Scafetta on UHI

By Rud Istvan – Re-Blogged From WUWT

Detection of UHI bias in China climate network using Tmin and Tmax surface temperature divergence

Nicola Scafetta a, Shenghui Ouyang b

Abstract Near-surface temperature records show that China warmed by about 0.8 °C from 1950 to 2010. However, there exists an ongoing debate about whether this warming might have been partially due to urbanization bias. In fact, homogenization approaches may be inefficient in densely populated provinces that have experienced a significant urban development since the 1940s. This paper aims to complement previous research on the topic by showing that an alternative approach based on the analysis of the divergence between the minimum (Tmin) and maximum (Tmax) near-surface temperature records since the 1940s could be useful to clarify the issue because urban heat island (UHI) effects stress the warming of nocturnal temperatures more than the diurnal ones.

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New USGS Study Shows Heat Retaining Concrete and Asphalt Have Encroached Upon US Climate Stations

By Anthony Watts – Re-Blogged From http://www.WattsUpWithThat.com

A new study from USGS by Keven Gallo and George Xian verifies what we’ve already learned and published on via the Surface Stations project; that concrete and asphalt (aka impervious surfaces) have increased near weather stations that are used to monitor climate. In this case, it is the much studied USHCN, that climate network I presented a poster on at AGU 2015. Details here.

What is most important about this paper is that it quantifies the percentage of stations that have had increased amounts of impervious surface area getting closer to the stations. As I have long since maintained, such things act as heat sinks, which increase the night-time temperature when they released the stored energy from the sun that was absorbed during the day as infrared, warming the air near the thermometer, and thus biasing the minimum temperature upwards.

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Flooding And Planning: We Don’t Need To Live Near Rivers Anymore

By Dr Tim Ball – Re-Blogged From http://www.WattsUpWithThat.com

Petula Clark sang, “Don’t sleep in the subway, darling. Don’t stand in the pouring rain.” More helpful advice would urge, “Don’t live in the floodplain, darling. Don’t you know it’s pouring rain?” It’s called a floodplain for a reason. The dangers of flooding mostly involve people living in dangerous places. Why are people allowed to live in these regions without being forced to accept full responsibility for their actions? They are encouraged by governments and insurance that enable bad practices, questionable, and unnecessary behavior.

There was a time when living near a river was important for transport, water supply, waste removal, and even food supply. We don’t need to live close to rivers or at least within the area identified as the floodplain. If you live there, flooding is inevitable, even if flooding protection is in place. In fact, the protection creates a false sense of security. Inevitably the protection will fail through neglect, accident, or water levels that exceed the design capacity.

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Theory and the Global Warming Pause

cropped-bob-shapiro.jpg   By Bob Shapiro

Global temperature is an average of the daily high and the daily low (Tmax & Tmin). If only it were that simple.

There are very few official weather stations on land, and a laughably low coverage for the 3/4 of the earth’s surface covered by water. To make up for this lack of data, climatologists divvy up the earth’s surface into grid boxes (5 degrees X 5 degrees), and “infill” any (A LOT) of the missing data, using readings from stations up to 1200 Km away – that’s like filling in Kansas from a thermometer in Galveston, TX.

Climatologists also do “quality control” on the instrumental data to adjust for station moves or other perceived problems. You would think that all the adjustments pretty much would cancel each other out, but if you think that is what actually happens, then I have a bridge I’d like to sell you. The reality is that older data is adjusted downward while newer data gets adjusted upward – and the same data gets adjusted several times, always in the direction which would make the trend look to be more steeply warming.

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