblet-Ducoudré ◽

P. Ciais

Keyword(s):

Land Use ◽

Land Cover ◽

Land Surface ◽

Surface Albedo ◽

Climate Models ◽

Vegetation Type ◽

Seasonal Pattern ◽

Satellite Observations ◽

Secondary Importance ◽

Past Land Use

Abstract. Regional cooling resulting from increases in surface albedo has been identified in several studies as the main biogeophysical effect of past land use-induced land cover changes (LCC) on climate. However, the amplitude of this effect remains quite uncertain due to, among other factors, (a) uncertainties in the extent of historical LCC and, (b) differences in the way various models simulate surface albedo and more specifically its dependency on vegetation type and snow cover. We derived monthly albedo climatologies for croplands and four other land cover types from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations. We then reconstructed the changes in surface albedo between preindustrial times and present-day by combining these climatologies with the land cover maps of 1870 and 1992 used by seven land surface models (LSMs) in the context of the LUCID ("Land Use and Climate: identification of robust Impacts") intercomparison project. These reconstructions show surface albedo increases larger than 10% (absolute) in winter, and larger than 2% in summer between 1870 and 1992 over areas that experienced intense deforestation in the northern temperate regions. The historical surface albedo changes estimated with MODIS data were then compared to those simulated by the various climate models participating in LUCID. The inter-model mean albedo response to LCC shows a similar spatial and seasonal pattern to the one resulting from the MODIS-based reconstructions, that is, larger albedo increases in winter than in summer, driven by the presence of snow. However, individual models show significant differences between the simulated albedo changes and the corresponding reconstructions, despite the fact that land cover change maps are the same. Our analyses suggest that the primary reason for those discrepancies is how LSMs parameterize albedo. Another reason, of secondary importance, results from differences in their simulated snow extent. Our methodology is a useful tool not only to infer observations-based historical changes in land surface variables impacted by LCC, but also to point out deficiencies of the models. We therefore suggest that it could be more widely developed and used in conjunction with other tools in order to evaluate LSMs.

Download Full-text

Changes in Local and Global Climate Feedbacks in the Absence of Interactive Clouds: Southern Ocean–Climate Interactions in Two Intermediate-Complexity Models

Journal of Climate ◽

10.1175/jcli-d-20-0113.1 ◽

2021 ◽

Vol 34 (2) ◽

pp. 755-772

Author(s):

Patrik L. Pfister ◽

Thomas F. Stocker

Keyword(s):

Southern Ocean ◽

Sea Ice ◽

Climate Models ◽

Lapse Rate ◽

Climate Feedback ◽

Ocean Heat Uptake ◽

Albedo Feedback ◽

Intermediate Complexity ◽

Global Mean ◽

Over Time

AbstractThe global-mean climate feedback quantifies how much the climate system will warm in response to a forcing such as increased CO2 concentration. Under a constant forcing, this feedback becomes less negative (increasing) over time in comprehensive climate models, which has been attributed to increases in cloud and lapse-rate feedbacks. However, out of eight Earth system models of intermediate complexity (EMICs) not featuring interactive clouds, two also simulate such a feedback increase: Bern3D-LPX and LOVECLIM. Using these two models, we investigate the causes of the global-mean feedback increase in the absence of cloud feedbacks. In both models, the increase is predominantly driven by processes in the Southern Ocean region. In LOVECLIM, the global-mean increase is mainly due to a local longwave feedback increase in that region, which can be attributed to lapse-rate changes. It is enhanced by the slow atmospheric warming above the Southern Ocean, which is delayed due to regional ocean heat uptake. In Bern3D-LPX, this delayed regional warming is the main driver of the global-mean feedback increase. It acts on a near-constant local feedback pattern mainly determined by the sea ice–albedo feedback. The global-mean feedback increase is limited by the availability of sea ice: faster Southern Ocean sea ice melting due to either stronger forcing or higher equilibrium climate sensitivity (ECS) reduces the increase of the global mean feedback in Bern3D-LPX. In the highest-ECS simulation with 4 × CO2 forcing, the feedback even becomes more negative (decreasing) over time. This reduced ice–albedo feedback due to sea ice depletion is a plausible mechanism for a decreasing feedback also in high-forcing simulations of other models.

Download Full-text

Varying Snow and Vegetation Signatures of Surface Albedo Feedback on the Northern Hemisphere Land Warming

10.5194/egusphere-egu21-13456 ◽

2021 ◽

Author(s):

Andrea Alessandri ◽

Franco Catalano ◽

Matteo De Felice ◽

Bart van den Hurk ◽

Gianpaolo Balsamo

Keyword(s):

Northern Hemisphere ◽

Temperature Increase ◽

Surface Albedo ◽

Environmental Research ◽

Underlying Surface ◽

Radiative Energy ◽

Wave Radiation ◽

Historical Period ◽

Regional Warming ◽

Albedo Feedback

<div> Changes in snow and vegetation cover associated with global warming can modify surface albedo (the reflected amount of radiative energy from the sun), therefore modulating the rise of surface temperature that is primarily caused by anthropogenic greenhouse-gases emission. This introduces a series of potential feedbacks to regional warming with positive (negative) feedbacks enhancing (reducing) temperature increase by augmenting (decreasing) the absorption of&#160;short-wave radiation. So far our knowledge on the importance and magnitude of these feedbacks has been hampered by the limited availability of relatively long records of continuous satellite observations. </div><div> Here we exploit a 31-year (1982-2012) high-frequency observational record of land data to quantify the strength of the surface-albedo feedback on land warming modulated by snow and vegetation during the recent historical period. To distinguish snow and vegetation contributions to this feedback, we examine temporal composites of satellite data in three different Northern Hemisphere domains. The analysis reveals and quantifies markedly different signatures of the surface-albedo feedback. A large positive surface-albedo feedback of +0.87 [CI 95%: 0.68, 1.05] W/(m2&#8727;K)&#160;absorbed solar radiation per degree of temperature increase is estimated in the domain where snow dominates. On the other hand the surface-albedo feedback becomes predominantly negative where vegetation dominates: it is largely negative (-0.91 [-0.81, -1.03] W/(m2&#8727;K)) in the domain with vegetation dominating, while it is moderately negative (-0.57 [-0.40, -0.72] W/(m2&#8727;K)) where both vegetation and snow are significantly present. &#160;Snow cover reduction consistently provides a positive feedback on warming. In contrast, vegetation expansion can produce either positive or negative feedbacks in different regions and seasons, depending on whether the underlying surface being replaced has higher (e.g. snow) or lower (e.g. dark soils) albedo than vegetation. The observational data and analysis from this work is supplying fundamental knowledge to model and predict how the surface-albedo feedback will evolve and affect the rate of regional temperature rise in the future. So far the simulation and prediction of albedo feedbacks shows a large spread and divergence among the available state-of-the-art Earth System Models (ESMs), due to uncertainties in the representation of vegetation-snow processes and the dynamics of vegetation and to uncertainties in land-cover parameters. By exploiting the unprecedented observational benchmarks to evaluate the ESMs currently engaged in CMIP6, this work will allow an improved and better constrained representation of the processes underlying surface albedo feedbacks in the next generation of ESMs.&#160; </div><div> This work is in now in Press and Open Access on Environmental Research Letters: https://doi.org/10.1088/1748-9326/abd65f </div>

Download Full-text