How Climate Can Be Predicted Why Some Predictability Exists, and How Predictions Can Be Made

Seasonal/interannual predictability comes from factors that exert a continuous influence over a period of time that includes many sequences of weather events. Such factors are: Sea surface temperatures (SST; ENSO, others) Land surface conditions: soil moisture, vegetation Radiative variations (volcanos, greenhouse gases) Intraseasonal processes (MJO)

Much of the predictable part of seasonal climate comes as a result of anomalies of sea surface temperature (SST) in tropical ocean basins.

ENSO: The strongest source of tropical SST variation Total SST Anomaly of SST

El Nińo La Nińa

Tropical Pacific SST anomaly in December of Year (0) of some El Nino episodes Climate Variability: Importance of ENSO and its Prediction

Total Anomaly

El Nińo La Nińa Normal

El Nińo El Nińo La Nińa La Nińa La Nińa Nińo3.4 region: 5ºN-5ºS, 120º-170ºW

El Nińo episodes often begin in April, May or June, and end in about months, in February, March, April, or May.

Mason and Goddard, 2001, Probabilistic precipitation anomalies associated with ENSO. Bulletin of the American Meteorological Society, 82,

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Is the ENSO phase predictable?

A Brief History of LDEO Model LDEO1: Original Cane and Zebiak model (Cane et al., Nature, 1986) LDEO2: LDEO1 plus coupled initialization (Chen et al., Science, 1995) LDEO3: LDEO2 plus sea level data assimilation (Chen et al., GRL,1998) LDEO4: LDEO3 plus statistical bias correction (Chen et al., GRL, 2000) LDEO5: LDEO4 plus additional correction on SST (Chen et al., Nature, 2004) LDEO1 LDEO2LDEO3 LDEO4 Forecast Skill LDEO month lead;

questionable forecast: Onset of La Nińa at unusual time of year questionable forecast: late onset of El Nińo questionable forecast: early dissipation of El Nińo due partly to MJO. *****

acceptable forecast: late onset of El Nińo acceptable forecast: Onset of La Nińa at unusual time of year acceptable forecast: early dissipation of El Nińo due partly to MJO. *****

Lead ‘04-06 ‘04-07 ‘04-06 ‘04-07 cor skill rmse skill

Lead ‘04-06 ‘04-07 ‘04-06 ‘04-07 cor skill rmse skill

Lead ‘04-06 ‘04-07 ‘04-06 ‘04-07 cor skill rmse skill ? ? ?

Correlation Skill for NINO3 forecasts Northern Spring barrier Useful long-lead skill Correlation between forecast and obs Skill of LDEO3 (Zebiak-Cane) simple dynamical model, for NINO3 Region

ENSO Predictability: Improvement from Mid-1980s to Today Improvements were large in the late 1980s, small to moderate in the 1990s, and not much in the 2000s. We do not know the upper limit of ENSO predictability. We still have a big problem predicting ENSO from the early part of a calendar year to the middle of that calendar year. The potential for better pre- dictions may be quite large, but it is also possible that it is only slightly better than what we can do now.

Forecast Skill Forecast lead time (days) Weather forecasts (from initial conditions) Potential sub-seasonal predictability Seasonal forecasts (from SST boundary conditions) Lead time and forecast skill super 0.9 good 0.6 fair 0.3 poor 0.0 (from MJO, land surface)

Skill of forecasts at different time ranges: 1-2 day weather good 3-7 day weather fair Second week weatherpoor, but not zero Third week weathervirtually zero Fourth week weathervirtually zero 1-month climate (day 1-31)poor to fair 1-month climate (day 15-45)poor, but not zero 3-month climate (day 15-99)poor to fair At shorter ranges, forecasts are based on initial conditions and skill deteriorates quickly with time. Skill gets better at long range for ample time-averaging, due to consistent boundary condition forcing

Predicting the atmospheric climate, based on the expected SST anomaly patterns: Climate prediction designs: Statistical – based on historical observed data for the predictand (e.g. rainfall, temperature) and for relevant predictors (e.g. SST, atmospheric pressure). Dynamical – using prognostic physical equations 2-tiered systems (first predict SST, then climate). 1-tiered systems (predict ocean and atmosphere together)

Prediction Systems: statistical vs. dynamical system ADVANTAGES Based on actual, real-world observed data. Knowledge of physical processes not needed. Many climate relationships quasi-linear, quasi-Gaussian Uses proven laws of physics. Quality observational data not required (but needed for val- idation). Can handle cases that have never occurred. DISADVANTAGES Depends on quality and length of observed data Does not fully account for climate change, or new climate situations Some physical laws must be abbreviated or statis- tically estimated, leading to errors and biases. Computer intensive. Stati- stical Dyna- mical

In Dynamical Prediction System: 2-tiered vs. 1-tiered forecast system ADVANTAGES Two-way air-sea interaction, as in real world (required where fluxes are as important as large scale ocean dynamics) More stable, reliable SST in the prediction; lack of drift that can appear in 1-tier system Reasonably effective for regions impacted most directly by ENSO DISADVANTAGES Model biases amplify (drift); flux corrections Computationally expensive Flawed (1-way) physics, especially unacceptable in tropical Atlantic and Indian oceans (monsoon) 1-tier tier

Climate forecasts need to be expressed probabilistically, because there is a wide distribution of possibilities even with our better-than-chance accuracy.

Below Normal Above Normal Historically, the probabilities of above and below are Shifting the mean by one half standard deviation and reducing the variance by 20% changes the probability of below to 0.15 and of above to Historical distribution (climatological distribution) (33.3%, 33.3%, 33.3%) Forecast distribution (15%, 32%, 53%) (Courtesy Mike Tippett) What probabilistic forecasts represent Near-Normal NORMALIZED RAINFALL FREQUENCY

| || ||| ||||.| || | | || | | |. | | | | | | | | | | Rainfall Amount (mm) Below| Near | Below| Near | Above  Below| Near | (Below normal,, near normal, above normal) (30 years of historical data for one station and season) Abbreviating a predicted shift in the probability distribution: Terciles Data: Climatological probabilities = 1/3 33% 33% 33%

Rainfall Amount (mm) Below| Near | Below| Near | Above  Below| Near | 10% 25% 65% Example of a climate forecast with a strong probability shift

Rainfall Amount (mm) Below| Near | Below| Near | Above  Below| Near | 25% 35% 40% Example of a climate forecast with a weak probability shift

Rainfall Amount (mm) Below| Near | Below| Near | Above  Below| Near | 33% 33% 33% Example of a climate forecast with no probability shift

OND A “strong” shift of odds in rainfall forecast for Kenya during El Nino |||||||| |||||||| 13% 29% 59%

OND A “strong” shift of odds in rainfall forecast for Kenya during El Nino |||||||| |||||||| 13% 29% 59% Steps in finding probabilities of each of the tercile- based categories (below, near and above normal). 1. Use regression to make a deterministic (single point) forecast. 2. Determine standard error of estimate to represent the uncertainty of the deterministic forecast. 3. Use standard error of estimate to form a forecast distribution (i.e., make the red curve). 4. Find what value of z on the forecast distribution coincides with the tercile boundaries of the climatological distribution (33%ile and 67%ile on the black curve). Then use z-table to get the probabilities associated with these z values..

Correlation Skill Predictor Signal=0.0 Predictor Signal +0.5 Predictor Signal +1.0 Predictor Signal +1.5 Predictor Signal F signal / 33 / 33 F signal / 33 / 33 F signal / 33 / 33 F signal / 33 / 33 F signal / 33 / F signal / 34 / 33 F signal / 34/ 37 F signal / 33 / 41 F signal / 33 / 45 F signal / 31 / F signal / 35 / 33 F signal / 34 / 38 F signal / 33 / 45 F signal / 31 / 51 F signal / 29 / F signal / 36 / 32 F signal / 35 / 40 F signal / 33 / 49 F signal / 30 / 57 F signal / 25 / F signal / 38 / 31 F signal / 37 / 42 F signal / 33 / 53 F signal / 27 / 64 F signal / 21 / F signal / 41 / 30 F signal / 38 / 44 F signal / 32 / 58 F signal / 23 / 72 F signal / 15 / F signal / 45 / 27 F signal / 41 / 46 F signal / 30 / 65 F signal / 17 / 81 F signal / 8 / F signal / 53 / 24 F signal / 44 / 48 F signal / 25 / 73 F signal * / 10 / 90 F signal ** / 3 / 97 *0.3 **0.04 Tercile probabilities for various correlation skills and predictor signal strengths (in SDs). Assumes Gaussian probability distri- bution. Forecast (F) signal = (Predictor Signal) x (Correl Skill). Note that it is hard to increase middle tercile prob

IRI’s Forecast System IRI is presently (in 2007) using a 2-tiered prediction system to probabilistically predict global temperature and precipitation with respect to terciles of the historical climatological distribution. We are interested in utilizing fully coupled (1-tier) systems also, and are looking into incorporating those. Within the 2-tiered system IRI uses 4 SST prediction scenarios, and combines the predictions of 7 AGCMs. The merging of 7 predictions into a single one uses two multi-model ensemble systems: Bayesian and canonical variate. These give somewhat differing solutions, and are presently given equal weight.

FORECAST SST SCENARIOS TROP. PACIFIC: THREE (multi-models, dynamical and statistical) TROP. ATL and INDIAN (2 and 3 multi-models) EXTRATROPICAL (damped persistence) GLOBAL ATMOSPHERIC MODELS ECPC(Scripps) ECHAM4.5(MPI) CCM3.6(NCAR) NCEP(MRF9) NSIPP(NASA) COLA2 GFDL Forecast SST Ensembles 3/6 Mo. lead Persisted SST Ensembles 3 Mo. lead IRI DYNAMICAL CLIMATE FORECAST SYSTEM POST PROCESSING MULTIMODEL ENSEMBLING PERSISTED GLOBAL SST ANOMALY 2-tiered OCEAN ATMOSPHERE 30 model weighting

NOV | Dec-Jan-Feb Jan-Feb-Mar Feb-Mar-Apr Mar-Apr-May IRI’s monthly issued probability forecasts of seasonal global precipitation and temperature We issue forecasts at four lead times. For example: Forecast models are run 7 months into future. Observed data are available through the end of the previous month (end of October in example above). Probabilities are given for the three tercile-based categories of the climatological distribution.

Observed SST

Predicted SST persisted SST anom

Observed SST

Predicted SST persisted SST anom