Detection of urban warming in recent temperature trends in Japan

Fujibe, Fumiaki

doi:10.1002/joc.1822

Cited by 167 publications

(114 citation statements)

References 35 publications

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“…For instance, long-term temperature data for Toronto show that the most recent increases in temperature are at urbanizing suburban stations (Mohsin and Gough, 2009). Recent warming in Japanese cities is largely attributed to regional temperature rises, but the urban signature amplifies where there is greatest population density (Fujibe, 2009). Clearly, any long-term changes in UHI behaviour should be interpreted on a case-by-case basis.…”

Section: Acknowledgementmentioning

confidence: 99%

Decadal variations in the nocturnal heat island of London

Wilby

Jones

Lister

2011

Weather

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Urban landscapes typically experience higher air temperatures than the surrounding countryside due to absorption of solar heat by the fabric of buildings and trapping of outgoing longwave radiation within enclosed spaces. There is also less evapotranspiration from paved areas compared with vegetated surfaces, leaving a greater fraction of solar energy for surface heating. Artificial heat sources from transport, industry, air conditioning and space heating can further contribute to local warming. Although such phenomena are well-understood, urban heat islands (UHIs) can be contentious because of their potential to corrupt estimates of global near-surface temperature trends (Parker, 2010). There are also concerns that UHIs may intensify under future climate conditions, thereby amplifying heat stress on urban residents and habitats (Wilby, 2008;McCarthy et al., 2010).Thanks to the pioneering work of Luke Howard (1833), the UHI of central London has probably attracted more attention than that of any other city. The earliest digitized temperature series for the London area originate from Kew Observatory (1881) (1931). These data show that the UHI varies over a range of timescales: on average peaking at night, towards the end of the week, and in summer (Wilby, 2003a;2008). The locus of the maximum temperature anomaly is known to be highly mobile as it drifts across central London in response to prevailing wind speed and direction (Graves et al., 2001; McGregor et al., 2006).London's UHI has also varied over recent decades. Using temperature differences between SJP (urban) and WIS (rural) during day and night, Lee (1992) found that the nocturnal UHI intensified during the period 1962-1989. The same index was used by Wilby (2003b) to show that the intensity of London's nocturnal UHI in summer increased by 0.12 degC per decade over the period . However, the most recent assessment suggests that temperatures at SJP have not increased any more than those at ROTH or WIS since 1907 (Jones and Lister, 2009). This is a finding of some consequence for urban planners and building designers; it further opens the debate about which meteorological stations can be included in global temperature datasets.The purpose of this article is to reconcile the apparent discrepancy between these studies by uncovering the causes of multidecadal variations in London's UHI. Accordingly, we investigate two groups of meteorological factors: first, potential influences of record length, homogeneity and quality on long-term trends, and secondly, possible changes in the frequency and properties of weather patterns favouring intense heat-island episodes. We pay particular attention to the summer nocturnal heat island because the climatic signature is strongest and has the greatest health implications at these times. Data and methodsWe employ both daily and monthly mean maximum (Tx) and minimum (Tn) temperatures measured at SJP and WIS. As the name suggests, SJP is not located in a highly developed area of central London and is known to be cooler than ...

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Section: Acknowledgementmentioning

confidence: 99%

Decadal variations in the nocturnal heat island of London

Wilby

Jones

Lister

2011

Weather

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“…These rural stations are not influenced by the SW-prevailing wind direction and therefore the impact of the city is negligible. Also, even if the urban heat island may not be zero in these small villages [11], they did not experience a rapid increase during the last century and therefore the ability of the urban infrastructure to prevent outgoing longwave radiation is not increasing and the anthropogenic heat input is stable. Table 1.…”

Section: Meteorological Weather Stationmentioning

confidence: 99%

Estimating Urban Heat Island Effects on the Temperature Series of Uccle (Brussels, Belgium) Using Remote Sensing Data and a Land Surface Scheme

Hamdi

2010

Remote Sensing

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Abstract:In this letter, the urban heat island effects on the temperature time series of Uccle (Brussels, Belgium) during the summers months 1960-1999 was estimated using both ground-based weather stations and remote sensing imagery, combined with a numerical land surface scheme including state-of-the-art urban parameterization, the Town Energy Balance Scheme. Analysis of urban warming based on remote sensing method reveals that the urban bias on minimum temperature is rising at a higher rate, 2.5 times (2.85 ground-based observed) more, than on maximum temperature, with a linear trend of 0.15 °C (0.19 °C ground-based observed) and 0.06 °C (0.06 °C ground-based observed) per decade respectively. The results based on remote sensing imagery are compatible with estimates of urban warming based on weather stations. Therefore, the technique presented in this work is a useful tool in estimating the urban heat island contamination in long time series, countering the drawbacks of a ground-observational approach.

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Section: Introductionmentioning

confidence: 99%

“…Urban anomaly in temperature trends is quite conspicuous at large cities in Japan (Fujibe 1995(Fujibe , 2008. Fujibe (2008;hereafter F08) analyzed the data of AMeDAS (Automated Meteorological Data Acquisition System) for 27 years, and found anomalous temperature trends even at slightly urbanized sites, with population density of less than 300 km 2 . The surface temperature can also be influenced by the microscale environment of the observation site (Hawkins et al 2004;Mahmood et al 2006;Runnalls and Oke 2006;Pielke et al 2007).…”

Section: Introductionmentioning

confidence: 99%

Relation between Long-term Temperature and Wind Speed Trends at Surface Observation Stations in Japan

Fujibe¹

2009

SOLA

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In order to detect microscale influences on observed surface air temperature changes, 29 years' data on the AMeDAS network were used to identify the relation between temperature trends and wind speed trends, which were regarded as reflecting the changes of site exposure. The results were found to differ according to time of the day and the mean wind speed of the site. For stations with relatively high mean wind speed ( 1.5 m s 1 ), a significant negative correlation is found between daytime temperature trends and wind speed trends. For stations with low wind speed (< 1.0 m s 1 ), evening temperature trends are negatively correlated with wind speed trends, whereas daytime temperature trends tend to be positively correlated with them. These facts give statistical evidence of the dependence of temperature trends on microscale environmental changes, with complications according to the degree of site exposure.

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Detection of urban warming in recent temperature trends in Japan

Cited by 167 publications

References 35 publications

Decadal variations in the nocturnal heat island of London

Decadal variations in the nocturnal heat island of London

Estimating Urban Heat Island Effects on the Temperature Series of Uccle (Brussels, Belgium) Using Remote Sensing Data and a Land Surface Scheme

Relation between Long-term Temperature and Wind Speed Trends at Surface Observation Stations in Japan

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