Spatial and temporal variability in energy and water vapour fluxes observed at seven sites on the Indian subcontinent during 2017

Bhat, G. S.; Morrison, Ross; Taylor, Christopher M.; Bhattacharya, B. K.; Paleri, Sreenath; Desai, Devansh; Evans, Jonathan; Pattnaik, Sandeep; Sekhar, M.; Nigam, Rahul; Sattar, Abdus; Angadi, S. S.; Kacha, DJ; Patidar, Anil; Tripathi, S. N.; Krishnan, K. V. M.; Sisodiya, Anshul

doi:10.1002/qj.3688

Cited by 18 publications

(29 citation statements)

References 28 publications

(27 reference statements)

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“…Based on radiosonde data obtained during the 2006-2009 monsoon seasons, they estimate a difference in mean temperature and humidity between wet and dry spells of 1-2 K and 0.5-2 g/kg in the lower troposphere. Such intraseasonal land-atmosphere feedbacks are consistent with new observations from the Indian surface flux network set up as part of INCOMPASS (Bhat et al, 2019). These confirm the existence of substantial intraseasonal fluctuations in evaporative fraction at drier sites.…”

Section: Discussionsupporting

confidence: 85%

A case‐study of land–atmosphere coupling during monsoon onset in northern India

Barton

Taylor

Parker

et al. 2019

Quart J Royal Meteoro Soc

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This article presents a land–atmosphere case‐study for a single day during monsoon onset, incorporating data from a research aircraft, satellite products and model outputs. The unique aircraft observations reveal temperature and humidity contrasts of up to 5 K and 4 g/kg in the planetary boundary layer induced by spatial variations in soil moisture. Both antecedent rain and irrigation were found to be drivers of this atmospheric variability. There is also evidence of soil moisture‐induced mesoscale circulations above some surfaces. This is the first time such responses have been observed in situ over India. Soil moisture‐driven temperature anomalies are larger than those found in previous observational studies in the African Sahel. Moreover, irrigation in the region is extensive, unlike in the Sahel, and has a similar atmospheric effect to antecedent rainfall. This implies that historical changes in irrigation practices are likely to have had an important influence on mesoscale processes within the Indian monsoon. We also examine evidence linking soil moisture and cloud formation. Above wetter soils we observed a suppression of shallow cloud, whilst the initiation of deep convection occurred mostly in areas affected by wet–dry soil moisture boundaries. To investigate the impact of soil moisture heterogeneity on large‐scale wind flow, three model depictions of the day are assessed: the European Centre for Medium‐Range Weather Forecasts ERA‐Interim and ERA5 reanalyses, and a high‐resolution (1.5 km) simulation generated using the Indian National Centre for Medium Range Weather Forecasting regional convection‐permitting Unified Model. We find evidence indicating surface flux uncertainties in the models lead to ∼3.5 hPa anomalies in the monsoon trough. This does affect the simulation of monsoon circulation and rainfall. Better representation of mesoscale land–atmosphere coupling is likely to improve the depiction of convection within weather and climate models over India.

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Section: Discussionsupporting

confidence: 85%

A case‐study of land–atmosphere coupling during monsoon onset in northern India

Barton

Taylor

Parker

et al. 2019

Quart J Royal Meteoro Soc

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“…The main ground instrumentation implemented by INCOMPASS is a series of eight eddy covariance flux towers spread across the country. Flux tower locations (listed in Table and shown in Figure ) were chosen to facilitate mapping across various hydroclimatic zones of India and different agricultural types (a brief description of the surrounding surface is found in the table; for more detail see Bhat et al ., ).…”

Section: Ground and Upper‐air Instrumentationmentioning

confidence: 97%

“…The main ground instrumentation implemented by INCOMPASS is a series of eight eddy covariance flux towers spread across the country. Flux tower locations (listed in Table 2 and shown in Figure 6) were chosen to facilitate mapping across various hydroclimatic zones of India and different agricultural types (a brief description of the surrounding surface is found in the table; for more detail see Bhat et al, 2020). Four of the towers (Berambadi, Chandan, Nawagam and Pusa) consisted of augmenting existing agro-meteorological (AMS) stations operated by the Indian Space Research Organisation (ISRO) with fast-response eddy covariance systems, while the remaining four (Bengaluru, Bhubaneswar, Dharwad and Kanpur) were new installations.…”

Section: Eddy Covariance Flux Towersmentioning

confidence: 99%

“…This article will outline the design of the INCOM-PASS field campaign and catalogue the flights performed and instruments installed. Other related works will discuss the first results from the network of eddy-covariance flux towers installed by INCOMPASS (Bhat et al, 2020) and a detailed description of the thermodynamic and dynamic structure of the atmosphere associated with the portion of our field campaign in southern India (Fletcher et al, 2020). In addition to describing the INCOM-PASS field campaign, in this study we will use airborne, ground and remote-sensing data to test a hypothesis suggested by Parker et al (2016) on the evolution of the dry intrusion during the progression of the monsoon onset in 2016.…”

Section: Introductionmentioning

confidence: 99%

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Interaction of convective organization with monsoon precipitation, atmosphere, surface and sea: The 2016 INCOMPASS field campaign in India

Turner¹,

Bhat²,

Martin³

et al. 2019

Quart J Royal Meteoro Soc

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The INCOMPASS field campaign combines airborne and ground measurements of the 2016 Indian monsoon, towards the ultimate goal of better predicting monsoon rainfall. The monsoon supplies the majority of water in South Asia, but forecasting from days to the season ahead is limited by large, rapidly developing errors in model parametrizations. The lack of detailed observations prevents thorough understanding of the monsoon circulation and its interaction with the land surface: a process governed by boundary‐layer and convective‐cloud dynamics. INCOMPASS used the UK Facility for Airborne Atmospheric Measurements (FAAM) BAe‐146 aircraft for the first project of this scale in India, to accrue almost 100 h of observations in June and July 2016. Flights from Lucknow in the northern plains sampled the dramatic contrast in surface and boundary‐layer structures between dry desert air in the west and the humid environment over the northern Bay of Bengal. These flights were repeated in pre‐monsoon and monsoon conditions. Flights from a second base at Bengaluru in southern India measured atmospheric contrasts from the Arabian Sea, over the Western Ghats mountains, to the rain shadow of southeast India and the south Bay of Bengal. Flight planning was aided by forecasts from bespoke 4 km convection‐permitting limited‐area models at the Met Office and India's NCMRWF. On the ground, INCOMPASS installed eddy‐covariance flux towers on a range of surface types, to provide detailed measurements of surface fluxes and their modulation by diurnal and seasonal cycles. These data will be used to better quantify the impacts of the atmosphere on the land surface, and vice versa. INCOMPASS also installed ground instrumentation supersites at Kanpur and Bhubaneswar. Here we motivate and describe the INCOMPASS field campaign. We use examples from two flights to illustrate contrasts in atmospheric structure, in particular the retreating mid‐level dry intrusion during the monsoon onset.

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“…In particular, how the land surface feeds back on the progression of the monsoon through its seasonal cycle, or during monsoon variability, is ripe for study since there is a distinct lack of observations particularly related to the land surface and its role in driving convection over India. For this reason, INCOMPASS set out to plug the gap by installing a series of flux towers across the country (early results described in Bhat et al ., 2020). By bringing together their outputs (e.g.…”

mentioning

confidence: 99%