How quickly has snow melted as the Earth’s Northern Hemisphere has warmed? Surprisingly, not all of our historical records of snow cover give the same answer. A new CanSISE publication titled 'Snow cover response to temperature in observational and climate model ensembles' is now available in Geophysical Research Letters (links below).

An exciting new paper titled 'Twenty-five winters of unexpected Eurasian cooling unlikely due to Arctic sea-ice loss' by CanSISE Research Associate Kelly McCusker and colleagues John Fyfe and Michael Sigmond is now available from Nature Geoscience.

Link - http://www.nature.com/ngeo/journal/vaop/ncurrent/full/ngeo2820.html

Congratulations to CanSISE Collaborator Michael Sigmond and CanSISE Investigator John Fyfe and for their publication in Nature Climate Change titled "Tropical Pacific impacts on cooling North American winters".

Link - http://www.nature.com/nclimate/journal/vaop/ncurrent/full/nclimate3069.html

View only - http://rdcu.be/i1TZ

The Globe and Mail release: http://www.theglobeandmail.com/news/national/canadas-extreme-winters-within-range-of-normal-variations-study/article30637250/

We are pleased to announce that the CanSISE Deliverable 1 report has been finalized, and is now available for download here:

Could you briefly describe your position within the CanSISE Network? 

I am a PhD student at the University of British Columbia working under William Hsieh, a funded CanSISE investigator.

What is your primary research goal?

The primary goal of my research is to develop high-resolution maps of snow across British Columbia (BC).  The value I'm interested in is snow water equivalent (SWE), which tells you how much water is in the snowpack.  Knowing the amount of snow water on a regional scale (100s of km) helps hydrologists and water resource managers to predict how much water will be released during the melt season as river runoff.  This is especially important in regions like BC, which depends on this water to make it through the drier summer months.

Can you tell us a bit more about regional snow estimation? 

There are different types of models or "gridded products" that attempt to represent snow, among other physical properties, on large scales.  These include land data assimilation systems, reanalyses, observation-based products and others that use blended techniques.  While such products work well in flat, open areas that don't see much snow, the conditions in many parts of BC are quite different -- mountainous, heavily forested, and receiving many metres of snow.  These conditions make it difficult to make good estimates of snow across the entire region.

Please explain how you investigate regional snow distribution?

I use machine learning methods such as artificial neural networks (ANNs) to attempt to estimate the snowpack.  The neural networks build statistical representations of the regional snowpack using many inputs.  Manual snow survey stations provide on the ground measurements of the actual snowpack, whereas the gridded products show estimates on a larger scale.  Additional information like elevation, latitude, longitude and time of year (known as "predictors") are also fed into the model, which can then estimate snow at other locations and times.  To check how well the model is doing, I use "cross-validation", a technique in which I leave out a fraction of survey stations spread throughout the province to use later as test stations and build the model using only data from the non-test stations. The model performance is then measured at the test stations. The process is repeated so all survey stations are utilized as test stations.

Tell us about something you've discovered.

The ANN model that uses only off-the-shelf gridded products plus the additional information as inputs (ANN_G in Figure 1) has a lower mean station root-mean-square error (RMSE) and therefore does a better job of estimating the snowpack than the individual products themselves.  The ANN using the gridded products even does better than the high-resolution Variable Infiltration Capacity (VIC) hydrologic model that has been run by the Pacific Climate Impact Consortium for 4 major BC watersheds (see https://pacificclimate.org/data/gridded-hydrologic-model-output for details on the VIC runs).

Figure 1: Mean Station RMSE for 3 evaluated gridded products plus VIC. Shown from left to right are the 3 best performing gridded SWE products (GLDAS2, ERALand and MERRA); mean, multiple linear regression (MLR) and ANN of those gridded products; SnowMelt model; MLR and ANN using gridded products and the SnowMelt model; and VIC. Error bars represent 95% confidence intervals.

I also setup a snow model (EcoHydRology SnowMelt) driven by a high-resolution temperature and precipitation data set (bias-corrected ANUSPLIN). The snow output from this model used as an extra predictor in the ANN model further enhances its performance (ANN_GS), such that the new ANN performs significantly better than VIC.

What implications does this have for future efforts to estimate the amount of snow?

Machine learning methods are a tool to bring together many sources of information and most likely better estimate snow water equivalent than individual methods or products.  In areas where it is very difficult to estimate snow, such as BC, such methods could become an important part of water planning and decision making.

How does your research fit into the broader scope of the CanSISE Network?

My project is part of research area A, focused on improving seasonal to multidecadal snow and sea ice prediction.  This model will be used to downscale and bias correct a prediction system (CanSIPS) that will lead to improved snow water forecasts.

What are your next steps?

Additional data sources will be incorporated into the model to try to gain further improvements.  Satellite microwave data, used to estimate snow water equivalent with some success in less complex environments, along with visible wavelength data that show forests and snow lines will be tested. 

On October 30th, 2015 more than twenty members of CanSISE gathered at the University of Victoria for a regional meeting hosted by our colleagues at PCIC. The day was filled with presentations from CanSISE investigators and HQP, as well as a talk by our invited guest Shawn Marshall from the University of Calgary (agenda below). 

Many of the presentations will be made available on our Events and Meetings page. Our next regional meeting will in Toronto in January 2016, hosted by our colleagues at Environment Canada. Plans the January 2016 meeting and the agenda will be developed over the next month.

A new aerosol emissions paper by CanSISE researchers from Environment Canada will soon be available. Be sure to check out the AGU press release and the Nature Research Highlight at the links below.

http://blogs.agu.org/geospace/2015/10/21/aerosol-reductions-could-account-for-up-to-40-percent-of-future-arctic-sea-ice-loss/

http://www.nature.com/nature/journal/v526/n7571/pdf/526008d.pdf

Could you briefly describe your position within the CanSISE Network?

I am a Post-doctoral Fellow (which falls under "Highly Qualified Personnel"), conducting research at the Canadian Centre for Climate modelling and analysis (CCCma) located at the University of Victoria. 

What is your primary research goal?

My primary research goal is to understand the impact that changes in Arctic sea ice have on atmospheric temperature and circulation, both locally in the Arctic and remotely in the midlatitudes. This is an exciting topic of research right now because Arctic  sea ice area has been declining rapidly  in the last decade or so, and while we know that this causes an

Can you tell us a bit more about the potential links between sea ice loss and temperature?

Sea ice acts as a cap over the Arctic Ocean, reflecting the Sun's energy and keeping it from being absorbed by the underlying seawater. When more sea ice melts during summer and autumn, more area of open water is available to absorb the Sun's energy. This heat is then later released during winter when the overlying air is colder than the newly opened areas of water beneath it. The result is that the near-surface  atmospheric  air temperature  warms over the  Arctic,  contributing to what is known as "Arctic amplification" where the Arctic warms more than the rest of the globe under increasing 

Please explain how you investigate these potential links?

In the real world, there are many components to the climate system that interact in tightly coupled, complex ways. In order to isolate the influence of sea ice alone, I use global climate computer models developed by Environment Canada to test how changes in sea ice area in isolation can affect the atmosphere. I also use observational data collected by satellites and others as a point for comparison.

Tell us about something unexpected that you have recently uncovered.

Perhaps the most surprising recent finding is that, in our idealized modelling results, we do not find that Arctic sea ice loss affects Eurasian winter temperature in a systematic way, which is counter to some previous findings. When simulated Arctic sea ice decreases, the response of Eurasian winter temperature is equally likely to cool or warm on decadal and even century-timescales. However, we do find that Eurasian winter can exhibit periods of cooling that are statistically significant on decadal/multi-decadal timescales, but are not robust to a larger sample size of model response years. In other words, it appears that internal climate variability (naturally chaotic variations in weather patterns) can produce Eurasian cooling that could be mistakenly attributed to sea ice loss.

What implications does this have for future studies?

One immediate implication of these results is that we have shown that many more ensemble members (or model run years) than are usually analyzed are necessary to determine the true response to sea ice loss, because internal climate variability is so large, especially over Eurasia in winter. Additionally, Barents-Kara sea ice loss does not appear to be a good predictor of Eurasian winter temperature, indicating that other sources for predictability should be the focus of future work.

How does your research fit into the broader scope of the CanSISE Network?

This work fits into Theme C of the CanSISE network, which is 'Snow and sea ice processes and climate interactions', with sub-focus on 'connecting snow and sea ice to the large-scale general circulation'. My research is on the modelling side of the network, as opposed to observational, and is an opportunity to provide feedback on the performance of the Canadian atmospheric and earth system climate models in the Arctic. I also am able to utilize in-network expert knowledge about observational datasets that I use as benchmarks and as boundary conditions to my model simulations.

What are your next steps?

Our next steps include investigating the role that ocean coupling plays in how Arctic sea ice loss influences the atmosphere -- Does the ocean communicate changes in sea ice to the atmosphere outside the Arctic? We will also turn that question on its head and investigate how ocean coupling both inside and outside of the Arctic can help us predict Arctic sea ice changes.

Check out this interesting article on CBC about changes in Arctic sea ice volume, with quotes from CamSISE PI, Paul Kushner:

http://www.cbc.ca/news/technology/arctic-sea-ice-volume-can-bounce-back-during-cooler-summers-1.3163741

Please briefly describe your position within the CanSISE Network.

I am a PhD student at the University of Waterloo, working under the supervision of Chris Fletcher (Professor, University of Waterloo) and Chris Derksen (MRD, Environment Canada) who are both funded CanSISE investigators.

What are your primary research goals?

The primary goals of my research are to investigate and quantify how well current global climate models represent snow cover, albedo, and snow albedo feedback (SAF). 

Can you tell us a bit more about SAF and the role it plays in climate models? 

SAF is a climate feedback process that amplifies warming through the melting of snow cover, which reveals a much darker (less reflective) surface. This increases the solar radiation that is absorbed by the ground surface. There is a large spread in how well various climate models represent this process, contributing to inter-model variation in global climate sensitivity (how much warming will occur in response to a certain amount of radiative forcing) with consequences for regional climate change.

Please explain in lay terms how you investigate this?  

We can calculate SAF “strength” by looking at how surface temperature, snow cover and surface reflectivity (albedo) change from one month to the next during the snow melt period (March-April-May-June). We use a number of observational estimates of these properties derived from satellites to evaluate the accuracy of the models.

Tell us about something new and exciting that your research has led to.

We recently published results showing that model performance related to simulating the seasonality of snow and albedo is poorest over the boreal forest (http://onlinelibrary.wiley.com/doi/10.1002/2015JD023325/full), where the way in which a model represents snow in forest canopies (the vegetation layer) can have a large impact on SAF accuracy (http://onlinelibrary.wiley.com/wol1/doi/10.1002/2014JD021858/full). This is a particularly complex environment because these forests retain their canopy vegetation year-round meaning they mask the reflective underlying surface. 

What kinds of implications does this have for understanding climate change?

The importance of SAF is best demonstrated by prior research showing that variability in SAF in climate models accounts for 40-50% of the spread in projections of 21st century warming over Northern Hemisphere land. Our work attempts to diagnose the physical processes that are leading to a large amount of model uncertainty so that improvements can be made. Better representation of snow and albedo in the models could help with improving projections of climate warming over this region.

How does your research fit into the broader scope of the CanSISE Network and how will your research contribute to improving the Canadian global climate model?

My research on snow albedo feedback (SAF) is a part of Research Area C (Snow and sea ice processes and climate interactions). This research will help determine how the Canadian climate model (CanESM2) performs in comparison with other global climate models when it comes to the simulation of snow and albedo, highlighting areas where development may be needed.

What are your next steps? 

My next steps include a number of novel climate model simulations where we hope to attribute the impact of current model biases related to snow and albedo on climate (i.e., temperature, atmospheric circulation).

The findings from a recent CanSISE publication on the topic of snow-cover changes (CanSISE researchers Marco Hernández-Henríquez and Stephen Déry) has been covered in a science news article released at EnvironmentalResearchWeb. A brief discussion and Q&A on their research can be found in the article linked here:

http://environmentalresearchweb.org/cws/article/news/61532

Be sure to read this exciting new paper published by our colleagues and CanSISE investigator John Fyfe at CCCma in Victoria, BC. For more detailed information on this very interesting publication you can find a press release at this link.

Here is a direct link to the paper on the Nature Climate Change journal website.

Hi, my name is Drew Snauffer and I'm a PhD student at the University of British Columbia with the CanSISE network. My advisor William Hsieh tackles tough environmental problems using machine learning methods, from the field of artificial intelligence (AI). My work involves using these methods to estimate snow across the province of British Columbia. Good snow estimates are crucial for water resources management and streamflow forecasts, especially in places that get much of their annual water from snowmelt, like BC.  The amount of water contained in a snowpack is called Snow Water Equivalent (SWE), discussed by my colleague Reinel in a previous post.  This can be thought of as the depth of water you'd get if you melted down all the snow in a certain area, and it gives you an indication of how much runoff there will be in streams and rivers when temperatures increase throughout the spring.

c) Snow models - estimates of snow accumulation and melt at individual points using maps of temperature and precipitation, as well as site information (I'll address these more in the future)

These methods are summarized in Table 1 below, along with links to detailed descriptions. They all create a map or grid of estimated snow, so I usually refer to them as "gridded products".  But basically they're large maps of snow estimates.

Figure 4:  Bias by Physiographic Region and Survey Month for BC's 5 physiographic regions: Coast Mountains and Islands (CMI), Interior Plateau (IP), Northern and Central Plateaus and Mountains (NPM), Great Plains (GP), and Columbia Mountains and Southern Rockies (CR)

Figure 6: Relative Bias by Physiographic Region and Survey Month for BC's 5 physiographic regions 

By Melissa Gervais

Two articles recently published in the July edition of journal of Climate (Gervais et al. 2014a, 2014b) examine the representation of precipitation in observations and Global Climate Models (GCMs). In the first article, the impact of station density on errors in the production of gridded precipitation data is explored.  An experiment was conducted in which station data in the United States were gridded repeatedly with subsequently fewer input stations. Two distinct error responses to loss of station density were found to characterize the western and the eastern United States. These were attributed to the relative spatial homogeneity of precipitation in these regions. As the station density decreases, the influence of stations farther from the analysis point increases, and therefore, if the distributions are inhomogeneous in space, the analysis point is influenced by stations with very different precipitation distributions.  Upper and lower bounds on these errors distributions across the entire United States can be used to estimate the range of errors associated with a given station density. 

Figure 1: Percent climatological error of JA and JF extreme precipitation (1979–2003) in the Rockies and Gulf regions, for all HRES grid boxes in a region and removal steps, as a function of station density (number of stations per 0.9° × 1.25° box). The colour of the symbols represents the concentration of climatological error points within 1% error bins, for a given station density.

The second article looks at the ability of a GCM to represent the full distribution of precipitation in North America.  As part of this analysis, the upper and lower bounds of errors determined in Gervais et al. 2014a were applied to a gridded precipitation dataset including both the contiguous US and Canada. Due to the low density of station observations in much of Canada, the range of errors due to station density are very large, on the order of 50% for extreme precipitation. 

Figure 2: (a),(c) Upper and (b),(d) lower bound on the percent bias in climatological annual (a),(b) median and (c),(d) extreme precipitation for the NAAP data using an experimentally derived relationship between upper and lower errors bounds and station density found in Gervais et al. (2014). Note that the colour scales are reversed between the upper and lower bound maps such that the magnitude of the colour schemes are identical but in opposing directions.

For more details please see:

Gervais, Melissa, L. Bruno Tremblay, John R. Gyakum, Eyad Atallah, 2014: Representing Extremes in a Daily Gridded Precipitation Analysis over the United States: Impacts of Station Density, Resolution, and Gridding Methods. J. Climate, 27, 5201–5218.

Gervais, Melissa, John R. Gyakum, Eyad Atallah, L. Bruno Tremblay, Richard B. Neale, 2014: How Well Are the Distribution and Extreme Values of Daily Precipitation over North America Represented in the Community Climate System Model? A Comparison to Reanalysis, Satellite, and Gridded Station Data. J. Climate, 27, 5219–5239.

-Melissa Gervais, CanSISE Graduate Student

By Reinel Sospedra-Alfonso 

Snow is the component of the cryosphere having the largest seasonal variation. A fundamental quantity describing the snow cover is Snow Water Equivalent (SWE = depth of water that results from the melting of a column of snow). To produce useful seasonal forecasts of SWE, a forecasting system must initialize this variable somewhat realistically, SWE itself must have potential predictability, and the forecasting system must be able to capitalize on this potential predictability to yield predictive skill.

We first assess the ability of CanSIPS (see description of CanSIPS below) to initialize SWE realistically, using gridded observational data products for comparison. We examine whether CanSIPS accurately reproduces the observed seasonal cycle of SWE and its interannual variations. We then study the temporal and geographical dependence of the potential predictability of SWE in the forecast runs.

CanSIPS is Environment Canada's operational seasonal prediction system, based on two versions of CCCma's coupled climate model, CanCM3 and CanCM4. Each model version produces 10 ensemble members, which are initialized by separate assimilation runs. Each such run assimilates the same observational data but originates from different initial conditions giving rise to ensemble spread. These assimilation run states provide forecast initial conditions for the atmosphere, land surface and sea ice.    

Potential predictability provides predictability in a world where the statistical characteristics of the observations equal the statistical characteristics of the model forecast runs. In the figure above, potential predictability of SWE from the Canadian Coupled Climate Model version 4 (CanCM4) forecast runs at days (from top left to right) 1, 8, 15 and 31.  Forecast initial time is February 1st.

-Reinel Sospedra-Alfonso

Reinel is a CanSISE supported Post-Doctoral Fellow who works with William Merryfield at CCCma in Victoria, BC.

By Alexander Slavin   Posted July 29, 2014

We show modeling evidence, supported by observations, for strong Ekman pumping velocities beneath active leads (Linear Kinematic Features, LFKs) where discontinuity in the sea-ice drifts and the surface ice-ocean stresses momentum transfer are present. The anomalous vertical velocities beneath LKFs extend hundreds of meters below the surface, well into the Atlantic Layer, and bring up large amount of ocean heat (of the order of hundreds of W/m2) into the mixed layer. Results show vertical ocean heat fluxes in winter of approximately 4.2 W/m2 (approximately 40 cm of sea-ice melt) when averaged over the Beaufort Sea, the region where most LKFs are present.

Fig. 1. 7th January 2005. a) - Sea-ice shear strain rate day-1, b) - vertical advective ocean heat flux, W/m2 at 40 m depth, c) - sea-ice stress curl s-1, d) - sea-ice divergence s-1, e) - vertical velocity m/s and f) - difference between the location beneath the mixed layer (where the NTSM is present) and the mixed layer maximum temperatures. The shear strain rate in (a) is capped at 0.2 day-1 to show a wider range of leads.'

We suggest that this process can be important in controlling the Arctic sea-ice mass balance and a potentially important player in the recent sea-ice decline in the Beaufort Sea, one that is not represented in lower-resolution global climate models. In a future climate with thinner and more mobile pack ice, this contribution from the ocean by this mechanism will only amplify.

-Alexander Slavin

Alexander Slavin is a CanSISE supported Post-Doctoral Fellow who works with Bruno Tremblay in the Department of Atmospheric and Ocean Sciences at McGill University.

By Do Hyuk Kang   Posted April 03, 2014

Air temperatures around the Fraser River in Hope, BC have risen significantly during the late 20th century. Results from a hydrological model indicate that deacreased runnoff, stemming from a reduction in snowfall, has lead to an altered hydrograph pattern in the Fraser River during the latter part of the 20th century.

-Do Hyuk Kang, CanSISE Post-Doctoral Fellow