incompass: interaction of convective organisation with ... · organisation with monsoon...
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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
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