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

How Climate Can Be Predicted

Why Some Predictability Exists,and How Predictions Can Be Made

Seasonal/interannual predictability comes from factorsthat exert a continuous influence over a period of timethat includes many sequences of weather events.

Such factors are:

Sea surface temperatures (SST; ENSO, others)Land surface conditions: soil moisture, vegetationRadiative variations (volcanos, greenhouse gases)Intraseasonal processes (MJO)

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

ENSO: The strongest source of tropical SST variation

TotalSST

Anomalyof SST

El Nińo

La Nińa

Tropical PacificSST anomaly inDecember ofYear (0) of someEl Nino episodes

Climate Variability:Importance of ENSO

and its Prediction

Total

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 episodesoften begin in April, May or June,and end in about10-12 months, in February, March,April, or May.

Mason and Goddard, 2001,Probabilistic precipitation anomalies associated with ENSO. Bulletin of the American Meteorological Society, 82, 619-638.

http://iri.columbia.edu/climate/forecast//enso/index.html






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http://iridl.ldeo.columbia.edu/SOURCES/.IRI/.Analyses/.ENSO-RP/.ver1950-2002/

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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)

0.4

0.5

0.6

0.7

0.8

1985 1990 1995 2000

LDEO1LDEO1

LDEO2LDEO2 LDEO3LDEO3

LDEO4LDEO4Forecast SkillLDEO5LDEO5

2005

6 month lead; 1970-1985

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questionableforecast:Onset of La

Nińa at unusual time of year

questionableforecast:

late onsetof El Nińo

questionableforecast:

early dissipationof El Nińo duepartly to MJO

.

*****

acceptableforecast:

late onsetof El Nińo

acceptableforecast:Onset of La

Nińa at unusual time of year

acceptableforecast:

early dissipationof El Nińo duepartly to MJO

.

*****

80 57 40

78 59 41

37 6 0

30 4 0

0 2 4 Lead

‘04-06‘04-07

‘04-06‘04-07

corskill

rmseskill

•

79 57 41

77 60 45

34 3 0

26 1 0

0 2 4 Lead

‘04-06‘04-07

‘04-06‘04-07

corskill

rmseskill

•

81 59 38

79 57 36

40 11 0

36 0 0

0 2 4 Lead

‘04-06‘04-07

‘04-06‘04-07

corskill

rmseskill

?

?

?

•

Correlation Skill for NINO3 forecasts

NorthernSpringbarrier

Usefullong-lead

skill

Correlation between forecast and obs

Skill of LDEO3 (Zebiak-Cane) simple dynamical model, 1970-2000 for NINO3 Region

ENSO Predictability: Improvement from Mid-1980s to Today

Improvements were large in the late 1980s, small to moderatein the 1990s, and not much in the 2000s. We do not know theupper limit of ENSO predictability. We still have a big problempredicting ENSO from the early part of a calendar year tothe middle of that calendar year. The potential for better pre-dictions may be quite large, but it is also possible that it is onlyslightly better than what we can do now.

Forecast Skill

Forecast lead time (days)

10 20 30 60 80 90

Weather forecasts (from initial conditions)

Potential sub-seasonal predictability

Seasonal forecasts (from SST boundary conditions)

Lead time and forecast skill

super 0.9good 0.6fair 0.3poor 0.0

(from MJO, land surface)

Skill of forecasts at different time ranges:

1-2 day weather good3-7 day weather fairSecond week weather poor, but not zeroThird week weather virtually zeroFourth week weather virtually zero1-month climate (day 1-31) poor to fair1-month climate (day 15-45) poor, but not zero3-month climate (day 15-99) poor to fair

At shorter ranges, forecasts are based on initialconditions 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 datafor the predictand (e.g. rainfall, temperature) and forrelevant 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-worldobserved data. Knowledge ofphysical processes not needed.

Many climate relationshipsquasi-linear, quasi-Gaussian------------------------------------Uses proven laws of physics.Quality observational data not required (but needed for val-idation). Can handle casesthat 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 inthe prediction; lack of driftthat can appear in 1-tier system

Reasonably effective for regionsimpacted 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

------

2-tier

Climate forecasts need to be expressedprobabilistically, because there is a widedistribution of possibilities even with ourbetter-than-chance accuracy.

BelowNormal

AboveNormal

Historically, the probabilities of above and below are 0.33. 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 0.53.

Historical distribution(climatological distribution)(33.3%, 33.3%, 33.3%)

Forecast distribution (15%, 32%, 53%)

(Courtesy Mike Tippett)

What probabilistic forecasts representNear-Normal

NORMALIZED RAINFALL

FR

EQ

UE

NC

Y

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 | || ||| ||||.| || | | || | | | . | | | | | | | | | |

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 inthe probability distribution: Terciles

Data:

Climatological probabilities = 1/3

33% 33% 33%

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200



10% 25% 65%

Example of a climate forecast with a strong probability shift

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200



25% 35% 40%

Example of a climate forecast with a weak probability shift

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200



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 representthe 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 theprobabilities 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 +2.0

0.00F signal 0.00

33 / 33 / 33

F signal 0.00

33 / 33 / 33

F signal 0.00

33 / 33 / 33

F signal 0.00

33 / 33 / 33

F signal 0.00

33 / 33 / 33

0.20F signal 0.00

33 / 34 / 33

F signal 0.10

29 / 34/ 37

F signal 0.20

26 / 33 / 41

F signal 0.30

23 / 33 / 45

F signal 0.40

20 / 31 / 49

0.30F signal 0.00

33 / 35 / 33

F signal 0.15

27 / 34 / 38

F signal 0.30

22 / 33 / 45

F signal 0.45

17 / 31 / 51

F signal 0.60

14 / 29 / 57

0.40F signal 0.00

32 / 36 / 32

F signal 0.20

25 / 35 / 40

F signal 0.40

18 / 33 / 49

F signal 0.60

13 / 30 / 57

F signal 0.80

9 / 25 / 65

0.50F signal 0.00

31 / 38 / 31

F signal 0.25

22 / 37 / 42

F signal 0.50

14 / 33 / 53

F signal 0.75

9 / 27 / 64

F signal 1.00

5 / 21 / 74

0.60F signal 0.00

30 / 41 / 30

F signal 0.30

18 / 38 / 44

F signal 0.60

10 / 32 / 58

F signal 0.90

5 / 23 / 72

F signal 1.20

2 / 15 / 83

0.70F signal 0.00

27 / 45 / 27

F signal 0.35

13 / 41 / 46

F signal 0.70

6 / 30 / 65

F signal 1.05

2 / 17 / 81

F signal 1.40

1 / 8 / 91

0.80F signal 0.00

24 / 53 / 24

F signal 0.40

8 / 44 / 48

F signal 0.80

2 / 25 / 73

F signal 1.20

0* / 10 / 90

F signal 1.60

0** / 3 / 97

*0.3 **0.04

Tercile probabilities for various correlation skills and predictorsignal strengths (in SDs). Assumes Gaussian probability distri-bution. Forecast (F) signal = (Predictor Signal) x (Correl Skill).

Note that it ishard to increasemiddle tercile prob

IRI’s Forecast System

IRI is presently (in 2007) using a 2-tiered prediction system to probabilistically predict

global temperatureand 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 andcanonical variate. These give somewhat differing solutions, and are presently given equal weight.

30

12

30

24

12

24

24

10

24

10

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

ForecastSST

Ensembles3/6 Mo. lead

PersistedSST

Ensembles3 Mo. lead

IRI DYNAMICAL CLIMATE FORECAST SYSTEM

POSTPROCESSING

MULTIMODELENSEMBLING

PERSISTED

GLOBAL

SST

ANOMALY

2-tiered OCEAN ATMOSPHERE

30

modelweighting

NOV | Dec-Jan-Feb Jan-Feb-Mar

Feb-Mar-AprMar-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 dataare available through the end of the previous month (end ofOctober in example above). Probabilities are given for thethree tercile-based categories of the climatological distribution.

Observed SST

Predicted SST persisted SST anom

Observed SST

Predicted SST persisted SST anom

Documents

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