CanSISE Research & Activities Blog

Thu

09

Feb

2017

Geophisical Research Letters Article

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).

Several different data sets of observed surface temperature and snow cover are used  to determine the sensitivity of snow cover to increases in surface temperature. This observed sensitivity is compared to that from a wide range of climate models.  The sensitivity simulated by climate models is shown to be within the observational range across the Arctic, but is too weak (smaller snow reductions for a given temperature increase compared to observations) for midlatitude and alpine regions. The use of multiple observational datasets highlights an inconsistent and physically unrealistic snow cover response characterized by one of the most prominent and frequently used observational datasets, the NOAA snow cover climate record.

 

Full article available here:

http://onlinelibrary.wiley.com/doi/10.1002/2016GL071789/full

 

Check out more of Lawrence’s research at

https://www.researchgate.net/profile/Lawrence_Mudryk

 

-Lawrence Mudryk, CanSISE Collaborator

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Mon

10

Oct

2016

Nature Geoscience Article

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.

'Georgian's Freeze' by Raymond Erwin Nikyuluw
'Georgian's Freeze' by Raymond Erwin Nikyuluw




"Winter cooling over Eurasia has been suggested to be linked to Arctic sea-ice loss. Climate model simulations reveal no evidence for such a link and instead suggest that a persistent atmospheric circulation pattern is responsible."

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Tue

28

Jun

2016

Nature Climate Change Article

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/

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Tue

03

May

2016

CanSISE Deliverable 1 Report Released

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

CanSISE Deliverable 1 Report (May 2016).
Adobe Acrobat Document 10.6 MB
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Thu

21

Jan

2016

Member Research Profile - Andrew Snauffer, PhD

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 (ANNG 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 (ANNGS), 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. 

 

 

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Fri

30

Oct

2015

CanSISE West Meeting - October 30th, 2015

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.


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Fri

23

Oct

2015

New Aerosol Emissions Paper 

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

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Wed

05

Aug

2015

Member Research Profile - Kelly McCusker, 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

Arctic sea ice extent on August 26, 2012 compared to median (click to enlarge). Image by Jesse Allen from the NASA Earth Observatory: http://tinyurl.com/ovoh4l8
Arctic sea ice extent on August 26, 2012 compared to median (click to enlarge). Image by Jesse Allen from the NASA Earth Observatory: http://tinyurl.com/ovoh4l8

increase in warming locally in the Arctic, there are open questions as to whether or not the sea ice loss can have an influence on weather patterns outside the Arctic. I am currently investigating whether sea ice loss in the Barents-Kara Seas region of the Arctic could cause colder winters in Eurasia by fundamentally changing atmospheric circulation in the region. Understanding this potential link is important for understanding how winter weather could change in the future, as sea ice declines further. 

 

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 

Areas of open water are sources for heat to the atmosphere in winter. Credit: Ted Scambos, NSIDC
Areas of open water are sources for heat to the atmosphere in winter. Credit: Ted Scambos, NSIDC

greenhouse gases. Thus on a planetary scale, sea-ice loss modifies the equator-to-pole temperature gradient, and this can have implications for the strength of the midlatitude westerly winds and other circulation features. At a regional level, sea ice loss in the Barents-Kara Seas can cause warming above it through the above mentioned mechanism, which thickens the atmosphere and can produce a weak high pressure aloft, especially in winter. This feature is hypothesized to drive polar air to the south over Eurasia, generating colder Eurasian winters. Of course temperature itself can change through other factors and influence the evolution of sea ice, so this is a very complex question. See next.

 

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.

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Wed

22

Jul

2015

Arctic Sea Ice in the news

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

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Fri

10

Jul

2015

Member Research Profile - Chad Thackeray, PhD Student

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. 

Old Jack Pine forest site, January 1997 (Source: berms.ccrp.ec.gc.ca)
Old Jack Pine forest site, January 1997 (Source: berms.ccrp.ec.gc.ca)

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).

 

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Tue

16

Jun

2015

CanSISE Research news article - Snow-cover changes

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

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Thu

29

Jan

2015

Nature Climate Change Publication

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.

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Thu

04

Dec

2014

An Introduction to a CanSISE graduate student and their work at UBC

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.

Figure 1:  Automatic Snow Pillow at Mission Ridge, BC (photo credit: Viasat Data Systems)
Figure 1: Automatic Snow Pillow at Mission Ridge, BC (photo credit: Viasat Data Systems)

There are many places throughout the province where snow is measured regularly. Two of the ways we get these measurements are Automatic Snow Pillow (ASP) stations (Figure 1) and Manual Snow Survey (MSS) stations (Figure 2). There are about 400 snow survey sites and 70 snow pillows are scattered all over BC, mostly in the southern part (a map is here). As many snow stations as there are throughout the province, it's not enough. Large gaps that exist in our survey network hamper our ability to see how much snow is there, and understand how it changes through time.

Figure 2: Surveyor measuring SWE using a snow tube on a snow course
Figure 2: Surveyor measuring SWE using a snow tube on a snow course

To be able to make good water management plans, we need good estimates of wintertime snow, but we have gaps in our measurement network of Automatic Snow Pillow (ASP) stations and Manual Snow Survey (MSS) stations.  There are a number of ways we try to fill these gaps. Methods that use satellite measurements of microwaves (like those in your microwave oven) that come off the earth have been used for many years. These methods work well in flat, open areas.  But not much of BC looks like this.  The province is known for its rugged, heavily forested mountains, and these factors make satellite microwave data hard to use.  Other methods include: 

a) Reanalyses - long term climate estimates produced using a large number of quality-checked atmospheric and land-based observations 

 

 

b) Land Data Assimilation Systems or LDAS - detailed land-surface models describing things like slope, soil type, etc. used to estimate water and energy movements according to observations

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.

Table 1 SWE Gridded Product Summary

Product

Type

Resolution

Time range

GlobSnow

observational

25 km

1979-2011

CMC

observational

24 km

1998-2012

GLDAS-1

LDAS

0.25 deg

2000-2012

GLDAS-2

LDAS

0.25 deg

1979-2010

ERAInterim

reanalysis

0.75 deg

1979-2012

ERALand

reanalysis

0.75 deg

1979-2010

MERRA

reanalysis

2/3 x 1/2 deg

1979-2010

MERRALand

reanalysis

2/3 x 1/2 deg

1980-2012

 

Figure 3:  BC's Physiographic Regions

Since it's hard to look at all 470 stations individually, I group them by region. BC has 5 main physiographic regions shown in Figure 3 on the left (and described in more detail in the BC Forest Service's 1994 Forest, Range & Recreation Resource Analysis).  By grouping the stations, I'm looking for regional characteristics or trends that describe how well these methods are doing.  It's also useful to see how they do year to year, so I break them out by month (e.g. all years for January, February, etc.).  


One way to see how well the methods are doing is to look at the average difference between the measurement and the estimate.  This is called the bias.  Bias tells me how much each estimate is over or under the measurement.  If I plot the average bias for several gridded products by region and month, I get Figure 4 below. 


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)


In figure 4, blues represent a negative bias, meaning the estimate is lower than what is measured, and greys mean insufficient data.  Whole season average values are printed to the left of each box.  Clearly there are regional differences in the bias across the methods I'm looking at, but all are underestimating to some extent.  Compare that to Figure 5 on the right showing mean snow accumulation (dark reds mean more snow).  The bias for all of these looks quite similar to the average snow accumulation.  This makes sense.  If gridded products are underpredicting, areas with more snow are likely to have a bigger difference between the estimate and the measurement.  A more fair comparison might be to divide the difference by the measured value.  This is known as relative bias.  


A similar plot for relative bias is show in Figure 6.  From this plot, I can see that while indeed most products are underestimating, ERA-Land and GLDAS-2 have the lowest magnitude relative biases across the two regions of highest accumulation, the Coast Mountain and Columbia & Rocky Mountain regions.  However MERRA and the CMC product have more modest relative biases in the Northern Plateaus and Great Plains respectively.  That tells me that these products perform better in the northern part of the province, and I can apply this knowledge to my efforts.  The ultimate goal will be to apply machine learning techniques to select and combine the SWE gridded products with other important information (elevation, forest cover, etc) and produce better snow estimates for BC.


Figure 5:  Mean snow accumulation by region and month for BC's 5 physiographic regions (as in Figure 4)


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

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Fri

29

Aug

2014

Recent Publications

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

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Fri

01

Aug

2014

Initialization and potential predictability of snow in CanSIPS

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.

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Tue

29

Jul

2014

Vertical ocean heat fluxes

Vertical ocean heat fluxes beneath Linear Kinematic Features: a potential mechanism for rapid sea-ice decline in the Arctic

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.

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Thu

03

Apr

2014

Fraser River Hydrology

Evaluating the Fraser River's Hydrologic Regime Shift in the late 20th century

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

 

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