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Urban microclimate: overcoming obstacles to high density resilient cities– 6 January 2017
Urban Climate: variations in density
Sue Grimmond1, Helen Ward1, Fredrik Lindberg2, Andy Gabey1,
Christoph Kent1, Simone Kotthaus1, Ting Sun, Leena Jarvi3, Tom Kokkonen3, TRUC team
1 Department of Meteorology, University of Reading
[email protected] University of Gothenburg, Sweden 3University of Helsinki, Finland
Acknowledge: University of Reading, King’s College London, NERC/CEH Jon Evans, Mark Pelling (KCL), Will Morrison, Kjell
zum Berge
Funding from: NERC/Belmont TRUC, EU emBRACE, Met Office CSSP/Newton Fund, University of Reading, H2020
UrbanFluxes, EU BRIDGE,NERC ClearfLo
Urban microclimate: overcoming obstacles to high density resilient cities– 6 January 2017
Urban climate scales
2
Sources: Crawford/Lindberg &Grimmond/Loridan/Grimmond
Urban microclimate: overcoming obstacles to high density resilient cities– 6 January 2017
Distinctiveness of Urban Areas from a Meteorological Perspective
• Differences in surface cover
• Differences in surface morphology
• Additional anthropogenic sources of heat, water, other gases and particulates
Urban microclimate: overcoming obstacles to high density resilient cities– 6 January 2017
Variability in urban surface implications (across & between) cities for:
• Wind flow
• Dispersion
• Flux partitioning
• Boundary Layer height
• Air quality
• Surface runoff
• Human Comfort
• Flow regimes
• Radiation
Grimmond & Oke 1999 JAM 38, 1262-92 Stewart & Oke (2009)
Urban microclimate: overcoming obstacles to high density resilient cities– 6 January 2017
Sky View Factors, London – Vary with density
Lindberg and Grimmond (2010) CR
Least Dense
Most Dense
Urban microclimate: overcoming obstacles to high density resilient cities– 6 January 2017
Variations in Height: Buildings and Vegetation
1 km
Lindberg and Grimmond (2010) CR
Urban microclimate: overcoming obstacles to high density resilient cities– 6 January 2017
Plan area density
vegetation
buildings
Vegetation Varies Across the City
Lindberg & Grimmond
2011 Urban Ecosystems
London
Height
(m)Height
(m)
9N
5N
1N
3S
7S
9S
9N
9S
Urban microclimate: overcoming obstacles to high density resilient cities– 6 January 2017
Surface cover by borough
City of London
70% built
5% veg
Increasing built fraction (Paved + Buildings)Havering
24% built
70% veg
* inner
boroughs
Surface cover by borough
Ward and Grimmond 2017 in review
Urban microclimate: overcoming obstacles to high density resilient cities– 6 January 2017
- input information - - biophysical model - - outputs - - exposure - - index -
SUEWS
meteorology
surface characteristics
energy use
water use
indicator
(IND)
energy fluxes
water fluxes WRI
vulnerability
Overview
ScenariosAssess impact
Variations in Surface attributes – Modify wide range of Urban Climate Processes
Ward and Grimmond 2017 in review
Ward and Grimmond 2017 in review
Urban microclimate: overcoming obstacles to high density resilient cities– 6 January 2017
SUEWS
meteorology
surface characteristics
energy use
water use
indicator
(IND)
energy fluxes
water fluxes
• land cover
• surface materials
• building height
• population density
Data source: census 2011 (ONS)
- input information - - biophysical model - - outputs - - exposure
Important input information
Ward and Grimmond 2017 in review
Indicator (for days in July 2012) by borough
City of London
IND = 371
Increasing built fraction (Paved + Buildings)Havering
IND = 146
Bexley
IND = 141
• Increasing built fraction linked to increasing indicator
* inner boroughs
Ward and Grimmond 2017 in review
Islington
Impact of surface cover changeChange tree cover across London from 20% to 25%1
Increasing vegetation fraction
more vegetation,
lower exposure
City of London
Bexley Havering
Tower Hamlets
1GLA (2011) Branching Out: The future for London's street trees Ward and Grimmond 2017 in review
increased
exposure
risk
reduced
exposure
risk
Impact of population change
City of London1. Increase population
without building
Increase population according to 2020 projection1 no new buildings
Increasing vegetation fraction
1GLA Projections (2020) via http://data.london.gov.uk/dataset/london-borough-profiles
Ward and Grimmond 2016 in review
Impact of population change
City of London1. Increase population
without building
Increase population according to 2020 projection – new buildings on vegetated lands
Increasing vegetation fraction
2. Increase population,
provide new buildings
Ward and Grimmond 2017 in review
Impact of population change
City of London1. Increase population
without building
Increase population according to 2020 projection – new buildings on bare land
Increasing vegetation fraction
2. Increase population,
provide new buildings
3. Increase population,
provide new buildings by
developing bare land
instead of vegetated
areas
Ward and Grimmond 2017 in review
Urban microclimate: overcoming obstacles to high density resilient cities– 6 January 2017
Final Comments
• Urban density modifies wide range of surface characteristics
• Changes in surface characteristics modifies the urban climate (wide range of processes)
• Scale at which you determine these attributes – will change both surface characteristics and urban climate model results
• Critical models are evaluated with observations across the range of densities and meteorological conditions (seasons, extremes)
Urban microclimate: overcoming obstacles to high density resilient cities– 6 January 2017
Reference
• Please contact me ([email protected]) if you would like copies
• Ward HC, S Grimmond 2017: Using biophysical modelling to assess the impact of various scenarios on summertime urban climate across Greater London Landscape and Urban Planning(in review)
• Ward HC. S Kotthaus, L Järvi, CSB Grimmond 2016: Surface Urban Energy and Water Balance Scheme (SUEWS): development and evaluation at two UK sites Urban Climate 18, 1–32 http://dx.doi.org/10.1016/j.uclim.2016.05.001
• http://urban-climate.net/umep/UMEP
• Other publications: CO2 and Anthropogenic heat related in London
• http://www.met.reading.ac.uk/userpages/xv904931.php
Data sources
• ONS (https://www.ons.gov.uk/)
• NESS (https://www.neighbourhood.statistics.gov.uk/)
• London Datastore (http://data.london.gov.uk/dataset/london-borough-profiles), including
GLA population projections
• GLA (2010) London: Garden City?
• GLA (2011) Branching Out: The future for London's street trees
• GreaterQF model (Iamarino et al. 2011)
• SUEWS model (Jarvi et al. 2011, 2014; Ward et al. 2016)
• Observational data (Kotthaus & Grimmond 2014a, b; Ward et al. 2013)
Contains National Statistics data © Crown copyright and database right 2012
Contains Ordnance Survey data © Crown copyright and database right 2012
WRI
S Susceptibility
C Coping capacity
A Adaptive capacity
V = [S • C • A] • (1/3) Vulnerability [V]
E Exposure (normalised) (indicator)Pi Population of boroughPT Total population
WRI = V • [E • (Pi/PT) ] WRI world risk index
Beck et al. (2012)
Stutgartt
SUEWS