NASA/TM-97-104606, Vol. 13

Technical Report Series on

Global Modeling and Data Assimilation

Max J. Suarez, Editor

Goddard Space Flight Center, Greenbelt, Maryland

Volume 13

lnterannual Variability and Potential

Predictability in Reanalysis Products

Wei Min, General Sciences Corporation, Laurel Maryland; and

Siegfried D. Schubert, Goddard Space Flight Center, Greenbelt, Maryland

National Aeronautics and

Space Administration

Goddard Space Flight Center

Greenbelt, Maryland 20771

December 1997
NASA Center for AeroSpace Information

800 Elkridge Landing Road

Linthicum Heights, MD 21090-2934

Price Code: A17

Available from:

National Technical Information Service

5285 Port Royal Road

Springfield, VA 22161

Price Code: A10
Abstract

The Data Assimilation Office (DAO) at Goddard Space Flight Center and the National Center for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) have produced multi-year global assimilations of historical data

employing fixed analysis systems. These "reanalysis" products are ideally suited for

studying short-term climatic variations. The availability of multiple reanaiysis products also provides the opportunity to examine the uncertainty in the reanalysis data.

The purpose of this document is to provide an updated estimate of seasonal and

interannuai variability based on the DAO and NCEP/NCAR reanalyses for the 15 year

period 1980-1995. Intercomparisons of the seasonal means and their interannual variations are presented for a variety of prognostic and diagnostic fields. In addition, atmospheric potential predictability is re-examined employing selected DAO reanalysis

variables.

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Contents

List of Figures vii

1 Introduction 1

2 Data Assimilation Systems 2

2.1 NASA/DAO system .............................. 2

2.2 NCEP/NCAR system ............................. 3

3 Intercomparison 3

3.1 Zonal mean fields ............................... 4

3.2 Global upper-air fields ............................ 36

3.2.1 Troposphere ............................. 36

3.2.2 Stratosphere .............................. 111

3.3 Global diagnostic fields ............................ 124

4 Potential Predictability 162

4.1 Data and data processing ........................... 162

4.2 Results ..................................... 165

5 Summary 189

V

List of Figures

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11

Seasonal means of zonally averaged u-wind for DAO reanalysis during

1980-1995. The contour interval is 5 m s-:. Negative values are shaded...

Seasonal means of zonally averaged u-wind for NCEP/NCAR reanalysis

during 1980-1995. The contour interval is 5 m s-1. Negative values are

shaded .....................................

Difference of the seasonal means of zonally averaged u-wind during 1980-

1995 (NCEP/NCAR minus DAO). The contour interval is 1 m s-1. Negative values are shaded .............................

Seasonal means of zonally averaged v-wind for DAO reanalysis during

1980-1995. The contour interval is 0.5 m s-:. Negative values are shaded..

Seasonal means of zonally averaged v-wind for NCEP/NCAR reanalysis

during 1980-1995. The contour interval is 0.5 m s-1. Negative values are

shaded .....................................

Difference of the seasonal means of zonally averaged v-wind during 1980-

1995 (NCEP/NCAR minus DAO). The contour interval is 0.2 m s-_. Negative values are shaded .............................

Seasonal means of zonally averaged temperature for DAO reanalysis during 1980-1995. The contour interval is 5°K. Values less than 210°K are

shaded .....................................

Seasonal means of zonally averaged temperature for NCEP/NCAR reanalysis during 1980-1995. The contour interval is 5°K. Values less than

210°K are shaded ................................

Difference of the seasonal means of zonally averaged temperature during

1980-1995 (NCEP/NCAR minus DAO). The contour interval is 1°K. Negative values are shaded .............................

7

Seasonal means of zonally averaged specific humidity for DAO reanalysis

during 1980-1995. The contour interval is 2 g kg -x. Values larger than 8 g

kg -1 are shaded .................................

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10

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14

15

Seasonal means of zonally averaged specific humidity for NCEP/NCAR

reanalysis during 1980-1995. The contour interval is 2 g kg -l. Values

larger than 8 g kg-: are shaded ........................ 16

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Differenceof the seasonalmeansof zonally averagedspecifichumidity

during 1980-1995(NCEP/NCARminusDAO).Thecontourintervalis0.3

g kg-I . Negativevaluesareshaded...................... 17

Seasonalmeansof zonallyaveragedmassstreamfunctionfor DAOreanalysisduring 1980-1995.Thecontourintervalis 2× 101°kg s-1. Negative

valuesareshaded................................ 18

Seasonalmeansof zonallyaveragedmassstreamfunctionfor NCEP/NCAR

reanalysisduring1980-1995.Thecontourintervalis 2x 101°kg s-1. Negativevaluesareshaded............................. 19

Differenceof the seasonalmeansof zonally averagedmassstreamfunction during 1980-1995(NCEP/NCARminusDAO). The contourinterval

is 1x 101°kg s-1. Negativevaluesareshaded................. 20

Standarddeviationsof seasonalmeanzonallyaveragedu-wind for DAO

reanalysisduring 1980-1995. The contourintervalis 1 m s-1. Values

largerthan2 m s-1 areshaded.........................

21

Standarddeviationsof seasonalmeanzonallyaveragedu-windfor NCEP/NCAR

reanalysisduring 1980-1995. The contourinterval is 1 m s-1. Values

largerthan2 m s'l areshaded......................... 22

Differencesof the standarddeviationsof seasonalmeanzonally averaged

u-wind during 1980-1995 (NCEP/NCAR minus DAO). The contour interval is 1 m s-x. Negative values are shaded ................... 23

Standard deviations of seasonal mean zonally averaged v-wind for DAO

reanalysis during 1980-1995. The contour interval is 0.1 m s-1. Values

larger than 0.2 m s-I are shaded ........................ 24

Standard deviations of seasonal mean zonally averaged v-wind for NCEP/NCAR

reanalysis during 1980-1995. The contour interval is 0.1 m s-1. Values

larger than 0.2 m s-1 are shaded ........................ 25

Differences of the standard deviations of seasonal mean zonally averaged

v-wind during 1980-1995 (NCEP/NCAR minus DAO). The contour interval is 0.1 m s-1. Negative values are shaded .................. 26

Standard deviations of seasonal mean zonally averaged temperature for

DAO reanalysis during 1980-1995. The contour interval is 0.5°K. Values

larger than I°K are shaded ...........................

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Standard deviations of seasonal mean zonally averaged temperature for

NCEP/NCAR reanalysis during 1980-1995. The contour interval is 0.5* K.

Values larger than I*K are shaded .......................

Differences of the standard deviations of seasonal mean zonally averaged

temperature during 1980-1995 (NCEP/NCAR minus DAO). The contour

interval is 0.2* K. Negative values are shaded .................

Standard deviations of seasonal mean zonally averaged specific humidity

for DAO reanalysis during 1980-1995. The contour interval is 0.05 g kg -1.

Values larger than 0.1 g kg- 1 are shaded ....................

Standard deviations of seasonal mean zonally averaged specific humidity

for NCEP/NCAR reanalysis during 1980-1995. The contour interval is

0.05 g kg -x. Values larger than 0.1 g kg -1 are shaded .............

Differences of the standard deviations of seasonal mean zonally averaged

specific humidity during 1980-1995 (NCEP/NCAR minus DAO). The contour interval is 0.05 g kg -1. Negative values are shaded ............

Standard deviations of seasonal mean zonally averaged mass stream function for DAO reanalysis during 1980-1995. The contour interval is 0.5 x 101°

kg s-1. Values larger than 1 x 101° kg s-1 are shaded .............

Standard deviations of seasonal mean zonally averaged mass stream function for NCEP/NCAR reanalysis during 1980-1995. The contour interval

is 0.5x 101° kg s-1. Values larger than 1 x 101° kg s-1 are shaded .......

Differences of the standard deviations of seasonal mean zonally averaged

mass stream function during 1980-1995 (NCEP/NCAR minus DAO). The

contour interval is 0.2 x 101° kg s-1 . Negative values are shaded .......

Seasonal means of 200 hPa u-wind for DAO reanalysis during 1980-1995.

The contour interval is 5 m s-1. Negative values are shaded .........

Seasonal means of 200 hPa u-wind for NCEP/NCAR reanalysis during

1980-1995. The contour interval is 5 m s-1. Negative values are shaded...

Difference of the seasonal means of 200 hPa u-wind during 1980-1995

(NCEP/NCAR minus DAO). The contour interval is 2 m s-1. Negative

values are shaded ................................

Seasonal means of 850 hPa u-wind for DAO reanalysis during 1980-1995.

The contour interval is 5 m s-1. Negative values are shaded .........

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Seasonal means of 850 hPa u-wind for NCEP/NCAR reanalysis during

1980--1995. The contour interval is 5 m s-1. Negative values are shaded...

Difference of the seasonal means of 850 hPa u-wind during 1980-1995

(NCEP/NCAR minus DAO). The contour interval is 2 m s-1. Negative

values are shaded ................................

Standard deviations of seasonal mean 200 hPa u-wind for DAO reanalysis

during 1980-1995. The contour interval is 2 m s-I. Values larger than 4 m

s-_ are shaded .................................

Standard deviations of seasonal mean 200 hPa u-wind for NCEP/NCAR

reanalysis during 1980-1995. The contour interval is 2 m s-1. Values

larger than 4 m s-I are shaded .........................

Difference of the standard deviations of seasonal mean 200 b_Pa u-wind

during 1980-1995 (NCEP/NCAR minus DAO). The contour interval is 0.5

m s-1. Negative values are shaded .......................

43

Standard deviations of seasonal mean 850 hPa u-wind for DAO reanalysis

during 1980-1995. The contour interval is 1 m s-_. Values larger than 2 m

s-_ are shaded .................................

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45

46

47

48

Standard deviations of seasonal mean 850 hPa u-wind for NCEP/NCAR

reanalysis during 1980-1995. The contour interval is 1 m s-_. Values

larger than 2 m s-_ are shaded ......................... 49

Difference of the standard deviations of seasonal mean 850 hPa u-wind

during 1980-1995 (NCEP/NCAR minus DAO). The contour interval is 0.5

m s-1. Negative values are shaded ....................... 50

Seasonal means of 200 hPa v-wind for DAO reanalysis during 1980-1995.

The contour interval is 2 m s-1. Negative values are shaded ......... 51

Seasonal means of 200 hPa v-wind for NCEP/NCAR reanalysis during

1980-1995. The contour interval is 2 m s-x. Negative values are shaded... 52

Difference of the seasonal means of 200 hPa v-wind during 1980-1995

(NCEP/NCAR minus DAO). The contour interval is 1 m s-1. Negative

values are shaded ................................ 53

Seasonal means of 850 hPa v-wind for DAO reanalysis during 1980-1995.

The contour interval is 2 m s-_. Negative values are shaded ......... 54

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57

58

Seasonal means of 850 hPa v-wind for NCEP/NCAR reanalysis during

1980-1995. The contour interval is 2 m s-1. Negative values are shaded...

Difference of the seasonal means of 850 hPa v-wind during 1980-1995

(NCEP/NCAR minus DAO). The contour interval is I m s-1. Negative

values are shaded ................................

Standard deviations of seasonal mean 200 hPa v-wind for DAO reanalysis

during 1980-1995. The contour interval is 1 m s-1. Values larger than 2 m

s-_ are shaded .................................

Standard deviations of seasonal mean 200 hPa v-wind for NCEP/NCAR

reanalysis during 1980-1995. The contour interval is 1 m s-x. Values

larger than 2 m s-x are shaded .........................

Difference of the standard deviations of seasonal mean 200 hPa v-wind

during 1980-1995 (NCEP/NCAR minus DAO). The contour interval is 0.5

m s-_. Negative values are shaded .......................

Standard deviations of seasonal mean 850 hPa v-wind for DAO reanalysis

during 1980-1995. The contour interval is 0.4 m s-_. Values larger than

0.8 m s-_ are shaded ..............................

Standard deviations of seasonal mean 850 hPa v-wind for NCEP/NCAR

reanalysis during 1980-1995. The contour interval is 0.4 m s-1. Values

larger than 0.8 m s-x are shaded ........................

Difference of the standard deviations of seasonal mean 850 hPa v-wind

during 1980-1995 (NCEP/NCAR minus DAO). The contour interval is 0.2

m s-_. Negative values are shaded .......................

Seasonal means of 200 hPa temperature for DAO reanalysis during 1980-

1995. The contour interval is 1" K. Values less than 222"K are shaded ....

Seasonal means of 200 hPa temperature for NCEP/NCAR reanalysis during 1980-1995. The contour interval is I*K. Values less than 222"K are

shaded .....................................

Difference of the seasonal means of 200 hPa temperature during 1980-

1995 (NCEP/NCAR minus DAO). The contour interval is 0.5*K. Negative

values are shaded ................................

Seasonal means of 850 hPa temperature for DAO reanalysis during 1980-

1995. The contour interval is 4*K. Values less than 284"K are shaded ....

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Seasonalmeansof 850hPatemperaturefor NCEP/NCARreanalysisduring 1980-1995.The contourintervalis 4*K. Valueslessthan284°K are

shaded. ....................................

Differenceof the seasonalmeansof 850 hPatemperatureduring 1980-

1995(NCEP/NCARminusDAO). The contourintervalis I*K. Negative

valuesareshaded................................

Standarddeviationsof seasonalmean200hPatemperaturefor DAO reanalysisduring 1980-1995.The contourintervalis 0.5*K. Valueslarger

thanI*K areshaded..............................

Standarddeviationsof seasonalmean200hPatemperaturefor NCEP/NCAR

reanalysisduring1980-1995.Thecontourintervalis 0.5°K. Valueslarger

than I*K areshaded..............................

Differenceof thestandarddeviationsof seasonalmean200 hPatemperature during1980-1995(NCEP/NCARminusDAO). Thecontourinterval

is 0.2°K.Negativevaluesareshaded......................

Standarddeviationsof seasonalmean850 hPatemperaturefor DAO reanalysisduring 1980-1995.The contourintervalis 0.40K.Valueslarger

than0.8*Kareshaded.............................

67

Standarddeviationsof seasonalmean850hPatemperaturefor NCEP/NCAR

reanalysisduring1980-1995.Thecontourintervalis 0.4*K. Valueslarger

than0.8*Kareshaded.............................

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72

73

Differenceof the standarddeviationsof seasonalmean850hPatemperatureduring 1980-1995(NCEP/NCARminusDAO).The contourinterval

is 0.2*K. Negativevaluesareshaded...................... 74

Seasonalmeansof 850 hPa specific humidity for DAO reanalysis during

1980-1995. The contour interval is 1 g kg -1. Values larger than 6 g kg -1

are shaded ................................... 75

Seasonal means of 850 hPa specific humidity for NCEPfNCAR reanalysis

during 1980-1995. The contour interval is 1 g kg -1. Values larger than 6 g

kg -1 are shaded ................................. 76

Difference of the seasonal means of 850 hPa specific humidity during 1980-

1995 (NCEP/NCAR minus DAO). The contour interval is 1 g kg -1. Negative values are shaded ............................. 77

xii
70 Standarddeviationsof seasonalmean850hPaspecifichumidity for DAO

reanalysisduring 1980-1995.The contourintervalis 0.2g kg-1. Values

largerthan0.4gkg-1 areshaded....................... 78

71 Standarddeviationsof seasonalmean850hPaspecifichumidityfor NCEP/NCAR

reanalysisduring 1980-1995.Thecontourintervalis 0.2 g kg-1. Values

largerthan0.4g kg-1 areshaded....................... 79

72 Differenceof the standarddeviationsof seasonalmean850 hPaspecific

humidity during1980-1995(NCEP/NCARminusDAO).Thecontourintervalis 0.2g kg-1. Negativevaluesareshaded................ 80

73 Seasonalmeansof 500hPaeddyheightfor DAO reanalysisduring 1980-

1995.Thecontourintervalis 40m. Negativevaluesareshaded....... 81

74 Seasonalmeansof 500hPaeddyheightfor NCEP/NCARreanalysisduring

1980-1995.Thecontourintervalis 40m. Negativevaluesareshaded.... 82

75 Differenceof theseasonalmeansof 500hPaeddyheightduring1980-1995

(NCEP/NCARminusDAO).Thecontourintervalis 10m. Negativevalues

areshaded................................... 83

76 Standarddeviationsof seasonalmean500hPaheightfor DAO reanalysis

during1980-1995.Thecontourintervalis 10m. Valueslargerthan20m

areshaded................................... 84

77 Standarddeviationsof seasonalmean500 hPaheight for NCEP/NCAR

reanalysisduring 1980-1995.Thecontourintervalis 10m. Valueslarger

than20m areshaded.............................. 85

78 Differenceof thestandarddeviationsof seasonalmean500hPaheightduring 1980-1995(NCEP/NCARminusDAO). Thecontourintervalis 5 m.

Negativevaluesareshaded........................... 86

79 Seasonalmeansof 200hPaeddystreamfunctionfor DAOreanalysisduring

1980-1995.The contourintervalis 5 x 10_°m_s-1. Negativevaluesare

shaded..................................... 87

80 Seasonalmeansof 200hPaeddystreamfunctionfor NCEP/NCARreanalysisduring 1980-1995.The contourintervalis 5 x 101°m2s-_. Negative

valuesareshaded................................ 88

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Difference of the seasonal means of 200 hPa eddy streamfunction during

1980-1995 (NCEP/NCAR minus DAO). The contour interval is 1 × 101°m2s -1.

Negative values are shaded ........................... 89

Seasonal means of 200 hPa velocity potential for DAO reanalysis during

1980-i995. The contour interval is 2 × 101°m2s -1. Negative values are

shaded ..................................... 90

Seasonal means of 200 hPa velocity potential for NCEP/NCAR reanalysis

during 1980-1995. The contour interval is 2 × 101°m_s -1. Negative values

are shaded ................................... 91

Difference of the seasonal means of 200 hPa velocity potential during 1980-

1995 (NCEP/NCAR minus DAO). The contour interval is 0.5 × 101°m2s-1.

Negative values are shaded ........................... 92

Standard deviations of seasonal mean 200 hPa eddy streamfunction for

DAO reanalysis during 1980-1995. The contour interval is 2 × 101°m_s -1.

Values larger than 4 × 101°m2s -I are shaded ................. 93

86 Standard deviations of seasonal mean 200 hPa eddy streamfunction for

NCEP/NCAR reanalysis during 1980-1995. The contour interval is 2 x 101°m2 s-1.

Values larger than 4 × 101°m2s -1 are shaded ................. 94

87 Difference of the standard deviations of seasonal mean 200 hPa eddy streamfunction during 1980-1995 (NCEP/NCAR minus DAO). The contour interval is 0.5 x 101°m2s -_. Negative values are shaded ............. 95

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9O

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Standard deviations of seasonal mean 200 hPa velocity potential for DAO

reanalysis during 1980-1995. The contour interval is 0.5 × 101°m 2s-1. Values larger than 1 × 101°m2s -1 are shaded ................... 96

Standard deviations of seasonal mean 200 hPa velocity potential for NCEP/NCAR

reanalysis during 1980-1995. The contour interval is 0.5 × 101°m 2s-1. Values larger than 1 × 101°m2s -1 are shaded ................... 97

Difference of the standard deviations of seasonal mean 200 hPa velocity

potential during 1980-1995 (NCEP/NCAR minus DAO). The contour interval is 0.2 × 10_°m2s -1. Negative values are shaded ............. 98

Seasonal means of mean sea level pressure for DAO reanalysis during

1980-1995. The contour interval is 5 hPa. Values larger than 1015 hPa

are shaded ................................... 99

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Seasonalmeansof meansealevel pressurefor NCEP/NCARreanalysis

during1980-1995.Thecontourintervalis 5 hPa.Valueslargerthan1015

hPaareshaded................................. 100

Differenceof theseasonalmeansof meansealevelpressureduring1980-

1995(NCEP/NCARminusDAO).Thecontourintervalis 2 hPa.Negative

valuesareshaded................................ 101

Standarddeviationsof seasonalmeansealevelpressurefor DAOreanalysis

during1980-1995.Thecontourintervalis 1hPa.Valueslargerthan2 hPa

areshaded................................... 102

Standarddeviationsof seasonalmeansealevelpressurefor NCEP/NCAR

reanalysis during 1980-1995. The contour interval is 1 hPa. Values larger

than 2 hPa are shaded .............................

103

Difference of the standard deviations of seasonal mean sea level pressure

during 1980-1995 (NCEP/NCAR minus DAO). The contour interval is 1

hPa. Negative values are shaded ........................

104

Seasonal means of total precipitable water for DAO reanalysis during 1980-

1995. The contour interval is 5 Kg m -2. Values larger than 40 Kg m -2 are

shaded .....................................

105

Seasonal means of total precipitable water for NCEP/NCAR reanalysis

during 1980-1995. The contour interval is 5 Kg m -2. Values larger than

40 Kg m -2 are shaded .............................

106

Difference of the seasonal means of total precipitable water during 1980-

1995 (NCEP/NCAR minus DAO). The contour interval is 2 Kg m -2. Negative values are shaded .............................

107

Standard deviations of seasonal mean total precipitable water for DAO reanalysis during 1980-1995. The contour interval is 1 Kg m -2. Values

larger than 2 Kg m -2 are shaded ........................

108

Standard deviations of seasonal mean total precipitable water for NCEP/NCAR

reanalysis during 1980-1995. The contour interval is 1 Kg m -2. Values

larger than 2 Kg m -2 are shaded ........................ 109

Difference of the standard deviations of seasonal mean total precipitable

water during 1980-1995 (NCEP/NCAR minus DAO). The contour interval

is 1 Kg m -2. Negative values are shaded ................... 110

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Seasonal means of 50 hPa u-wind for DAO reanalysis during 1980-1995.

The contour interval is 5 m s-1 . Negative values are shaded ......... 112

Seasonal means of 50 hPa u-wind for NCEP/NCAR reanalysis during 1980-

1995. The contour interval is 5 m s-1. Negative values are shaded ...... 113

Difference of the seasonal means of 50 hPa u-wind during 1980-1995

(NCEP/NCAR minus DAO). The contour interval is 2 m s-1. Negative

values are shaded ........ . ....................... 114

Standard deviations of seasonal mean 50 hPa u-wind for DAO reanalysis

during 1980-1995. The contour interval is 2 m s-1. Values larger than 4 m

s-I are shaded ................................. 115

Standard deviations of seasonal mean 50 hPa u-wind for NCEP/NCAR reanalysis during 1980-1995. The contour interval is 2 m s-_. Values larger

than 4 m s- _ are shaded ............................ 116

Difference of the standard deviations of seasonal mean 50 hPa u-wind during 1980-1995 (NCEP/NCAR minus DAO). The contour interval is 1 m

s-1 . Negative values are shaded ........................ 117

Seasonal means of 50 hPa temperature for DAO reanalysis during 1980-

1995. The contour interval is 2°K. Values less than 222*K are shaded.... 118

Seasonal means of 50 hPa temperature for NCEP/NCAR reanalysis during

1980-1995. The contour interval is 2°K. Values less than 222"K are shaded. 119

Difference of the seasonal means of 50 hPa temperature during 1980-1995

(NCEP/NCAR minus DAO). The contour interval is 1°K. Negative values

are shaded ................................... 120

Standard deviations of seasonal mean 50 hPa temperature for DAO reanalysis during 1980-1995. The contour interval is 1 °K. Values larger than 2°K

are shaded ................................... 121

Standard deviations of seasonal mean 50 hPa temperature for NCEP/NCAR

reanalysis during 1980-1995. The contour interval is I°K. Values larger

than 2°K are shaded .............................. 122

Difference of the standard deviations of seasonal mean 50 hPa temperature during 1980-1995 (NCEP/NCAR minus DAO). The contour interval

is 0.2* K. Negative values are shaded ...................... 123

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Seasonalmeansof precipitationfor DAO reanalysisduring 1980--1995.

Thecontourintervalis 2 mm day-1. Valueslargerthan4 mm day-1 are

shaded ..................................... 126

Seasonal means of precipitation for NCEP/NCAR reanalysis during 1980-

1995. The contour interval is 2 mm day -1. Values larger than 4 mm day -1

are shaded ................................... 127

Difference of the seasonal means of precipitation during 1980-1995 (NCEP/NCAR

minus DAO). The contour interval is 2 mm day -1. Negative values are

shaded ..................................... 128

Standard deviations of seasonal mean precipitation for DAO reanalysis during 1980-1995. The contour interval is 0.5 mm day -1. Values larger than

1 mm day- x are shaded ............................. 129

Standard deviations of seasonal mean precipitation for NCEP/NCAR reanalysis during 1980-1995. The contour interval is 0.5 mm day -1 . Values

larger than 1 mm day -1 are shaded ...................... 130

Difference of the standard deviations of seasonal mean precipitation during

1980-1995 (NCEP/NCAR minus DAO). The contour interval is 0.5 mm

day -1. Negative values are shaded. _...................... 131

Seasonal means of outgoing longwave radiation for DAO reanalysis during

1980-1995. The contour interval is 20 W m -2. Values less than 220 W

m -2 are shaded ................................. 132

Seasonal means of outgoing longwave radiation for NCEP/NCAR reanalysis during 1980-1995. The contour interval is 20 W m -2. Values less than

220 W m -2 are shaded ............................. 133

Difference of the seasonal means of outgoing longwave radiation during

1980-1995 (NCEP/NCAR minus DAO). The contour interval is 10 W m -2.

Negative values are shaded ........................... 134

Standard deviations of seasonal mean outgoing longwave radiation for DAO

reanalysis during 1980-1995. The contour interval is 5 W m -2. Values

larger than 10 W m -2 are shaded ....................... 135

Standard deviations of seasonal mean outgoing longwave radiation for NCEP/NCAR

reanalysis during 1980-1995. The contour interval is 5 W m -_. Values

larger than 10Wm -2 are shaded ....................... 136

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Difference of the standard deviations of seasonal mean outgoing longwave

radiation during 1980-1995 (NCEP/NCAR minus DAO). The contour interval is 5 W m -2. Negative values are shaded ................ 137

Seasonal means of evaporation for DAO reanalysis during 1980-1995. The

contour interval is 1 mm day -1. Values larger than 4 mm day -1 are shaded. 138

Seasonal means of evaporation for NCEP/NCAR reanalysis during 1980-

1995. The contour interval is 1 mm day -1. Values larger than 4 mm day -I

are shaded ................................... 139

Difference of the seasonal means of evaporation during 1980-1995 (NCEP/NCAR

minus DAO). The contour interval is 1 mm day -_. Negative values are

shaded ..................................... 140

Standard deviations of seasonal mean evaporation for DAO reanalysis during 1980-1995. The contour interval is 0.5 mm day -_. Values larger than

1 mm day -1 are shaded ............................. 141

Standard deviations of seasonal mean evaporation for NCEP/NCAR reanalysis during 1980-1995. The contour interval is 0.5 mm day -_. Values

larger than 1 mm day -I are shaded ...................... 142

Difference of the standard deviations of seasonal mean evaporation during

1980-1995 (NCEP/NCAR minus DAO). The contour interval is 0.5 mm

day -I. Negative values are shaded ....................... 143

Seasonal means of sensible heat flux for DAO reanalysis during 1980-

1995. The contour intervals are -60, -40, -20, 0, 10, 30, 60, 90, 120, 150 W

m -2. Values larger than 60 W m -2 are shaded ................. 144

Seasonal means of sensible heat flux for NCEP/NCAR reanalysis during

1980-1995. The contour intervals are -60, -40, -20, 0, 10, 30, 60, 90, 120,

150 W m -2. Values larger than 60 W m -2 are shaded ............. 145

Difference of the seasonal means of sensible heat flux during 1980-1995

(NCEP/NCAR minus DAO). The contour intervals are -60, -40, -20, 0, 10,

20, 30 W m -?. Negative values are shaded .................. 146

Standard deviations of seasonal mean sensible heat flux for DAO reanalysis

during 1980-1995. The contour intervals are 5, 10, 20, 30, 40, 50, 60 W

m -2. Values larger than 10 W m -9- are shaded ................. 147

.°,

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141

142

143

144

145

146

147

Standard deviations of seasonal mean sensible heat flux for NCEP/NCAR

reanalysis during 1980-1995. The contour intervals are 5, 10, 20, 30, 40,

50, 60 W m -2. Values larger than 10 W m -2 are shaded ........... 148

Difference of the standard deviations of seasonal mean sensible heat flux

during 1980-1995 (NCEP/NCAR minus DAO). The contour intervals are

-40, -30, -10, -5, 0, 5, 10 W m -2. Negative values are shaded ......... 149

Seasonal means of temperature at 2 meters for DAO reanalysis during

1980-1995. The contour interval is 5°K. Values larger than 290°K are

shaded ..................................... 150

Seasonal means of temperature at 2 meters for NCEP/NCAR reanalysis

during 1980-1995. The contour interval is 5°K. Values larger than 290°K

are shaded ................................... 151

Difference of the seasonal means of temperature at 2 meters during 1980-

1995 (NCEP/NCAR minus DAO). The contour interval is 5°K. Negative

values are shaded ................................ 152

Standard deviations of seasonal mean temperature at 2 meters for DAO

reanalysis during 1980-1995. The contour interval is I°K. Values larger

than 2°K are shaded... ........................... 153

Standard deviations of seasonal mean temperature at 2 meters for NCEP/NCAR

reanalysis during 1980-1995. The contour interval is I*K. Values larger

than 2°K are shaded .............................. 154

Difference of the standard deviations of seasonal mean temperature at 2

meters during 1980--1995 (NCEP/NCAR minus DAO). The contour interval is I°K. Negative values are shaded ..................... 155

Seasonal means of u-wind at 10 meters for DAO reanalysis during 1980-

1995. The contour interval is 2 m s-1. Negative values are shaded ...... 156

Seasonal means of u-wind at 10 meters for NCEP/NCAR reanalysis during

1980-1995. The contour interval is 2 m s-1. Negative values are shaded... 157

Difference of the seasonal means of u-wind at 10 meters during 1980-1995

(NCEP/NCAR minus DAO). The contour interval is 1 m s-_. Negative

values are shaded ................................ 158

xix
148

149

150

151

152

153

154

155

i56

157

Standard deviations of seasonal mean u-wind at 10 meters for DAO reanalysis during 1980--1995. The contour interval is 0.5 m s-1. Values larger

than 1 m s-1 are shaded ............................ 159

Standard deviations of seasonal mean u-wind at 10 meters for NCEP/NCAR

reanalysis during 1980-1995. The contour interval is 0.5 m s-1. Values

larger than 1 m s-1 are shaded ......................... 160

Difference of the standard deviations of seasonal mean u-wind at 10 meters

during 1980-1995 (NCEP/NCAR minus DAO). The contour interval is 0.2

m s-1 . Negative values are shaded ....................... 161

Annual variation of the 15-year mean annual cycle of 500 hPa height at

60°N, 150°W. The thick line represent the smoothed seasonal cycle fitted

with truncated Fourier harmonics and the thin line denotes the unsmoothed

values ...................................... 170

Lag correlations of 500 hPa height at selected grid points. Solid line is the

autocorrelation estimated directly from the data, the broken line denotes the

estimate of the autocorrelation by fitting a first order autoregressive (AR)

model, and the dash-dotted line is the estimate of the autocorrelation by

fitting a second order AR model ........................ 171

Characteristic time scales for 500 hPa height in winter season. (a) Direct

estimate; Co) First order AR model fit; (c) Second order AR model fit. Unit

: days ...................................... 172

Intraseasonal variances of 500 hPa height from the DAO reanalysis during 1980-1995. The contour interval is 30× 104m 2. Values larger than

150× 104m 2 are shaded ............................. 173

Intraseasonal variances of sea level pressure from the DAO reanalysis during 1980-1995. The contour interval is 30 (hPa)L Values larger than 150

(hPa) 2 are shaded ................................ 174

Intraseasonal variances of 200 hPa streamfunction from the DAO reanalysis during 1980-1995. The contour interval is 30x 10_2m4s -2. Values

larger than 150x 10_2m4s -2 are shaded .................... 175

Intraseasonal variances of 200 hPa velocity potential from the DAO reanalysis during 1980-1995. The contour interval is 30x 1011m4s-2. Values

larger than 150× 10tlm4s -2 are shaded .................... 176

XX
158

159

160

161

162

163

164

165

166 •

167

168

Interannual variances of 500 hPa height from the DAO reanalysis during 1980-1995. The contour interval is 10x 104mL Values larger than

40× 104m 2 are shaded ............................. 177

Interannual variances of sea level pressure from the DAO reanalysis during

1980-1995. The contour interval is 10 (hPa) 2. Values larger than 40 (hPa) 2

are shaded ................................... 178

Interannual variances of 200 hPa streamfunction from the DAO reanalysis

during 1980-1995. The contour interval is 10× 10X2m4s -2. Values larger

than 40× 10X2m4s -2 are shaded ........................ 179

Interannual variances of 200 hPa velocity potential from the DAO reanalysis during 1980-1995. The contour interval is 10× 101Xm4s -2. Values

larger than 40× 1011m4s -2 are shaded ..................... 180

Characteristic time scales of 500 hPa height from the DAO reanalysis during 1980-1995. The contour interval is 1 day. Values larger than 5 days are

shaded ..................................... 181

Characteristic time scales of sea level pressure from the DAO reanalysis

during 1980-1995. The contour interval is 1 day. Values larger than 5 days

are shaded ................................... 182

Characteristic time scales of 200 hPa streamfunction from the DAO reanalysis during 1980-1995. The contour interval is 1 day. Values larger than 5

days are shaded ................................. 183

Characteristic time scales of 200 hPa velocity potential from the DAO reanalysis during 1980-1995. The contour interval is 1 day. Values larger

than 5 days are shaded ............................. 184

The F ratio of 500 hPa height from the DAO reanalysis during 1980-1995.

The contour interval is 2. Light, intermediate and dark shadings denote

values larger than 2, 4, and 6, respectively. .................. 185

The F ratio of sea level pressure from the DAO reanalysis during 1980-

1995. The contour interval is 2. Light, intermediate and dark shadings

denote values larger than 2, 4, and 6, respectively. .............. 186

The F ratio of 200 hPa streamfunction from the DAO reanalysis during

1980-1995. The contour interval is 2. Light, intermediate and dark shadings denote values larger than 2, 4, and 6, respectively. ........... 187

xxi
169 The F ratio of 200 hPa velocity potential from the DAO reanalysis during 1980-1995. The contour interval is 2. Light, intermediate and dark

shadings denote values larger than 2, 4, and 6, respectively .......... 188

xxii
1 Introduction

Recently, several efforts have been devoted to the reanalysis of historical data with an unvarying assimilation system (Bengtsson and Shukla 1988; Schubert et al. 1993; Gibson et

al. 1996). The motivation for these efforts is that operational assimilation systems undergo

frequent updates and thus introduce spurious signals in the analysis output that may contaminate the quality of the atmospheric state and its variability particularly at seasonal or

longer time scales. Even the dominant climate events (e.g. the E1 Nifio/Southern Oscillation, ENSO) can be masked by these signals (see, for example, Fig. 10 in Arpe 1990).

This makes the analysis output poorly suited for climate studies. While some quantities

are not sensitive to system changes, others are more susceptible due to their sensitivity to

model formulation, and the weak constraints imposed by limited and/or poor quality observations. This is particularly true for the hydrological cycle (e.g., Mo and Higgins 1996;

Wang and Paegle 1996; Min and Schubert 1997). The use of a fixed assimilation system

has eliminated some of these problems, however, discontinuities remain in the output as a

result of changes to the observing systems. The main purpose of this document is to provide an updated estimate of seasonal and interannual variability based on the NASA/DAO

and NCEP/NCAR reanalyses for the 15 year period 1980-1995. Intercomparisons of the

seasonal means and their interannual variations axe presented for a variety of prognostic

and diagnostic fields.

In addition to understanding short term climate variations, one of the major goals of climate

research is the capability to provide useful predictions on seasonal and longer time scales.

Atmospheric predictability is determined by both internal dynamics and the influence of

slowly varying boundary conditions such as sea surface temperature, sea ice, snow cover,

and soil moisture. Numerous studies (e.g. Leith 1978, Lorenz 1982) indicate that, in the absence of anomalous boundary forcing, the inherent nonlinearity of the atmosphere results in

an rms error-doubling time of about two days, and a current practical limit of useful predictions of about two weeks. On seasonal and longer time scales, however, boundary forcing

has a substantial influence on atmospheric variability. Under the assumption that the variability induced by internal dynamics is unpredictable on seasonal and longer time scales,

there would still be a potential for predictability of time averages if the variability due to

changes in boundary conditions is sufficiently large compared to that induced by internal

dynamics. Early studies using the operational analysis data sets to assess this potential predictability (see Section 4 for definitions and discussion of earlier studies) suffered from the

spurious signals introduced by changes in the analysis system and the measurement instruments. In this study, we will also re-examine atmospheric potential predictability using the

NASA/DAO reanalysis data set.

The document is divided into two parts. The first part is focused on the seasonal means

and their interannual variability of selected prognostic and diagnostic fields from both the
NASA/DAOand NCEP/NCARreanalyses.We notethat Higgins et al. (1997)havealreadypresentedarathercomprehensivecomparisonof meanquantitiesandwithin season

variability from bothreanalyses.This is anextensionof thatwork to includemoreyears,

thetransitionseasons,furtherdiagnosticquantitiesand,in particular,interannualvariability. Thesecondpart examinesatmosphericpotentialpredictabilityusingtheNASA/DAO

reanalysis.Theresultsaresummarizedin termsof variances,characteristictime scalesand

signal-to-noiseratio.

Section2 givesa brief overviewof the NASA/DAO and the NCEP/NCARassimilation

systems.Section3 comparesthe seasonalmeansand its interannualstandarddeviation

of selectedquantitiesfrom thetworeanalyses.Theanalysisof atmosphericpotentialpredictabili.tyispresentedin section4. Section5 summarizestheresults.

2 Data Assimilation Systems

In this section, we will give a brief overview on the NASA/DAO and the NCEP/NCAR

data assimilation systems. Detailed descriptions can be found in Schubert et al. (1993)

and Kalnay et al. (1996). There are differences between the two systems in many aspects,

including the model characteristics, the analysis techniques, and the input data. These

differences may at least partially account for the differences in the reanalysis datasets.

2.1 NASA/DAO system

The NASA/DAO dataset (Schubert et. al. 1993) is the multiyear reanalysis produced with

version 1 of the Goddard Earth Observing System (GEOS-1). The general circulation

model (GCM) is described in Takacs et al. (1994) and the analysis system is described

in Pfaendtner et al. (1995). The analysis scheme consists of a 3-dimensional multivariate

optimal interpolation (OI) carried out on 14 standard pressure levels extending from 1000

to 20 hPa. The GEOS-1 GCM is a gridpoint model employing the Aries/GEOS dynamical

core described in Suarez and Takacs (1995). For the reanalysis the GCM was run at a

horizontal resolution of 2*latitude by 2.5* longitude and with 20 sigma levels in the vertical

(top at 10 hPa). The assimilation system does not include an initialization scheme and

relies on the damping properties of a Matsuno time differencing scheme to control initial

imbalances generated by the insertion of observations. However, the initial imbalances and

spin-up have been greatly reduced by introducing an incremental analysis update (IAU)

procedure (Bloom et al. 1996).

The GEOS-1 model has 4 levels below 850 hPa and 4 levels between 100 and 10 hPa

which includes parameterizations of all major physical processes: radiation, convection,

2
large-scaleprecipitation,boundarylayerphysics,andverticalandhorizontaldiffusion. In

theGEOS-1system,theseasurfacetemperature(SST)isupdatedusingthemonthlymean

blendedSSTanalyses(ReynoldsandMarsico1993).ThepenetrativeconvectionisparameterizedusingtheRelaxedArakawa-Schubert(RAS) scheme(Moorthi andSuarez1992).

The soil moisturein theNASA/DAOassimilationsystemis computedoff-line basedona

simplebucketmodelusingobservedmonthlymeansurfaceair temperatureandprecipitation (Schemmet al. 1992).

2.2 NCEP/NCAR system

The NCEP/NCAR reanalysis products were also produced with a fixed assimilation system (Kalnay et. al. 1996) consisting of the NCEP Medium Range Forecast spectral model

and the operational NCEP Spectral Statistical Interpolation scheme, a 3-dimensional variational analysis scheme (SSI; Parrish and Derber 1992) with improved error statistics and a

divergence tendency constrain. The assimilation was performed at a horizontal resolution

of T62 and with 28 sigma levels in the vertical (top at about 3 hPa). The implementation

of SSI in the analysis makes it unnecessary to employ an initialization step. The analysis is performed on the model sigma levels employing mandatory pressure level data, and

significant level winds. The bogus surface data (POABs) from the Australian Bureau of

Meteorology are also assimilated in the NCEP/NCAR system 1

This version of the NCEP spectral model has 7 levels below 850 hPa and 6levels between

100 and 10 hPa which includes parameterizations of all major physical processes, including

radiation (diurnal cycle and interaction with clouds), convection, large-scale precipitation,

shallow convection, boundary layer physics, interactive surface hydrology, gravity wave

drag, and vertical and horizontal diffusion. Details of this model are described in Kanamitsu (1989). Recent changes are the use of a simplified Arakawa-Schubert convective

parameterization scheme (Pan and Wu 1994) and a diagnostic cloud scheme (Campana et

al. 1994). The NCEP/NCAR system incorporates the weekly global optimal interpolation

SST analyses (Reynolds and Smith 1994) from 1982-present and the UKMO GISST for

earlier years. The soil moisture is updated during the analysis cycle with a simple soil

model (Pan and Mahrt 1987).

3 Intercomparison

In this section, we compare the seasonal means and their interannual vaiability from the

NASA/DAO and NCEP/NCAR (hereafter we use DAO and NCEP for simplicity) reanal1The bogus data were inadvertently incorporated with a 180*phase shift in the NCEP product.
yses for various quantifies. Conventional definitions of seasons are used in this study, i.e.,

December-January-February (DJF) for northern winter season, March-April-May (MAM)

for northern spring season, June-July-August (JJA) for northern summer season, and SeptemberOctober-November (SON) for northern fall season. Both the seasonal means and their interannual variability (or more specifically the standard deviation) are based on the 15-year

time period March 1980 through February 1995, which was the time period common to

both reanalyses at the time of this study. The results of the zonally averaged prognostic

fields are presented first. Next the global maps are presented for various fields at selected

levels, including one level in the lower stratosphere. The intercomparison of selected diagnostic variables is presented in subsection 3.3. We note that this is primarily intended as an

atlas of seasonal means and their variability, and as convenient reference for assessing the

uncertainties in these quantities based on the differences between the two reanalyses. As

such the discussions of the various figures are rather terse and are intended only to highlight

some of the salient features.

3.1 Zonal mean fields

Figures 1 - 30 present the seasonal means and the interannual standard deviations of zonally

averaged seasonal mean quantities (zonal mean zonal (u) and meridional (v) winds, zonal

mean temperature (T), zonal mean specific humidity (q), and mass streamfunction). The

seasonal means of u-wind are very similar in the two reanalyses. The difference maps

(Fig.3) show, however, that during all four seasons the DAO product has weaker easterlies

in tropics, weaker westerlies in the subtropics and stronger westerlies in the middle and

high latitudes thanth e NCEP pr_uct. The differences in the middle an d high latitudes of

the northern hemisphere are smaller than in the southemhemisphere. The seasonal mean vwind has similar patterns in the two reanalyses (Figs. 4 and 5). However, large differences

in the magnitudes are seen in DJ'F and MAM indicating a considerably weaker Hadley Cell

during these months in the DAO product. During JJA and SON the DAO Hadley Cell is

somewhat stronger with the upper branch extending further into the middle troposphere

just north of the equator.

For the zonal mean temperature profiles (Figs. 7-9), the seasonal means clearly show systematic differences between the DAO and NCEP products; the DAO temperature is warmer

in much of the troposphere and colder than NCEP above 100 hPa and in the middle troposphere (300-500 hPa) near the south pole. The large differences near the surface in the

southern hemisphere (SH) polar region are likely related to the interpolation and extrapolation in regions of high topography. The zonal mean moisture profiles (Figs. 10--12) show

that the DAO product is wetter in the tropical middle troposphere and northern hemisphere

(NH) subtropics near the surface, and drier between the surface layer and approximately

850 hPa. In the midlatitudes, the DAO data is consistently drier than the NCEP product

4
abovethesurfacelayer. TheJJAmoisturedifferencesaresomewhatdifferentfrom other

seasonswith theDAOwetterin theSHsubtropicsandsubstantiallydrierin theNH middle

andhighlatitudes.Themassstreamfunction(Figs. 13-15)showsthattheDAOproducthas

a strongerHadleycell in JJAandweakerHadleycell in DJF,indicatinga strongerannual

cyclein theDAOdata.Thisis consistentwith thev-wind resultsshownpreviously.

Theinterannualvariability of theseasonalmeanzonalmeanu-wind(Figs. 16-18)is quite

similar in the two products,exceptin the tropical lower stratospherewherethe NCEP

productshowsconsiderablymorevariability,andin theSH polarregions(Fig.18),where

theDAO productshowsmorevariability.TheDAO productshowsmorevariability in the

v-wind for all seasonsexceptMAM (Figs. 19-21). DuringthisseasontheNCEPproduct

exhibitsits largestvariability,while theDAO producthas the smallestvariability. The

patternsin thetemperaturevariability (Figs.22-24) arequitesimilarin thetwo reanalyses

but largedifferencesareobservedin thetropospherenearthe SH polarregion,with the

DAO product showinggreatervariability. The DAO productshowsconsistentlylarger

variability in themoistureprofiles(Figs. 25-27) andthe massstreamfunction(Figs. 28-

30).

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3.2 Global upper-air fields

3.2.1 Troposphere

Figures 31-102 display the global maps of selected quantities from the DAO and the NCEP

reanalyses. The seasonal means and the interannual standard deviations are presented.

For the mean zonal wind, the DAO and NCEP products are very similar for all seasons. The

largest differences occur near the polar region of the southern hemisphere and in the tropics

at both 200 and 850 hPa (Figs.33 and 36). The DAO wind shows weaker easterlies in the

tropics and stronger westerlies in the middle and high latitudes of the southern hemisphere.

At 200 hPa, both the DAO and the NCEP products show a large seasonal variation in the

interannual variability with a maximum during DJF and a minimum during JJA (Figs.37-

38). A suggestion of tropical/extratropical coupling is also observed, between the central

tropical Pacific and the midlatitudes of both hemispheres, particularly during the DJF. The

differences in the standard deviations between the two datasets are generally small (Fig.39).

The largest differences occur in the polar region of the southern hemisphere. At 850 hPa,

the seasonal variation of the interarmual variability of zonal wind is also evident for both

the DAO and the NCEP reanalyses (Figs.40--41). The maximum variability occurs over

the central and western tropical Pacific. During DJF and MAM seasons, this maximum

spreads over the entire tropical Pacific. Large variability is also seen in other regions, such

as the Pacific and Atlantic storm track regions. The differences between the DAO and

the NCEP indicate that the DAO has substantially larger interannual variability over the

tropical Pacific especially during DYE

Figures 43--51 show the seasonal mean meridional wind and the interannual variability at

200 and 850 hPa. The differences in the seasonal mean reveal rather more complicated

patterns than the zonal wind. Systematic differences are observed at 200 hPa in the tropical

region (Fig.45). Compared to the NCEP product, the DAO product shows stronger northerlies over Africa, Indian ocean, northern South America and stronger southerlies over the

central tropical Pacific. The largest differences are observed in the polar region of the

southern hemisphere, which appears to be related to the topography. The differences at 850

hPa are rather noisy, and appear to be associated with topographic features. The meridional wind at both 200 and 850 hPa shows large variability in the middle and high latitudes

of both hemispheres. At 200 hPa the variability is particularly large over western North

America during DJF and SON, and over Europe and southern Asia during DJF. At 850 hPa

the largest variability occurs over the NH polar regions, Europe, and the NH high latitude

Pacific and Atlantic oceans during DJF. Large variability also occurs over the SH high latitude Pacific Ocean during JJA. The differences in the interannual variability between the

two reanalyses show that the DAO in general has larger variability than the NCEP with the

largest differences occurring in the polar region of the southern hemisphere.

36
In section3.1,we haveshownthat thezonal meantemperatureprofileshavesystematic

differencesbetweentheDAO andtheNCEPreanalysesin thetroposphereaswell asin the

lowerstratosphere.Here,ourfocusis ontheglobaldistributionsof thetemperatureat 200

and850hPa. The comparisonfor thelower stratospherewill bepresentedin subsection

3.2.2.Thedifferencesin temperatureat200hPashowthattheDAO productis colderthan

theNCEPproductnearthepoleof bothhemispheres,andthattheDAOproductis warmer

thantheNCEPproductthroughoutthe tropicsandmidlatitudes(Fig.57). In themidlatitudesof thenorthernhemisphere,abandof largenegativedifferences(DAOtemperatures

warmer)is observedfor all seasons.At 850hPa(Fig.60),largenegativedifferencesare

foundin the easternPacificnearthecoastand in the southernIndian Oceanwhile large

positivedifferencesareseenoverthecontinentsnearhighterrain.Fortheinterannualvariability,bothreanalysesshowsimilarpatternsandhavelargeseasonalvariationsat200and

850hPa. In general,largevariability occursat middleand high latitudesin bothhemispheres.At 850hPatheNCEPtemperaturetendsto havemorevariability overthetropical

oceansandin the subtropicsoff the westcoastsof thecontinents,whiletheDAO temperaturetendsto havemorevariability overthemiddlelatitude oceansandAfrica. At 200

hPatheDAO temperatureislessvariablethanNCEPinthenorthernhemispherewhile it is

considerablymorevariablethanNCEPoverAntarctica(Figs.63and66).

The meanDAO specifichumidity at 850hPashowsa muchmoreconcentratedtropical

maximumassociatedwith theITCZ comparedwith NCEP(Figs.67and68). The differenceplot (Fig.69) showsthatthe DAO productis drierjust to thewestof the continents

(justoff the equatorovertheeasternPacificocean,the Atlantic ocean,andeastof Australia),andit is wetteroverthewesternPacific,the African continentandtheAmericas.

Thelargestvariability occursin thetropicsandsubtropics(Figs.70-72),withtheDAOspecific humidity showingconsiderablymorevariability thanNCEPthroughouttheseasons.

The500hPaeddygeopotentialheightshowslarge(> 10m)differencesin themiddleand

high latitudesof the southernhemisphereandoverthemidlatitudeoceansof thenorthern

hemisphere(Figs.73--75).Theinterannualvariability,in general,showscoherentpatterns

in thetwo reanalyses.However,largedifferencescanbefoundin thepolarregionsof both

hemispheres(Fig.75).

The seasonalmeansof the200hPa eddy streamfunction show substantial differences between the two reanalyses over the subtropics especially during JJA in the southern hemisphere (Fig.81). For the 200 hPa velocity potential, however, the large differences are found

in the tropical region (30"S-30"N) where heavy rainfall occurs (Fig.84). The stronger gradients suggest that the NCEP system tends to produce stronger deep convection than DAO

over the western Pacific, Indian ocean and the South America continent. Also, NCEP has

stronger subsidence over the eastern Pacific ocean, the eastern Atlantic in the tropics and in

the northern Africa. This indicates a stronger tropical east-west (Walker) circulation in the

NCEP system. For the interannual variability, both reanalyses show large seasonal varia37
tions in the200hPaeddystreamfunctionwith themaximumvariability occurringduring

DJF inbothhemispheres(Figs.85-87).Thissuggestsanimportantrole fortropicalforcing

duringthis seasonparticularlyovertheeasternPacific.The DAOeddystreamfunctionhas

somewhatlargerinterannualvariability duringDJF overtheeasternsubtropicaloceansof

both hemispheres.Otherwise,thedifferencesaregenerallylargerin the southernhemispherethanin thenorthernhemisphere.For the 200 hPavelocity potential(Fig.88-90),

the largestvariability occursin thetropicswith maximaoverthe westernPacificand the

easternPacificnearthecoastof theAmericancontinent.TheDAO velocity potentialhas

lessvariability overthe southernIndianoceanandthe centralandeasternPacificOcean

andmorevariabilityoverthemonsoonregion.

Thedifferencesin theseasonalmeansof themeansealevelpressure(Figs.91-93)largely

follow thetopography,suggestingdifferencesin themodeltopographyandthe algorithm

usedto reducethe surfacepressureto the sealevel. The _nter_ual variability generally

has the samepatternas500 hPageopotentialheight, showinglargevariability nearthe

preferredregionsof persistentatmosphericanomalies.The differencesbetweenthe two

reanalysesindicatelargervariability in theDAO sealevel pressure,particularly nearthe

polarregions.

Theseasonalmeantotalprecipitablewaterdifferences(Fig.99)betweenthetworeanalyses

aresimilarto thosefor the850hPaspecifichumidity (Fig.69).In general,theDAO product showsa moreconcentratedtropicalmaximum. The DAO:atm0sphereis wetterthan

NCEPoverthecentralandwesternPacific,the tropical Indian ocean,Africa, the South

Americancontinent,andIndia andsoutheasternAsia, and it is drier thanNCEPwestof

Australia,theeasternPacificoceanandthetropicalAtlanticoce_. Thelargestinterannual

variability occursovertheoceanwith maximaoverthecentralandeasternPacific(Fig.100

and 101).Thedifferencesin theinterannualstandarddeviationindicatethattheDAO has

substantiallylargervariabilitythantheNCEP.

38
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3.2.2 Stratosphere

We look in this section in more detail at the differences between the two reanalysis in the

lower stratosphere. Neither system has a highly resolved stratosphere. This is particularly

true for this version of the DAO assimilation system (GEOS-1) which has 20 model levels

and a top at 10 hPa. We note that the new version of the DAO system does have a well

resolved stratosphere with 70 model levels and a top at 0.01 hPa. We have already shown

that the zonal mean fields in both wind and temperature (Figs.3 and 9) have substantial

systematic differences between the DAO and the NCEP products in the lower stratosphere.

In the following, we will examine the global distributions of the zonal wind and temperature

at 50 hPa.

The seasonal mean zonal wind at 50 hPa shows distinct seasonal variations (Fig. 103-104).

Westerlies prevail in the northern hemisphere during DJF while strong easterlies occur in

the subtropics of the southern hemisphere. This structure is reversed in JJA with the corresponding westerlies considerably stronger in the southern hemisphere high latitudes. The

transition seasons show a more symmetrical structure with respect to the equator, with easterlies in the tropics and westerlies in the extratropics. The differences between MAM and

SON are largest in the southern hemisphere extratropics which show considerably stronger

westerlies during SON. The differences between the reanalyses (Fig.105), in general, show

positive values in the subtropical region of the northern hemisphere and negative values

in the subtropical and polar regions of the southern hemisphere. The interannual standard

deviation of 50 hPa u-wind shows large seasonal variations in the middle and high latitudes

(Figs.106 and 107). Large variability is observed in the tropics with maximum over the

western Pacific, which is likely associated with the Quasibiennial Oscillation (QBO). The

difference between the two reanalyses shows that the DAO has larger variability than the

NCEP except for the tropical western Pacific and the tropical Indian ocean. The largest

differences are found in the tropical central and eastern Pacific.

The seasonal mean temperature at 50 hPa shows large seasonal variations with a warm

pole in the summer hemisphere (Figs.109 and 110). There is evidence of noise near the

pole in the DAO datasets for all seasons. NCEP shows consistently warmer temperature at

50 hPa over the entire globe (Fig. 111). Generally, large interannual temperature variability

is found in the high latitudes during the winter seasons. The transition seasons show some

asymmetry with the largest variability occurring in the northern hemisphere during MAM,

and in the southern hemisphere during SON. The differences in the variability between the

two reanalyses are small (Figs.112-114).

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In this subsection, we will compare the seasonal means and the interannual variability of

the seasonal means for selected diagnostic quantities.

The precipitation shows a clear seasonal evolution in the tropics as well as in the subtropics

(Figs. 115 and 116). Key features include the boreal winter precipitation in the storm track

regions, the Indian summer monsoon rainfall, and the enhancement of summer rainfall over

land. The largest differences between the two reanalyses are in the vicinity of heavy tropical

rainfall where the DAO product generally has more rainfall than NCEP. The NCEP system

generates more precipitation over the storm tracks. There is also considerable small scale

noise in the NCEP product at high latitudes. The largest interannual variability is found

in the normally heavy rainfall regions of the tropics and the subtropics. Large interannual

variability is also observed over the northern Europe, eastern Asia and the eastern coast of

the United States during IIA. The difference field (Fig. 120) shows that the DAO rainfall

has larger interannual variability than NCEP in the regions of heaviest tropical rainfall.

Figures 121-126 show the seasonal mean and interannual variability of the outgoing longwave radiation (OLR) of the two reanalyses. The differences in the seasonal means (Fig.123)

again suggests that in the tropics and subtropics the DAO system generates stronger convection (and higher clouds) than the NCEP system. The extratropical differences are largely

negative. This is especially evident over the storm tracks, consistent with the generally

greater rainfall in the NCEP product in these regions. The largest variability occurs in the

tropics and subtropics with a maximum in the western Pacific regions. Again, the NCEP

product shows less variable than the DAO throughout the year.

The evaporation depends on the magnitude of the surface wind, the vertical moisture gradient near the surface and the surface layer stability. Since the techniques used in determining

evaporation are very similar in the two reanalyses, the differences in the evaporation reflects

differences in the surface wind or the vertical moisture gradient near the surface. In general,

the DAO product shows less evaporation over most of the ocean area and more evaporation

over land (Fig.129). The interannual variability is generally small and spotty (Figs.130 and

131). The largest differences in the variability occur over land, where the DAO product

shows consistently larger values.

The sensible heat flux is determined by the magnitude of the surface wind, the vertical

temperature gradient near the surface and the surface roughness. In general, the sensible

heat flux is from the surface to the atmosphere except for regions covered with snow or ice

(Figs.133 and 134). The main differences between the two reanalyses occur over land, and

over ocean where SST gradients are the largest (Fig.135). The DAO has larger sensible

heat flux over land and less over the Gulf Stream and the Kuroshio current. The differences

of the sensible heat flux over the ocean may reflect in part the differences in the sea surface

124
temperature(SST)betweenthetwo reanalyses.TheSSTdatasetusedin theDAO incorporatesthemonthlymeanblendedSSTanalysesof RenoldsandMarsico(1993)whilethe

NCEPusesthetheimprovedweeklyglobaloptimuminterpolatedSSTanalysesof Renolds

andSmith(1994).The majordifferencesbetweenthetwo SSTdatasetsarein regionsof

largeSSTgradients,suchas theGulf Streamandthe Kuroshio. The largest interannual

variability in the sensible heat flux occurs over land, and in the regions with large SST

gradients and the ocean area near the south pole where the variability is associated with the

migration of the sea ice concentration (Figs. 136 and 137). Figure 138 shows that the DAO

sensible heat flux has generally larger variability than NCEP--exceptions include the Gulf

Stream and Europe during DJF and MAM.

Figures 139-144 show the seasonal means and the interannual standard deviations of temperature at 2 meters. The seasonal means are similar in the two reanalyses. However, large

differences are observed over the cold season middle and high latitude continents of both

hemispheres (Fig.141), with considerably colder temperatures in the DAO product. Both

reanalyses show a large seasonal cycle in the regions of maximum variability. Note also

that there is a local maximum of interannual variability over the tropical eastern Pacific.

This is likely linked to the ENSO phenomenon. The differences map shows that the DAO

two meter temperature generally has larger variability than NCEP.

The seasonal means of the 10 meter zonal wind show similar pattems in the two reanalyses

with the largest differences occurring near the polar regions, the coastal regions and the

central tropical Pacific (Figs.145-147). The main features in the seasonal means of the 10

meter wind are the prevailing easterlies in the tropics and subtropics with maxima off the

equator, and the westerlies in the midlatitudes. Large interannual variations are observed

in the storm track regions, such as the North Pacific, the North Atlantic and the east of

New Zealand. Large variability is also found in the central and western Pacific which may

be linked to ENSO variability. The difference map, while noisy, shows a tendency for

the DAO winds to have larger variability over the tropics and subtropics, while the NCEP

winds have greater variability over the extratropics.

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4 Potential Predictability

Leith (1973) conceptualized the problem of climate predictability by analyzing the signalto-noise ratio in the time averages and stated that the noise in the climate means is closely

dependent on the persistence in the daily Weather fluctuations. Madden (1976)pioneered

the study of potential predictability of the atmosphere by examining the mean sea level

pressure in the Northern Hemisphere using a 74-year dataset. He concluded that there is

little potential predictability between 40°N and 60°N in all seasons and that potential predictability increases to the north and south of this mid-latitude band. In a similar study,

Madden and Shea (1978) showed that there is fairly strong evidence for potential predictability in surface temperature over the United States in the winter season but less in

other seasons. Shukla and Gutzler (1983), using 15 years of NMC analyzed 500 hPa height

in the northern hemisphere, showed stronger evidence for potential predictability in January

and July, though the distribution of the ratio of interannual to intraseasonal variability has

the same characteristics as that in Madden (1976). These differences illustrate the sensitivity of the results to the methods employed for estimating potential predictability (Zwiers

1987). Further aspects of the sensitivity will be examined below.

In this section, we document the interannual variability and seasonal potential predictability

in the DAO reanalysis products. The approach basically follows the formulation given in

Trenberth (1985b). The current study, however, employs a fit to an autoregressive process

to estimate the autocorrelation which, in turn, is used to determine the characteristic time

scales. The data and a brief description of the methodology are given in subsection 4.1.

The results are presented in subsection 4.2.

4.1 Data and data processing

a. Data

The data consist of the daily averages of mean sea level pressure (SLP), 500 hPa geopotential height, 200 hPa stream function and 200 hPa velocity potential generated from the

DAO multiyear reanalysis for the 15 year period of March 1980--February 1995 (Schubert

et al. 1995). The daily mean values were computed from the 6-hourly fields sampled at a

resolution of 2 ° lat x 2.5" Ion.

b. Annual cycle

The 15-year mean annual cycle of the daily data is represented by a truncated Fourier

expansion (Madden 1976; Madden and Shea 1978). The full amplitudes of the first four

harmonics were included and harmonics five through seven were weighted by 0.75, 0.50,

and 0.25 respectively. The smoothed annual cycle was then used to generate the anomalies

162
by removingthe annualcyclefrom thedaily data. Note that these anomalies retain any

interannual variations. Figure 151 gives an example of the original and truncated Fourier

representation of the mean annual cycle of the 500 hPa geopotential height at 60°N and

150°W.

Conventional seasons were used for the northern spring (March, April and May : MAM),

summer (June, July and August : JJA), fall (September, October and November : SON)

and winter (December, January and February : DJF).

c. Estimating the autocorrelation

For the anomaly time series, {xi,j}, the autocovariance for a particular season at lag L for

the jth year is estimated by

where

1 N-L

Cj(L) = -_ _ (xi,j - _j)(Xi+L,j -- _j), (1)

i----1

1 N

_'J _ -N E Xi,j _

i--1

and N is the sample size (number of days in the season). Then mean autocovariance and

autocorrelafion over J years are given by

1 J

C(L) = -_ _ Cj(L), (2)

j=l

and

C(L)

p(L) = C(0)' (3)

respectively. Jones (1975) and Trenberth (1985) note that the p(L) estimate from (1)-(3) is

unreliable at large lags for small sample sizes. In this study, we employ an autoregressive

process to fit the autocorrelation (e.g. Jones 1975). Figure 152 shows an example of the

autocorrelation calculated from (3) and the first and second order AR fits. In general, the

second order AR model fits the time series better than a red noise approximation (first order

AR process).

d. Definition of potential predictability

There are generally two approaches to estimating potential predictability: one in the time

domain (e.g., Trenberth 1985b) and another in frequency domain (e.g., Madden 1976; Madden and Shea 1978; Zwiers 1986). Th two approaches are essentially similar. The method

used in this study is described in detail in Trenberth (1985b) and outlined below.

163
For eachseasonof yearj, there are N daily values, xi,j, with the mean annual cycle removed. An unbiased estimate of the interannual variance is

-2 J__ 2

= J-li=lZJ= j-lsm'

(4)

where

1 J

_Z -2 Srn = j xj.

j=l

(5)

This is a measure of the climatic signals. In order to test whether there is a significant

contribution from potentially predictable sources, we need to estimate independently the

variance due to the noise in the time averages. The noise is the high frequency variability

of the daily mean values.

The variance of the seasonal means due to climate noise, O'_v, is given

0 "2 o.2Zo

a_ = N_f-"'_ = _' (6)

where o.2 is the unbiased estimate of total variance, N_ff is the effective number of independent observations, and To the characteristic time between independent samples as defined

in Leith (1973)as

To = i+ 2 _(i - )p(L). (7)

L--1

The estimate of To is sensitive to the estimate of autocorrelation at lag L, as it is shown in

Fig.153. Different estimates of the autocorrelation result in very different To values. For

example, the T0 values calculated from the first order AR model are very different from

those based on the original sample estimates of the autocorrelation.

An unbiased estimate of o.2 based on intraseasonal variances is given by

N

o.2_ s2, (8)

N-To

where s 2 is the mean intraseasonal variance and is given by

1 J

$2 __ ._j (9)

164
andwhere

1 U

The variance due to climatic noise is

(10)

T0 ,32

a_v = _ . (11)

Under the null hypothesis that there is no potentially predictable signal, the ratio,

2

(7"m

F = zz-, (12)

(YN

will approximately follow an F distribution with J-1 and J(Ne.tl-1) degrees of freedom.

The null hypothesis will be rejected if F > F_ where Fc is the critical value of F at an

appropriate significant level.

4.2 Results

a. Intraseasonal variances

Figures 154 - 157 show the intraseasonal variances of the 500 hPa height, sea level pressure, 200 hPa stream function and 200 hPa velocity potential for the four seasons. The

seasonal variations of the variances of the 500 hPa height, SLP and 200 hPa stream function are larger in the northern hemisphere than in the southern hemisphere. In the northern

hemisphere, the winter (DJF) has the largest variability while the summer (JJA) has the

least variability. The height field (Fig. 154) shows three centers of large variability over

the North Pacific/Gulf of Alaska, the North Atlantic extending over Europe, and the Arctic. This is consistent with previous studies using the NMC analysis (e.g., Blackmon 1976;

Blackmon et al. 1986; Min and Kung 1997). The transition seasons show similar variability patterns, with somewhat larger variability over Europe and eastern Asia during SON.

For the southern hemisphere, the seasonal variation is weaker with maximum variability

occurring during JJA over the South Pacific. The regions of large variability are consistent

with those found in Trenberth (1985b). That study was based on about 8 years of 0000

UTC analyses from Australian World Meteorology Center. The pattern of the intraseasonal

variance is, however, more zonally symmetric than in the Northern Hemisphere, with the

maximum variability occurring at about 60* S.

The regions of high SLP variability (Fig. 155) are similar to those of the height field, though

the location of the maxima tend to be displaced somewhat to the east with respect to the

height variance maxima. The regions of high stream function variability (Fig. 156) tend

165
to be displaced equatorward of the height variances, and are more closely identified with

fluctuations in the major subtropical and midlatitude jet streams. The 200 hPa velocity

potential shows large variability in the tropics with large seasonal variations (Fig.157).

For DJF, the maximum is located over the tropical Indian Ocean. In contrast, JJA shows

substantially weaker variability over the Indian Ocean and somewhat stronger variability

over central America, and just east of Australia. There is a large difference in the variance

for the transition seasons, with a broad tropical maximum in variability for MAM and

much weaker tropical variability during SON with the maximum over the Indian Ocean.

In general, the MAM variability is more like DJF, while the SON variability is more like

JJA. It is likely that a substantial fraction of the intraseasonal variances in 200 hPa velocity

potential comes from the 40-50 day oscillation (Madden and Julian 1972). We note that

this variance is treated as climatic noise in the estimate of potential predictability in this

study.

b. lnterannual variances

The interannual variability is shown in Figs.158-161. The maxima in the height and SLP

variance occur over the North Pacific, the North Atlantic, the Arctic, and the South Pacific

near 120* S. The Northern Hemisphere has a large seasonal variation with relatively large

variability occurring during DJF and very weak summer time variability. The Southern

Hemisphere shows less season variation in the variance with, however, the weakest variance

again occurring during the summer. These results are consistent with earlier studies (e.g.,

Madden 1976; Trenberth 1985b) for both the winter and summer seasons. In the northern

hemisphere, interannual variance maxima tend to be displaced equatorward of the maxima

in the intraseasonal variances, while in the southern hemisphere they are displaced to the

south. The interannual variance is about 1/4 of the intraseasonal variance.

The interannual variance of the 200 hPa stream function is largest during DJF. During both

DJF and MAM the meridional sequence of variance maxima in the central/eastern Pacific

suggest a coupling between the tropics and extratropics. Maxima in variance also occur

over the Gulf of Alaska, Southeast Asia, the Arctic, the North Atlantic, and the South

Pacific (southeast of New Zealand). During JJA, the interannual variability is substantially

reduced with the largest variability apparently tied to fluctuations in the subtropical jets.

The variability is also weak during SON, except for a local maximum which occurs east of

Australia in the region of the South Pacific Convergence Zone (SPCZ).

The interannual variance of the 200 hPa velocity potential is primarily concentrated in the

tropics. Surprisingly, the transition seasons (MAM and SON) show the largest variability.

The major maxima in Variance during MAM occur over the western Pacific and the Americas, while during SON the primary maximum is located over Indonesia and Australia, with

a secondary maximum located over the central Pacific. During DJF there is a broad maxi166
mumacrossthetropicalPacificwhich extendsinto middlelatitudesoverboththewestern

PacificandMexico.Thevariability issubstantiallyreducedduringJJA,with weakmaxima

overIndia, southernAfrica, andthecentraltropicalPacific.

c. Characteristic time scales

Previous studies have presented To values for sea level pressure (Madden 1976), surface

temperature (Madden and Shea 1978), and geopotential height (Shukla and Gutzler 1983)

in the northern hemisphere. Trenberth (1985a) provided To values for 500 hPa and 1000

hPa height in the southern hemisphere. Stefanick (1981) presented global distributions of

an "integral time scale" (_ 1⁄2To) for wind, temperature and geopotential height at different

levels for DJF. In view of the sensitive of the characteristic time scale to the estimate of

autocorrelation we have examined several different estimates and found that a second order

AR process provides in many cases an improved estimate (see section 4.1 c and references

cited therein).

Figures 162-165 show the characteristic time scales obtained by fitting the data with an

AR(2) process. The results are generally consistent with previous studies for the 500 hPa

height and SLP for DJF and JJA seasons (e.g., Madden 1976; Stefanick 1981; Shukla

and Gutzler 1983; Trenberth 1985a). In general, To ranges from 2 days to more than

one week with minima in the midlatitudes in both hemisphere and maxima in the tropics

and polar regions. During the northern winter (DJF), the To values for 500 hPa height

and SLP are larger than 6 days over the eastern North Pacific and North Atlantic regions.

These are the preferred regions of winter blocking. Regions with relatively smaller To

correspond to regions of frequent cyclogenesis. Also during DJF, the tropical height field

is characterized by a very narrow band (centered on the equator) of long time scales (greater

than 5 days) extending eastward from eastern Africa across South America. In view of the

small fluctuations of the height field in the deep tropics, these estimates may not be very

reliable. For the SLP, the long tropical time scales cover a broader band with the longest

time scales (greater than 7 days) occurring over Indonesia and the western Pacific. During

JJA and SON the largest tropical time scales in the SLP are over the Indian Ocean and

the western Pacific, while during MAM the region of long tropical time scales moves east,

extending from Indonesia across the Pacific.

The To values of the 200 hPa stream function show characteristics similar to the 500 hPa

geopotential height and the sea level pressure in the middle and high latitudes for all seasons, though the values tend to be larger (e.g. about 2 weeks over Antarctic during DJF).

The major differences are in the subtropical and the tropical regions. The To values show

a narrow band of minimum To along the equator with maxima in the subtropics of both

hemispheres for all seasons but JJA. The 200 hPa velocity potential generally shows time

scales of one to two weeks throughout much of the tropical and subtropical eastern hemi167
spherefor all seasonexceptJJA.DJF andMAM patternsarequite similarwith long time

scalesextendingfrom thetropicspolewardto +/-60*in someregions.Duringthenorthern

summer,the longesttime scalesarefoundoff theeastcoastsof Asia andNorth America,

andovertropicalSouthAmerica.

d. Signal-to-Noise Ratios

Figures 166-169 present the F-ratios (signal-to--noise ratios) computed from (12) for the

500 hPa geopotential height, SLP, 200 hPa stream function and velocity potential for all

seasons. Following Jones (1975), we make the null hypothesis that there is no potentially

predictable signal in the data. The ratio (12) then has an F distribution. Regions where the

ratio is larger than the critical value for a given significant level are considered potentially

predictable. For convenience, we take everywhere values of the F-ratio less than 2 to indicate regions with no potential predictability. This is based on the fact that at the 97.5%

confidence level, Fc is approximately 2.0 for the 200 hPa stream function in DJF over

Antarctica: this represents the worst case from a statistical sampling point of view, since it

has the longest time scales and therefore has the smallest number of effective samples.

For the 500 hPa geopotential height (Fig.166), the largest values of F-ratio are found in the

tropical regions (20"S-20°N), highlighting the importance of SST forcing of the tropical

atmosphere. This region of large signal-to-noise ratios is consistent with the findings of

Schubert et al. (1997). That study found substantial coherence extending well into the upper troposphere between the tropical temperature from the reanalysis and the temperature

from a simulation employing the observed SSTs as boundary conditions. In the extratropics, the small signal-to-noise ratios are generally consistent with Shukla and Gutzler (1983)

for the northern hemisphere, and with Trenberth (1985b) for the southern hemisphere. We

note, however, the F-values do exceed 2 throughout much of the northern extratropical

oceans for all seasons except JJA, at which point the values greater than 2 tend to shift

to the summer continents. In the Southern Hemisphere, signal-to--noise ratios exceeding

4 are found extending eastward from Australia into the central South Pacific. These signals, which may be related to the Southern Oscillation (Trenberth 1976; Nicholls 1981; Van

Loon 1984), were not evident in the Trenberth (1985b) study. For the northern hemisphere

winter season, the major differences between the current results and those of Shukla and

Gutzler (1983) are over the central and eastern United States and over the North Atlantic

(see Fig.2 in Shukla and Gutzler i983). The large F values over the central and eastern

United States in Shukla and Gutzler are not found in the current study, while Shukla and

Gutzler do not obtain the large F values over the North Atlantic where persistent anomalies

or blocking events tend to occur.

The sea level pressure (SLP) depicts similar patterns ofF values as the 500 hPa geopotential

height in the middle and high latitudes of both hemisphere (Fig.167). In the subtropical and

168
tropicalregions,however,localmaximaareobserved.For example,duringthe northern

winter (DJF) largeF values are found over the southeastern Pacific, the western Pacific

region, the Indian ocean, and the southern Atlantic ocean. During the northern summer

(JJA), large F values are observed over Africa, the central Pacific, and just off the coast

of Peru. Large values over southern Asia and the southwestern United States are likely

associated with the summer monsoons.

Figures 168 and 169 show the F values of the 200 hPa streamfunction and velocity potential. For the 200 hPa streamfunction, two regions of locally enhanced F values straddle the

tropical central Pacific for all seasons. These regions of enhanced signal-to-noise move

east/west and change strength with the seasons. During DJF they are located just east of

the dateline. During MAM, the patterns are slightly to the east of th DJF position and

have weaker signal-to-noise ratios. During J'JA the signal-to-noise ratios are smallest and

located in the western and central Pacific. During SON the regions have the largest signalto-noise ratio and are located in the central Pacific. Large F values also occur over the

tropical/subtropical Atlantic during DJF, MAM and JJA. Surprisingly large F values also

occur over much of the Southern Hemisphere middle latitudes during DJF.

The velocity potential exhibits large seasonal variations of the F values. During DJF the

largest F values are located over the eastern Pacific extending across North America, and

over Antarctica. During MAM the high F values have switched from the Antarctica to the

North Polar region, and the eastern Pacific region of high F values has moved further east

to cover most of the Americas. During JJA the highest signal-to--noise ratios are located

over the central Pacific and the high latitudes of both hemispheres. The largest F values

during SON are located over Australia.

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5 Summary

The results presented in this document are intended as an updated arias of seasonal and

interannual variability. The results are based on a 15-year period from March 1980 to

February 1995 employing assimilated data (the NASA/DAO and NCEP/NCAR reanalyses)

which do not suffer from artificial climate signals introduced by system changes typical of

operational analyses. Thecomparisons between the two reanalyses serve to assess the

quality and consistency of the various quantities at seasonal and longer time scales.

The results show that, in general, the seasonal means of the prognostic fields are consistent in the two reanalyses. However, systematic differences do occur even in the zonal

wind and temperature fields. For example, the DAO product shows a consistently colder

lower stratosphere than the NCEP product. The largest differences tend to occur over regions where observations are sparse, though it is not clear how many of these differences

are associated with model bias, the input observations, or the analysis techniques. In particular, differences in the weighting of conventional and satellite observations may play

an important role. Systematic differences in the moisture are found throughout the troposphere with the largest occurring in the lower levels which contribute substantially to the

differences in the hydrological cycle. These differences also occur largely over data sparse

regions, such as the tropical oceans and South America. Since the current analyses only

use radiosonde moisture observations it is expected that the incorporation of alternative

high quality sources of moisture observations will have a major positive impact on future

reanalyses.

The differences tend to be largest for the diagnostic quantities (especially those involving

the hydrological cycle) and those quantities involving the divergent wind. For example,

the mass streamfunction in the DAO system is stronger than in the NCEP system during

the northern summer (JJA) while the opposite is true during the northern winter (DJF).

The key moisture source terms also show substantial differences. For example, the tropical

precipitation is consistently larger in the DAO product, especially over the Pacific warm

pool. The NCEP/NCAR evaporation is larger over the oceans compared to the DAO and

less than the DAO over land during the warm seasons.

While it is clear there are still substantial bias' in the reanalysis fields, the most promising

products of the reanalysis are the estimates of interannual variability. This is potentially the

major improvement over the operational products, though it must be pointed out that the inhomogeneities in the observations can still induce spurious signals. The comparisons show

that, in general, the differences in the wind and temperature variability tend to be small,

except in the lower stratosphere and the high latitudes of the Southern Hemisphere. On the

other hand, the variability of the Hadley Cell is quite different in the two reanalyses with

the DAO product showing consistently stronger interannual variability. The variability of

the moisture also shows substantial differences with consistently greater variability in the

189
DAOmoisturefield.Similarly,variousnearsurfacequantitiesandsurfacefluxesexamined

hereshowsubstantialdifferencesin variability. Thesequantitiesareonly indirectly constrainedby theobservations,anddependstronglyonthephysicalparameterizationsof the

assimilatinggeneralcirculationmodels.While in mostcasesthepatternsof variabilityare

similar,theDAO productsshowa tendencyfor largervariability thantheNCEPproducts

in boththetropicsandthemidlatitudes.Theseresultssuggestssubstantialuncertaintystill

existsin the estimatesof seasonalvariability,especiallyin the diagnosticquantities.We

notethatotherstudies(e.g.Schubertet.al. 1995;Min andSchubert1997)suggestgreater

agreementand/ormoreaccurateestimatesof variability on monthly time scales,andof

variability associatedwith extremeevents.

Finally, wehavere-examinedatmosphericpotentialpredictabilitywith selectedvariables

from theDAOreanalysisproducts.Theresultsaregenerallyconsistentwith previousstudies in thatpotentialpredictabilityis smallestin the midlatitudes(40*-60*) of bothhemispheresand increasesbothto thenorthand south. On theotherhand,the useof the reanalysisdata,andcarefulfitting of theautocorrelation,appearto haveprovidedimproved

estimatesof thesignal-to--noiseratio,whicharemoreconsistentwith knownclimatesignals. For example,we findenhancepotentialpredictabilityoverAustraliaandtheSouth

Pacific,apparentlylinkedto theSouthernOscillation.Wehavealsoseena strongseasonal

cycleinthepotentialpredictabilityof mostquantitiesexaminedhere.For example, the seasonal evolution of patterns of strong signal-to-noise ratios tied to the tropical SSTs can be

tracked across the Pacific during the course of the annual cycle. These estimates appear to

be sufficiently reliable to now provide a basis for assessing the veracity of signal-to-noise

ratios generated with general circulation models.

Acknowledgments. This work was supported by NASA's Earth Observing Systems (EOS)

projects on 4D data Assimilation and Computing.

190
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194
Previous Volumes in This Series

Volume 1

September 199.{

Documentation of the Goddard Earth Observing System

(GEOS) general circulation model - Version 1

L.L. Takacs, A. Molod, and T. Wang

Volume 2

October 199_

Direct solution of the implicit formulation of fourth order

horizontal diffusion for gridpoint models on the sphere

Y. Li, S. Moorthi, and J.R. Bates

Volume 3

December 199_

An e_cient thermal infrared radiation parameterization for

use in general circulation models

M.-D. Chou and M.J. Suarez

Volume 4

January 1995

Documentation of the Goddard Earth Observing System

(GEOS) Data Assimilation System - Version 1

James Pfaendtner, Stephen Bloom, David Lamich,

Michael Seablom, Meta Sienkiewicz, James Stobie,

and Arlindo da Silva

Volume 5

April 1995

Documentation of the Aries-GEOS dynamical core: Version 2

Max J. Suarez and Lawrence L. Takacs

Volume 6

April 1995

A Multiyear Assimilation with the GEOS-1 System:

Overview and Results

Siegfried Schubert, Chung-Kyu Park, Chung-Yu Wu,

Wayne Higgins, Yelena Kondratyeva, Andrea Molod,

Lawrence Takacs, Michael Seablom, and Richard

Rood

Volume 7

September 1995

Proceedings of the Workshop on the GEOS-1 Five-Year

Assimilation

Siegfried D. Schubert and Richard B. Rood

195
Volume 8

March I996

Volume 9

March 1996

Documentation of the Tangent Linear Model and Its Adjoint

of the Adiabatic Version of the NASA GEOS-1 C-Grid GCM:

Version 5.2

Weiyu Yang and I. Michael Navon

Energy and Water Balance Calculations in the Mosaic LSM

Randal D. Koster and Max J. Suarez

Volume 10

April 1996

Dynamical Aspects of Climate Simulations Using the GEOS

General Circulation Model

Lawrence L. Takacs and Max J. Suarez

Volume 11

May 1997

Documentation of the Tangent Linear and its Adjoint Models

of the Relaxed Arakawa-Schubert Moisture Parameterization

Package of the NASA GEOS-1 GCM (Version 5.2)

Weiyu Yang I. Michael Navon, and Ricardo Todling

Volume 12

August 1997

Comparison of Satellite Global Rainfall Algorithms

Alfred T.C. Chang and Long S. Chiu

196

REPORT DOCUMENTATION PAGE Form Approved

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1, AGENCY USE ONLY (Leave b/ank) 2, REPORT DATE 3. REPORT TYPE AND DATES COVERED

December 1997 Technical Memorandum

4. TITLE AND SUBTITLE

Technical Report Series on Global Modeling and Data Assimilation

Volume 13--Interannual Variability and Potential Predictability in

Reanalysis Products

6. AUTHOR(S)

Wei Min and Siegfried D. Schubert

Max Suarez, Editor

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS (ES)

Data Assimilation Office

Climate and Radiation Branch

Goddard Space Flight Center

Greenbelt, Maryland

9. SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS (ES)

National Aeronautics and Space Administration

Washington, DC 20546-0001

11. SUPPLEMENTARYNOTE'S

Wei Min: General Sciences Corporation, Laurel, Maryland

5. FUNDING NUMBERS

Code 910.3/913

8. PEFORMING ORGANIZATION

REPORT NUMBER

97A00357

10. SPONSORING I MONITORING

AGENCY REPORT NUMBER

TM-97-104606, Vol. 13

12a. DISTRIBUTION ! AVAILABILITY STATEMENT 12b. DISTRIBUTION CODE

Unclassified-Unlimited

Subject Category: 46

Availability: NASA CASI (301) 621-0390.

13. ABSTRACT (Maximum 200 words)

The Data Assimilation Office (DAO) at Goddard Space Flight Center and the National Center for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) have produced multi-year

global assimilations of historical data employing fixed analysis systems. These "reanalysis" products are

ideally suited for studying short-term climatic variations. The availability of multiple reanalysis products also

provides the opportunity to examine the uncertainty in the reanalysis data. The purpose of this document is to

provide an updated estimate of seasonal and interannual variability based on the DAO and NCEP/NCAR

reanalyses for the 15-year period 1980-1995. Intercomparisons of the seasonal means and their interannual

variations are presented for a variety of prognostic and diagnostic fields. In addition, atmospheric potential

predictability is re-examined employing selected DAO reanalysis variables.

14. SUBJECT TERMS

DAO, NCEP/NCAR, reanalysis products, seasonal variability, interannual variability

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OF REPORT OF THIS PAGE OF ABSTRACT

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