scholarly journals Assessing Snow Albedo Feedback in Simulated Climate Change

2006 ◽  
Vol 19 (11) ◽  
pp. 2617-2630 ◽  
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.

Evaluation of snow albedo feedback simulated by CMIP6 coupled climate models

2020 ◽  
Author(s):  
jiangling hu ◽  
duoying ji
Keyword(s):  
Climate Change ◽  
Northern Hemisphere ◽  
Land Surface ◽  
Climate Models ◽  
Coupled Model ◽  
Cmip5 Models ◽  
Simulation Research ◽  
Albedo Feedback ◽  
Snow Albedo ◽  
Autumn And Winter

<p>As the land surface warms, a subsequent reduction in snow and ice cover reveals a less reflective surface that absorbs more solar radiation, which further enhances the initial warming. This positive feedback climate mechanism is the snow albedo feedback (SAF), which will exacerbate climate warming and is the second largest contributor to Arctic amplification. Snow albedo feedback will increase the sensitivity of climate change in the northern hemisphere, which affects the accuracy of climate models in simulation research of climate change, and further affects the credibility of future climate prediction results.</p><p>Using the latest generation of climate models from CMIP6 (Coupled Model Intercomparison Project Version 6), we analyze seasonal cycle snow albedo feedback in Northern Hemisphere extratropics. We find that the strongest SAF strength is in spring (mean: -1.34 %K<sup>-1</sup>), second strongest is autumn (mean: -1.01 %K<sup>-1</sup>), the weakest is in summer (mean: -0.18 %K<sup>-1</sup>). Except summer, the SAF strength is approximately 0.15% K<sup>-1</sup> larger than CMIP5 models in the other three seasons. The spread of spring SAF strength (range: -1.09 to -1.37% K<sup>-1</sup>) is larger than CMIP5 models. Oppositely, the spread of summer SAF strength (range: 0.20 to -0.56% K<sup>-1</sup>) is smaller than CMIP5 models. When compared with CMIP5 models, the spread of autumn and winter SAF strength have not changed much.</p>

Surface Albedo Feedback Estimates for the AR4 Climate Models

2006 ◽  
Vol 19 (3) ◽  
pp. 359-365 ◽  
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.

scholarly journals What Controls the Strength of Snow-Albedo Feedback?

2007 ◽  
Vol 20 (15) ◽  
pp. 3971-3981 ◽  
Author(s):  
Xin Qu ◽  
Alex Hall
Keyword(s):  
Climate Change ◽  
Snow Cover ◽  
Surface Albedo ◽  
Intergovernmental Panel ◽  
Representative Sampling ◽  
Albedo Feedback ◽  
Snow Albedo ◽  
Warming Climate ◽  
Large Spread ◽  
Transient Climate Change

Abstract The strength of snow-albedo feedback (SAF) in transient climate change simulations of the Fourth Assessment of the Intergovernmental Panel on Climate Change is generally determined by the surface-albedo decrease associated with a loss of snow cover rather than the reduction in snow albedo due to snow metamorphosis in a warming climate. The large intermodel spread in SAF strength is likewise attributable mostly to the snow cover component. The spread in the strength of this component is in turn mostly attributable to a correspondingly large spread in mean effective snow albedo. Models with large effective snow albedos have a large surface-albedo contrast between snow-covered and snow-free regions and exhibit a correspondingly large surface-albedo decrease when snow cover decreases. Models without explicit treatment of the vegetation canopy in their surface-albedo calculations typically have high effective snow albedos and strong SAF, often stronger than observed. In models with explicit canopy treatment, completely snow-covered surfaces typically have lower albedos and the simulations have weaker SAF, generally weaker than observed. The authors speculate that in these models either snow albedos or canopy albedos when snow is present are too low, or vegetation shields snow-covered surfaces excessively. Detailed observations of surface albedo in a representative sampling of snow-covered surfaces would therefore be extremely useful in constraining these parameterizations and reducing SAF spread in the next generation of models.

Snow albedo feedback

2016 ◽  
Vol 40 (3) ◽  
pp. 392-408 ◽  
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.

scholarly journals Dynamics of air temperature in the main types of mountain Crimea regional ecosystems

2021 ◽  
Vol 29 (1) ◽  
pp. 39-54
Author(s):  
Roman V. Gorbunov ◽  
Vladimir A. Tabunshchik ◽  
Tatyana Yu. Gorbunova ◽  
Maria S. Safonova
Keyword(s):  
Climate Change ◽  
Standard Deviation ◽  
Northern Hemisphere ◽  
Air Temperature ◽  
Temperature Fields ◽  
Crimean Peninsula ◽  
Geoinformation Modeling ◽  
The Mean ◽  
Temperature Standard

Climate change in Crimea is characterized by spatial heterogeneity in the displacement of air temperature fields, due to the influence of regional and local factors. There are currently no works devoted to the study of the reaction of regional ecosystems to changes in air temperature in Crimea. Based on open databases of reanalysis, geoinformation modeling the results of studies of the dynamics of air temperature in the main types of ecosystems of the Mountain Crimea under conditions of climate change are presented. For each circulation epoch and period of the Northern Hemisphere, maps of average annual temperatures were obtained along the landscape contours of the Crimean Peninsula. A map of the standard deviation of temperature within the landscape contours was made. For key areas, the mean annual air temperature, standard deviation, and factorial entropy were calculated. The main regularities of air temperature dynamics in the main types of Mountain Crimea ecosystems with the change of circulation epochs and periods of the Northern Hemisphere are revealed. Based on the analysis of the dynamics of the standard deviation and factor entropy, the role of changes in air temperature in the formation of strategies for the development or stabilization of the main types of regional ecosystems in Mountain Crimea is shown.

scholarly journals Reduced global warming from CMIP6 projections when weighting models by performance and independence

2020 ◽  
Vol 11 (4) ◽  
pp. 995-1012
Author(s):  
Lukas Brunner ◽  
Angeline G. Pendergrass ◽  
Flavio Lehner ◽  
Anna L. Merrifield ◽  
Ruth Lorenz ◽  
...  
Keyword(s):  
Climate Change ◽  
Retrospective Analysis ◽  
Climate Models ◽  
Cmip5 Models ◽  
Future Climate Change ◽  
Historical Period ◽  
Climate Change Scenarios ◽  
Uncertainty Range ◽  
Future Warming ◽  
The Mean

Abstract. The sixth Coupled Model Intercomparison Project (CMIP6) constitutes the latest update on expected future climate change based on a new generation of climate models. To extract reliable estimates of future warming and related uncertainties from these models, the spread in their projections is often translated into probabilistic estimates such as the mean and likely range. Here, we use a model weighting approach, which accounts for the models' historical performance based on several diagnostics as well as model interdependence within the CMIP6 ensemble, to calculate constrained distributions of global mean temperature change. We investigate the skill of our approach in a perfect model test, where we use previous-generation CMIP5 models as pseudo-observations in the historical period. The performance of the distribution weighted in the abovementioned manner with respect to matching the pseudo-observations in the future is then evaluated, and we find a mean increase in skill of about 17 % compared with the unweighted distribution. In addition, we show that our independence metric correctly clusters models known to be similar based on a CMIP6 “family tree”, which enables the application of a weighting based on the degree of inter-model dependence. We then apply the weighting approach, based on two observational estimates (the fifth generation of the European Centre for Medium-Range Weather Forecasts Retrospective Analysis – ERA5, and the Modern-Era Retrospective analysis for Research and Applications, version 2 – MERRA-2), to constrain CMIP6 projections under weak (SSP1-2.6) and strong (SSP5-8.5) climate change scenarios (SSP refers to the Shared Socioeconomic Pathways). Our results show a reduction in the projected mean warming for both scenarios because some CMIP6 models with high future warming receive systematically lower performance weights. The mean of end-of-century warming (2081–2100 relative to 1995–2014) for SSP5-8.5 with weighting is 3.7 ∘C, compared with 4.1 ∘C without weighting; the likely (66%) uncertainty range is 3.1 to 4.6 ∘C, which equates to a 13 % decrease in spread. For SSP1-2.6, the weighted end-of-century warming is 1 ∘C (0.7 to 1.4 ∘C), which results in a reduction of −0.1 ∘C in the mean and −24 % in the likely range compared with the unweighted case.

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

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> <p>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 <span>to</span> regional warming with positive<span> (negative)</span> feedback<span>s</span> enhancing<span> (reducing)</span> temperature increase by augmenting<span> (decreasing) the absorption of </span>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.</p> </div><div> <p>Here we exploit a 3<span>1</span>-year (1982-2012) high-frequency observational record of land data to quantify the strength of the surface-albedo feedback on land warming <span>modulated by snow and vegetation </span>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 <span>the </span>surface-albedo feedback. A large positive surface-albedo feedback of<span> +0</span>.87 [CI 95%: 0.68, 1.05] W/(m<sup>2</sup>∗K) <span>absorb</span>ed 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 (<span>-</span>0.91 [<span>-</span>0.81, <span>-</span>1.03] W/(m<sup>2</sup>∗K)) in the domain with vegetation dominating, while it is moderately negative (<span>-</span>0.57 [<span>-</span>0.40, <span>-</span>0.72] W/(m<sup>2</sup>∗K)) where both vegetation and snow are significantly present.  <span>S</span>now cover reduction consistently provides a positive feedback on warming<span>. In contrast,</span> vegetation<span> expansion</span> can produce <span>either</span> positive <span>or</span> negative feedbacks<span> 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.</span></p> <p><span>The observational data and analysis from this work is </span><span>supplying</span> fundamental knowledge to model and predict how <span>the </span>surface-albedo feedback will evolve and affect the rate of regional temperature rise in the future<span>. </span><span>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. </span><span>By exploiting the</span><span> 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.</span> </p> </div><div> <p><span>This work is in now in Press and Open Access on Environmental Research Letters:</span> https://doi.org/10.1088/1748-9326/abd65f</p> </div>

scholarly journals Northern Hemisphere Stationary Waves in a Changing Climate

2019 ◽  
Vol 5 (4) ◽  
pp. 372-389 ◽  
Author(s):  
Robert C. J. Wills ◽  
Rachel H. White ◽  
Xavier J. Levine
Keyword(s):  
Climate Change ◽  
Atmospheric Circulation ◽  
Northern Hemisphere ◽  
Climate Models ◽  
Regional Climate ◽  
Stationary Wave ◽  
Future Research ◽  
Stationary Waves ◽  
The Pacific ◽  
Longitudinal Variations

Abstract Purpose of Review Stationary waves are planetary-scale longitudinal variations in the time-averaged atmospheric circulation. Here, we consider the projected response of Northern Hemisphere stationary waves to climate change in winter and summer. We discuss how the response varies across different metrics, identify robust responses, and review proposed mechanisms. Recent Findings Climate models project shifts in the prevailing wind patterns, with corresponding impacts on regional precipitation, temperature, and extreme events. Recent work has improved our understanding of the links between stationary waves and regional climate and identified robust stationary wave responses to climate change, which include an increased zonal lengthscale in winter, a poleward shift of the wintertime circulation over the Pacific, a weakening of monsoonal circulations, and an overall weakening of stationary wave circulations, particularly their divergent component and quasi-stationary disturbances. Summary Numerous factors influence Northern Hemisphere stationary waves, and mechanistic theories exist for only a few aspects of the stationary wave response to climate change. Idealized studies have proven useful for understanding the climate responses of particular atmospheric circulation features and should be a continued focus of future research.

scholarly journals Impact of internal variability on climate change for the upcoming decades: analysis of the CanESM2-LE and CESM-LE large ensembles

2019 ◽  
Vol 156 (3) ◽  
pp. 299-314 ◽  
Author(s):  
Gabriel Rondeau-Genesse ◽  
Marco Braun
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Ghg Emissions ◽  
Internal Variability ◽  
Mean Annual Temperature ◽  
Large Ensemble ◽  
Climate Change Effects ◽  
Large Ensembles ◽  
The Mean

Abstract The pace of climate change can have a direct impact on the efforts required to adapt. For short timescales, however, this pace can be masked by internal variability (IV). Over a few decades, this can cause climate change effects to exceed what would be expected from the greenhouse gas (GHG) emissions alone or, to the contrary, cause slowdowns or even hiatuses. This phenomenon is difficult to explore using ensembles such as CMIP5, which are composed of multiple climate models and thus combine both IV and inter-model differences. This study instead uses CanESM2-LE and CESM-LE, two state-of-the-art large ensembles (LE) that comprise multiple realizations from a single climate model and a single GHG emission scenario, to quantify the relationship between IV and climate change over the next decades in Canada and the USA. The mean annual temperature and the 3-day maximum and minimum temperatures are assessed. Results indicate that under the RCP8.5, temperatures within most of the individual large ensemble members will increase in a roughly linear manner between 2021 and 2060. However, members of the large ensembles in which a slowdown of warming is found during the 2021–2040 period are two to five times more likely to experience a period of very fast warming in the following decades. The opposite scenario, where the changes expected by 2050 would occur early because of IV, remains fairly uncommon for the mean annual temperature, but occurs in 5 to 15% of the large ensemble members for the temperature extremes.

Sign in / Sign up

Export Citation Format

Share Document