Chad W. Thackeray

Quantifying the skill of CMIP5 models in simulating seasonal albedo and snow cover evolution

Effectively modeling the influence of terrestrial snow on climate in general circulation models is limited by imperfect knowledge and parameterization of arctic and subarctic climate processes and a lack of reliable observations for model evaluation and improvement. This study uses a number of satellite-derived data sets to evaluate how well the current generation of climate models from the Fifth Coupled Model Intercomparison Project (CMIP5) simulate the seasonal cycle of climatological snow cover fraction (SCF) and surface albedo over the Northern Hemisphere snow season (September–June). Using a variety of metrics, the CMIP5 models are found to simulate SCF evolution better than that of albedo. The seasonal cycle of SCF is well reproduced despite substantial biases in simulated surface albedo of snow-covered land (αsfc_snow), which affect both the magnitude and timing of the seasonal peak in αsfc_snow during the fall snow accumulation period, and the springtime snow ablation period. Insolation weighting demonstrates that the biases in αsfc_snow during spring are of greater importance for the surface energy budget. Albedo biases are largest across the boreal forest, where the simulated seasonal cycle of albedo is biased high in 15/16 CMIP5 models. This bias is explained primarily by unrealistic treatment of vegetation masking and subsequent overestimation (more than 50% in some cases) of peak αsfc_snow rather than by biases in SCF. While seemingly straightforward corrections to peak αsfc_snow could yield significant improvements to simulated snow albedo feedback, changes in αsfc_snow could potentially introduce biases in other important model variables such as surface temperature.

The influence of canopy snow parameterizations on snow albedo feedback in boreal forest regions

Variation in snow albedo feedback (SAF) among Coupled Model Intercomparison Project phase 5 climate models has been shown to explain much of the variation in projected 21st century warming over Northern Hemisphere land. Prior studies using observations and models have demonstrated both considerable spread in the albedo and a negative bias in the simulated strength of SAF, over snow-covered boreal forests. Boreal evergreen needleleaf forests are capable of intercepting snowfall throughout the winter and consequently exert a significant impact on seasonal surface albedo. Two satellite data products and tower-based observations of albedo are compared with simulations from multiple versions of the Community Climate System Model (CCSM4) to investigate the causes of weak simulated SAF over the boreal forest. The largest bias occurs in April and May, when simulated SAF is one half the strength of SAF in observations. This is traced to two features of the canopy snow parameterizations used in the land model. First, there is no mechanism for the dynamic removal of snow from the canopy when temperatures are below freezing, which results in albedo values in midwinter that are biased high. Second, when temperatures do rise above freezing, all snow on the canopy is melted instantaneously, which results in an unrealistically early transition from a snow-covered to a snow-free canopy. These processes combine to produce large differences between simulated and observed monthly albedo and are the source of the weak bias in SAF. This analysis highlights the importance of canopy snow parameterizations for simulating the hemispheric scale climate response to surface albedo perturbations.

Evaluating biases in simulated snow albedo feedback in two generations of climate models

This study presents a comprehensive evaluation of snow albedo feedback (SAF) in two generations of climate models (Coupled Model Intercomparison Project versions 3 (CMIP3) and 5 (CMIP5)). A comparison of the models is performed against a multiobservation-based reference data set (mOBS) derived from the seasonal cycle of albedo, snow cover, and temperature. The observed total SAF shows low uncertainty and is generally well simulated by the CMIP3 and CMIP5 ensemble mean, except for a low (high) bias over the Arctic (northern boreal forest). Most CMIP5 models overestimate the snow cover component of SAF (SNC) and underestimate the temperature sensitivity component (TEM). The high bias in SNC is due to simulated snow albedos 4–5% brighter than observed driving unrealistically large albedo contrasts. However, overall representation of surface albedo—and mean climate—has improved, as fewer CMIP5 models exhibit large cold temperature, or high snow, biases. The low bias in TEM is related to overly persistent snow albedo during spring, particularly over southern Eurasia and North America. There is large observational uncertainty in the reference data set mOBS that is traced primarily to the different snow cover products, with a secondary contribution from the albedo products and a small contribution from the temperature products. The conclusion is that the model mean tends to simulate the multiobservation mean very closely; however, this masks considerable spread in both models and observations. There is clear motivation for producing improved submonthly snow cover products for the purpose of model evaluation.

Snow albedo feedback Current knowledge, importance, outstanding issues and future directions

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.

Snow cover response to temperature in observational and climate model ensembles

The relationship between land surface temperature and snow cover extent trends is examined in three distinct types of ensembles over the 1981-2010 period: an observation-based ensemble, a representative selection of CMIP5 coupled climate model output, and two large initial condition coupled climate model ensembles. Observation-based estimates of snow cover sensitivity are stronger than simulated over midlatitude and alpine regions. Observed sensitivity estimates over Arctic regions are consistent with simulated values. Anomalous snow cover extend trends present in one dataset, the NOAA climate record, obscure the relationship to surface temperature seen in the rest of the analyzed data. The spread in modeled snow cover trends reflects roughly equal contributions from inter-model variability and from natural variability. Together, the anomalous relationship between surface temperature and snow cover expressed in the NOAA climate record and the large influence of natural variability present in the simulations highlight the importance of ensemble-based approaches.

Quantifying the Uncertainty in Historical and Future Simulations of Northern Hemisphere Spring Snow Cover

AbstractProjections of twenty-first-century Northern Hemisphere (NH) spring snow cover extent (SCE) from two climate model ensembles are analyzed to characterize their uncertainty. Phase 5 of the Coupled Model Intercomparison Project (CMIP5) multimodel ensemble exhibits variability resulting from both model differences and internal climate variability, whereas spread generated from a Canadian Earth System Model–Large Ensemble (CanESM-LE) experiment is solely a result of internal variability. The analysis shows that simulated 1981–2010 spring SCE trends are slightly weaker than observed (using an ensemble of snow products). Spring SCE is projected to decrease by −3.7% ± 1.1% decade−1 within the CMIP5 ensemble over the twenty-first century. SCE loss is projected to accelerate for all spring months over the twenty-first century, with the exception of June (because most snow in this month has melted by the latter half of the twenty-first century). For 30-yr spring SCE trends over the twenty-first century, int...