1. Biases in simulated boreal snow cover and their influence on snow albedo feedback Chad Thackeray University of Waterloo CanSISE Regional Meeting February 10, 2014

2. Introduction Snow albedo feedback (SAF) is a positive feedback climate mechanism that plays a crucial role in directing shortwave forcing in a changing climate. A large spread in SAF strength among the CMIP5 models explains much of the variation in predictions of warming over the Northern Hemisphere. Previous work has shown that modeled SAF strength is significantly weaker than observed over the boreal forest during the spring melt period. Partitioning between SAF components (CON versus TEM) is different between observational studies, and remained largely unchanged between CMIP3 and CMIP5. The primary goal of this work is to investigate the boreal SAF weakness in greater detail, making use of multiple observational products (MODIS, APP-x, and BERMS). We also strive to diagnose the underlying cause of differences in observed versus simulated SAF. Historical runs of CCSM4 and CLM4 are used here.

3. Figure 1 – Total (NET) Snow Albedo Feedback strength (%/K) over the boreal forest (>75%) for CCSM4 (green), CLM4 (red), APP-x (yellow), and MODIS (blue). During April-May SAF in MODIS and APP-x is approximately twice the simulated SAF from both offline and freely coupled runs. Strongly influencing the seasonal (MAMJ) mean feedback strength. SAF strength over the boreal forest is weaker than what is seen over the rest of the Northern Hemisphere because of its low peak albedo.

4. Figure 2: Map of NET snow albedo feedback bias (MODIS - CCSM4) for April-May. The dark green line shows the 50% boreal region, while black stippling indicates the >75% area. (a) This bias (between MODIS and CCSM4) is large over regions with high fractions of boreal forest cover. CCSM4 is largely underestimating the NET feedback strength over a majority of the hemisphere.

5. Figure 3 – Monthly Albedo and snow cover fraction (SCF) change for boreal forest (>75%). Monthly changes are climatologies over the 2000-2004 period for CLM4, CCSM4, MODIS, and APP-x. Snow products used include CLM4, CCSM4, MODIS, and GlobSnow. The grey box encompasses a time period when observational uncertainty is high due to large solar zenith angles. In observations snow albedo increases or stays constant throughout the winter, until Mar-Apr when it decreases sharply until MJ. By contrast, the models transition to springtime snow-free albedo much earlier, with large decreases during Jan-Mar, and only weak decreases during Apr-Jun. In Jan-Feb and Feb-Mar, there is a difference in the sign of monthly albedo change for the observations (positive month to month albedo change) versus the models (negative month to month albedo change).

6. Table 1: Mean Snow Depth Biases from CLM4 in relation to GlobSnow (derived from SWE) CLM4GlobSnowModel - Obs (cm) 90th Percentile Depth (cm)48.8237.5511.27 Max. Depth (cm)52.9639.9313.03 For the rest of this work, we focus on CLM4, with the understanding that this model is representative of the biases in CCSM4 – primary reasons for this are: 1) observation-based forcing, so the comparison with observations is much cleaner 2) daily snow cover and albedo data were archived for CLM4 but not CCSM4. Model biases in spring snow cover fraction (Fig.3) and pre-melt maximum snow depth do not account for the differences in observed and simulated albedo.

7. Figure 4: Daily climatological Snow Depth and Albedo scatterplots for (a) CLM4 over Pure Boreal Forest (>90%) and (b) Old Jack Pine (a) (b) There is a strong decline in CLM4 albedo during Feb and Mar (pre-melt) in spite of snow depths that exceed 40 cm. The observational albedo peaks in early December once depth exceeds ~10 cm. Model peak albedo occurs closers to a 20 cm threshold. The seasonal evolution of simulated snow depth versus albedo is very different from observations at a tower site. Partially due to litter (pine needles and dirt) on the snow pack that is not captured by the model.

8. Figure 5: Relationship between snow depth, albedo, and temperature for a Boreal forest grid cell in CLM4 (a) Canopy Sticking (00-01) (b) Warming Events (01-02) (c) OJP 01-02. Scatterplots of daily albedo and temperature are shown for (a) Canopy sticking (b) Warming events (c) Tower data Note the albedo ‘sticking’ at peak value within CLM4 (panel a and b). Tower data shows a greater clustering of points at a snow covered but non-peak albedo (circled in red). In a year with more mid-winter warming events, we see a greater number of days where albedo is between the peak and minimum snow albedos (Panel B). There is a complete absence of boreal grid cells with albedo > 0.3 at OJP 01-02 even at temps <250 K, indicating the model albedo is unrealistically high in these situations. (c) (b) (a)

9. Figure 6: (a) Monthly climatological change in water on canopy (blue) and albedo (red) from CLM4 –Northern Hemisphere Boreal Forest (>75%). (b) Same relationship shown in a scatterplot Changes in boreal canopy snow storage account for the winter season changes in simulated albedo and hence the disagreement with observations. There is a sharp decrease in boreal canopy snow storage during Jan-Mar that drives the albedo decrease. It is the canopy albedo that is driving seasonal changes, while surface variations are muted.

10. Figure 7: The relationship between NET SAF bias (OBS-CCSM4) and Exposed Leaf Area Index over a region with at least 1% boreal evergreen forest (green) and with at least 75% boreal evergreen forest (orange) for March-April. Areas where the previously discussed SAF bias is greatest tend to have a high Leaf Area Index (LAI), suggesting that it is in denser canopies where the bias is greatest. A majority of these locations are within the boreal region (orange) shown on Figure 2.

11. Conclusions Large disagreements in albedo between models and satellite retrievals have a significant impact on the total snow albedo feedback strength. A decrease in canopy snow in January - March is the main cause of these albedo differences. Development of a more realistic snow-canopy parameterization based on satellite and site measurements will help to minimize observational discrepancies. Future work should address the positive SAF bias over the Canadian subarctic tundra, and also attempt to use multi-model ensembles.