Earth Atmospheric Land Surface Temperature and Station Quality in the Contiguous United States

Muller, Richard A.; Wurtele, J. S.; Rohde, R. A.; Perlmutter, S.; Rosenfeld, Arthur H.; Groom, Donald; Wickham, Charlotte; Mosher, Steven W.

doi:10.4172/2327-4581.1000107

Cited by 11 publications

(9 citation statements)

References 15 publications

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“…A source of error in all these climate surfaces is certainly the use of stations with poor data, however it has been estimated that the use of low quality stations does not have major negative effects or bias the results (Muller et al , ); however, we think if data is of extremely poor quality surely the results could also be poor quality. Instead, the use of more stations improves interpolations, especially in complex climatic areas like Mexico, where a low number of stations may not reflect climatic variations (Daly et al , ; New et al , ).…”

Section: Discussionmentioning

confidence: 99%

An update of high‐resolution monthly climate surfaces for Mexico

Cuervo-Robayo

Téllez‐Valdés

Albores

et al. 2013

Intl Journal of Climatology

158

109

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Climate surfaces are digital representations of climatic variables from a region in the planet estimated via geographical interpolation techniques. Climate surfaces have multiple applications in research planning, experimental design, and technology transfer. Although high-resolution climatologies have been developed worldwide, Mexico is one of the few countries that have developed several climatic surfaces. Here, we present an updated high-resolution (30 arc sec) climatic surfaces for Mexico for the average monthly climate period 1910-2009, corresponding to monthly values of precipitation, daily maximum, and minimum temperature, as well as 19 bioclimatic variables derived from the monthly precipitation and temperature values. To produce these surfaces we applied the thin-plate smoothing spline interpolation algorithm implemented in the ANUSPLIN software to nearly 5000 climate weather stations countrywide. As an additional product and unlike the previous efforts, we generated monthly standard error surfaces for the three climate parameters, which can be used for error assessment when using these climate surfaces. Our climate surface predicted slightly drier and cooler conditions than the previous ones. ANUSPLIN diagnostic statistics indicated that model fit was adequate. We implemented a more recent error assessment, a set of withheld stations to perform an independent evaluation of the model surfaces. We estimate the mean absolute error and mean error, with the withheld data and all the available data. Average RTGCV for monthly temperatures was of 1.26-1.12 • C and 24.67% for monthly precipitation, and a RTMSE of 0.48-0.56 • C and 11.11%. The main advantage of the surfaces presented here regarding the other three developed for the country is that ours cover practically the entire 20th century and almost the entire first decade of the 21st century. It is the most up to date high-resolution climatology for the country.

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Section: Discussionmentioning

confidence: 99%

An update of high‐resolution monthly climate surfaces for Mexico

Cuervo-Robayo

Téllez‐Valdés

Albores

et al. 2013

Intl Journal of Climatology

158

109

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“…The remaining term ( ⃑ ) is the approximately timeinvariant long-term mean temperature of a given location, often referred to as the climatology. In our construction we treat this via equation [3] a function of latitude, altitude, and a smoothed local average calculated using equation [24]. As mentioned earlier, the latitude and altitude components account for about 95% of the structure.…”

Section: Climatologymentioning

confidence: 99%

Berkeley Earth Temperature Averaging Process

Rohde¹,

Muller

Jacobsen³

et al. 2013

Geoinfor Geostat: An Overview

Self Cite

342

357

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A new mathematical framework is presented for producing maps and large-scale averages of temperature changes from weather station data for the purposes of climate analysis. This allows one to include short and discontinuous temperature records, so that nearly all temperature data can be used. The framework contains a weighting process that assesses the quality and consistency of a spatial network of temperature stations as an integral part of the averaging process. This permits data with varying levels of quality to be used without compromising the accuracy of the resulting reconstructions. Lastly, the process presented here is extensible to spatial networks of arbitrary density (or locally varying density) while maintaining the expected spatial relationships. In this paper, this framework is applied to the Global Historical Climatology Network land temperature dataset to present a new global land temperature reconstruction from 1800 to present with error uncertainties that include many key effects. In so doing, we find that the global land mean temperature has increased by 0.911 ± 0.042 C since the 1950s (95% confidence for statistical and spatial uncertainties). This change is consistent with global land-surface warming results previously reported, but with reduced uncertainty. 3 IntroductionWhile there are many indicators of climate change, the long-term evolution of global surface temperatures is perhaps the metric that is both the easiest to understand and most closely linked to the quantitative predictions of climate models. It is also backed by the largest collection of raw data. According to the summary provided by the Intergovernmental Panel on Climate Change (IPCC), the mean global surface temperature (both land and oceans) has increased 0.64 ± 0.13 C from 1956 to 2005 at 95% confidence (Trenberth et al. 2007).During the latter half of the twentieth century weather monitoring instruments of good quality were widely deployed, yet the quoted uncertainty on global temperature change during this time period is still ± 20%. Reducing this uncertainty is a major goal of this paper. Longer records may provide more precise indicators of change; however, according to the IPCC, temperature increases prior to 1950 were caused by a combination of anthropogenic factors and natural factors (e.g. changes in solar activity), and it is only since about 1950 that man-made emissions have come to dominate over natural factors. Hence constraining the post-1950 period is of particular importance in understanding the impact of greenhouse gases.The Berkeley Earth Surface Temperature project was created to help refine our estimates of the rate of recent global warming. This is being approached through several parallel efforts to A) increase the size of the data set used to study global climate change, B) bring additional statistical techniques to bear on the problem that will help reduce the uncertainty in the resulting averages, and C) produce new analysis of systematic effects, including data selection bias, urban hea...

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“…S5ab). Although often capturing the general climatic trend (Muller et al 2013), stations are disregarded by CRU due to poor-quality data, gaps in records, or incomplete coverage of the full period targeted (from 1961 onwards; Harris et al 2009). Climate interpolations based on the selected six stations might thus not be able to capture all the regional heterogeneity.…”

Section: Notwithstanding the Availability Of Coarse-resolution Data mentioning

confidence: 99%

An empirically tested overlap between indigenous and scientific knowledge of a changing climate in Bolivian Amazonia

et al. 2017

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Existing climate data for the Bolivian Amazonia rely on observations from a few sparse weather stations, interpolated on coarse-resolution grids. At the same time, the region hosts numerous indigenous groups with rich knowledge systems that are hitherto untapped in the quest to understand local climate change. Drawing on an empirical dataset of climate change observations by an Amazonian native society, we assess the potential use of indigenous knowledge for complementing available climate data. We find indigenous observations to be robustly associated with local station data for climatic changes over the last five decades. By contrast, there are discrepancies between gridded climate data and both indigenous observations and local station observations. Indigenous knowledge can be instrumental to enhance our understanding of local climate in data-deficient regions. Indigenous observations offer a tool to ground-truth gridded descriptions of climatic changes, thereby making adaptation strategies more robust at local scales. We contend that the use of indigenous knowledge could help to assist the climate interpolation process and address the prevailing uncertainties in local assessments of climate change.

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Earth Atmospheric Land Surface Temperature and Station Quality in the Contiguous United States

Cited by 11 publications

References 15 publications

An update of high‐resolution monthly climate surfaces for Mexico

An update of high‐resolution monthly climate surfaces for Mexico

Berkeley Earth Temperature Averaging Process

An empirically tested overlap between indigenous and scientific knowledge of a changing climate in Bolivian Amazonia

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