Case study of an urban heat island in London, UK: Comparison between observations and a high resolution numerical weather prediction model
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1 Case study of an urban heat island in London, UK: Comparison between observations and a high resolution numerical weather prediction model Siân Lane, Janet Barlow, Humphrey Lean With thanks to Christos Halios, Aurore Porson, Sylvia Bohnenstengel, and John Lally
2 Why is this interesting? Over 80% of the UK population live in urban areas (Office for National Statistics, 2010). Lots of people rely on forecasts of urban weather, but cities can be difficult to Urban areas have their own microclimate (urban represent in forecast models. heat island, increased pollution etc.) With a grid-spacing of ~1 km, NWP models like the UK Met Office s UKV model are able to resolve some observations to assess their accuracy features of cities, but many features which have a significant effect on and the impact. boundary layer are still too small to be resolved, so must be parameterized. Urban schemes must be compared with
3 Met Office 1.5 km model grid Regent s Park Hyde Park Marylebone Road
4 Representation of urban surfaces in the UKV Separate Roof and canyon tiles Accounts for incanyon radiation exchange Roof thickness and canyon dimensions can be altered to suit local morphology MORUSES (new) (Porson et al. 2010) Best scheme (operational) (Best 2005) Single slab with defined roughness and heat capacity Radiatively coupled with soil (like a vegetation canopy) Simpler to implement
5 Science questions Can the UKV (1.5 km version of the Unified Model) accurately represent the structure of the urban boundary layer? Can the UKV reproduce the diurnal behaviour of temperature, heat fluxes etc. in London? Does the new heat flux scheme of MORUSES produce more physically realistic behaviour? What is causing any differences noted between the UKV and observations?
6 Case study (30/09 01/ ) Wind Chosen because no clouds or fronts affected the area simple(ish) situation. Conditions would be expected to produce a strong UHI, and remained the same for several days. The UBL would be strongly influenced by the urban surface energy balance in these conditions Chilbolton (~100 km WSW of London) used as a rural reference site
7 Height (m) Day time BL depth is underestimated in both rural and urban areas Time (UTC) London, Chilbolton model = pale lines
8 Using MORUSES increases sensible heat flux and reduces model time lag. Rooftop BT Tower (Model pre-scaling denoted by plain line)
9 Rural site shows good daytime match, but model is too negative at night Chilbolton (1.5 m)
10 Improvement in H doesn t translate to an improvement in temperature Rooftop (21 m) BT Tower (190 m) - scaled Suppressed diurnal cycle at rooftop site is similar to modelled rural behaviour. Delayed warming and cooling seems to be an urban effect.
11 Temperature (K) Rural site shows similar suppressed diurnal cycle, but no lag Chilbolton Time (UTC) Observations, operational UKV, UKV + MORUSES
12 Model captures structure of the wind field well, but tends to underestimate speed slightly Time (UTC) Time (UTC)
13 T UKV T BT (K) Long-term comparison between model and observations doesn t show a clear difference Temperature (150 days) Underestimate during afternoon? Time (UTC)
14 Wind UKV Wind BT (ms -1 ) Long-term comparison between model and observations doesn t show a clear difference Hourly average wind speed (150 days) The case study results may only apply in similar conditions the longer dataset requires filtering to determine the affects of different meteorological conditions Time (UTC)
15 Conclusions Some features of the UBL are reproduced by the model (higher BL top in urban area, wind field structure), but some are not (BL growth rate and maximum depth). This is influenced by the background state of the model. The operational UKV underestimates heat flux in London, and produces a phase lag of ~ 1-2 hours in the diurnal cycles of T and H. (heat capacity too large?) Using MORUSES improves the modelled values and timing of H, but only gives a slight improvement in T. These conclusions cannot be generalised to the whole dataset filtering by different conditions is required sian.lane@metoffice.gov.uk
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