Seasonal albedo and snow cover evolution of CMIP5 models in boreal forest regions Chad Thackeray CanSISE East Meeting July 25, 2014

Motivation  Previous research found that two features of the canopy snow parameterization within the Community Land Model (CLM4), combine to produce large differences between simulated and observed monthly albedo.  They are the source of a negative bias (~40% weaker than observed) in snow albedo feedback (SAF) over the boreal region.  We found the largest SAF bias to occur in April-May, when simulated SAF is one-half the strength of SAF in observations.

Motivation  Previous research found that two features of the canopy snow parameterization within the Community Land Model (CLM4), combine to produce large differences between simulated and observed monthly albedo.  They are the source of a negative bias (~40% weaker than observed) in snow albedo feedback (SAF) over the boreal region.  We found the largest SAF bias to occur in April-May, when simulated SAF is one-half the strength of SAF in observations.

Motivation  1) No mechanism for the dynamic removal of snow from the canopy when temperatures are below freezing.  2) 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 snow-free canopy.  Since some climate models share common components they are likely to have similar biases (Masson and Knutti, 2011; Flato et al., 2013).  Does this issue exist within other models, and where CCSM4 fits within the hierarchy of CMIP5 models?

Methods

Monthly Albedo Change  A majority of the CMIP5 models have an albedo decrease that begins one month earlier than observations.  Models that fall outside of the zone of model consensus either struggle with:  The timing of changes  Or the magnitude of those changes.  The ensemble mean reproduces observations from MODIS fairly well during the melt period.

Where does CCSM4 fit?  CCSM4 is not only decreasing before the observations, but also well before the multi-model ensemble mean and the model consensus.  The model does well up until when the canopy snow melt begins.  By the observed melt period in spring, CCSM4 albedo has already decreased substantially resulting in the smallest amount of albedo change in Apr-May of any model.

Snow Cover Fraction  We also look at SCF because of the direct linkage that it has with model calculations of surface albedo.  A small group of models see a dramatic decrease in snow cover one month before observations (Mar-Apr).  The ensemble mean very accurately captures the timing of snow accumulation and melt.  However, the magnitude of observed changes are much larger than model consensus.

Snow Cover Fraction  We also look at SCF because of the direct linkage that it has on model calculations of surface albedo.  A small group of models see a dramatic decrease in snow cover one month before observations during Mar-Apr.  The ensemble mean very accurately captures the timing of snow accumulation and melt.  However, the magnitude of observed changes are much larger than model consensus.

Boreal Skill Scores  The creation of this metric allows for model improvements to the treatment of the boreal forest to be measured and tracked.  It tests the models for both their ability to properly simulate the timing of snow accumulation and melt, along with an accurate peak albedo value.  The CMIP5 models are better at simulating SCF (mean of 0.895) over the boreal region than albedo (mean of 0.842). ModelSSalbSSscfSStot ACCESS N/A BCC-CSM CanESM CCSM CESM1-BGC CESM1-CAM CRCM-CM CSIRO-MK FGOALS-g GFDL-CM GFDL-ESM2G0.856 N/A GFDL-ESM2M GISS-E2-R HadCM N/A HadGEM2-CC0.869 N/A HadGEM2-ES INMCM IPSL-CM5A-LR0.847 N/A IPSL-CM5A-MR0.843 N/A IPSL-CM5B-LR0.841 N/A MIROC MIROC-ESM-CHEM MIROC-ESM MPI-ESM-LR MPI-ESM-MR MRI-CGCM NorESM1-ME NorESM1-M

Discussion  Lower scoring models in terms of Ssalb (MIROC-ESM, INMCM4) capture the timing of albedo changes, but have max albedos that are far too high ( ) when compared to obs (~0.3).  The peak albedo value is important here because a model that is biased high will have a SAF that is too strong (positive bias).  Because the possible decrease from snow-covered to snow-free is larger than observed.

Conclusions  CCSM4 falls in the middle of the CMIP5 hierarchy despite its timing discrepancies because of an accurate peak boreal albedo and good simulation of snow cover.  Its very weak albedo change in the melt period makes it the ideal test case to track improvements.  Improvements to the boreal canopy snow scheme within CLM4, as suggested by Thackeray et al. (2014), should result in an increased skill score.  This work has quantified where CCSM4 fits within the CMIP5 hierarchy of models, while also showing that the issues with canopy snow are only also present in NorESM1.  We would have less confidence in assigning skill scores over more northern regions (where SAF biases also exist in CCSM4) because of increased observational uncertainty in satellite retrievals of albedo.