Urban Climate: variations in density

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 climate scales

2

Sources: Crawford/Lindberg &Grimmond/Loridan/Grimmond


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


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)


Sky View Factors, London – Vary with density

Lindberg and Grimmond (2010) CR

Least Dense

Most Dense


Variations in Height: Buildings and Vegetation

1 km

Lindberg and Grimmond (2010) CR


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


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


- 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




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


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


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


1GLA Projections (2020) via http://data.london.gov.uk/dataset/london-borough-profiles




without building

Increase population according to 2020 projection – new buildings on vegetated lands


2. Increase population,

provide new buildings




without building

Increase population according to 2020 projection – new buildings on bare land



provide new buildings


provide new buildings by

developing bare land

instead of vegetated

areas



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)


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

http://www.sciencedirect.com/science/journal/22120955/18/supp/C

http://dx.doi.org/10.1016/j.uclim.2016.05.001

http://urban-climate.net/umep/UMEP

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

https://www.ons.gov.uk/

https://www.neighbourhood.statistics.gov.uk/

http://data.london.gov.uk/dataset/london-borough-profiles

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

Environment

Urban Climate: variations in density