A closer look at United States and global surface temperature change

Hansen, James E.; Ruedy, R.; Sato, Makiko; Imhoff, Marc L.; Lawrence, William T.; Easterling, David R.; Peterson, Thomas C.; Karl, Thomas R.

doi:10.1029/2001jd000354

Cited by 494 publications

(460 citation statements)

References 34 publications

(11 reference statements)

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“…In general the air temperature defined UHI has a strong diurnal cycle and is more important at night. The potential impact of UHI's on long term air temperature trend analyses is well known and a considerable effort has been made to correct air temperature biases when comparing UHI effects across different regions (e.g., Gallo et al, 1993;Hansen et al, 2001;Karl et al, 1988;Kukla et al, 1986).…”

Section: Introductionmentioning

confidence: 99%

Remote sensing of the urban heat island effect across biomes in the continental USA

Imhoff

Zhang

Wolfe

et al. 2010

Remote Sensing of Environment

1,189

686

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Impervious surface area (ISA) from the Landsat TM-based NLCD 2001 dataset and land surface temperature (LST) from MODIS averaged over three annual cycles (2003)(2004)(2005) are used in a spatial analysis to assess the urban heat island (UHI) skin temperature amplitude and its relationship to development intensity, size, and ecological setting for 38 of the most populous cities in the continental United States. Development intensity zones based on %ISA are defined for each urban area emanating outward from the urban core to the nonurban rural areas nearby and used to stratify sampling for land surface temperatures and NDVI. Sampling is further constrained by biome and elevation to insure objective intercomparisons between zones and between cities in different biomes permitting the definition of hierarchically ordered zones that are consistent across urban areas in different ecological setting and across scales. We find that ecological context significantly influences the amplitude of summer daytime UHI (urban-rural temperature difference) the largest (8°C average) observed for cities built in biomes dominated by temperate broadleaf and mixed forest. For all cities combined, ISA is the primary driver for increase in temperature explaining 70% of the total variance in LST. On a yearly average, urban areas are substantially warmer than the non-urban fringe by 2.9°C, except for urban areas in biomes with arid and semiarid climates. The average amplitude of the UHI is remarkably asymmetric with a 4.3°C temperature difference in summer and only 1.3°C in winter. In desert environments, the LST's response to ISA presents an uncharacteristic "U-shaped" horizontal gradient decreasing from the urban core to the outskirts of the city and then increasing again in the suburban to the rural zones. UHI's calculated for these cities point to a possible heat sink effect. These observational results show that the urban heat island amplitude both increases with city size and is seasonally asymmetric for a large number of cities across most biomes. The implications are that for urban areas developed within forested ecosystems the summertime UHI can be quite high relative to the wintertime UHI suggesting that the residential energy consumption required for summer cooling is likely to increase with urban growth within those biomes.Published by Elsevier Inc.

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Section: Introductionmentioning

confidence: 99%

Remote sensing of the urban heat island effect across biomes in the continental USA

Imhoff

Zhang

Wolfe

et al. 2010

Remote Sensing of Environment

1,189

686

View full text Add to dashboard Cite

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“…This number of components is also the one provided by the model selection criteria as shown later in Table 8. Indeed, as mentioned by Nguyen and McLachlan (2016), Hansen et al (2001) found that the data could be segmented into two periods of global warming (before 1940 and after 1965), separated by a transition period where there was a slight global cooling (i.e. 1940 to 1965).…”

Section: Temperature Anomalies Data Setmentioning

confidence: 88%

“…The data consist of n = 135 yearly measurements of the global annual temperature anomalies (in degrees C) computed using data from land meteorological stations for the period of 1882 − 2012. These data have been analyzed earlier by Hansen et al (1999Hansen et al ( , 2001 and recently by Nguyen and McLachlan (2016) by using the Laplace mixture of linear experts (LMoLE).…”

Section: Temperature Anomalies Data Setmentioning

confidence: 99%

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Robust mixture of experts modeling using thetdistribution

Chamroukhi¹

2016

Neural Networks

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Mixture of Experts (MoE) is a popular framework for modeling heterogeneity in data for regression, classification, and clustering. For regression and cluster analyses of continuous data, MoE usually use normal experts following the Gaussian distribution. However, for a set of data containing a group or groups of observations with heavy tails or atypical observations, the use of normal experts is unsuitable and can unduly affect the fit of the MoE model. We introduce a robust MoE modeling using the t distribution. The proposed t MoE (TMoE) deals with these issues regarding heavy-tailed and noisy data. We develop a dedicated expectation-maximization (EM) algorithm to estimate the parameters of the proposed model by monotonically maximizing the observed data log-likelihood. We describe how the presented model can be used in prediction and in model-based clustering of regression data. The proposed model is validated on numerical experiments carried out on simulated data, which show the effectiveness and the robustness of the proposed model in terms of modeling non-linear regression functions as well as in model-based clustering. Then, it is applied to the real-world data of tone perception for musical data analysis, and the one of temperature anomalies for the analysis of climate change data. The obtained results show the usefulness of the TMoE model for practical applications.

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“…The causal role of CO 2 as a greenhouse gas is derived from the well-understood physical properties of the CO 2 molecule. There is also positive feedback in that increased 1 The observed decline is shown in Figure 3.1 of the IPCC report (Solomon et al 2007) as a negative deviation from the 1961 to 1990 mean temperature, based on several data reconstructions (Brohan et al 2006;Hansen et al 2001;Lugina et al 2005;Smith and Reynolds 2005). warming of the oceans can cause a decrease in the saturation concentration of CO 2 in the ocean waters. As a result, CO 2 is released into the atmosphere from warming oceans and can cause further atmospheric warming.…”

Section: Interpreting Ice Core Datamentioning

confidence: 96%

Climate Change: Evidence of Human Causes and Arguments for Emissions Reduction

2011

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In a recent editorial, Raymond Spier expresses skepticism over claims that climate change is driven by human actions and that humanity should act to avoid climate change. This paper responds to this skepticism as part of a broader review of the science and ethics of climate change. While much remains uncertain about the climate, research indicates that observed temperature increases are human-driven. Although opinions vary regarding what should be done, prominent arguments against action are based on dubious factual and ethical positions. Thus, the skepticisms in the recent editorial are unwarranted. This does not diminish the general merits of skeptical intellectual inquiry.

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A closer look at United States and global surface temperature change

Cited by 494 publications

References 34 publications

Remote sensing of the urban heat island effect across biomes in the continental USA

Remote sensing of the urban heat island effect across biomes in the continental USA

Robust mixture of experts modeling using thetdistribution

Climate Change: Evidence of Human Causes and Arguments for Emissions Reduction

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