1. Indrani Roy & Mat Collins
SAPRiSE
Project
Identifying the role of the Sun and the El Niño Southern Oscillation on Indian Summer Monsoon
Indrani Roy
&
Mat Collins
25th June, 2013

2. Outline I: Observation Role of Sun and ENSO on ISM
Background
Analyses
II : CMIP5 Model Output
Some features of ISM, ENSO in CMIP5 models
ISM and ENSO teleconnection
Hydrological Cycle in CMIP5 models and ISM

3. Role of sun and ENSO on ISM

4. Background: General Circulation
Walker Cell:
Hadley Cell:
India
Hadley Cell: Thermally driven rising air around ITCZ and falling 30 lat.
Walker Cell: EW circulation in tropics and changes direction in warm and cold phases of ENSO

5. Background : Indian Summer Monsoon(ISM)
SLP: Climatology
Monsoon means seasonal
wind reversals. For ISM, it is
from NE ly (Jan) to SW ly
(July).
Associated with movement of
ITCZ.
Heavy rains in summer (JJAS)
due to moisture rich air from
ocean.
Walker circulation and Hadley
circulation both play role.
ISM has changed in last few
decades (IPCC, 2007)
January
July

6. Background: Major Modes of Variability
Tahiti
(Australia)
Different regions affected by various modes

7. Why NAO? Anticorrelation between NAO and ISM [Sen Roy, 2011]
Positive Phase
Negative Phase
Anticorrelation between NAO and ISM [Sen Roy, 2011]
ISM is strongly modulated by the NAO [Liu & Yanai, 2001]
Relationship bet. temperature W. Eurasia and ISM is stronger,
over same period the rel. between the ENSO, ISM weakened.
[Chang et al. [2001]

8. Why Indian Ocean Dipole (IOD) ?
Positive Phase
Negative Phase
Connection: ISM- Australia- E. African rainfall
IOD and ENSO have complementarily affected ISM during (Ashok et al, 2001)

9. Atmos-Ocean coupling was different during 1950s to 1997
Global warming caused weakening of tropical circulation:
more in the Walker cell than the Hadley cell
Strong decrease in intensity of Walker circulation after 1950s
Modest intensification since (Vecchi, et al. 2007)
[McPhaden and Zhang, 2004]
Could climate change during that period have modified ISM?

10. Solar signal on ISM detected using Solar peak year compositing
JJA Max-yr
JJA Min-yr
Anomaly
Van Loon and Meehl (2012) only used solar max-yr compositing on SLP and rainfall and suggested sun enhances ISM
Is it true solar signal?

11. Solar Peak years and ENSO
Average SSN
ENSO (DJF)
[Roy and Haigh, 2010]
Almost all solar peak years are with –ve ENSO index
ENSO signal in peak-yr compositing might be mis- interpreted as solar
Is any other strong signal also mixed up in compositing method?

12. Solar compositing on SLP
Min r.t
Max r.t
Min r.t
Max r.t
Max-yr significant signal around Azore High, min-yr Icelandic Low. Unaffect with change of period of anomaly.
SH is mostly affected due to climate change signal during
During same period, land (+ve)-sea(-ve) SLP contrast favours ISM rainfall

13. ISM in solar compositing covering India
Min r.t
Max r.t
Min r.t
Max r.t
Max-yr as well as min-yr compositing suggest similarly around Indian subcontinent.
Max-yr suggest stronger effect on ITCZ that min-yr compositing.

14. Multiple Regression Analysis
Trend
SSN
Volcano
ENSO 14

15. ENSO Signal in JJA
ENSO captures SO in SLP, but major changes around Australia in later period. Australia (Darwin), one lobe of SO is also coincidentally one end of IOD.
-ve NAO pattern observed in later period.
Local N-S Hadley circulation, as manifest as NAO in NH and IOD in SH may have played role in modulating ISM in later period.

16. Solar Signal in JJA
Using regression, no significant signal is detected around regions of Indian subcontinent.
This is true irrespective of the period considered

17. Some connections between Sun and ISM
+ve ENSO index +
-ve ENSO index *
Rainfall deficit years are usually associated with warm phase of ENSO
and vice versa (L.H.S).
Some connections, solar cycle and ISM rainfall- different since 1950s.
Decadal solar forcing on trade winds (Meehl, et al 2008, Roy & Haigh
,2012) that acts alongside inter-annual ENSO may be responsible.

18. Summary I
Solar influence on ISM rainfall, using method of solar peak year compositing, may not be robust and can be influenced by factors as ENSO and trends.
Compositing suggests SH is mostly affected by climate change signal. Min year detects signal around Icelandic low and max year around Azore high.
During 2nd half of last century, the weakened Walker circulation due to climate change seems to be overtaken by local N-S Hadley circulation, as manifest as NAO in NH and IOD in SH.
Some connections between solar cycle and monsoon rainfall, which are different since 1950s.

19. ISM and ENSO in CMIP5 models

20. Comparison with Observation and another model - last 50 years
Observation-CRU : black; Model-all forcing: red
Temporal
Behaviour
(Bollasina, et al
2011, Science)
5-yr running mean JJAS(relative ) over central-N India (box)
Observation-CRU
Model (NOAA GFDL CM3) all forcing
Spatial
Behaviour
-ve anomaly in box for both cases.

21. Spatial Pattern (Historical Run)
Precipitation Anomaly w.r.t. ( )
No consistent pattern – even in box (starting from +ve to –ve shown)

22. Precipitation Anomaly w.r.t. (1940-2005)
Temporal Pattern (Historial Run)-last 50 years
Precipitation Anomaly w.r.t. ( ) 1 1 1 2 2 2
Better results 11 year running mean
Decreasing trend : inmcm4, Access1-0, MIROC-ESM, NorESM1-M
Increasing : IPSL-CM5B-LR, MPI-ESM-LR, MPI-ESM-P, IPSL-CM5A-MR 1 2

23. Analysis: Historical +rcp (w.r.t 1985-2005)
Rainfall (box), 11 year running mean, blue rcp scenario. 2 1
Most models rising trend in rcp, some overall no trend (e.g. ),
some falling trend in rcp scenario (e.g. ).
Similar observation not only in box but also for overall rainfall. 1 2

24. SST(Nino 3.4 in JJAS): rcp (w.r.t 1985-2005)
Model with least trend in Nino
Model high variable Nino
All models rising trend in rcp, exception 1 2 3

25. ISM and ENSO Teleconnection
Model
Correlation : Rainfall vs. Nino3.4
Whole India Box
I II I II
(His) (rcp) (His) (rcp)
MIROC-ESM-CHEM
NorESM1-M
MIROC-ESM
Inmcm4
ACCESS1-0
CSIRO-Mk3-6-0
IPSL-CM5A-MR
MPI-ESM-LR
CanESM2
-ve correlation - both in historical (I) and rcp (II)- after removing trend.
True in the box region as well.

26. Hydrological Cycle in CMIP5 models and ISM

27. Global Hydrological Cycle : CMIP3
(Vecchi and Soden, 2007)
Moistening vary model to model, but all models exhibit a nearly linear relationship between column water vapour and surface temperature.
The rate of this increase is 7.5% /K, following Clausius Clapron (C-C) equation.

28. Precipitation Time Series:CMIP5
Global
Monsoon
Global:
annual & JJAS resemble.
Uncertainty increases with time.
Matches CMIP3 study.
Nearterm prediction skill good.
Monsoon:
large uncertainty throughout.
Magnitude-wise much higher.

29. Hydrological cycle and ISM:CMIP5
Annual
Atm water and temp
Precipitation, vert. vel and temp
JJAS
Ppt, vert. vel and temp (Globe)
Precipitation, vert. vel and temp (India)
Although there can be significant regional changes in relative humidity among models, the global-mean behaviour closely resembles that expected from (C–C) arguments also in CMIP5.

30. Questions to answer/future work
It is usually said that monsoon increases due to
increase in water vapour offset by weakening
circulation.
Hypothesis: Can this explain variations between
CMIP5 models?
Does global change reflect regional change?
Studies using HADCM3/ RM3 with perturbed physics
will be carried to understand monsoon dynamics.
Emphasis will be on circulation fields.

31. Summary II ISM - General Features vary model to model in CMIP5.
Model FGOALS-g2 does not show any trend in nino3.4 for historical or rcp scenario.
ISM and ENSO teleconnection studied. All models suggest -ve correlation in historical as well as rcp scenario.
Hydrological Cycle and ISM were analysed using CMIP5 models. Hydrological cycle matches to that of earlier CMIP3 study.
Monsoon rainfall suggests large uncertainty in CMIP5.