Page 243 - Contributed Paper Session (CPS) - Volume 2

CPS1846 Maryam I. et al.

Monthly Data Set Quantifying Uncertainties in
Past Global Temperatures
1
1,2
Maryam Ilyas , Serge Guillas , Chris Brierleyc
3
1 Department of Statistical Science, University College London, London, U.K
2 College of Statistical and Actuarial Sciences, University of the Punjab, Lahore, Pakistan
3 Department of Geography, University College London, London, U.K

Abstract
Instrumental temperature records are derived from the network of in situ
measurements of land and sea surface temperatures. This observational
evidence is seen as 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 used to quantify the coverage errors. It
accounts for the variation in the model parameters and variance of the spatial
process at multiple spatial scales. 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 monthly temperatures over the
entire globe that samples the combination of coverage, parametric and
observational uncertainties.

Keywords
multi-resolution kriging; ABC; uncertainties in temperatures

1. Introduction
The instrumental surface temperature data sets are widely used to monitor
climate. For example, for climate change assessment (e.g. Hansen et al., 2010;
Morice et al., 2012; Good, 2016) and to evaluate the numerical weather
prediction and climate models (e.g. Milton and Earnshaw, 2007; Edwards et al.,
2011; Suklitsch et al., 2011). Raw data are obtained from thousands of
meteorological stations around the globe. The stations are based on land and
ships and buoys in the oceans (Kennedy et al.,2011b). Temperature data bases
are generally created by blending the land and sea surface temperature
records. The land component of the data sets is mostly collected from the
global historical network of meteorological stations (e.g. Jones et al., 2012).
These are obtained from World Meteorological Organization (WMO) and
Global Climate Observation System (GCOS). On the other hand, sea surface
temperatures are largely compiled by International Comprehensive Ocean-
Atmosphere Data Set (ICOADS) (Woodruff et al., 2011). These are collected

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