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Changes of Seasonal Predictability Associated with Climate Change Kyung Jin and In-Sik Kang Climate Environment System Research Center Seoul National University.

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Presentation on theme: "Changes of Seasonal Predictability Associated with Climate Change Kyung Jin and In-Sik Kang Climate Environment System Research Center Seoul National University."— Presentation transcript:

1 Changes of Seasonal Predictability Associated with Climate Change Kyung Jin and In-Sik Kang Climate Environment System Research Center Seoul National University

2  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 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

3 Model Description and Experimental Design ResolutionDynamicsPhysics T42 L21 Spectral 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) ModelInstituteResolutionIntegrated PeriodEnsemble Number SNU/GCPSSNU/KMAT42L21Jan1897-Nov19984 member NSIPPNASA2 o x2.5 o L43Jan1930-Nov19989 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)

4 CES/SNU Linear Trend of Surface Temperature Oberved trend : 0.61 o C/100yrSimulated trend: 0.55 o C/100yr  Using anomaly data subtracted the climatology during 1961-1990  Observation comes from CRU surface temperature and Hadley SST

5  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 region Global 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. 1921-19501968-1997 0.630.38 0.660.53 (a) Surface Temperature (b) Precipitation

6  5-year running mean Perfect Model Correlation of DJF Anomalies over Global region 1921-19501968-1997 0.630.38 0.660.53 (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

7  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 region Global Pattern Correlation SNU NSIPP 0.63 0.65 0.38 0.44 1921-19501968-1997 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.

8 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 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.

9 Ratio of Temporal Perfect Model Correlation – SNU (1968-1997) vs. (1921-1950) (a) Surface Temperature (c) Precipitation (c) Surface Temperature (d) Precipitation DJF JJA   :  Red denotes that latter (1968-1997) period show higher predictability than former (1921-1950) period and blue denotes to the contrary. COR 1968-1997 and COR 1921-1950 means perfect model temporal correlation during 1968-1997 and 1921-1950, respectively.

10 1921-1950 vs. 1968-1997 Plausible Source of Improvement of Potential Predictability in AMIP run Increase of Forced Variance Improvemen t 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

11 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.

12 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.

13 Longitude-Time Cross section of SST Anomalies over 5 o N-5 o S (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.

14 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.

15 Relationship between DJF Global PRCP Perfect Model Correlation and SST DJF Global Pattern Correlation of Precipitation 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).

16  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 1921-19501968-1997 (c) Ratio of (b)/(a) (f) Ratio of (e)/(d) NINO 3.4PM Corr. 1 σ

17  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) 1921-19501968-1997

18 Summary  In AGCM ensemble simulations for 20 th 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.

19 Model Experiment  Boundary Condition: 1921-1950 Climatology + 1968-1997 Anomaly  30 years simulation with 4 ensemble member Perfect Model Corr. Global Mean SST New experiment New experiment SST

20 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

21 Perfect Model Correlation of AGCMs DJF PRCP Anomalies over Global region 0.63 0.65 0.38 0.44 1921-19501968-1997 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.

22 Climatology of DJF SST– (1921-1950) vs. (1968-1997) (a) 1921-1950 (b) 1968-1997 (e) Difference (b) - (a) DJF Climatology of SST

23 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)

24 EOF analysis of 30-yearr ANOVA of DJF PRCP   For the EOF analysis of analysis of variance, 1914 in x-axis denotes analysis of v ariance during 1899-1928. Forced VarianceRatio of Forced/Free Variance (a) 1 st mode (b) 1 st mode (c) PC time series (d) PC time series

25 DJF Global Perfect Model Correlation and NINO3.4 Index DJF Global Pattern Correlation (a) Surface Temperature (b) Precipitation NINO3.4 Index Perfect Model Corr.

26 Perfect Model Correlation of SNUGCM DJF PRCP Anomalies over Global region Using 1897-1997 Climatology 0.66 0.73 0.53 0.58

27 Perfect Model Correlation of SNUGCM DJF PRCP Anomalies Using 1897-1997 Climatology for both periods

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

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

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

31  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) 1921-19501968-1997

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