Changes of Seasonal Predictability Associated with Climate Change

Kyung Jin and In-Sik Kang

Climate Environment System Research CenterSeoul National University

International project coordinated by Hadley Centre and COLA Goal: Characterize climate variability and predictability of the last ~130 years through analysis of observational data and ocean-forced atmospheric general circulation models (AGCM) “Classic” experimental design: Hadley Centre provides HadISST1.1 SST and sea ice data as lower boundary conditions

- Integrate over 1871-2002 (at least 1949-2002)- Ensembles of at least 4 members

Background and ObjectiveBackground and Objective

International Climate of the Twentieth Century Project (C20C)

Perfect Model Correlation- Average of correlations between each member and ensemble mean using other members in the ensemble simulations of GCM- Hence, it can be the indication of upper limit of GCM potential predictability due to SST boundary forcing

Change of Predictability following to the use of different climatology

In this study, we examine Changes of potential seasonal predictability in 100-year AGCM ensemble simulation Plausible sources of regulation of potential predictability in AMIP run

Model Description and Experimental DesignModel Description and Experimental Design

Resolution Dynamics Physics

T42 L21Spectral model

using semi-implicit method

•2-stream k-distribution radiation scheme (Nakajima and Tanaka 1986)• Simplified Arakawa-Schubert cumulus convection scheme based on RAS scheme (Moorthi and Suarez 1992)• Orographic gravity-wave drag (McFarlane 1987)• Bonan’s land surface model (Bonan 1996)• Mon-local PBL/vertical diffusion (Holtslag and Boville 1993)

SNU/KMA Global Climate Prediction System (GCPS)

Model Institute Resolution Integrated Period Ensemble Number

SNU/GCPS SNU/KMA T42L21 Jan1897-Nov1998 4 member

NSIPP NASA 2ox2.5o L43 Jan1930-Nov1998 9 member

Used Model Dataset

Performed Experimental Design in SNU/GCPS

International Climate of the Twentieth Century Project (C20C) Integration Period: Jan 1897 to Nov 1998 Boundary Conditions

- HadSST and Sea ice 1.1 (Jones et al. 2001)- PCMDI vertical ozone distribution- Atmospheric CO2 concentration: 321.07 ppm (100-yr mean)

CES/SNULinear Trend of Surface Temperature

Oberved trend : 0.61oC/100yr Simulated trend: 0.55oC/100yr

Using anomaly data subtracted the climatology during 1961-1990

Observation comes from CRU surface temperature and Hadley SST

Perfect Model Correlation- Average of correlations between each member and ensemble mean using other members in the ensemble simulations of GCM- Hence, it can be the indication of upper limit of GCM potential predictability due to SST boundary forcing

Change of Predictability following to the use of different climatology

Perfect Model Correlation of DJF Anomalies over Global regionGlobal Pattern Correlation

Perfect Model Correlation

-Considering one member of the ensemble as an observation

-Making spatial correlation between the model observation and the ensemble mean of the other members

- Average of correlations between each member and ensemble mean using other members in the ensemble simulations of GCM.

1921-1950 1968-19970.630.38

0.66

0.53

(a) Surface Temperature

(b) Precipitation

5-year running mean

Perfect Model Correlation of DJF Anomalies over Global region

1921-1950 1968-19970.630.38

0.66

0.53

(a) Surface Temperature

(b) Precipitation

5-year running mean

Not shown here, the increase is also detected in the case of boreal summer, even though the difference is rather weak.

The changes of predictability due to the use of different climatology is negligible in this case.

Global Pattern Correlation

In NSIPP results, the ascent of potential predictability is also shown, and moreover, the interannual variability of predictability is also coincide with that of SNU.

Increase of potential predictability of recent years can be the general feature of GCM ensemble simulations

The change of SST as the boundary condition for two models, has to be estimated to fine the origin of predictability 5-year running mean

Perfect Model Correlation of AGCMs DJF PRCP Anomalies over Global regionGlobal Pattern Correlation

SNU NSIPP0.63

0.650.380.44

1921-1950 1968-1997

0.66

0.73

0.53

0.58

(a) Surface Temperature

(b) Precipitation

5-year running mean

In NSIPP results, the ascent of potential predictability is also shown, and moreover, the interannual variability of predictability is also coincide with that of SNU.

Increase of potential predictability of recent years can be the general feature of GCM ensemble simulations.

The change of SST as the boundary condition for two models, has to be estimated to fine the origin of predictability.

Analysis of Variance: SNU DJF PRCP – P1(1921-1950) vs. P2(1968-1997)

Free variance

Intrinsic transients due to natural variability

Forced variance

Climate signals caused by external forcing

N

ii XX

N 1

2)(1

1

N

i

n

jiij XX

nN 1 1

2)()1(

1

1968-1997 (b) Forced variance

(d) Free variance

(f) Forced/Free variance

(a) Forced variance

(c) Free variance

(e) Forced/Free variance

1921-1950

The improvement of potential predictability is coming from the increase of forced part generated by SST. The SST has an important role to regulate the potential predictability in model results.

Ratio of Temporal Perfect Model Correlation – SNU (1968-1997) vs. (1921-1950)

(a) Surface Temperature

(c) Precipitation

(c) Surface Temperature

(d) Precipitation

DJFJJA

:

Red denotes that latter (1968-1997) period show higher predictability than former (1921-1950) period and blue denotes to the contrary.

19501921

19971968

COR

CORRatio COR1968-1997 and COR1921-1950 means perfect model temporal

correlation during 1968-1997 and 1921-1950, respectively.

1921-1950 vs. 1968-1997

Plausible Source of Improvement of Potential Predictability in AMIP runPlausible Source of Improvement of Potential Predictability in AMIP run

Increase of Forced

Variance

Improvement of Potential Predictabilit

y

Change of SST

boundary forcing

Increase of Global Mean SST

Change of climatological SST field

Increase of Tropical Forcing over Eastern Pacific Increase of remote forcing to whole globe

Increase of Intensity of SST variability

Increase of variability of absolute value of SST anomalies

Plausible Source

Two periods during 30 years

To find the origin of interannual characteristics of predictability

DJF Global Perfect Model Correlation and Global SST

Perfect Model Corr.Global Mean SST

DJF Global Pattern Correlation

(a) Surface Temperature

(b) Precipitation

5-year running mean

The improved predictability roughly looks some connection with global warming trend, but inconsistencies exist in the sense of interannual predictability.

Mean Absolute Value of SST Anomalies – P1(1921-1950) vs. P2(1968-1997)

(a) 1921-1950

(b) 1968-1997

(e) Ratio (b)/(a)

Mean of Absolute Value of DJF SST anomalies during 30 years

Red denotes that latter (1968-1997) period show larger variability than former (1921-1950) period and blue denotes to the contrary.

Longitude-Time Cross section of SST Anomalies over 5oN-5oS

(b) 1968-1997(a) 1921-1950

It show the clear intensification of SST variability for latter period including both increase of intensity and frequency of ENSO and warming trend over the Indian Ocean.

Regression of the Absolute Value of DJF SST Anomalies by Perfect Model Correlation

(b) PNA PRCP (c) Monsoon PRCP

(d) Global 500hPa GPH (e) Monsoon 500hPa GPH

(a) Global pattern correlation of rainfall

The region of SST variability regulating the potential predictability in AGCM is almost same for various variables and regions. The interannual variability of SST over the eastern Pacific looks to have an important role for predictability.

Relationship between DJF Global PRCP Perfect Model Correlation and SST

DJF

Glo

bal

Pat

tern

Co

rrel

atio

n o

f P

reci

pit

ati

on

DJF SST anomalies

(a) Global Mean SST (b) NINO3.4 Index

(c) Absolute value of NINO3.4

Characteristics of improved predictability - The improvement of predictability during ENSO years are clear for both El Nino and La Nina.- Even in the normal year, latter period (1968-1997, blue dots) show higher predictability than former years (1921-1950, red dots).

8 cases are selected for high and low skill, respectively Using 1897-1997 Climatology for both periods

Composite of Absolute Value of DJF SST Anomalies by PRCP Predictability

(a) High Skill

(d) Low Skill

(b) High Skill

(e) Low Skill

1921-1950 1968-1997 (c) Ratio of (b)/(a)

(f) Ratio of (e)/(d)

NINO 3.4PM Corr.1 σ

5 cases are selected for high and low skill in normal year (not ENSO), respectively Even in the normal cases, the increase of tropical SST variability is traced. In Particular, low skill case show much larger increase over the whole tropical ocean. It is well matched with the previous results showing the higher predictability for recent years even in the non-ENSO years having small value of NINO index.

Composite of Absolute Value of DJF SST Anomalies by PRCP Predictability

(a) High Skill for Normal Year

(d) Low Skill for Normal Year

(b) High Skill for Normal Year

(e) Low Skill for Normal Year

(c) Ratio of (b)/(a)

(f) Ratio of (e)/(d)

1921-1950 1968-1997

SummarySummary

In AGCM ensemble simulations for 20th century, the increase of potential predictability is clearly shown, especially for the surface variables.

As the plausible causes of this, the change of characteristics of SST following to the global climate change can be considered: Global warming trend, intensity of ENSO activity, and the amplitude of SST anomalies are considered.

The potential predictability over the globe is very much related to the intensity of ENSO.

The magnitudes of SST anomalies over the tropics are also important for the predictability for even non-ENSO years.

To quantify the effect of each origins exactly, model experiments using regulated SST boundary condition and statistical approach are needed.

Model Experiment

Boundary Condition: 1921-1950 Climatology + 1968-1997 Anomaly 30 years simulation with 4 ensemble member

Perfect Model Corr.Global Mean SSTNew experimentNew experiment SST

Perfect Model Corr.

Model Experiment

Boundary Condition: 1921-1950 Climatology + 1968-1997 Anomaly 30 years simulation with 4 ensemble member

NINO3.4 Index

New experiment

New experiment SST

Perfect Model Correlation of AGCMs DJF PRCP Anomalies over Global region

0.630.65

0.380.44

1921-1950 1968-1997

0.66

0.73

0.53

0.58

SNU NSIPP

(a) Surface Temperature

(b) Precipitation

Global Pattern Correlation for DJF Precipitation

Perfect Model Correlation- Average of correlations between each member and ensemble mean using other members in the ensemble simulations of GCM- Hence, it can be the indication of upper limit of GCM potential predictability due to SST boundary forcing

Change of Predictability following to the use of different climatology

Perfect Model Correlation- Average of correlations between each member and ensemble mean using other members in the ensemble simulations of GCM.- Hence, it can be the indication of upper limit of GCM potential predictability due to SST boundary forcing.

Change of predictability following to the use of different climatology is not detected.

Climatology of DJF SST– (1921-1950) vs. (1968-1997)

(a) 1921-1950

(b) 1968-1997

(e) Difference (b) - (a)

DJF Climatology of SST

Perfect Model Correlation of SNUGCM DJF PRCP Anomalies

SNU Pattern Correlation for DJF Precipitation(a) Surface Temperature

(b) Precipitation

5-year running mean

Global Region(0-360oE, 90oS-90oN) Asian Monsoon Region(40-160oE, 20oS-40oN)

EOF analysis of 30-yearr ANOVA of DJF PRCP

For the EOF analysis of analysis of variance, 1914 in x-axis denotes analysis of variance during 1899-1928.

Forced Variance Ratio of Forced/Free Variance

(a) 1st mode (b) 1st mode

(c) PC time series (d) PC time series

DJF Global Perfect Model Correlation and NINO3.4 Index

DJF Global Pattern Correlation(a) Surface Temperature

(b) Precipitation

NINO3.4 IndexPerfect Model Corr.

Perfect Model Correlation of SNUGCM DJF PRCP Anomalies over Global region

Using 1897-1997 Climatology

0.66

0.73

0.53

0.58

Perfect Model Correlation of SNUGCM DJF PRCP Anomalies

Using 1897-1997 Climatology for both periods

Analysis of Variance – SNU DJF Z500 - P1(1921-1950) vs. P2(1968-1997)

Analysis of Variance – NSIPP DJF PRCP – P1(1930-1950) vs. P2(1968-1997)

Analysis of Variance – NSIPP DJF Z500 – P1(1930-1950) vs. P2(1968-1997)

5 cases are selected for high and low skill in normal year (not ENSO), respectively Using 1921-1950 and 1968-1997 Climatology, respectively

Composite of Absolute Value of DJF SST Anomalies by PRCP Predictability

(a) High Skill for Normal Year

(d) Low Skill for Normal Year

(b) High Skill for Normal Year

(e) Low Skill for Normal Year

(c) Ratio of (b)/(a)

(f) Ratio of (e)/(d)

1921-1950 1968-1997