Identifying the role of the Sun and the El NiñoSouthern Oscillation on Indian Summer Monsoon

Indrani Roy

&

Mat Collins

25th June, 2013

SAPRiSEProject

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

Role of sun and ENSO on ISM

Background: General Circulation

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

Hadley Cell: Walker Cell:

India

Background : Indian Summer Monsoon(ISM)

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)

SLP: Climatology

January

July

Background: Major Modes of Variability

Different regions affected by various modes

Tahiti (Australia)

Positive Phase Negative Phase

Why NAO?

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]

Why Indian Ocean Dipole (IOD) ?

Connection: ISM- Australia- E. African rainfall

IOD and ENSO have complementarily affected ISM during 1958-1997 (Ashok et al, 2001)

Positive Phase Negative Phase

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 1998 (Vecchi, et al. 2007)

Atmos-Ocean coupling was different during 1950s to 1997

Could climate change during that period have modified ISM?

[McPhaden and Zhang, 2004]

Solar signal on ISM detected using Solar peak year compositing

Van Loon and Meehl (2012) only used solar max-yr compositing on SLP and rainfall and

suggested sun enhances ISM

JJA Max-yr

JJA Min-yr

Is it true solar signal?

Anomaly

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?

Solar Peak years and ENSO

Ave

rage

SS

N

ENSO (DJF) [Roy and Haigh, 2010]

Solar compositing on SLP Min r.t. 1956-1997 Max r.t. 1956-1997

Min r.t. 1936-1975 Max r.t. 1936-1975

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 1956-1997.

During same period, land (+ve)-sea(-ve) SLP contrast favours ISM rainfall

Min r.t. 1936-1975 Max r.t. 1936-1975

Min r.t. 1956-1997 Max r.t. 1956-1997

ISM in solar compositing covering India

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

ENSO Signal in JJA1856-1955 1956-1997

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.

Solar Signal in JJA1856-2004

1856-1955 1956-1997

Using regression, no significant signal is detected around regions of Indian subcontinent.

This is true irrespective of the period considered

Some connections between Sun and ISM

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.

+ve ENSO index +

-ve ENSO index *

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.

ISM and ENSO in CMIP5 models

-ve anomaly in box for both cases.

(Bollasina, et al

2011, Science)

5-yr running mean JJAS(relative 1940- 2005) over central-N India (box)

Observation-CRU : black; Model-all forcing: red

Observation-CRU Model (NOAA GFDL CM3) all forcing

Comparison with Observation and another model - last 50 years

Temporal Behaviour

SpatialBehaviour

Spatial Pattern (Historical Run) Precipitation Anomaly w.r.t. (1986-2005)

No consistent pattern – even in box (starting from +ve to –ve shown)

Temporal Pattern (Historial Run)-last 50 years Precipitation Anomaly w.r.t. (1940-2005)

Better results 11 year running meanDecreasing trend : inmcm4, Access1-0, MIROC-ESM, NorESM1-MIncreasing : IPSL-CM5B-LR, MPI-ESM-LR, MPI-ESM-P, IPSL-CM5A-MR

1

1

1

2

2

1

2 2

Analysis: Historical +rcp (w.r.t 1985-2005) Rainfall (box), 11 year running mean, blue rcp scenario.

• 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.

2 1

2

1

SST(Nino 3.4 in JJAS): rcp (w.r.t 1985-2005)

3

1

2

• Model with least trend in Nino • Model high variable Nino • All models rising trend in rcp, exception 3

12

• -ve correlation - both in historical (I) and rcp (II)- after removing trend.• True in the box region as well.

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

-.15 .10 .07 -.20

-.78 - 0.71 -.47 -.40

-.23 -0.23 -.03 -.10

-.44 -0.39 -.37 -.27

-.29 -0.16 -.11 -.15

-.26 -0.30 -.24 -.39

-.66 -0.54 -.60 -.50

-.35 -0.50 -.33 -.52

-.49 -0.65 -.26 -.31

Hydrological Cycle in CMIP5 models and ISM

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.

Global Hydrological Cycle : CMIP3(Vecchi and Soden, 2007)

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.

Hydrological cycle and ISM:CMIP5

JJAS

Annual

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.

Atm water and temp Precipitation, vert. vel and temp

Precipitation, vert. vel and temp (India)

Ppt, vert. vel and temp (Globe)

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.

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.