Effects of historical urbanization in the Brussels Capital Region on surface air temperature time series: a model study
R. Hamdi*, A. Deckmyn*, P. Termonia*, G. Demarée*, P. Baguis*, S. Vanhuysse**, E. Wolff** *Royal Meteorological Institute, Brussels, Belgium
**Free University of Brussels, Faculty of Sciences, IGEAT
[email protected]
ABSTRACT In the present study we examine the local impact of change in impervious surfaces in the Brussels Capital Region (BCR, Belgium) on trends in maximum, minimum, and mean temperatures between 1960 and 1999. Specifically, we combine data from remote sensing imagery and a land surface model including state­of­the­art urban parameterization, the Town Energy Balance scheme. In order to: (i) isolate effects of urban growth on near surface temperature independent of atmospheric circulations and (ii) be able to run the model over very long period without any computational cost restrictions, we run the land surface model in a stand­alone mode coupled to downscaled ERA­40 reanalysis data. We also consider BCR as a lumped urban volume and the rate of urbanization was assessed by estimating percentage of impervious surfaces from Landsat images acquired for various years. Model simulations show that: (1) the annual mean urban bias (AMUB) on minimum temperature is rising at a higher rate (slightly 3 times) than on maximum temperature, with a linear trend of 0.14°C and 0.05°C per decade respectively, (2) the 40­year AMUB on mean temperature is estimated to be 0.62°C, (3) 45% of the overall warming trend is attributed to intensifying urban heat island effects rather than to changes in local/regional climate, (4) during summertime, a stronger dependence between the increase of urban bias on minimum temperature and the change in percentage of impervious surfaces is found.
Modeling strategy
Estimation of mean urban bias on the temperature record of Uccle
The expansion of the built­up area was found to be the main factor in long­term changes in air temperatures (Huang et al., 2008; Shouraseni and Fei, 2009). Therefore, in this study we will update previous research on the BCR region by analyzing the local impact of change in impervious surfaces on long­term trends in mean and extreme temperatures between 1960 and 1999. Specifically, we combine data from remote sensing imagery and the newly developed surface scheme of Météo­France SURFEX (SURFace Externalisée) (Martin et al., 2007) including the multi­layer version of the Town Energy Balance (TEB) (see Hamdi and Masson (2008) for more details). In the present study: (i) we use SURFEX in a stand­alone mode, coupled to downscaled ERA­40 reanalysis data, (ii) we consider BCR as a lumped urban volume. ERA40
DOWNSCALING
m ambient temperature of the urban and the rural simulations. It can further be noticed that even if the forcing level using by the SBL scheme is relatively high (50 m), we may miss the vertical structure of the UHI that would be present at 50 m of height. However, at this level the UHI intensity is less than at the surface because of the rapid increase of temperature in the rural area during nighttime stable conditions where the UHI is best observed. Atmospheric forcing at 50 m
T, P, RH, Wind, Precipitation,
downward Radiation
10 km resolution
over Belgium
ADAPTATION
The urban bias on the temperature record of Uccle is estimated as the difference between the 2­
FORCING
SURFEX
Surface air
temperature
The Brussels Capital Region (BCR)
To isolate effects of urbanization on local near surface climate conditions, we performed model simulations according to two scenarios, which correspond to different states of urbanization: (i) the ''rural'' scenario represented a hypothetical situation with no urban areas inside the domain during the 40 years. Radiative and thermal properties of the vegetation cover (albedo, roughness length, emissivity, thermal inertia, leaf area index, etc.) are taken from the ECOCLIMAP database The focus of our study is the Brussels Capital Region, centrally located in Belgium, with a size of (Masson et al., 2003) and remained fixed through the simulation. 161.78 km2 and a registered population of 1 031 215 on January 1st 2007, estimated by the National (ii) the ''urban'' scenario represented the climate in the presence of urban areas using the evolution Institute of Statistics (INS). The area covered by the BCR is quite circular with a diameter of 12 km of surface cover fractions. For this run, the surface cover fractions are updated each year using a and the effects of urbanization variability dominate the topographic influence (orography is beneath linear interpolation. In this study, geometrical, thermal, and radiative properties of roofs, walls, and 150 m). The national recording station of the Royal Meteorological Institute (RMI) of Belgium is roads were averaged over available data (Hamdi and Masson, 2008, Trusilova et al., 2008) and situated some 6 km south of the center of the capital, in the Uccle suburban. The temperature time were set to values representing a typical midsize European city.
series has a long history, dating to 1833, and was homogenized for different sources of errors (Demarée et al., 2002) but not for urbanization of the station environment. Evaluation of the model
The 40­year annual mean urban bias on mean temperature at Uccle is 0.62°C.
Estimation of urban bias on the annual trend
To evaluate model performance, a comparison is made between the urban run and the routine observations of the Uccle ground station.
As indicated by the spacing between the curves, annual mean urban bias (AMUB) on minimum temperature is shown to be rising at a higher rate (slightly 3 times) than on maximum
temperature, with a linear trend of 0.14°C and 0.05°C per decade respectively.
Impact of urbanization of the station environment has not been assessed in this time series. The signal of urban warming of the city of Brussels is embedded together with other aspects as global warming within the temperature time series of Uccle. The Uccle mean annual time series together with the mean annual worldwide time series as given by the CRU/Hadley Center and by the NASA/GISS are shown. All values are calculated as deviations from the 1951­1980 period. An abrupt change in the Uccle time series is noted by the end of the 1980s. The aim of the present study is to quantify which part of this warm bias with respect to the global trend can be explained by urbanization of the city of Brussels.
Evolution of surface cover fractions: 1955­2006
Using the AMUB on mean temperatures, we now estimate that 45% (ratio between the linear trend of the AMUB and the urban scenario) of the overall warming trend is attributed to intensifying urban heat island effects rather than to changes in local/regional climate. This should correspond to ~0.63 °C of the 20th century warming trend of 1.4 °C in the Uccle series. Seasonal trend
Performance statistics for daily maximum TMAX, minimum TMIN, and mean TMEAN=(TMIN+TMAX)/2
temperature based on formulas by Wilmott (1982). RMSESYS and RMSEUNSYS are the
systematic and unsystematic root mean square error, respectively. With the advent of remote sensing methodology, it has become possible to monitor local urban climate changes associated with land use changes over rapidly expanding urban areas like BCR. The evolution of surface cover fractions over the study region was derived from Vanhuysse et al. (2006) study. This study aims to assess the evolution of the fraction of impervious surfaces in the BCR since the 1950s date of the acceleration of urban growth linked to widespread use of car as a new mode of transport. Correlation
Index of
agreement
Bias (°C)
TMAX
0.96
0.97
1.12
1.38
2.33
TMIN
0.96
0.97
-0.43
0.66
1.89
TMEAN
0.98
0.98
0.34
0.81
1.69
RMSESYS (°C) RMSEUNSYS (°C) This figure shows time series of monthly average daily mean temperature observed and simulated by the urban run, the term ''monthly'' refers to the average of mean daily data in that month. It can be seen that the model captures quite well the intraseasonal, interannual, and interdecadal variability. This figure demonstrates also that the model is able to produce a consistent climatology for the BCR area. The SMUB on minimum temperature shows increasing trends throughout the four seasons. Fitting coefficients of the linear trend are 0.15°C, 0.14°C, 0.14°C, and 0.11°C per decade for fall, spring, summer, and winter respectively. During summertime, the coefficient of determination is maximum (0.74), which indicates a stronger dependence between the increase of SMUB on TMIN and the changes in the percentage of impervious surfaces.
Winter
Summer
References:
The increase in impervious surfaces is very important since the 1950s, from 26% in 1955 to 47% in 2006, slightly a doubling. The largest increase (both in absolute and relative terms) occurs between 1955 and 1970. Then a slight decline in the upward trend is observed. Between 1985 and 1993, there was a decline in growth due to a crisis in the real estate market during the 1980s. After 1993, the increase in impervious surface becomes again very important, remaining so until now. The spatial distribution of the percentage of impervious surfaces in the catchment basins including BCR in 1955, 1970, 1985, and 2006, clearly reveals that as Brussels grows the Uccle observatory becomes surrounded by impervious surfaces with increasing percentage over time. In fact, the change in urbanization over time is smaller for a station which originally was established in a densely built­up area than for a station originally installed in a rural or only light urbanized environment that has experienced growth. Demarée, G. R., P. J. Lachaert, T. Verhoeve, and E. Thoen, 2002: The long­term daily central Belgium temperature (CBT) series (1767­1998) and early instrumental meteorological observations in Belgium. Climatic Change, 53, 269­293.
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Strongest gradients are found after sunrise (between 06:00 and 09:00 LT) with a rapid decrease of the urban bias which confirm the result presented in the top. Interestingly, during this period the urban bias is kept constant during the 40­year independently of the increase in the percentage of impervious surfaces.
There is less evidence for urban bias in the maximum temperature series. During daytime period (note that the night is longer during winter) the urban bias is very weak and is hardly modified with the increase in the percentage of impervious surfaces during the 40 years.