Dave SauchynPrairie Adaptation Research Collaborative

University of Regina

National Workshop: Development of Scenarios of Climate Variability and Extremes: Current Status and Next Steps

Victoria, BC, 16-17 October 2003

The paleo-perspective: what can paleo-data tell us about past extremes which

is useful for the future?

Acknowledgements

Collaborators: Dr. Elaine Barrow, Dr. Ge Yu

Graduate Students: Antoine Beriault, Jennifer Stroich

Funding:

Climate is Always Changing

From GSC Misc. Report 71 (2001)

Ice cores, tree rings, lakes and oceans sediments:windows on the past

(Leavitt, U of R)

Climatic VariabilityA projected increase in climate variability, including more frequent drought and major hydroclimatic events, is the most challenging climate change scenario. Social and biophysical systems respond to short-term climate variability and to extreme events long before they respond to gradual changes in mean conditions. More extreme climate anomalies are likely to exceed natural and engineering thresholds beyond which the impacts of climate are more severe.

Ron Hopkinson, MSC

IPCC Workshop on Changes in Extreme Weather and Climate Events: Workshop Report. 11 – 13 June, 2002, Beijing, China, 107 pp. • assess whether recent changes in the intensity, frequency and

duration of extremes are unusual in the context of instrumental and proxy records;

•  more paleoclimatic records/analyses and proxy indicators of pre-instrumental extremes,

• palaeo circulation records for the 'recent' 1000-2000 years should be used to put recent trends and variations in circulation-related extremes in the context of a longer history of natural variations

• the available palaeo records and to assess the information they contain on extremes

• whether indices calculated from model data have realistic variability

• long coupled control simulations (1000 to as long as 10000 years in length) should be analysed for interannual, decadal and centennial variations in simulated extremes

Mirror Lake, NWT

1750 1800 1850 1900 1950 2000

9

10

11

12

13

14

ReconstructedO bserved11-yr Running M ean

aR 2: 39.5%

M ean July Tem perature, Tungsten, NW T, 1751-2000

Tree-Ring Chronologies

Near Outlook, Saskatchewan, May 2, 2002

Oldman River

Whaleback Ridge

White Spruce, Cypress Hills

1750 1800 1850 1900 1950 2000

-300

-200

-100

0

100

1750 1800 1850 1900 1950 2000

-200

0

200

400

600

June - Ju ly P recip ita tionM edicine H at, A lberta , 1754-2001

August - Ju ly P recip ita tion,H avre, M ontana, 1727-2001

Departures From Median Precipitation

Widespread dune activity induced by late 18th century dryness Wolfe, et al. 2001

At Edmonton House, a large fire burned “all around us” on April 27th (1796) and burned on both sides of the river. On May 7th, light canoes arrived at from Buckingham House damaged from the shallow water. Timber intended to be used at Edmonton House could not be sent to the post “for want of water” in the North Saskatchewan River. On May 2nd, William Tomison wrote to James Swain that furs could not be moved as, “there being no water in the river.” (Johnson 1967: 33-39, 57)

In 1800 “Fine weather” continued into April at Edmonton House. On April 18th, James Bird repeated his observation that the poor trade with both the Slave and Southern Indians was the result of “the amazing warmness of the winter” diminishing both the bison hunt and creating a “want of beaver.” Bird reported “clear weather except for the smoke which almost obscures the sun. The country all round is on fire.” On June 15th, he noted that the “amazing shallowness of the water” prevented the shipment of considerable goods from York Factory (Johnson 1967: 240-248)

Fort Edmonton – HBC Archives

Monte-Carlo Probability Analysis

Reconstructed extremes can be compared in only probability terms. Monte Carlo methods were used to obtain a normally-distributed random sample of 10,000 errors for each year and thereby produce 10,000 error-added reconstructions (Touchan et al., 1999; Meko et 2001). Applying sampling error weighted probability considers the uncertainties in the observed dataUsing Monte Carlo random sampling to obtain error-added reconstructions enables us to establish the probability that reconstructed precipitation in any year or group of years was lower than the record-low of gauge precipitation.

Drought criteria (Xc): Lowest gauge precipitation

0%

25%

50%

75%

100%

1700 1750 1800 1850 1900 1950 2000

Prob

abili

ty (x

< X

c)

16.4mmm

80%CI: 1812 1883 1902 1936 1961

50% prob in the dry-half of the distribution is equivalent to 100% prob in two tails of the probability distribution.

15-yr droughts (running average)

Drought criteria (Xc): Lowest gauge precipitation

0%

25%

50%

75%

100%

1700 1750 1800 1850 1900 1950 2000

Prob

abili

ty (x

< X

c)

84.0mm90%CI: 1850, 1851

Markov Chain Monte Carlo Simulation (MCMC)Markov Chain is a series where the realization of the next element in the series, Y, is dependent only on the current state, X, and occurs with probability, P(Y|X). i.e, a model of sequences of events where the probability of an event occurring depends upon the fact that a preceding event occurred (Papoulis, 1984).

Seven states were based on the probability distribution of the time series in order to build Markov Chain.

1). Extreme dry: highest (<1th percentile)

2). Dry: < 10th percentile

3). Dry: < 20th percentile

4). Normal: between 20~80th percentiles

5). Wet: > 80th percentile

6). Wet: > 90th percentile

7). Extreme wet: highest(>99th percentile)

a. 7-class climate probability

0 927

178

303 1

0

50

100

150

200

1 2 3 4 5 6 7Class

Freq

uenc

y

`

b. Sample error-estimated 7-class climate probability

0.42 9.8637.89

154.39

35.648.42 1.35

0

50

100

150

200

1 2 3 4 5 6 7Class

Freq

uenc

yThe sampling error weights can be considered as the influence on the class probability through the entire time series Pr(A):

Pr(A) = Pr(Bi) Pr(A|Bi)

Where Pr(B) is a probability for event B, Pr(A|B) is a probability of event A under condition of event B (Papoulis, 1984). Here we apply the sampling error weight as Pr(Bi), Pr(A|Bi) is the probability of one classe throughout the series conditional on the yearly sampling errors.

Simple probability Sampling error- conditional probability

CGCM2 climate simulation for 1000-yr control run versus proxy precipitationThe CGCM2 1000-year simulation with late 20th century atmospheric concentration of greenhouse gases (Flato et al., 2000) downloaded from the IPCC-DDC and the CCCMA webs (http://www.ipcc-ddc.cru.uea.ac.uk and www.cccma.bc.ec.gc.ca/)

State CodeObservation(sample=1000)

Simulation(sample=20,000) T-test F-test

for Mean for SDSample/df 1000 20000 df1 20998 999Frequency Dry (<1th %) 1 0.011 0.012 df2 19999

Dry (<10th %) 2 0.089 0.090Dry (<20th %) 3 0.110 0.109Normal 4 0.591 0.585Wet (>80th %) 5 0.100 0.104Wet (>99th %) 6 0.089 0.090Wet (>90th %) 7 0.010 0.010 alpha 0.05 0.05

Mean 3.987 3.990 Lower limit 0.0627 0.92595Median 4 4 Upper limit 1.9601 1.07675Stdev 1.054 1.065 Statistics 0.59636 0.97981Skew 0.005 -0.021 Conclusion H0: M1=M2 H0: SD1=SD2Kurt 0.866 0.825 Accepted Accepted

Mean and standard deviation from 20,000 iteration MCMC simulation are equal to mean and SD from TRI chronologies, gauge precipitation and GCM precipitation simulation at 95% confidence level by T-test and F-test.

Comparison of 7-state distribution in three climate series for the Cypress Hills

Steady-state distribution probability:

Observation of Pjj

0.0

0.5

1.0

1 2 3 4 5 6 7

State

Pro

babi

lity

MCMC simulation for TRI (sample=20,000)

0.0

0.5

1.0

1 2 3 4 5 6 7

State

Pro

babi

lity

MCMC simulation forPjj (sample=20,000)

0.0

0.2

0.4

0.6

0.8

1 2 3 4 5 6 7State

Prob

abilit

y

MCMC simulation forCGCM2 CTL-run Pjj

(sample=20,000)

0.0

0.2

0.4

0.6

0.8

1 2 3 4 5 6 7State

Prob

abilit

y

MCMC simulation forTRI (sample=20,000)

0.0

0.2

0.4

0.6

0.8

1 2 3 4 5 6 7State

Prob

abilit

y

The similarity among the probabilities suggests that the GCM modeling has simulated a similar distribution to the real climate; probabilities of extreme dry (States 1) and extreme wet (State 7) are slight smaller in GCM than the gauge precipitation or tree-ring reconstructions, suggesting the GCM CTRL_run insufficiently simulates the two tails of the distribution of events.

CPH State 1 State 2 State 3 State 4 State 5 State 6 State 7TRI 0.012336 0.089502 0.101555 0.598476 0.097218 0.088818 0.012095Gauge 0.016986 0.084648 0.102524 0.590239 0.102227 0.085537 0.017839CGCM2 0.011011 0.089089 0.110110 0.590591 0.100100 0.089089 0.010010

For 20~25-yr and 10~13 yr timescales, the GCM model is consistent with TRI and gauge records, but differs in longer timescale of hundred years.

Spectral analysis

Cypress Hills

TRI

2.0

2.5

3.0

3.5

1 10 100 1000Period (yr)

Log1

0 Sp

ectr

um

TRI

0

200

400

600

800

1000

0.0 0.1 0.2 0.3 0.4 0.5Frequency (1/yr)

Spec

trum

T_max =24.8 yrT_2nd_max = 125.0 yrT_3rd_max = 13.1yr

GCM

0200400600800

10001200

0.0 0.1 0.2 0.3 0.4 0.5

Spec

trum

T_max = 90.9 yrT_2nd_max = 38.5 yrT_3rd_max = 20.5 yr

Pjj

0

1000

2000

3000

4000

5000

0.0 0.1 0.2 0.3 0.4 0.5

Spec

trum

T_max = 13.1 yr

Pjj

2.5

3.0

3.5

4.0

1 10 100 1000

Log1

0 Sp

ectr

umGCM

2.5

3.0

3.5

4.0

1 10 100 1000

Log1

0 Sp

ectr

um

• MODEL GFLD (Geophysical Fluid Dynamics Laboratory, USA) is a coupled ocean-atmosphere GCM, i.e. R15L9 (atmosphere) and 4degL12 (ocean).

• MODELING The emission scenario “Is92a-GS” was forced on the basis of the IPCC-IS92a scenario, starting CO2 and aerosol forcing at 1766 levels, and running it through the present (historical equivalent CO2 + aerosols from 1766 to 1990) and out to year 2065. A control integration is also performed keeping concentrations of sulfate and carbon dioxide fixed at 1765 levels (Haywood et al., 1997). The simulations investigate changes in surface air temperature, hydrology and the thermohaline circulation due to the radiative forcing of anthropogenic greenhouse gases and sulfate aerosols in the GFDL coupled ocean-atmosphere model.

GFDL-R15 climate simulation for the last 250-years

• Simulations of the mean and maximum June-July precipitation are much higher (ca. 70mm and 200mm) than both observed and proxy climate due to model bias.

• The range of reconstructed precipitation is smaller (ca. 80mm) than the observed value because regression methods causes the reconstruction to be biased towards the calibration-period mean.

Cypress Hill: 1754-2001AD 50.02~49.5N and -110~-110.72W

0

100

200

300

400

1750 1800 1850 1900 1950 2000

Prec

ipita

tion

(mm

)

TRI Gauge GFDL-Is92a-GS 10 per. Mov. Avg. (GFDL-Is92a-GS)

Three drought years (1883, 1924 and 1980) in the modeling are consistent with the tree-ring derived observations.

Comparison of Drought Years (Criteria < 0.1 percentile)

-200

-100

0

100

200

300

400

1754 1779 1804 1829 1854 1879 1904 1929 1954 1979

Prec

ipita

tion

(mm

)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

TRI

GCM departure TRI

Timing/frequency of drought

Comparison of 10-yr moving droughts (Criteria < 0.1 percentile)

-80

-40

0

40

80

120

160

1754

1764

1774

1784

1794

1804

1814

1824

1834

1844

1854

1864

1874

1884

1894

1904

1914

1924

1934

1944

1954

1964

1974

1984

Prec

ipita

tion

(mm

)

0.5

0.7

0.9

1.1

TRI

GCM departure TRI

0.1 percentile

0.1 percentile

Comparison of 5-yr moving droughts (Criteria < 0.1 percentile)

-80

-40

0

40

80

120

160

1754

1774

1794

1814

1834

1854

1874

1894

1914

1934

1954

1974

1994

Prec

ipita

tion

(mm

)

0.5

0.7

0.9

1.1

TRI

GCM departure TRI

0.1 percentile

0.1 percentile

Low-Pass Gaussian Filter (p>10 yr for 50% frequency response) show the periods that for 13~15 yr timescales, the model is found to be consistent with TRI and gauge records, but differs in timescale longer than 2 decades.

(3) Spectral analysis

TRI

1.5

2.5

3.5

1 10 100 1000Period (yr)

Log1

0 Sp

ectr

um

TRI

0

200

400

600

800

1000

0.0 0.1 0.2 0.3 0.4 0.5Frequency (1/yr)

Spec

trum

T_max =24.8 yrT_2nd_max = 125.0 yrT_3rd_max = 13.1yr

GCM

0

4000

8000

12000

0.0 0.1 0.2 0.3 0.4 0.5

Spec

trum

T_max = 29.1yrT_2nd_max = 49.7 yrT_3rd_max = 14.9 yr

Pjj

0

1000

2000

3000

4000

5000

0.0 0.1 0.2 0.3 0.4 0.5

Spec

trum

T_max = 13.1 yr

Pjj

2.5

3.5

4.5

1 10 100 1000

Log1

0 Sp

ectr

um

GCM

2.5

3.5

4.5

1 10 100 1000

Log1

0 Sp

ectr

um

Comparison of GDFL-Is92a-GS historical climate modeling with proxy climate observations

• A T-test suggests that modeled precipitation means are not equal to the TRI or gauge data at a 0.95 confidence level. Correlation between the model and observation is poor and F-test suggests that the modeled standard deviation is not equal to those of the TRI or gauge data at a 0.95 CI. The model simulates more variability than the observations.

• There is little agreement in the timing of annual precipitation in lowest 10th percentile. There is more agreement when the drought criterion is relaxed to the 20th percentile. These results suggest that the model poorly simulates the most extreme events.

• There are obvious peaks of ca. 13 year period in spectral power of the TRI and gauge series. The simulated series also has strong spectral power of this period with 2 years. The strongest power at ca. 23 year period can be found in both observation and modeling with 2 years. The power spectrum for the simulated series diverges from the spectra for the TRI and gauge data at timescales longer than the two decades.

raw proxy datafiltered data (signal)paleoclimatic and paleo-environmental recordstrends, variability, frequencies, probabilitiestemporal analoguesclimate changeand impact scenarios

Paleo Data (Products)