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Wildfire	Season	Forecast
Forecast 2018
May	16,	2018
Richard	Carr,	Wildland	Fire	Research	Analyst,	
Canadian	Forest	Service,	Natural	Resources	Canada
1
2
Agenda
This presentation
• Provides some background information
• Revisits last year’s fire season
• Summarizes current global and Canadian conditions
• Describes the forecast methodology
• Provides the 2018 forecast (May 1 run)
2
3
Background information
3
Forecasts:	General	points
Forecasts of seasonal wildland fire risk are available on
• Canadian Wildland Fire Information System (CWFIS) web site:
http://cwfis.cfs.nrcan.gc.ca
• North American Seasonal Forecast Assessment Outlook:
http://www.predictiveservices.nifc.gov/outlooks/NA_Outlook.pdf
Forecasts are based on:
• Environment and Climate Change Canada (ECCC) seasonal forecasts
• Information contained in the CWFIS
• North American outlook areas use visual assessment of other climate
models’ output
• Advice provided by provincial agencies
4
Wildland	fire	in	Canada
5
2017
Fire numbers and areas burned are highly variable
• Number of fires decreasing
• Area burned increasing
6
Canadian	Wildland	Fire	Information	
System	(CWFIS)
The CWFIS provides:
• Fire weather
• Fire behaviour
• Fire locations
• Interactive map
• Seasonal forecast and
other information
URL:
http://cwfis.cfs.nrcan.gc.ca
6
7
Canadian	Forest	Fire	Weather	Index	(FWI)	
System
7
Temperature
Humidity,
Wind, Rain
Temperature,
Humidity,
Rain
Wind
Temperature,
Rain
Fire Weather
Observations
DC
Drought Code
DMC
Duff Moisture Code
ISI
Initial Spread Index
BUI
Buildup Index
FWI
Fire Weather Index
FFMC
Fine Fuel Moisture Code
Fire Behavior
Indexes
Fuel Moisture
Codes
Snow depth
Barometric Pressure
Horizontal Visibility
Cloud cover and type
Upper air data
DSR/MSR/SSR
Severity Rating
Seasonal forecasts use the severity rating anomaly
8
8
Drought	Code
The Canadian FWI System allows for adjustment of fall
conditions to the spring using winter precipitation.
Fuel	moisture	codes	represent	moisture	at	three	forest	floor	depths:
Moisture Depth Layer Woody	Fuel Precip
Code Threshold
FFMC 0-2	cm Litter	layer	L Fine	twigs,	needles,	etc 0.5mm
DMC 2-5	cm Fibric layer	F Bark,	small	branches 1.5mm
DC 5+	cm Humic layer	H Logs,	large	branches 2.8mm
Drought	Code	(DC)	based	on	forest	floor	moisture	capacity	of	~200mm
L
F
H
9
What	affects	spring	DC?
• Spring DC (and FWI) can depend on El Niño/Southern
Oscillation (ENSO) events
• La Niña (negative phase) and El Niño (positive phase) are major
drivers of seasonal weather
• Other oscillations (PNA, PDO, AO, MJO, …) likely effect DC,
but at different timescales, intensities; effects not as well-
described
9
10
10
El	Niño	– Southern	Oscillation	(ENSO)
El Niño:
• Warm ocean temperature anomaly in eastern Pacific; atmospheric
pressure higher over Darwin thanTahiti
La Niña:
• Cold ocean temperature anomaly in eastern Pacific; atmospheric
pressure higher overTahiti than Darwin
Events may cause extreme global weather (floods, droughts)
Events persist 9-12 months; sometimes 2+ years
Events recur every 2 to 7 years
Develop often during April-June
Reach maximum strength Dec-Feb
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Fire	problems	in	ENSO	Springs
El Niño:
• Warm, windy,
and dry in
western Canada
La Niña:
• Arctic surface
highs bring dry
air, strong wind
around edges
• Temperature
may be cool
Summer fire
problems may
depend on other
influences
Year DJF JFM FMA MAM AMJ MJJ JJA JAS ASO SON OND NDJ
1989 -1.7 -1.4 -1.1 -0.8 -0.6 -0.4 -0.3 -0.3 -0.2 -0.2 -0.2 -0.1
1990 0.1 0.2 0.3 0.3 0.3 0.3 0.3 0.4 0.4 0.3 0.4 0.4
1991 0.4 0.3 0.2 0.3 0.5 0.6 0.7 0.6 0.6 0.8 1.2 1.5
1992 1.7 1.6 1.5 1.3 1.1 0.7 0.4 0.1 -0.1 -0.2 -0.3 -0.1
1993 0.1 0.3 0.5 0.7 0.7 0.6 0.3 0.3 0.2 0.1 0.0 0.1
1994 0.1 0.1 0.2 0.3 0.4 0.4 0.4 0.4 0.6 0.7 1.0 1.1
1995 1.0 0.7 0.5 0.3 0.1 0.0 -0.2 -0.5 -0.8 -1.0 -1.0 -1.0
1996 -0.9 -0.8 -0.6 -0.4 -0.3 -0.3 -0.3 -0.3 -0.4 -0.4 -0.4 -0.5
1997 -0.5 -0.4 -0.1 0.3 0.8 1.2 1.6 1.9 2.1 2.3 2.4 2.4
1998 2.2 1.9 1.4 1.0 0.5 -0.1 -0.8 -1.1 -1.3 -1.4 -1.5 -1.6
1999 -1.5 -1.3 -1.1 -1.0 -1.0 -1.0 -1.1 -1.1 -1.2 -1.3 -1.5 -1.7
2000 -1.7 -1.4 -1.1 -0.8 -0.7 -0.6 -0.6 -0.5 -0.5 -0.6 -0.7 -0.7
2001 -0.7 -0.5 -0.4 -0.3 -0.3 -0.1 -0.1 -0.1 -0.2 -0.3 -0.3 -0.3
2002 -0.1 0.0 0.1 0.2 0.4 0.7 0.8 0.9 1.0 1.2 1.3 1.1
2003 0.9 0.6 0.4 0.0 -0.3 -0.2 0.1 0.2 0.3 0.3 0.4 0.4
2004 0.4 0.3 0.2 0.2 0.2 0.3 0.5 0.6 0.7 0.7 0.7 0.7
2005 0.6 0.6 0.4 0.4 0.3 0.1 -0.1 -0.1 -0.1 -0.3 -0.6 -0.8
2006 -0.8 -0.7 -0.5 -0.3 0.0 0.0 0.1 0.3 0.5 0.7 0.9 0.9
2007 0.7 0.3 0.0 -0.2 -0.3 -0.4 -0.5 -0.8 -1.1 -1.4 -1.5 -1.6
2008 -1.6 -1.4 -1.2 -0.9 -0.8 -0.5 -0.4 -0.3 -0.3 -0.4 -0.6 -0.7
2009 -0.8 -0.7 -0.5 -0.2 0.1 0.4 0.5 0.5 0.7 1.0 1.3 1.6
Year DJF JFM FMA MAM AMJ MJJ JJA JAS ASO SON OND NDJ
2010 1.5 1.3 0.9 0.4 -0.1 -0.6 -1.0 -1.4 -1.6 -1.7 -1.7 -1.6
2011 -1.4 -1.1 -0.8 -0.6 -0.5 -0.4 -0.5 -0.7 -0.9 -1.1 -1.1 -1.0
2012 -0.8 -0.6 -0.5 -0.4 -0.2 0.1 0.3 0.3 0.3 0.2 0.0 -0.2
2013 -0.4 -0.3 -0.2 -0.2 -0.3 -0.3 -0.4 -0.4 -0.3 -0.2 -0.2 -0.3
2014 -0.4 -0.4 -0.2 0.1 0.3 0.2 0.1 0.0 0.2 0.4 0.6 0.7
2015 0.6 0.6 0.6 0.8 1.0 1.2 1.5 1.8 2.1 2.4 2.5 2.6
2016 2.5 2.2 1.7 1.0 0.5 0.0 -0.3 -0.6 -0.7 -0.7 -0.7 -0.6
2017 -0.3 -0.1 0.1 0.3 0.4 0.4 0.2 -0.1 -0.4 -0.7 -0.9 -1.0
2018 -0.9 -0.8 -0.6
Most area burned
2nd most area burned
Chisholm, AB
Kelowna, BC
Virginia Hills, AB
Slave Lake, AB
Fort McMurray, AB
12
ENSO	forecasts:	Climate	Ensembles
ENSO and other influences represented in climate models
Models used by ECCC’s Canadian Meteorological Centre (CMC):
• Were developed by Canadian Centre for Climate modeling and analysis
(CCCma)
• Provide temperature and precipitation forecasts
• Ensembles of ten members each produce 12-month forecasts
• CanCM3 uses atmospheric model CanAM3 (AGCM3)
• CanCM4 uses atmospheric model CanAM4 (AGCM4)
• CanSIPS: Canadian Seasonal to Interannual Prediction System
12
13
Ensemble	Forecasts:	FWI	calculations
Predicted temperatures and precipitation amounts are
entered into the Canadian FWI system.
13
14
14
Ensemble	Forecasts:	Confidence
The ensemble approach
provides a measure of
confidence indicated by
the spread of the
ensemble members.
15
The 2017 Fire Season
15
16
16
2017	National	Statistics
Date 2017	fires 10-yr	Avg fires %	normal 2017	Area	(ha) 10-yr	Avg Area	(ha) %normal
May	31 1,041 1,909 55 8,481 210,967 4
June	28 1,614 2,911 55 165,330 753,071 22
August 2 3,288 4,640 71 1,202,646 2,134,800 56
August	
30
4,870 5,780 84 2,983,460 2,527,647 118
End? 5,305 7,500* 71 3,500,000 2,500,000* 140
*NFDB 1990-2016, otherwise CIFFC/CFS Sitrep
17
17
2017	Season
YT, NT, MB, and PC had
slightly more fires than
normal
BC,YT, and NT had much
higher than normal area
burned.
18
18
2017	Season
Sep 30
Aug 31
June 30
July 15
July 31
Aug 15
19
19
2017	Predictions
March June
The seasonal fire severity forecast changed
with ENSO forecast changes.
Weak “Modoki” ENSO, positive PDO, and
MJO may have helped shape the 2017
season.
20
20
2017	Predictions	– May	and	July	runs
June July August
New look (at the time)
The “new new” look appears later
May run
July run
21
21
2017	NASFAO:	2-month	lead	time
May’s forecast for July June’s forecast for August July’s forecast for September
http://www.predictiveservices.nifc.gov/outlooks/NA_Outlook.pdf
• Uncertainty in climate predictions affected severity forecasts
• 2-month lead time resulted in poor predictions
• Countries use different methodologies
22
22
Oops!	We	found	a	coding	error	in	
the	cm4	precipitation	file	reading
…
Fixed	for	May	1	run
Year DJF JFM FMA MAM AMJ MJJ JJA JAS ASO SON OND NDJ
2012 -0.8 -0.6 -0.5 -0.4 -0.2 0.1 0.3 0.3 0.3 0.2 0.0 -0.2
2013 -0.4 -0.3 -0.2 -0.2 -0.3 -0.3 -0.4 -0.4 -0.3 -0.2 -0.2 -0.3
2014 -0.4 -0.4 -0.2 0.1 0.3 0.2 0.1 0.0 0.2 0.4 0.6 0.7
2015 0.6 0.6 0.6 0.8 1.0 1.2 1.5 1.8 2.1 2.4 2.5 2.6
2016 2.5 2.2 1.7 1.0 0.5 0.0 -0.3 -0.6 -0.7 -0.7 -0.7 -0.6
2017 -0.3 -0.1 0.1 0.3 0.4 0.4 0.2 -0.1 -0.4 -0.7 -0.9 -1.0
2018 -0.9 -0.8 -0.6 ?
Possible	problems	with	2017	predictions	
2016-18:	Rapid	ENSO	fluctuations
23
2018 Seasonal Prediction
Starting Conditions
23
24
24
2017	Fall	conditions
Many locations in top 10th percentile
for the time of year
Drought Code
Oct 28, 2017
• 0.0 – 250.0
• 250.0 – 500.0
• 500.0 – 750.0
• 750.0 – 1000.0
• 1000.0 – 1250.0
Does CWFIS need a
new DC class?
25
25
2018	Spring	start-up	conditions
Past 6 months precipitation: CDM – end of April
Dry in MB, BC Chilcotin;
wet in northeast BC and mountains
Snow depth anomaly:
April 10, May 6
26
26
2018	Spring	start-up	conditions
DC in much of Canada is higher than in mid-May 2017
27
2018 Seasonal Prediction
Climate and CMC Forecasts
27
28
ENSO	– Current	SST
28
29
29
ENSO	Forecasts
Models predict neutral conditions during spring and summer,
and weak El Niño by late fall/early winter
30
30
ENSO	Forecasts
Increasing chance El Nino develops in winter of 2018-19
31
0 50 100 150
051015202530
CYEG
Days from Apr 1
Temp-cm3
Seasonal	Forecast	Methodology
1.Use fall conditions to help assess
spring start-up
2. Calculate average daily weather
4. Calculate fire weather anomaly 3. Apply seasonal predictions
31
32
32
CanSIPS Forecasts: May,	June,	July
Higher probability of temperature above normal in BC, central
and eastern regions. Increased chance of low precipitation in
Prairie Provinces.
33
33
CanSIPS Forecasts:	July,	Aug,	Sept
Above-normal temperatures possible for most of Canada
No precipitation trends evident
34
34
Other	models
We make visual
assessments of
other models’
output.
These are
temperature plots,
but precipitation is
also evaluated
35
2018 Seasonal Prediction
NRCan-CFS Model Predictions
35
36
36
NRCan-CFS	Seasonal	Prediction	-- New
More fully automated – less chance of failed runs
Classification of anomalies: shows below average and more
distinct classes
New summary map content: areas may use visual assessment
of other models’ predictions
• Included in NASFAO but not CWFIS yet
37
37
NRCan-CFS	Prediction:	May	run,	for	May
Anomaly
Predicted values normalized against average weather
NASFAO
BNs
The “newer look” maps feature areas
with expected below normal fire risk.
38
38
NRCan-CFS	Prediction:	May	run,	for	June
Anomaly
Predicted values normalized against average weather
NASFAO
BNs
39
39
NRCan-CFS	Prediction:	May	run,	for	July
Anomaly
Predicted values normalized against average weather
NASFAO
BNs
40
40
NRCan-CFS	Prediction:	May	run,	for	August
Anomaly
Predicted values normalized against average weather
BNs
41
Temperature Precipitation
Cranbrook,	BC
41
https://www.ndstudies.gov
Cranbrook: positive SSR trend mostly dependant on forecast
low rainfall
42
Temperature Precipitation
Fort	St.	John,	BC
42
https://www.ndstudies.gov
Fort St John: Warm temperatures probable by summer, hint
rainfall may be lacking
43
Temperature Precipitation
Fort	Simpson,	NT
43
https://www.ndstudies.gov
Fort Simpson: On the edge of our higher risk area this summer
44
Temperature Precipitation
The	Pas,	MB
44
https://www.ndstudies.gov
The Pas: CM4 temperature trend, probable low rainfall driving
trend in MB in spring/summer
45
Temperature Precipitation
Sioux	Lookout,	ON
Sioux Lookout forecasts are for warm temperatures, dry, and
positive severity rating trend
45
https://www.ndstudies.gov
46
Temperature Precipitation
Wabush Lake,	NF
46
https://www.ndstudies.gov
Wabush Lake: May be warmer than normal, but forecast
precipitation looks adequate
47
What’s up with the large temperature departure on
one ensemble element?
47
Notes
Forecast temperature higher in many places in May
run than April’s
Does positive Pacific Decadal Oscillation reduce
spring rain in southwestern Canada?
48
48
NRCan-CFS	Seasonal	Prediction– Future?
One more step needed to refine anomaly map classes
Add software to CWFIS architecture
Better snowmelt estimate
Understand ensemble members and apparent biases
Input from other climate models
Reconcile CWFIS and NASFAO presentation methods
Incorporate provincial methodologies
Assessment of model performance
49
49
Watch	for	…
New forecasts done in the first week of each month
New anomaly class scheme
Daily conditions on the CWFIS
and/or provincial fire weather
web sites
50
50
Questions?
Contact:
Richard Carr
Fire Research Analyst
Natural Resources Canada –
Canadian Forest Service
Richard.Carr@canada.ca
5320 122 Street NW
Edmonton, AB, Canada
T6H 3S5
780-435-7313
?
?
?
?
?

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