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

2. 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.

3. 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.

4. 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?

5. Methods

6. 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.

7. 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.

8. 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.

9. 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.

10. 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 ACCESS1.00.876 N/A BCC-CSM1.10.878 0.904 0.891 CanESM20.878 0.912 0.895 CCSM40.845 0.888 0.867 CESM1-BGC0.848 0.895 0.872 CESM1-CAM50.849 0.909 0.879 CRCM-CM50.883 0.893 0.888 CSIRO-MK3.6.00.880 0.896 0.888 FGOALS-g20.840 0.890 0.870 GFDL-CM30.814 0.882 0.848 GFDL-ESM2G0.856 N/A GFDL-ESM2M0.854 0.888 0.871 GISS-E2-R0.911 0.898 0.905 HadCM30.867 N/A HadGEM2-CC0.869 N/A HadGEM2-ES0.885 0.881 0.883 INMCM40.742 0.914 0.828 IPSL-CM5A-LR0.847 N/A IPSL-CM5A-MR0.843 N/A IPSL-CM5B-LR0.841 N/A MIROC50.768 0.895 0.831 MIROC-ESM-CHEM0.714 0.913 0.813 MIROC-ESM0.716 0.914 0.815 MPI-ESM-LR0.847 0.864 0.855 MPI-ESM-MR0.844 0.851 0.848 MRI-CGCM30.864 0.891 0.877 NorESM1-ME0.861 0.909 0.885 NorESM1-M0.858 0.899 0.879

11. 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 (0.5-0.65) 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.

12. 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.