2014
DOI: 10.1002/gdj3.8
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The international surface temperature initiative global land surface databank: monthly temperature data release description and methods

Abstract: Described herein is the first version release of monthly temperature holdings of a new Global Land Surface Meteorological Databank. Organized under the auspices of the International Surface Temperature Initiative (ISTI), an international group of scientists have spent three years collating and merging data from numerous sources to create a merged holding. This release in its recommended form consists of over 30 000 individual station records, some of which extend over the past 300 years. This article describes… Show more

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Cited by 108 publications
(86 citation statements)
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“…We follow a method which is similar to the International Surface Temperature Initiative (ISTI, Rennie et al, 2014). The ISTI methodology maps separations (distance and height) into decaying exponential probability curves.…”
Section: Merging Stationsmentioning
confidence: 99%
“…We follow a method which is similar to the International Surface Temperature Initiative (ISTI, Rennie et al, 2014). The ISTI methodology maps separations (distance and height) into decaying exponential probability curves.…”
Section: Merging Stationsmentioning
confidence: 99%
“…HadCRUT4 combines land data from CRUTEM4 with SST data from HadSST3 (Kennedy et al, 2011a(Kennedy et al, , 2011b; see also Kennedy, 2014). NCEI use land data from the ISTI database (Rennie et al, 2014) and their ERSSTv4 dataset of SST anomalies over the ocean (Huang et al, 2015;Liu et al, 2015). GISS data are derived by first averaging all the land station data (from NCEI) into 160 approximately equal-area boxes, and then combining these with SST values (currently using ERSSTv4) from marine areas.…”
Section: Introductionmentioning
confidence: 99%
“…Especially in case of temperature, interest in an analysis of these series is also motivated by the aim of better understanding the uncertainty of global temperature datasets and their trends (Jones, 2016). To allow studying systematic biases at a global scale, the Parallel Observations Science Team (POST) of the International Surface Temperature Initiative (ISTI, see Rennie et al, 2014) compiles a database with parallel measurements.…”
Section: Introductionmentioning
confidence: 99%