The Making and Utility of Seasonal Forecasts - Lennard

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Presentation by Chris Lennard.

CCAFS workshop titled "Using Climate Scenarios and Analogues for Designing Adaptation Strategies in Agriculture," 19-23 September in Kathmandu, Nepal.

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The Making and Utility of Seasonal Forecasts - Lennard

  1. 1. The Making and Utility Why make a seasonal forecast?of Seasonal Forecasts Chris Lennard Climate Systems Climate Systems Analysis Group Analysis GroupHow do we make a seasonal forecast? What are we trying to achieve....skill? For a given spatial scale, variable, and application, the prediction skill is a function of time scale Theoretical 3-6 month into future Required skill of deterministic limit of Seasonal forecast model results for predictability decision making Prediction Skill Ensemble Seasonal Forecast Actual skill Perturb the SST ? of current field to account We can’t do this model Sea Surface resultsTemperatures for SST variability Downscaling We need to do thisTwo types of sst fields:Persisted SSTs: last observed month modified by forecast month climatologyForecast SSTs: from a fully coupled model run into the future Daily 2-3 weeks Months Seasonal Decadal Century Climate Systems Climate Systems Analysis Group Analysis Group
  2. 2. Reasons for improving forecasts..... The challenge of observational data, and the lack thereof!The future is already beginning to exceed the operating parameters ofmany social and physical systems. Impact comes through exceeding • New developments in highthresholds. resolution reanalysis data sets • Some moderate data rescue HadCM3 ECHam 4 balanced by network decline • Supplemented (but not replaced) by growing satellite products Changes in the length of the growth period •Despite this, we try to by 2050 with the improve forecasts/predictions SRES B1 emissions through downscaling them scenario Oh oh.... Source: ILRI/TERI/ACTS, 2006 Climate Systems Climate Systems Analysis Group Analysis GroupDownscaling large scale climate data to a regional (relevant?) scale Downscaling large scale climate data to a regional (relevant?) scale Global Climate Model resolution Numerical Downscaling Statistical Downscaling Global Climate Model Data from the GCM is used Statistical relationships by Regional Climate between weather stations Downscaling Models (RCMs) to on the ground and numerically simulate the atmospheric circulations climate characteristics at a are established. GCM- much higher resolution. produced atmospheric Results in a gridded circulations can then be product. downscaled to the station scale. Ouagadougou There are two methods used to downscale Climate Systems Climate Systems Analysis Group Analysis Group
  3. 3. What do stakeholders need/want from seasonal forecast? What do stakeholders need/want from seasonal forecast? What would you like - metrics -Onset; Disrt of wet and dry spell within season; Temp extremes; eg decadal (10 Data? days)distribution of rainfall; Rainfall intensity (numbers); exceedence and thresholds; end of rainy season; Number of rain days and monthly disrtibution; thermal stress (# days/hours above threshold eg 30 degrees); number of fog days; sunshine hours and radiation intensity; When is a forecast skillful? If planting and harvest was successful; fog days in correct season (NDJF); did you irrigate correctly in the season; hit-miss score; Text Anything else? Capture extremes generally; monthly distribution of rain; lead time forecast better Climate Systems Climate Systems Analysis Group Analysis GroupThe questions that producers of seasonal forecasts The questions that producers of seasonal forecastsneed to answer in the context of their stakeholders: need to answer in the context of their stakeholders: 1. Is my forecast plausible: Does it fall within the envelope of natural variability? Climate Systems Climate Systems Analysis Group Analysis Group
  4. 4. The questions that producers of seasonal forecasts The questions that producers of seasonal forecasts need to answer in the context of their stakeholders: need to answer in the context of their stakeholders: 1. Is my forecast plausible: Does it fall within the envelope of 1. Is my forecast plausible: Does it fall within the envelope of natural variability? natural variability? 2. Is the forecast defensible: On a regional scale, am I able 2. Is the forecast defensible: On a regional scale, am I able to explain in physical process terms why the forecast shows to explain in physical process terms why the forecast shows what it does? what it does? 3. Is the forecast actionable: at the time and space scales of user decision making, can I defend the actions with real monetary implications based on the information of my forecast? (Would I spend my own money?) Why “No” to question 3? Climate Systems Climate Systems Analysis Group Analysis GroupMany sources of uncertainty...... Many sources of uncertainty...... PRUDENCE : Sources of uncertainty in temperature and precipitation change (2071-2100 PRUDENCE : Sources of uncertainty in temperature and precipitation change (2071-2100 minus 1961-1990) minus 1961-1990) (Adapted from Deque et al. 2007) (Adapted from Deque et al. 2007) RCMs RCMs “NOISE” “NOISE” GCMs GCMs SCENARIO Sea Surface SCENARIO (ElNino mode?) Temperatures (ElNino mode?) Are we able to answerT-JJA for seasonal forecasting?(F. Giorgi, 2008) T-DJF this P-DJF P-JJA Are we able to answerT-JJA for seasonal forecasting?(F. Giorgi, 2008) T-DJF this P-DJF P-JJA Climate Systems Climate Systems Analysis Group Analysis Group
  5. 5. Sources of concern......Uptake of the forecasts Filling some gaps: Physical understanding Stakeholder interface Uptake of forecast per farm activity 1. Investigate past, natural climate 1. Evaluate the uptake of the 100% variability to understand current seasonal forecast and assess if/ climatic conditions how it was used in decision making 90% 80% processes 2. Understand the effect of land 70% surfaces on the regional climate to 2. Tailored forecasts for specific user % respondents 60% did not use improve the seasonal forecast environments and sectors (what is 50% used important to you?) 40% 3. Downscale the seasonal forecasts 30% to regionally relevant scales 3. Does downscaling add any value 20% to the forecast from the user 10% 4. Develop a decadal climate perspective 0% prediction system Amount of Type of Selection Fertiliser Irrigation Borrowing Crop Stocking 4. Dissemination of the forecast, how Land Planting crops of crop purchase planning rates money 5. Introduce another model into the can we do this optimally given our prepared date planted cultivars mix; widen the ensemble spread contexts From P. Johnston 6. Are we close to the limits of our Activity predictability capacity Climate Systems Climate Systems Analysis Group Analysis Group Thank you......Some useful (?) sites..... CSAG http://www.gfcsa.net/csag.html IRI http://portal.iri.columbia.edu/portal/server.pt? open=512&objID=944&PageID=7868&mode=2 JAMSTEC http://www.jamstec.go.jp/frsgc/research/d1/iod/ ECMWF http://www.ecmwf.int/products/forecasts/d/charts/seasonal/forecast/ seasonal_range_forecast/ Climate Systems Climate Systems Analysis Group Analysis Group
  6. 6. 1. What are the minimum rainfall requirements needed by the different end users?2. How do you communicate the seasonal forecast and gain the trust of the different endusers?3. Are the tools we are currently using the correct ones in a changing climate4. What level of forecast skill is useful for the user?5. How do we reduce SST bias in the models?6. How do we link the physical dynamics to the forecast and then communicate thisinformation?7. How do we verify the forecast in a country with no observations?8. The request for tailored forecasts was made (again).Then from Manhiques talk re user requests:1. Onset and cessation (for agri)2. Quantative rainfall forecast (agri and water management)3. Wet and dry spells in the rainy season (agri and water management)4. Temperature and heat wave duration (agri)5. Number of cyclones to affect regions (for emergency services budgeting)6. Improved spatial resolution Climate Systems Analysis Group 21

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