PROFESSIONAL DEVELOPMENT for CLIMATE PREDICTION and

PROJECTION

Peter J. Lamb

WMO-CCl-XV Conference on Changing Climate and Demands for Climate for Sustainable Development

Antalya, Turkey February 16-18, 2010

Cooperative Institute for Mesoscale Meteorological Studies and School of Meteorology The University of Oklahoma

[Reprinted from BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY, Vol. 62, No. 7, July 1981]

Printed in U.S.A.

Do We Know What We Should Be Trying to Forecast-Climatically? Peter J. Lamb, Climatology Section, Illinois State Water Survey, Champaign Ill. 61820

Proposed

Three demanding, reasonably sequential prerequisites for climate prediction/projection to have maximum societal value: 1. Identify human activities most impacted by climate variability by region, season, and weather.

2. Determine how affected regional economies can adjust or change to capitalize on

availability of skillful climate predictions/projections. 3. Satisfaction of 1 and 2 should focus development of climate prediction/projection

schemes that have the greatest societal value.

CHALLENGE IS NOT JUST “METEOROLOGICAL”

PROFESSIONAL DEVELOPMENT TO ENHANCE CLIMATE PREDICTION/PROJECTIONRequirements extend substantially beyond Atmospheric Sciences

Should include appreciation of need for and competence in:

1.Development of “impacts” data sets (e.g., agriculture, health)

2.Combined statistical analysis of impacts and climate data to quantify linkages

3.Use of decision models to estimate values of alternative economic adjustments to climate variation as well as

4.Increased understanding of behavior and predictability of climate system

SUCCESS of 1, 2, and 3 WILL DETERMINE SOCIETAL VALUE OF 4

ILLUSTRATION #1 UNITED STATES WINTER

EFFECTS OF TEMPERATURE ON RESIDENTIAL NATURAL GAS CONSUMPTION

impacts data set combined climate-impacts analysis

climate system predictability

(from Timmer and Lamb, J. Appl. Meteor. Clim., 2007)

CONTROL OF EL NIÑO ON PRECIPITATION

climate system predictability challenge for socioeconomic management

(from Montroy, Richman, and Lamb, J. Climate, 1998)

MOTIVATION

Prior to mid-1980s -- gas prices were federally regulated due to concern about “monopoly power”

Results of policy -- prices were not sufficiently market driven and often correlated poorly with demand (i.e., temperature)

Value of seasonal climate forecasts for managing energy resources was limited

Full deregulation by 1989 -- market became more sensitive to temperature-related demand increased price volatility “spot” and “futures” markets emerged usefulness/value of seasonal climate forecasts required temperature-gas consumption quantification (vs socioeconomic factors)

NATURAL GAS PRICE

1999 $

Unadjusted $

DATA SETS

Richman–Lamb Daily Temperature(Max, Min) Data Set for 1949-2000

U.S. Department of EnergyPetroleum Administration forDefense Districts (PADDs) (monthly/state data)

Number of Richman-Lamb Stationsin each PADD

TEMPERATURE STATIONS

NATURAL GAS REGIONS

COMBINED DATA SET

Fig. 4. Mean seasonal residential natural gas consumption [first number below PADD name, billions of cubic feet (Bcf)], percentage of total U.S. residential natural gas consumption (second number below PADD name), and per capita consumption [bottom number; thousands of cubic feet (Mcf)] for each PADD for (a) 3-month (December–February) winters and (b) 4-month (November–February) winters during 1989–2000. Gas data were obtained from the Energy Information Administration Internet site given in the text. PADD populations were averages of census totals for 1990 and 2000 obtained online (see http://txsdc.utsa.edu/txdata/apport/respop_b.php).

IMPORTANCE OF NATURAL GAS

Per capita consumption in 2B >> 1X despite similar latitudePer capita consumption in 2A > 1Y despite similar latitudeOther fuels are used for heating in 1X and 1Y (mountain barrier)

(a) 3-month (Dec-Feb) (b) 4-month (Nov-Feb)

2 C

2 D

3 B

3 A 1 Z

2 A

2 B

1 Y

1 X1 1 04 . 8 %1 2 . 4

1 5 86 . 9 %1 2 . 3

1 2 35 . 4 %6 . 5

5 4 22 3 . 7 %1 6 . 7

8 53 . 7 %6 . 1

2 2 81 0 . 0 %1 1 . 3

2 5 11 1 . 0 %7 . 8

3 1 71 3 . 9 %9 . 1

1 0 04 . 4 %3 . 1

2 C

2 D

3 B

3 A 1 Z

2 A

2 B

1 Y

1 X1 3 34 . 9 %1 5 . 0

1 8 46 . 8 %1 4 . 3

1 4 45 . 3 %7 . 6

6 5 62 4 . 1 %2 0 . 2

9 73 . 6 %7 . 0

2 7 41 0 . 1 %1 3 . 5

2 9 91 1 . 0 %9 . 3

3 7 51 3 . 8 %1 0 . 7

1 1 94 . 4 %3 . 6

MAXIMUM GAS CONSUMPTION-TEMPERATURE CORRELATIONS

Top = 3 month wintersBottom = 4 month winters

Monthly Seasonal

Stronger in N than SN weaker in New EnglandDBP/HDD very similar (esp. N)S weakened in Jan (esp) & Feb

N/S contrast reducedNew England contrast reducedLess effect of Jan in SInclusion of Nov also contributes

SEASONAL PREDICTION TOOLS ?Example set of regression equations -- seasonal, 3-month (Dec-Feb) winters, T-mean daily temperature index -- for DBP/HDD that gave largest correlations with natural gas consumption

Winter temperature predictive skill (ENSO-based) is highest for areas where temperature-gas consumption relations are very strong Wisconsin and Illinois (western PADD 2B), Kentucky and Tennessee (southern PADD 2A), OR moderately strong Carolinas (northern PADD 1Z),Minnesota and eastern North Dakota (eastern PADD 2C). Reverse is case for New England (PADD 1X).

“Transition to Operations” would need underpinning by statistical analyses of historical temperaturedata translate categorical NWS seasonal predictions into DBP/HDD ranges appropriate for ensemble-type prediction approach.

CONTROL OF EL NIÑO ON PRECIPITATION EL NIÑO COMPOSITE

(mm)

Monthly Anomaly

1998 EL NIÑO 2010 EL NIÑO

6 MONTH LEAD TIME

SUBSTANTIAL OPPORTUNITYfor SOCIETALRESPONSE

IS POTENTIALBEING REALIZED?

Mar 1998

STRONG PREDICTABILITY

Feb 2010

Similar to January?

Mar 2010

?

Jan 2010

ILLUSTRATION #2 WEST AFRICAN MONSOON RAINFALL

CONTROL OF INTERTROPICAL FRONT (ITF) ON RAINFALL

based on climate data set availability of impacts data sets? (e.g., malaria, meningitis)

ITF-BASED PREDICTABILITY OF RAINFALL

relation to climate system? applicability to impacts?

(from Lélé and Lamb, J. Climate, 2010)

LONG-TERM AVERAGE CLIMATOLOGY

OF ITF DISPLACEMENT (1974-2003)

• Oriented WNW-ESE

• Gradual northward advance of about 0.8o dek-1 (8.8 km day-1) on average

• Reaches its maximum position near 20oN in early August

• Abrupt southward retreat of 1.4o dek-1 (15.5 km day-1) on average.

• Retreat almost twice as fast as advance – WHY ?

5 0 10 1510

12

14

16

18

20

22

24

Latit

ude

(Nor

th)

April dekad 2April dekad 3

M ay dekad 1M ay dekad 2

M ay dekad 3

June dekad 1June dekad 2June dekad 3

July dekad 1July dekad 2

July dekad 3

10W 5E 20E

ITFAdvance

Longitude

10

12

14

16

18

20

22

24La

titud

e (

Nor

th)

10W 5 0 5E 10 15 20E

ITFRetreat

LONG-TERM AVERAGE CLIMATOLOGYOF ITF LOCATION (1974-2003)

LONG-TERM AVERAGE ITF LOCATIONS AND RAINFALL PATTERN

10W 0 10E 20E 10W 0 10E 20E

10W 0 10E 20E 10W 0 10E 20E

ITF

JUNE

10N

15N

20N

25N

JULY

ITF

1 2 3 4 5 6 7 8 9

OCT-OBER

ITF

SEPT-EMBER

ITF

10N

15N

20N

25N10W 0 10E 20E

AUG-UST

ITF

10N

15N

20N

25N

10N

15N

20N

25N

APRIL

ITF

MAY

ITF ITF Advance = northward displacement of rainfall zones

ITF Retreat = southward displacement of rainfall zones

Peak of the rainfall season = northernmost ITF position

mm d-1

14

16

18

20

74 76 78 80 82 84 86 88 90 92 94 96 98 00 02

0

10

20

30

40

14 15 16 17 18 19 20 21

0

10

20

30

40

12

14

16

18

20

74 76 78 80 82 84 86 88 90 92 94 96 98 00 02

0

10

20

30

40

12 13 14 15 16 17 18 19 20 21

0

10

20

30

40

10

12

14

16

74 76 78 80 82 84 86 88 90 92 94 96 98 00 02

0

4

8

12

16

10 11 12 13 14

0

4

8

12

12

14

16

18

20

74 76 78 80 82 84 86 88 90 92 94 96 98 00 02

0

10

20

30

40

50

12 13 14 15 16 17 18

0

10

20

30

40

50

ITF LATITUDE VERSUS RAINFALL – EARLY/LATE SEASONLINEAR CORRELATIONS

Rain ITF

APRIL

MAY

JUNE

OCTOBER

ITF LATITUDE VERSUS RAINFALL -- RAINY SEASON CORELINEAR CORRELATIONS

Rain ITF

JULY

AUGUST

SEPTEMBER

APRIL ITF LATITUDE PREDICTION OF MAY-JULY ITF/RAIN

PoD = 54.44% TCE = 11.67% PoD = 47.77% TCE = 11.67%

PoD = 45.55% TCE = 18.33% PoD = 43.33% TCE = 21.67%

PoD = 48.89% TCE = 6.67% PoD = 42.22% TCE = 10.00%

RAINFALL PoDs (TCEs) NOT GREATLY REDUCED (INCREASED) FROM CONCURRENT VALUES (esp. JULY)

APRIL ITF LATITUDE PREDICTION OF AUG-OCT ITF/ RAIN

PoD = 31.11% (38.89%) TCE = 40.00% (28.33%) PoD = 35.56% (37.77%) TCE = 36.67% (33.33%)

SEPTEMBER-OCTOBER PoDs (TCEs) ARE LARGER (SMALLER) FOR OPPOSITE DIAGONALS (EXTREME TERCILES) ….. ITF TENDS TO RETREAT SOUTH EARLY (LATE) AFTER ADVANCING NORTH EARLY (LATE)

CLIMAT-OLOGY

GENERALIZED MODELING FRAMEWORK

DECISION MODEL (often includes economics)

PROCESS MODEL

e.g., plant growth water monitoring disease transmission

REQUIRES DAILY WEATHER DATA

PROFESSIONAL DEVELOPMENT …

MUST RECOGNIZE THE NEED TO USE DAILY DATA

WMO RESOLUTION 40 (June 1995)

Members should provide to the research and education communities, for their non-commercial activities, free and unrestricted access to all data andproducts exchanged under the auspices of WMO …”

“In adopting the new policy, Congress stressed that WMO was committing itselfto broadening and enhancing the free and unrestricted international exchangeof meteorological and related data and products. The new practice states that: