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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
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