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Dave Sauchyn Prairie Adaptation Research Collaborative University of Regina National Workshop: Development of Scenarios of Climate Variability and Extremes:

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Presentation on theme: "Dave Sauchyn Prairie Adaptation Research Collaborative University of Regina National Workshop: Development of Scenarios of Climate Variability and Extremes:"— Presentation transcript:

1 Dave Sauchyn Prairie Adaptation Research Collaborative University of Regina National Workshop: Development of Scenarios of Climate Variability and Extremes: Current Status and Next Steps Victoria, BC, October 2003 The paleo-perspective: what can paleo- data tell us about past extremes which is useful for the future?

2 Acknowledgements Collaborators: Dr. Elaine Barrow, Dr. Ge Yu Graduate Students: Antoine Beriault, Jennifer Stroich Funding:

3 Climate is Always Changing From GSC Misc. Report 71 (2001) Ice cores, tree rings, lakes and oceans sediments: windows on the past

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6 (Leavitt, U of R)

7 Climatic Variability A 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.

8 Ron Hopkinson, MSC

9 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' 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 years in length) should be analysed for interannual, decadal and centennial variations in simulated extremes

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12 Mirror Lake, NWT

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14 Tree-Ring Chronologies

15 Near Outlook, Saskatchewan, May 2, 2002

16 Oldman River Whaleback Ridge

17 White Spruce, Cypress Hills

18 Departures From Median Precipitation

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

20 At Edmonton House, a large fire burned “all around us” on April 27 th (1796) and burned on both sides of the river. On May 7 th, 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 2 nd, 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 18 th, 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 15 th, he noted that the “amazing shallowness of the water” prevented the shipment of considerable goods from York Factory (Johnson 1967: ) Fort Edmonton – HBC Archives

21 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 data Using 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.

22 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)

23 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: < 10 th percentile 3). Dry: < 20 th percentile 4). Normal: between 20~80 th percentiles 5). Wet: > 80 th percentile 6). Wet: > 90 th percentile 7). Extreme wet: highest(>99 th percentile)

24 a. 7-class climate probability Class Frequency ` b. Sample error-estimated 7-class climate probability Class Frequency The 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

25 CGCM2 climate simulation for 1000-yr control run versus proxy precipitation The CGCM 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 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.

26 Comparison of 7-state distribution in three climate series for the Cypress Hills Steady-state distribution probability: 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.

27 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

28 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 CO 2 and aerosol forcing at 1766 levels, and running it through the present (historical equivalent CO 2 + aerosols from 1766 to 1990) and out to year 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

29 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.

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

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32 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

33 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 10 th percentile. There is more agreement when the drought criterion is relaxed to the 20 th 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.

34 raw proxy data filtered data (signal) paleoclimatic and paleo- environmental records trends, variability, frequencies, probabilities temporal analogues climate change and impact scenarios Paleo Data (Products)


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