Ilyas, M;
Nychka, D;
Brierley, C;
Guillas, S;
(2021)
Global ensemble of temperatures over 1850-2018: quantification of uncertainties in observations, coverage, and spatial modeling (GETQUOCS).
Atmospheric Measurement Techniques
, 14
(11)
pp. 7103-7121.
10.5194/amt-14-7103-2021.
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Abstract
Instrumental global temperature records are derived from the network of in situ measurements of land and sea surface temperatures. This observational evidence is seen as being fundamental to climate science. Therefore, the accuracy of these measurements is of prime importance for the analysis of temperature variability. There are spatial gaps in the distribution of instrumental temperature measurements across the globe. This lack of spatial coverage introduces coverage error. An approximate Bayesian computation based multi-resolution lattice kriging is developed and used to quantify the coverage errors through the variance of the spatial process at multiple spatial scales. It critically accounts for the uncertainties in the parameters of this advanced spatial statistics model itself, thereby providing, for the first time, a full description of both the spatial coverage uncertainties along with the uncertainties in the modeling of these spatial gaps. These coverage errors are combined with the existing estimates of uncertainties due to observational issues at each station location. It results in an ensemble of 100 000 monthly temperatures fields over the entire globe that samples the combination of coverage, parametric and observational uncertainties from 1850 to 2018 over a 5∘×5∘ grid.
| Type: | Article |
|---|---|
| Title: | Global ensemble of temperatures over 1850-2018: quantification of uncertainties in observations, coverage, and spatial modeling (GETQUOCS) |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.5194/amt-14-7103-2021 |
| Publisher version: | https://doi.org/10.5194/amt-14-7103-2021 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| Keywords: | Science & Technology, Physical Sciences, Meteorology & Atmospheric Sciences, SEA-SURFACE TEMPERATURE, APPROXIMATE BAYESIAN COMPUTATION, HISTORICAL CLIMATOLOGY NETWORK, MEASURED IN-SITU, HYPERCUBE, HOMOGENIZATION, RELIABILITY, VARIABLES, EXPOSURE |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science UCL > Provost and Vice Provost Offices > UCL SLASH UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Geography |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10139480 |
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