Climate Diagnostics of Large Fires in the Upper Colorado River

Basin

Nick NauslarM.S. Student/Research Assistant

Department of Atmospheric SciencesUniversity of Nevada, Reno & Desert Research Institute

Climate and Wildfires• Climate fluctuations heavily impact length and severity of fire season.• Southern Oscillation Index have teleconnections with western US

climate.• Scientists research other indices to identify connections to fire

season.

Climate and Wildfires• Fire season in the western US: May through

October (94% of fires & 98% of acres burned).• 50-80% of precipitation in the West falls from

October to March.• Peak fire season during July and August

(climatologically hottest and driest months).

General Colorado Fire Climate

• Areas dominated by dry shrubs and grasslands depend on climate conditions 10-18 months before fire season.

• Colorado Rockies show wet (dry) conditions previous year contributing (inhibiting) fuel loading.

• Wet (dry) conditions during current year suppressing (enhancing) fire activity by moistening (drying) fuels.

Why Investigate?• Costs: ~$500 million,

80,000+fires burning 3.5 million acres

• Loss of life and property• Better prediction and

information can lead to more efficient and productive fire management

Geographical Area Coordination Centers (GACC)

Rocky Mountain GACC

Upper Colorado River Basin (UCRB)

COLORADO

UTAH

I-70

I-25

Grand Junction

UCRB Climate

• Region varies greatly with precipitation and temperature values and from season to season

• Yearly snowfall from 12 in. to 200+ in. and total precipitation from 8 in. to 20+ in

• Average high and low temperatures as much as 25° different for the year

UCRB Fuels

UCRB Fuels

• Varies greatly from east to west• Fine (grasses and shrubs) to heavy (Aspen and

conifer trees) fuels • Fine fuels react quicker to weather/climate

changes. Easier to ignite and burn when dry• Heavy fuels react slower to weather/climate

changes. Burn longer and produce crown fires (very destructive)

Palmer Drought Severity Index (PDSI)

• Chose to investigate relationship between PDSI and anomalous fire season severity.

• PDSI- Autoregressive measure of combined precipitation, evapotranspiration, and soil moisture conditions.

• Represents accumulated precipitation anomalies and to a lesser extent temperature anomalies.

PDSI: 17 Jan 2009

Standard Precipitation Index (SPI)

• SPI- Number of standard deviations that observed value would deviate from the long-term mean for normally distributed random variable

• Single numeric value assigned to precipitation accumulation

• Can compare across different climate regions

SPI 1 Month Dec 2008

PRISM Data• PDSI and SPI values are from Parameter-elevation

Regressions on Independent Slopes Model (PRISM) data

• Uses point measurements of precipitation, temperature, and other climatic factors

• Produces continuous, digital grid estimates of monthly, yearly, and event-based climatic parameters

• 4 km grid that accounts for topography and its effects

PDSI and SPI PRISM Data• Devised a FORTRAN

program that mapped the closest PRISM data point and retrieved the average monthly value

• Obvious drawbacks: if fire occurred early in the month and only using one data point

Arctic Oscillation (AO)• Dominant pattern of non-seasonal sea-level

pressure (SLP) variations north of 20°N• Characterized by SLP anomalies of one sign in

the Arctic and the opposite sign centered about 37°-45°N

• Below normal Arctic SLP (warm phase) and above normal Arctic SLP (cold phase)

• Leading mode of Empirical Orthogonal Function (EOF) analysis

Pacific/North American Pattern (PNA)

• Based on analysis of Rotated Principal Component Analysis (RPCA)

• Process identifies primary teleconnection patterns for all months

• Daily PNA for last 120 days• Each daily value standardized by standard

deviation of monthly index from 1950-2000• Positive phase -> higher heights near Hawaii and

over intermountain west

Oceanic Niño Index (ONI) (El Niño 3.4 Region)

• Based on +/- 0.5°C threshold for ONI

• ONI- 3 month running mean of sea surface temperature (SST) anomalies (5°N-5°S and 120°-170°W)

• Warm Phase-> El Niño, Cold Phase-> La Niña

Multivariate ENSO Index (MEI)• Comprised of variables over tropical Pacific• SLP, surface wind (u, v components), SST,

surface temperature, and cloudiness• Calculated from 1st Principal Component

Spearman Rank Correlation (SRC)

• Values between -1 and 1• Rank data in pairs• More robust and resistant than Pearson’s r• Statistically significant dependent on data and

final results• Set for α=.05, Confidence Interval= 95%

Principal Component Analysis (PCA)

• Purpose is to reduce a data set with many variables to fewer variables but still representing most of the data

• 1st PC explains most variance• Subsequent PC’s explain less and less variance

and are uncorrelated to each other

PDSI Histogram

• PDSI negative for all fires

• However, majority of large fires occurred during near-normal conditions (-1.9 to 1.9)

SPI 1 and 3 Month Histograms

SPI 6 and 12 Month Histograms

SPI Scatter Plot

PDSI and SPI PCATable 1

Factor 1 = 67% Factor 2= 21%

PDSI 0.21 0.07

SPI 1month 0.23 0.13

SPI 3month 0.20 0.26

SPI 6month 0.24 ~0

SPI 12month 0.12 0.53

PDSI and SPI Summary

• Deficit precipitation in 3 and 6 month SPI• SPI 1 could be higher than normal due to

previous suggestions• SPI 12 month and PDSI near normal• Possibly a buildup fuels with near or above

normal precipitation then adequate shorter term drying = large fires

• Stays in line with previous studies

Climate Pattern CorrelationsPNAJAN PNAJUNspearman= 0.37p=.007CI=99%

DJFENSO MJJENSOspearman= 0.29p=.04

CI= 96%

MEIDJ MEIJJspearman= 0.40p=.002CI=99.5%

JanAO JuneAOspearman= 0.38p=.003CI=99%

AO PCA

Factor 1=41% Factor 2=20% Factor 3=12%

JanAO 0.36 0.08 0.21FebAO 0.28 ~0 0.04MarAO 0.03 0.42 0.43AprAO ~0 0.01 0.04MayAO 0.02 0.01 ~0JuneAO ~0 0.03 ~0JulyAO ~0 0.02 ~0AugAO ~0 0.01 ~0SepAO ~0 0.04 ~0OctAO 0.02 ~0 0.09NovAO 0.11 0.10 0.02DecAO 0.17 0.29 0.17

PNA PCA

Factor 1=28% Factor 2=20% Factor 3=17%

PNAJAN ~0 0.09 0.01PNAFEB 0.01 0.11 ~0PNAMAR 0.14 0.02 0.05PNAAPR 0.24 0.22 ~0PNAMAY 0.03 0.05 0.01PNAJUN 0.01 0.13 ~0PNAJUL 0.02 0.05 0.67PNAAUG 0.13 0.14 0.16PNASEP 0.23 0.05 ~0PNAOCT 0.03 0.08 ~0PNANOV 0.12 0.03 0.08PNADEC 0.04 0.05 0.01

ENSO 3.4 PCAFactor 1=77% Factor 2=21%

DJFENSO 0.02 0.43JFMENSO 0.02 0.29FMAENSO 0.02 0.13MAMENSO 0.02 0.06AMJENSO 0.04 0.01MJJENSO 0.06 ~0JJAENSO 0.08 0.01JASENSO 0.09 0.01ASOENSO 0.11 0.01SONENSO 0.14 0.02ONDENSO 0.20 0.02NDJENSO 0.21 0.02

MEI PCAFactor 1=63% Factor 2=29%

MEIDJ 0.04 0.22MEIJF 0.04 0.22MEIFM 0.04 0.18MEIMA 0.06 0.10MEIAM 0.06 0.02MEIMJ 0.07 ~0MEIJJ 0.08 0.02MEIJA 0.06 ~0MEIAS 0.12 0.05MEISO 0.14 0.05MEION 0.14 0.07MEIND 0.15 0.06

Climate Pattern Summary• Correlations show statistically significant

connection between winter and summer months

• Fall and winter months in Niño patterns important, explain most of the variance

• PNA inconclusive although spring and summer seem to have some connection

• AO appears to have connection in winter months

Climate Pattern Summary• Winter patterns seem

to have the most impact on following fire season

• Explain most of the variance, studies show connection with some of these patterns and the Rockies

Colorado Rockies

Cor

rela

tion

(PD

SI

&

acre

s bu

rned

)

-0.5

0

0.

5-40 -30 -20 -10 0

Months before August

Conclusions• Near normal conditions further back with

drought conditions closer to fire start• Large scale climate patterns show some

promise of teleconnections (esp. ENSO)• Preliminary results are encouraging, merit

further investigation• Investigate further back with indices• Perform more complete statistical analysis

References• “Climate and Wildfire in the Western United States”- Westerling,

Gershunov, Brown, Cayan, and Dettinger, 2001• “Statistical Forecast of the 2001 Western Wildfire Season Using

Principle Components Regression” - Westerling, Gershunov, Brown, Cayan, and Dettinger, 2002

• “ENSO Influence on Intraseasonal Extreme Rainfall and Temperature Frequencies in the Continental United States: Observations and Model Results”- Gershunov, Barnett, 1998.

• Wildland Fire Assessment System (WFAS)• Climate Prediction Center (CPC)• Program for Climate, Ecosystems, and Fire Applications (CEFA)• Western Regional Climate Center (WRCC)• ATMS 706: Data Analysis taught by Dr. Ryan Banta• National Interagency Fire Center (NIFC)