Dr Pankiewicz's presentation - NERC

Atmospheric modelling of the 

monsoon: key research issues 

George Pankiewicz, Met Office, MoES/NERC workshop: February 2013 

Thanks to Gill Martin, Richard Levine, Sean Milton, Stu Webster, Andrew Turner, 

Nicholas Klingaman, Stephanie Bush, Gopal Iyengar, E.N. Rajagopal, Ashis Mitra 

© Crown copyright Met Office

Understanding and evaluating 

monsoon processes 

• The monsoons: still a challenge for modelling on a 

range of timescales 

• Known sensitivities: 

• convection and boundary layer parametrisation 

• cloud microphysics 

• land surface properties 

• orography 

• model resolution (atmosphere, ocean, horizontal, vertical) 

• coupled model SST biases 


Rapid development of biases: 

1 - 5 days 

1-day NWP forecast 

5-day forecast error 

• Rapid growth of west equatorial Indian Ocean precipitation bias in 

the first 5 days 

• Also lack of a wave-like pattern in the low-level circulation. 

• Rapid growth of error suggests that it is a direct impact of 

parameterisations and not due to a non-linear feedback process 

operating on longer time-scales. 


Common systematic biases: CMIP5 

CanESM2 CNRM-CM5 CSIRO-Mk3-8-0 inmcm4 

IPSL-CM5-LR MIROC-ESM-CHEM MIROC-ESM MIROC4h 

Few models can simulate the major precipitation 

centres and their interannual variation. 

Equatorial Indian Ocean precipitation / SST 

biases are common in many models. 

MIROC5 HadCM3 HadGEM2-CC HadGEM2-ES 

Many models underestimate rainfall over India. 

Himalayan rainfall bias common in many models. 

MPI-ESM-LR MRI-CGCM3 NCAR/CCSM4 NorESM1-M 


The role of Arabian Sea SST bias 

Strong monsoons depend on Arabian 

Sea moisture in observations 

• Large systematic cold SST 

errors in Arabian Sea reduce 

monsoon rainfall (up to 30%) 

and delay onset by weakening 

local evaporation. 

• Cold Arabian Sea SST errors 

develop in winter due (in part) 

to excessive winter monsoon 

northerlies. 

• This tends to reduce Indian 

rainfall, particularly in June. 

• Inter-model spread of early 

monsoon rainfall for future 

scenarios is more constrained 

in CMIP5 models with large 

cold Arabian Sea SST biases. 

© Crown copyright Met Office 

Idealised representation of SST bias 

Coupled model SST biases and the effect of (1) & (2) on 

HadGEM3-A (rainfall and vertically-integrated moisture flux) 

Richard Levine

Convection time-series analysis 

Suppression of intense deep convection results in less intermittent behaviour 

at time-step level allowing more continuous rainfall 

Over India there are improvements consistent with feedback from suppressing 

equatorial convection and direct improvement of diurnal cycle (rainfall at night) 

Equatorial Indian Ocean (67.5E,0N): 

deep convective precip 

Control 

Test 

Indian land (81E,21N): deep (solid) / 

mid-level (dotted) convective precip 


Time-step 

Time-step

INTEGRATE Progress 

End of Year 1 

Goal: Build coupled atmosphere-ocean-ice-aerosol model 

Reduce Model Systematic Errors 

• Model development ► evaluation ► processes ► development 

• GA4.0 Released 

• UM User workshop Review & Report 

• ENDGame 

• Improved Variability - Tropics & Extratropics. 

• Improved Stability. 

• Warm Bias at TTL - issue for Stratospheric Chemistry 

• GA5.0 Physics? 

• Convection - Entrainment increase - Improved MJO 

• 5A GWD 

• GO & GSI 5.0 under development

Frequency-wavenumber spectra of 

precipitation 

Preliminary GA5.0 Package (GA4.0#73.4.1) 

Observed 

N96 ENDGame N96 GA4.0 package AMIP GA4.0#73.4.1 


Limited Area Model 

Configurations 

Model 

Climate 

NWP like 

Like 

4 km 2.2 km 1.5 km 

Horiz Grid 50 x 50 200 x200 1150 x 1150 2000 x2000 3000 x 3000 

Vertical Levels L70, 80 km lid L118, 78 km lid 

Timestep (s) 1200 600 10 

Convection Parametrized Explicit 

Sub-grid mixing 1D BL 3D Smagorinsky 

Critical RH 

92% (z=0) ⇒ 80% 

(z≥3km) 

99% 

4500km 

4500km 

• All LAMs span the same domain, nested directly inside the global model 

• All LAMs initialised with the same data (the 18th 00z global model flow 

fields) and thereafter free running 

• All LAMs forced with the same lateral boundary conditions, and with 

SSTs updated daily using Met Office OSTIA analyses 

• Configuration therefore allows as clean an assessment of the impact of 

horizontal resolution as possible 


1.5 km model 

NWP model 

Climate Model 

NWP & climate models show typical conv. parameterisation behaviour: 

• Rainfall grid-scale in nature in both time and space. 

• Strong diurnal cycle over land peaking at 07Z (local noon) rather than 

early evening 

• Plot to right indicates far too little rainfall over land relative to TRMM 

• Too much rainfall over the sea (not shown) 

All these features much improved in 1.5 km explicit convection simulation


Future challenges 

• Representation of flow and rainfall over steep orography: 

• What does it really look like? Are the observations good enough? 

• High-resolution (convection-permitting) regional simulations may help 

• Coupled model SST biases – errors in mean state feed back on 

monsoon climatology, variability and teleconnections 

• Should these be the priority? (e.g. equatorial Indian Ocean) 

• Climatology versus variability 

• Hard to get both right 

• More wide-spread, high frequency rainfall observations (land & ocean) 

• Poor ENSO - monsoon teleconnections 

• Influenced by SST biases and overall tropical circulation biases 

• Incorrect convection-dynamics coupling and/or incorrect diabatic heating? 

• Future projections 

• More complex processes needed to simulate complex feedbacks? 

• But need to reduce basic model systematic biases

Thank You 


GA4.0 

GA5.0 - GA4.0 

ENDGame/GA5.0 Impacts: 

Tropical Water Cycle 

JJA AMIP 

GA4.0 - MERRA GA5.0 - MERRA 

Hadley Circulation - 

Less ascent on equator 

Reduced Errors 

GA4.0 

GA5.0 - GA4.0 

GA4.0 - GPCP 

GA5.0 - GPCP 

Tropical Precipitation - 

Reduced ITCZ precipitation 

Reduced Indian Ocean Precip 

& 

Increase over India 

Preliminary GA5.0 Package 

(GA4.0#73.4.1)

Process Evaluation Groups 

1. African processes 

2. MJO & its teleconnections 

3. South Asian Monsoon 

4. Blocking and Stormtracks 

5. Tropical Cyclones 

6. ENSO & its teleconnections 

7. Ocean Biases (North Atlantic & Southern Ocean Focus) 

8. Clouds, Radiation and Light Rain 

9. Continental Surface Temperature Biases 

10. Conservation 


Summary 

• Representing monsoon systems, including variability and response to 

climate change, is an on-going challenge for modelling groups 

• Many common errors are forced locally (in time and space) although 

some are related to biases which develop during the preceding winter 

(e.g. Arabian Sea SST cold bias) 

• Systematic errors develop rapidly (within 2 weeks for atmosphere and 

first month for SSTs) 

• Sensitivity to model resolution is weaker than sensitivity to model 

physics 

• The additional functionality afforded by including extra model processes 

can be offset by interactions with other model errors 

• Observations of cloud characteristics and rainfall intensity, on sub-daily 

timescales, over Indian land and ocean, would help greatly

Dr Pankiewicz's presentation - NERC

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