Urban signals in high-resolution weather and climate simulations: role of urban land-surface characterisation

Abstract

Two urban schemes within the Joint UK Land Environment Simulator (JULES) are evaluated offline against multi-year flux observations in the densely built-up city centre of London and in suburban Swindon (UK): (i) the 1-tile slab model, used in climate simulations; (ii) the 2-tile canopy model MORUSES (Met Office–Reading Urban Surface Exchange Scheme), used for numerical weather prediction over the UK. Offline, both models perform better at the suburban site, where differences between the urban schemes are less pronounced due to larger vegetation fractions. At both sites, the outgoing short- and longwave radiation is more accurately represented than the turbulent heat fluxes. The seasonal variations of model skill are large in London, where the sensible heat flux in autumn and winter is strongly under-predicted if the large city centre magnitudes of anthropogenic heat emissions are not represented. The delayed timing of the sensible heat flux in the 1-tile model in London results in large negative bias in the morning. The partitioning of the urban surface into canyon and roof in MORUSES improves this as the roof tile is modelled with a very low thermal inertia, but phase and amplitude of the grid box-averaged flux critically depend on accurate knowledge of the plan-area fractions of streets and buildings. Not representing non-urban land cover (e.g. vegetation, inland water) in London results in severely under-predicted latent heat fluxes. Control runs demonstrate that the skill of both models can be greatly improved by providing accurate land cover and morphology information and using representative anthropogenic heat emissions, which is essential if the model output is intended to inform integrated urban services.

High‐resolution global climate simulations: Representation of cities

“…Underestimation of Q H is also caused by the relatively large JULES–GL7.0 urban albedo used in HAD–1T (α = 0.18) compared to the observed value of 0.11 used in CTRL–1T (Table A1), leading to a reduction of energy input ( Q N ) through an increase of K ↑ (Figure S4a,c). In both configurations, Q H has a substantial phase delay (rise and peak times), as a result of the large thermal inertia (through C and z h ) of the urban slab impacting the temporal response of surface temperatures (Hertwig et al ., 2020). The phase delay, also present in the diurnal cycle of L ↑ (Figure S4b), impacts diagnostics like T air (Section 4.2).…”

“…Furthermore, modelled JJA daytime temperatures in central London are overestimated by up to 2 C. This is partially explained by the large heat capacity and roughness length for heat used in the scheme. The JULES two-tile canopy model MORUSES, with separate surface-energy balance calculations for roofs and street canyons, can improve this by explicitly modelling the bulk radiative, thermal and aerodynamic parameters as a function of building morphology (Hertwig et al, 2020). At grid-box scale, the fast response of the (insulated) roof tile to radiative forcing can partially offset the large heat storage and correspondingly delayed sensible heat flux of the canyon tile (Porson et al, 2010).…”

“…In these simulations, irrigation or other anthropogenic moisture sources are not modelled for urban or vegetation tiles. Following precipitation, the built surface sheds most of the water immediately (i.e., urban run‐off rates are large; Hertwig et al ., 2020) as the water‐holding capacity of the impervious tiles is limited (Best and Grimmond, 2016a). Hence, in most situations (i.e., in the absence of rainfall), the turbulent latent heat flux ( Q E ) is limited to the vegetated tiles, which did not receive any urban runoff.…”

“…β ≈ 0.54-0.33) and more spatially homogeneous. To illustrate the sensitivity of JULES surface fluxes to land cover and urban parameter choices, JULES is run offline mimicking the HadGEM3-PRIMAVERA GL7.0 setup at a site in central London (KCL) for which highquality flux measurements are available for 3 years (2011Ward et al, 2016, Hertwig et al, 2020. A description of JULES settings, parameters (Table A1) and observations are given in Appendix A.…”

“…Details about site characteristics and measurements are given in Grimmond (2014a, 2014b). For an overview of JULES forcing/evaluation variables see Table 5 in Hertwig et al (2020).…”