MOCA-09 Montréal, Canada

Snow-monsoon teleconnections: testing competing mechanisms using idealized snow forcing conditions in a GCM

Andrew Turner1 & Julia Slingo2,1

1NCAS-Climate, University of Reading, UK

2Met Office, Exeter, UK

Historical perspective

Long history of using Himalayan snow to forecast ISM rainfall, dating back to Blanford (1884).

More recent work examines the influence of snow from Himalaya, but also West/North Eurasia, both with negative teleconnections.

Difficulties involve ENSO, snow measure used, region of influence:– Himalaya: how can such a small region perturb

the monsoon?– West/North Eurasia: what is the remote

mechanism?

right: using west Eurasia snow index

HadCM3 snow composite difference evolutions (1050yr control run)

MAM snow (kg/m2)

Apr 1.5m temp & Z500

May 200hPa wind

JJA 850hPa wind & rain

MAM snow (kg/m2)

May 200hPa wind

JJA 850hPa wind & rain

left: using Himalaya snow index

Apr 1.5m temp & Z500

Composite evolutions generated from heavy minus light snow years followed by weak minus strong monsoon rainfall under neutral ENSO conditions

In HadCM3, weak monsoon summers can be preceded by heavy Eurasian OR Himalayan snow.

Aims of this study

Can the Hadley Centre model simulate snow-monsoon teleconnections?

Which region dominates?

Can we make a detailed assessment of the mechanisms involved?

AGCM ensemble experiment design

Hadley Centre Land-atmosphere model HadAM3 (3.75˚x2.5˚xL30)

Snow forcing derived from the HadCM3 coupled run; using climatology with ±2σ anomalies in FMA snow indices over:• Eurasia (30-110˚E, 50-65˚N) WNEur• Himalaya (67.5-100˚E, 27.5-40˚N) HimTP

Climatological SST forcing (to avoid ENSO).Experiments initialized 1Nov, 6 month spin-up with snow depth updated hourly to chosen forcing.

32 member ensemble begun 1Apr for 8 months [initial conditions from the 15Mar–16Apr period].

Snow no longer constrained [free to melt].

HimTP ensemble results

These diagrams show ensemble mean differences between HimTPpos and HimTPneg experiments.

Snow amount

temperature

Z500 UV200 UV850

April May June

HimTP ensemble results

Himalaya AGCM ensemble results consistent with coupled run composites.

Strong Himalaya snow forcing weakened Indian monsoon (June).

precip

June July August

WNEur ensemble results

These diagrams show ensemble mean differences between WNEurpos and WNEurneg experiments. UV200 UV850Z500

temperature

Snow amount

MayApril June

WNEur ensemble results

WNEur results contrary to coupled run composites.

Strong Eurasia snow forcing strong Indian monsoon (June) due to contamination from induced Himalaya anomaly.

Both exp. support the Blanford hypothesis.

WNEur

HimTP

June July August

HimTP sensitivity tests

To test mechanism, use sensitivity tests over HimTP:

HimTP1000 (1000kg/m2 snow = 4m)

HimTPzero (0kg/m2)

Qualitative agreement with standard HimTP experiment but larger in magnitude.

Significant weakening of the early Indian monsoon.

Redistribution of East Asian monsoon rainfall.

Effect on tropospheric temperature (600-200hPa, 40-100˚E)

Heavy snow forcing over Himalaya/TP cools mid-troposphere 15-40˚N at Indian longitudes by around 3˚C.

Measure of reversal of meridional TT gradient (Xavier et al., 2007): 600-200hPa mass weighted temperature over 40-100˚E, difference between 5-35˚N and 15˚S-5˚N regions

(from Xavier et al. 2007)

Difference in character between ENSO and snow effects on the monsoon: growing vs. decaying modes

The Blanford mechanism in HimTP

D

ow

nw

ard

The effect of snow albedo

Effect of snow albedo as part of the Blanford mechanism is tested in a further version of HimTP1000.

HimTP1000sfa: snow albedo over HimTP set to snow-free value for that region.

Albedo: 0.67 0.20 averaged over HimTP.

The effect of snow albedo

Do

wn

ward

The effect of snow albedo

Much more rapid snow melt, reduced reflected shortwave, sensible heating over HimTP reduced by 50% compared to HimTP1000 ensemble.

Reduced tropospheric cooling compared to HimTP1000. Remaining cooling caused by reduced upward longwave (insulating effect of snow).

Summary

Coupled model (HadCM3) can simulate weak Indian monsoon following strong spring snow forcing in either HimTP or WNEur regions.

Tests with HadAM3 AGCM show that HimTP is dominant in this model, supporting the Blanford hypothesis.

Snow albedo plays a crucial role.

Model bias may inhibit teleconnection from further north.

Similar teleconnection in coupled (mixed-layer) model; air-sea feedbacks need further exploration.

Thank you!

a.g.turner@reading.ac.uk

Please see my poster this afternoon:

“Uncertainties in future projections of extreme precipitation in the Asian monsoon regions”