Enhancing the Scale and Relevance of Seasonal Climate Forecasts -

• Advancing knowledge of scales• Space scales• Weather within climate

• Methods for information creation• Pure dynamical systems• Model output statistics• Empirical predictors, lead-time

issues

Issues for practical improvement

N. Ward - IRIacknowledgments to colleagues at IRI and partners

this presentation with L. Sun and A. Robertson

Climate Prediction and Agriculture: Advances and Challenges, WMO, Geneva, May 11th, 2005

Collaborative Work in Regions

Skill of Model Hindcasts Using Observed SST

Part 1: Advances in Understandingof Predictability at Smaller Spatialand Temporal scales

(a) Space Scales

Example of driving a Regional Climate Modelwith output from a Global Climate Model.

Surface Wind at One Time Step

DYNAMICALDOWNSCALING

RSM Precipitation Forecast from Jan for Feb-Mar-Apr (Avg of 10 ensemble)

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Correlation 0.79

Regional models can represent influence on local climate from detailed landscape – e.g. elevation, land cover type …

Even in this situation, how to estimate predictability at the field scale?

Quantifying decline in skill at smaller scales: General: Barnston et alNE Brazil example: Sun et al

Leading patternof small-scalerainfall anomaliesover Ceara(a) Observed(b) Regional Model

Hypothesis:Local physiography induces systematicvariability features

      

Contingency tables for 3 subregions of Ceara State at local scales (FMA 1971-2000)

OBS

Coast B N A

B 5 3 2

N 3 4 3

A 2 3 5

Central B N A

B 5 2 3

N 4 5 1

A 1 3 6

Southern B N A

B 4 3 3

N 3 5 2

A 3 2 5

RSM

RSM

RSM

StatisticalDownscalingResults forSri Lanka, 1951-80 Verification

Map shows correlationskill (shading) alongwith contours of elevation

StatisticalDownscalingResults forSenegal, 1968-2002 Verification

Map shows correlationskill (red positive) forSeasonal rainfall (upper)And NDVI (lower)

Large-scale predictability doescascade into predictability at smaller

spatial scales

There is need to represent the localphysiographic forcing to best

estimate the small scale seasonal climate

Part 1: Advances in Understandingof Predictability at Smaller Spatialand Temporal scales

(b) Weather within Climate

Predictability of weather statisticsthrough the season

……

Predictability of the interannual variability of weather statistics over Ceara, NE BrazilBlue = Observed, Pink dash = Predicted by RSM (no statistical correction)

Number ofDry spells

LongestDry spell

Number of Days withoutrain

d r y s p e l l n u m b e r s

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1 9 7 4 1 9 7 6 1 9 7 8 1 9 8 0 1 9 8 2 1 9 8 4 1 9 8 6 1 9 8 8 1 9 9 0 1 9 9 2 1 9 9 4 1 9 9 6 1 9 9 8 2 0 0 0

y e a r

numbers

o b s r s m

l o n g e s t d r y p e r i o d

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y e a r

days

o b s r s m

d a y s w i t h o u t r a i n f a l l

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1 9 7 4 1 9 7 6 1 9 7 8 1 9 8 0 1 9 8 2 1 9 8 4 1 9 8 6 1 9 8 8 1 9 9 0 1 9 9 2 1 9 9 4 1 9 9 6 1 9 9 8 2 0 0 0y e a r

days

o b s r s m

c o r r = 0 . 8 0

c o r r = 0 . 6 2

c o r r = 0 . 7 3

Model Simulation vs. Observation

Seasonal Rainfall totalR=0.84

Drought IndexR=0.74

Flooding IndexR=0.84

Weather IndexR=0.69

Rainfall states (S Georgia / N Florida, USA)

HMM rainfall parameters “learned” from the data

Rainfall occurrence probability

Average rainfall amount on wet days(from parameters of mixed exponential distribution)

Illustration of concepts in statistical downscaling to weather series:(From a study using the Hidden Markov Model approach)

Estimated state sequence

1924-1998March to August

seasonality, sub-seasonal and interannual variability

Estimated state sequence forMarch-May rainfall in Kenya

March April May

- “dry” state (#3, yellow) tends to occur in March

- “wet” states (#1, green), (#2, blue) tend to occur in April–May

To get rainfall sequence: P(Rt | St)

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.

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Day in Season

33 333 33 333 33 33 333 32 222 23 33 333 33 31 112 22 222 21 11 112 21 111 11 11 112 22 22 222 22 222 22 33 333 33 333 21 11 111 1

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11 133 33 333 33 22 222 33 333 33 33 111 11 11 111 11 111 12 22 222 22 222 22 22 222 22 22 222 22 222 22 22 222 22 233 22 22 233 3

22 233 33 333 33 33 333 31 111 12 22 333 33 11 112 22 222 11 22 222 22 222 22 33 332 22 22 223 33 222 22 21 111 11 111 11 11 111 1

33 333 33 333 33 33 333 33 333 22 23 322 11 11 111 11 111 22 22 211 11 222 22 22 222 22 22 112 22 112 21 11 112 22 333 33 32 111 1

12 211 11 111 11 22 222 22 222 22 33 221 11 11 111 23 332 22 22 222 22 222 22 22 223 32 22 211 11 111 11 11 111 11 333 31 11 122 2

22 233 33 333 33 33 333 33 333 33 33 322 22 22 222 33 322 22 22 222 21 111 12 22 112 22 11 222 22 222 22 22 233 32 223 32 22 221 1

33 333 33 333 33 33 333 32 322 22 21 111 12 33 333 31 111 11 11 111 11 111 11 11 111 12 22 222 22 222 22 22 111 11 111 11 11 112 2

33 333 32 222 33 32 211 11 222 22 22 222 22 22 222 22 222 22 22 211 11 222 22 22 211 11 12 222 22 222 22 11 111 11 111 22 22 222 2

33 333 33 333 33 33 333 33 333 11 11 111 11 33 333 22 222 22 22 222 22 222 22 22 222 23 22 211 12 221 12 22 222 22 211 11 13 333 3

33 333 33 333 33 33 333 31 233 33 33 333 33 33 333 33 333 22 11 111 11 111 11 11 112 23 22 222 22 111 22 21 222 22 222 22 21 111 1

33 333 33 333 33 33 333 33 333 33 33 333 33 33 333 33 333 32 21 122 22 222 22 21 111 11 11 111 11 111 11 11 111 11 211 11 12 233 3

33 323 33 333 33 33 332 22 111 11 11 111 22 11 111 11 112 22 22 222 22 222 22 23 333 32 21 111 11 112 22 22 222 11 111 11 12 233 3

33 333 33 333 33 33 333 33 333 22 23 333 33 33 333 33 222 22 11 211 12 112 22 22 211 11 11 111 11 111 11 12 211 12 233 33 11 111 1

33 333 33 333 33 33 333 21 111 11 11 111 11 11 122 22 222 22 22 222 23 333 22 22 222 22 22 222 22 222 33 33 332 21 111 11 11 111 1

33 333 33 331 11 11 112 22 222 22 22 222 22 22 222 22 222 22 22 222 21 111 11 11 111 12 22 222 22 222 22 22 211 11 111 11 11 111 1

33 333 33 333 33 33 333 32 223 33 33 322 22 22 211 12 211 11 11 222 22 222 22 22 222 22 22 222 22 222 22 22 222 22 111 12 22 233 3

33 333 33 333 33 33 333 33 333 33 33 333 33 11 122 11 111 11 11 111 11 112 22 22 222 22 22 222 22 222 21 11 221 11 112 11 11 111 1

33 333 33 333 33 22 233 33 333 33 33 333 33 33 333 33 222 22 22 222 11 111 12 22 111 11 11 111 11 111 11 11 111 11 111 11 11 111 1

33 333 33 333 33 33 333 32 222 21 12 222 22 22 222 22 222 22 22 222 22 222 22 22 111 22 22 222 22 222 22 22 222 21 111 11 11 111 1

33 222 22 223 33 33 322 11 223 33 31 111 11 11 111 22 222 21 11 111 11 122 22 22 222 22 22 222 22 111 11 22 222 22 222 22 11 122 2

33 333 33 322 23 33 332 23 333 33 33 333 33 33 332 22 222 22 22 111 22 222 21 11 111 22 21 122 22 221 11 11 211 11 122 33 22 233 2

33 322 33 333 33 33 333 22 222 22 12 333 33 32 112 22 221 11 12 222 22 222 22 22 222 22 33 222 22 111 11 11 112 22 111 11 11 111 1

33 333 33 333 33 33 333 22 221 12 11 122 22 22 222 22 222 21 11 111 11 112 22 22 222 21 11 112 22 222 22 22 222 11 111 11 11 111 1

22 222 22 222 22 22 111 11 112 22 33 322 22 22 222 22 222 22 22 222 33 322 11 11 111 13 33 333 22 211 22 11 333 33 222 11 11 111 1

33 333 31 233 33 33 333 33 333 33 33 333 32 11 111 11 111 11 11 111 11 111 11 11 111 11 22 222 22 112 22 22 221 11 111 11 11 111 1

33 333 33 333 33 33 333 33 333 22 22 223 33 33 332 21 111 11 11 111 22 222 22 22 111 22 22 111 11 112 22 22 221 22 111 12 33 333 3

22 222 22 222 33 32 113 33 331 11 11 112 21 11 222 22 222 22 11 111 11 111 11 12 222 22 33 332 22 222 11 12 222 11 111 11 11 111 1

12 333 33 333 33 33 333 22 221 11 12 222 22 23 332 22 233 22 11 111 11 111 11 11 111 11 11 111 11 112 22 22 222 22 222 22 22 111 1

Predictability of seasonal means doescascade into predictability of weather

statistics through the season

Rainfall onset involves the specific timingof a set of weather events. The limit of forecasting

the specific timing of weather events is about 2 weeks

However, it is reasonable to think that information about the likelihood of a set of weather events over a certain time-

period could be provided in situations where there is strong SST forcing on the large-scale circulation

Furthermore, the possibility for projecting forward information about large-scale intraseasonal structures

is open to further analysis

Part 2: Tools for Prediction

Predicted

SSTA

Persisted

SSTA

GlobalECHAM 4.5

(T42)Regional

NCEP RSM10 members

(60km)

Post-Processing

IRI

Downscaling prediction system

Precipitation Forecast FMA 2004, using persisted SST

Note: not the raw model output -already an elementof statistical transformation ofmodel output

EOF 1 of 850mb Oct-Dec zonal wind from GCM (ECHAM4) GCM was driven with observed SST 1950-1980

To be used as predictor for observed 20kmx20km rainfall over Sri Lanka

Statistical Transformation/Downscaling Methods can be applied to the output of all categories of dynamical prediction systems

StatisticalDownscalingResults forSri Lanka, 1951-80 Verification

Map shows correlationskill (shading) alongwith contours of elevation

Statistical Downscaling to NDVI

Using a GCM with Sept SST to predict December vegetation

(about 25km resolution) across East Africa 1982-1998

Spatial variations in skill may reflect-variations in climate predictability

-variations in climate-NDVI couplingHypotheses to explore using RCMs.

Time series of area-average predicted NDVI over NE Kenya

(r=0.76)

Units are correlation

skill

Contours are elevationCorrected high resolution NDVI provide by USGS

Climate Predictability Tool (CPT)

Example of Reservoir Inflow in Ceara, NE BrazilProbabilistic forecasts based on 2 SST indices in July of previous year

Model trained on 1912-1992 data

AnnualInflow

Forecast Year

Part 3: Some Further Key Issues for Practical Improvement in SI Prediction Systems

Lead-time (SST development)Land surface (initial conditions, interaction)

Presence of Low-frequency Climate

UKMO model, results published early 1990s

Early example of 2-tier GCM forecast experiments using persisted SSTA – Sahel Seasonal Rainfall Total

Sensitivity of skill toSST developmentfrom April to June

Example of Reservoir Inflow in Ceara, NE BrazilProbabilistic forecasts based on 2 SST indices in July of previous year

Model trained on 1912-1992 data

AnnualInflow

Forecast Year

Exploring Enhancementof Predictabilityfrom GlobalInitial Soil MoistureConditions

The NCEP RSM Land Module

Enhancing the Scale and Relevance of Seasonal Climate Forecasts -

• Advancing knowledge of scales• Space scales• Weather within climate

• Methods for information creation• Pure dynamical systems• Model output statistics• Empirical predictors, lead-time

issues

Issues for practical improvement

N. Ward - IRIacknowledgments to colleagues at IRI and partners

this presentation with L. Sun and A. Robertson

Climate Prediction and Agriculture: Advances and Challenges, WMO, Geneva, May 11th, 2005

Reservoir Management Tool

Input: ProbabilitySeasonal Forecastsand Reservoir SystemProperties

Output: Properties ofReservoir operationWith and withoutSeasonal forecasts

-40.0

-35.0

-30.0

-25.0

-20.0

-15.0

-10.0

-5.0

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5.0

1950 1960 1970 1980 1990

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Forecast - Climatology (Reliability = 0.9)

Observed Flows