INCOMPASS: Interaction of Convective Organisation with Monsoon Precipitation, Atmosphere, Surface & Sea: 2015-2018 GS Bhat, AG Turner and many others

UpdatetoMonsoonsPanel,Sept.2016

  From Sperber et al. (2013) Climate Dynamics.

Modelbiases

  Large biases in CMIP3 and CMIP5 models

Mean JJAS precipitation (left) and bias versus GPCP obs (right)

INCOMPASSOBJECTIVESINCOMPASS update

INCOMPASSobjecCves

Grand objective: improve the skill of rainfall prediction in operational weather & climate models by better understanding/representation of interactions between land surface, boundary layer, convection, the large-scale environment and monsoon variability on range of scales

Specific objectives:

1a) document and evaluate characteristics of monsoon rainfall on sub-daily to intraseasonal scales, as influenced by surface, thermodynamic and dynamic forcing, as monsoon air moves from the ocean inland and across the subcontinent

1b) evaluate representation of these processes in MetUM at various resolutions, indicating priorities for model development

2) Quantify land surface properties & fluxes, using in-situ and remote sensing measurements, as they interact with the monsoon on hourly-to-monthly and km-to-continental scales

INCOMPASSobjecCves(cont’d)

Specific objectives:

3a) Quantify role of Indian land surface in progression of monsoon onset, and in monsoon variability (and relate it to role of ocean)

3b) Evaluate impact of improved land-surface representation on monsoon prediction and make recommendations for future land-atmosphere modelling strategy

4a) Evaluate influence of local and short-term structures in convection and boundary layer, on rainfall variability on intraseasonal and seasonal timescales, using observations, idealized models and a range of operational models

4b) Make recommendations for priorities in the parametrization of convective rainfall in the monsoon system

HOWTOACHIEVETHESEOBJECTIVES?INCOMPASS update

Combined field and modelling campaign

Field campaign involving aircraft, ground instruments, upper air measurements

High-resolution nested modelling

INCOMPASS2015-2018

  INCOMPASS: INteraction of Convective Organisation with Monsoon Precipitation, Atmosphere, Surface and Sea

  NERC/MoES Drivers of Variability in South Asia directed call

http://www.incompass.org.uk

Team

Personnel

Reading: Andy Turner + Arathy Menon

Met Office: Gill Martin, Stu Webster, Sean Milton +…

Leeds: Doug Parker, John Marsham + Jennifer Fletcher +…

CEH: Chris Taylor, Jon Evans, Danijel Belusic, Ross Morrison

Indian Inst. Science (IISc, Bangalore): GS Bhat, M Sekhar, PDRA

NCMRWF: Rajagopal, Mitra +…

IMD: Madan + many others

IIT Bhubaneswar: Sandeep Pattnaik

IIT Kanpur: S Tripathi +…

NAL: Mrudula, Venkatesh…

  ISRO: partnership with Bimal Bhattacharya

SpaCalvariaConsinthemonsoon

Monsoonprogression

GROUNDINSTRUMENTATIONINCOMPASS update

SurfacefluxobservaCons

(Koster et al., Science, 2004)

v  Huge area equipped for irrigation in northern India

v  Evidence in models for strong coupling between land and atmosphere in this region

v  Contrasts between wet and dry soils

Despite all these factors, measurements of the land and its interaction with the atmosphere are sorely lacking

Fluxtowers

N1=IIT Kanpur, installed

N2=Kabini/Berambadi (Karnataka), installed

N3=Dharwad (Karnataka), installed

U0=IIT Bhubaneswar (Odisha), installed

U1=Nawagam/Anand, semi-arid site (Gujarat), installed

U2=Jodphur/Jaisalmer, arid site (Rajasthan), installed

U3=Samastipur (Bihar), installed

U4=Sagar (MP), preparing equipment

IIT-Bhubaneswar

  New EC flux tower installed   Permanent vertical

precipitation radar installed 1 July 2016

  Permanent microwave radiometer (MWR) also installed, particularly for atmospheric temperature profiles

IIT-Kanpursupersite(~85kmLKO)

Flux tower: permanent installation; surface flux data currently going through QA in UK Lidar ceilometer: permanent installation; test data have successfully tracked height of cloud base Microwave radiometer: permanent, TBA Radiosonde receiving station: temporary, July 2016

Radiosondes

v  2400+ additional radiosondes to be distributed across India, operated by staff recruited by Prof. Bhat for IMD

v  Focus on 19 stations

v  Supplemented by intensive (~8/day) launches at Kanpur

RS will also supply information to data denial/OSSE experiments What benefit do additional or particular launches have for the analysis?

IMDLucknowvisit

AIRCRAFTCAMPAIGNINCOMPASS update

INCOMPASS–originalflightplans

Aircra`campaignschedule

v Maximum 40 days science flying

v  120 hours

Day-to-dayforecasts

gws-access.ceda.ac.uk/public/incompass/restricted/MetUM/

Flightsperformed

June23a`ernoonflight

  Going west across the Ghats

MulCpleflightobjecCves

v  Measuring fixed features of the monsoon v Across coasts, orography; across climatological gradients;

across known regions of irrigation; monsoon trough

v  Measuring contrasts in time: v Capturing diurnal cycle v From pre-monsoon to during the mature phase

v  In response to transient features v Recently wetted soils v Weather (dry intrusions; monsoon depressions)

v  Low-level flights (e.g. 500ft) to capture detail of “land atmosphere coupling”

v  Variations in boundary layer over different soil types

Orographyandland-seacontrastsv  By the time the mission arrives in Bangalore, a

consistent feature of the monsoon is well established v  Heavy, frequent rainfall upstream of the Western Ghats v  Rain shadow

LKOweatheropportunityflights

  E.g. to sample a monsoon depression that might be passing along the monsoon trough – depression flight completed on 7 July 2016

  High-level runs across the feature (e.g. depression)

BoBBLErendezvous

Overflight of Bae-146 on June 27th with RV Sindhu Sadhana

  Aiming for proximal measurements of atmospheric structure (INCOMPASS) and underlying ocean (BoBBLE)

  Measure boundary layer and tropospheric vertical profile over land and ocean rain shadow

  Measure contrasts from land to sea across the rain shadow

  Contemporaneous radiosonde and CTD launches from the ship

NESTEDMODELLINGINCOMPASS update

MetUMnestedmodellingsuite

v  For most model configurations: 5-35N; 50-100E

v  100m: to be selected based on interesting observed case studies

Othermodelling

CRM: LAM: GCM: Remote sensing

AMMA field studies

Earlier work Theory and process studies

HAPEX-Sahel (1992): Taylor and Lebel Taylor and Clark Taylor and Ellis

JET2000: Taylor et al. 2003: thermodynamic feedback, and evidence of rainfall response. Parker et al. 2005a/b, some evidence of dynamic response.

AMMA research flights: Soil moisture feedbacks exist and are significant Taylor et al. 2007, 2010 Dixon et al. 2012

Vegetation forcing of PBL and cloud demonstrated: Garcia-Carreras et al. 2010

Mechanisms of local feedback explained. CRM/LEM shows suppressed precip over forest. Garcia-Carreras et al. 2010,2011

Soil moisture triggers storms (1/8). Taylor et al. Nature Geo., 2011

UM at 4km can represent storm initiation. Gravity wave and soil moisture both necessary. Birch et al 2012

Taylor et al. Nature 2012: GCMs have wrong sign of feedback on afternoon rainfall.

Cascade soil moisture stats. (AMMA-2: in progress).

Hartley project in progress: how is mesoscale rainfall controlled by vegetation?

Parker 2008 models dynamics of coupling

Taylor et al. 2005: AEWs have a significant coupling with soil moisture.

Baldi/Dalu 2008

Composition controlled by mesoscale surface: Taylor/Stewart; Crumeyolle et al. ; Ferreira et al.

Albedo control on Sahara Messager et al. 2010 Marsham et al. (GERBILS ) Cuesta et al. ASL 2009

Bain et al. AEW (2011) involves soil moisture in model.

AMMA-UK land-atmosphere interaction studies 2005-2012. (Slide courtesy Doug Parker)

A solved problem? Surface state controls the daytime PBL, with convergence and instability on downwind edge of hot surface. This controls 1/8 of storm initiations in the region – a process which GCMs represent wrongly, although explicit-convection models capture it. At the same time, rainfall can be suppressed over cooler adjacent areas. Inversely, organised convection tends to propagate over available moisture, and rains more on wet surfaces. Synoptic AEWs have a soil moisture signal with evidence of feedback.

Surface data used to explain PBL response to rain: Kohler et al. 2010

MCS propagates towards soil moisture in COSMO model. Gantner and Kalthoff 2010

Observations - > -> - > -> - > -> - > -> - > -> - > -> - > -> - > -> Models

Theend

Thank you!

a.g.turner@reading.ac.uk