Beyond Climate Predictions

Transcription

1 Lisa Goddard Beyond Climate Predictions International Research Institute for Climate & Society The Earth Institute of Columbia University "

2 Beyond Climate Predic1ons Many Thanks to Walter Baethgen, Remi Cousin, Paula Gonzalez, Arthur Greene, Andy Robertson, Simon Mason, Mike

3 Climate Variability & Change Globally" Annual Mean Temperature 65% 13% Temperature Most of the variability in the globally- averaged temperature is contained in the slowly varying climate change component. (Greene, Goddard & Cousin, 2010, EOS) 21% Time Scales Decomposition Map Room hkp://iridl.ldeo.columbia.edu/maproom/global/time_scales/ 23 May 2013 CICAR Symposium

4 Temperature Trends: % of total variance! 20 th Century Gridded Observations -- Annual Means! hkp://iridl.ldeo.columbia.edu/maproom/global/time_scales/

5 Precipitation Trends: of total variance! 20 th Century Gridded Observations -- Annual Means! hkp://iridl.ldeo.columbia.edu/maproom/global/time_scales/

6 Climate Variability & Change in Colorado" Annual Mean Temperature Temperature Precipita1on 12% 21% Most of the variability in the globally- averaged temperature is contained in the slowly varying climate change component. 1% 19% 65% 75% 23 May 2013 CICAR Symposium

7 Example of 3-Category Seasonal Rainfall Forecast Regional 3-month average Probabilistic

8 What probabilispc forecasts represent Near-Normal Below Normal Above Normal FREQUENCY Historical distribution Forecast distribution NORMALIZED RAINFALL Historically, the probabilities of above and below are Shifting the mean by half a standard-deviation and reducing the variance by 20% changes the probability of below to 0.15 and of above to (Courtesy of Mike Tippett) (Courtesy Mike Tippett)

9 Mul1- model ensembling Probabilistic skill scores (RPSS for 2m Temperature (JFM ) Combining models reduces deficiencies of individual models

10 A Major Goal of Probabilistic Forecasts Reliability! Forecasts should mean what they say.

11 (a) 80N 60N 40N 20N 0 20S 40S 60S (c) Model Calibra1on (spa1al): Based on model performance 80N 60N 40N 20N 0 20S 40S Raw RPSS for forecasts of DJF 2m-temperature t2m counting mean rpss = W 100W 50W 0E 50E 100E 150E t2m eofs mean rpss = (b) N N N N S S S N N N N S S 0.3 t2m ridge regression mean rpss = W 100W 50W 0E 50E 100E 150E Lasso Regression (d) t2m eofs lasso grid bma mean rpss = S 150W 100W 50W 0E 50E 100E 150E 60S W 100W 50W 0E 50E 100E 150E 0.4 (TippeK et al., in prep)

12 Need for more flexible forecast informapon: Agriculture Climate InformaPon Relevant to Agricultural Decision Making in Uruguay Probability that Yields of a Maize Hybrid (Medium Cycle) Planted in October in SW Uruguay Fall Below 80% of Expected Yields Versus DJF Precipitation Anomalies (Logistic Regression. DJF Climatology: 311mm) Prob(Yield<0.8*Mean Yield) Marginal Effect= On average a 10 percent increase in the anomaly will reduce the chance of falling below % of the expected yield by 8.2% (Courtesy of Miguel Carriquiry) Percent Anomaly in DJF Precipitation

13 Need for more flexible forecast informapon: Health Seasonal Climate Forecasts for Malaria Control Planning in Southern Africa Relevant categories for malaria planning are 25%- ile tails (i.e. probability of climate being in wekest/driest 25% of years) (Courtesy of Simon Mason) (Thomson et al. 2006, Nature)

14 FORECASTING THE FULL PDF (Cousin et al. in prep)

15 Regional Scale Decadal PredicPons? Western Africa : Annual-Mean Temperature Southern Africa : Annual-Mean Temperature Climate Change Projections cannot deliver predictions of decadal variability

16 CMIP5 Experimental PredicPon Design

17 Time Scales Seasonal- to- Interannual Forecasts Longer Timescales Decadal Predic1ons: Skill sbll to be demonstrated Multi-model Ensemble (12 models: Equal Weighting ) Correlation Mean Squared Skill Score Annual T BIG CHALLENGE Jul-Aug-Sep P (based on Goddard et al. 2013, Climate Dynamics; See also 23 May May 2013 CICAR Symposium CICAR Symposium

18 (Greene, et al. 2012)

19 Decadal Variability

20 Climate Informa1on for Adapta1on, Resilience & Decision Support 1 Consider all Pmescales for establishing resilience, for informing management, and for planning, evaluapon 2 PresentaPon can be as important as quality 3 Good quality observapons are cripcal 4 Strive for informed uncertainty (won t get that straight from models)

21 There are no answers, only choices. Stanislaw Lem/Steven Soderbergh (Solaris)

22 Thank You /climatesociety

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