The EUSTACE Project: Delivering Global, Daily Information on Surface Air Temperature

Rayner, Nick; Auchmann, Renate; Bessembinder, Janette; Brönnimann, Stefan; Brugnara, Yuri; Capponi, Francesco; Carrea, Laura; Dodd, Emma; Ghent, Darren; Good, Elizabeth; Høyer, Jacob L.; Kennedy, John J.; Kent, Elizabeth C.; Killick, Rachel; Linden, Paul van der; Lindgren, Finn; Madsen, Kristine S.; Merchant, Christopher J.; Mitchelson, J; Morice, Colin; Nielsen‐Englyst, Pia; Ortiz, P.; Remedios, J. J.; Schrier, Gerard van der; Squintu, Antonello A.; Stephens, Ag; Thorne, Peter; Tonboe, Rasmus; Trent, Tim; Veal, Karen L.; Waterfall, A. M.; Winfield, K. A.; Winn, Jonathan; Woolway, R. Iestyn

doi:10.1175/bams-d-19-0095.1

Cited by 21 publications

(16 citation statements)

References 60 publications

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“…Recent developments in satellite retrievals of surface skin temperatures present a new possibility for near‐surface temperature monitoring, bringing the potential for detailed spatial information with sustained measurement over a time frame that is now of sufficient length for climate studies. Recent work (Rayner et al., 2020) has explored the potential of combining air temperature information inferred from satellite skin temperatures with traditional in situ observations, expanding on the understanding of relationships between satellite‐derived skin temperatures and traditional near‐surface air temperature observations, and on the stability of these relationships over time that is required to construct merged data products. Alternatively, dynamical reanalyzes, that combine numerical weather prediction models with a range of varied observational data sources, are increasingly being used to monitor the climate (e.g., ERA5, Hersbach et al., 2020; JRA‐55, Kobayashi et al., 2015; and MERRA‐2, Gelaro et al., 2017).…”

Section: Introductionmentioning

confidence: 99%

An Updated Assessment of Near‐Surface Temperature Change From 1850: The HadCRUT5 Data Set

et al. 2021

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We present a new version of the Met Office Hadley Centre/Climatic Research Unit global surface temperature data set, HadCRUT5. HadCRUT5 presents monthly average near‐surface temperature anomalies, relative to the 1961–1990 period, on a regular 5° latitude by 5° longitude grid from 1850 to 2018. HadCRUT5 is a combination of sea‐surface temperature (SST) measurements over the ocean from ships and buoys and near‐surface air temperature measurements from weather stations over the land surface. These data have been sourced from updated compilations and the adjustments applied to mitigate the impact of changes in SST measurement methods have been revised. Two variants of HadCRUT5 have been produced for use in different applications. The first represents temperature anomaly data on a grid for locations where measurement data are available. The second, more spatially complete, variant uses a Gaussian process based statistical method to make better use of the available observations, extending temperature anomaly estimates into regions for which the underlying measurements are informative. Each is provided as a 200‐member ensemble accompanied by additional uncertainty information. The combination of revised input data sets and statistical analysis results in greater warming of the global average over the course of the whole record. In recent years, increased warming results from an improved representation of Arctic warming and a better understanding of evolving biases in SST measurements from ships. These updates result in greater consistency with other independent global surface temperature data sets, despite their different approaches to data set construction, and further increase confidence in our understanding of changes seen.

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Section: Introductionmentioning

confidence: 99%

An Updated Assessment of Near‐Surface Temperature Change From 1850: The HadCRUT5 Data Set

et al. 2021

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“…Similar observational uncertainty approaches have previously been used in detection and attribution studies (e.g., Jones and Kennedy 2017). Future developments in global surface air temperature datasets, which do not rely on SSTs, may also help in providing consistent model and observation comparisons (e.g., Rayner et al ., 2020).…”

Section: Resultsmentioning

confidence: 99%

“…It is therefore simplistic to assume that the observed SSTs are representative of mean temperatures throughout a top layer of the oceans (Kennedy, 2014; Kent et al ., 2017). Night marine air temperatures, NMAT, are much more robust than daytime marine temperatures (Kent et al ., 2013) and together with other data sources, such as from satellites, could provide an alternative to using SSTs in future global temperature datasets (Rayner et al ., 2020).…”

Section: Introductionmentioning

confidence: 99%

"Apples and Oranges": On comparing simulated historic near‐surface temperature changes with observations

Jones

2020

Quart J Royal Meteoro Soc

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Simulated historic near-surface air temperature variations are often compared with observations of land air temperatures blended with sea surface temperatures. This study investigates claims that this is not a "true like-with-like" comparison, which may cause small biases in simulated twentieth century temperature changes, with implications for different climate attribution and projection studies. A more appropriate analysis, it is claimed, should use simulated sea surface temperatures blended with land air temperatures; an apparent discrepancy with observed trends is then reduced. As the temperature of the uppermost level in a model's ocean is used to represent simulated sea surface temperatures, that models have inconsistent ways of representing land, and that simulations have differing sea ice coverages, the claim of an idealised analysis approach is challenged. An examination of Coupled Model Intercomparison Project simulations, compared with an observational dataset of near-surface temperatures, suggests there is a bias in simulated historic trends when upper-ocean temperatures are used instead of marine air temperatures, but this bias is small compared to other model and observational uncertainties and the impact of analysis choices. The results indicate that it is generally appropriate to use global near-surface air temperature diagnostics to compare simulated historic climate change with observed temperature changes. Alternative model diagnostics are not necessarily superior to those used in standard approaches, and the emphasis of model and observational discrepancies may be based on overconfident reasoning. K E Y W O R D S climate change, climate models, observed temperatures, CMIP5 1 INTRODUCTION The assessing of past and future changes in climate has a long history (Le Treut et al., 2007; Bindoff et al., 2013; Collins et al., 2013), which has been dominated by the use of surface temperatures, as measurement records are particularly long, have reasonable global coverages, and arguably have the most important impact on our lives. Multiple datasets indicate that global mean near-surface temperatures have increased by about 1 K since the end This article is published with the permission of the Controller of HMSO and the Queen's Printer for Scotland.

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“…Observations from polar orbiting satellites offer a very good supplement to the in situ observations through a high spatial and temporal coverage of the high latitudes and may improve the surface temperature products and the assessment of the Arctic climate changes. Daily near surface air temperatures derived from satellites temperature observations therefore have the potential to increase the amount of information in the data sets and improve the quality of the climate records, as recognized in Merchant et al (2013) and (Rayner et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…However, within EUSTACE this has been done using a statistical model to combine the satellite derived clear sky near surface air temperatures (i.e. the product derived in this paper over ice and similar clear sky temperature products over land, ocean and lakes) and in situ observations, and their respective uncertainty estimates (Morice et al, 2019;Rayner et al, 2020). This paper is structured such that Sect.…”

Section: Introductionmentioning

confidence: 99%

Deriving Arctic 2 m air temperatures over snow and ice from satellite surface temperature measurements

Nielsen‐Englyst¹,

Høyer²,

Madsen³

et al. 2021

Preprint

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Abstract. The Arctic region is responding heavily to climate change, and yet, the air temperature of ice covered areas in the Arctic is heavily under-sampled when it comes to in situ measurements, resulting in large uncertainties in existing weather- and reanalysis products. This paper presents a method for estimating daily mean clear sky 2 meter air temperatures (T2m) in the Arctic from satellite observations of skin temperature, using the Arctic and Antarctic ice Surface Temperatures from thermal Infrared (AASTI) satellite dataset, providing spatially detailed observations of the Arctic. The method is based on a linear regression model, which has been tuned against in situ observations to estimate daily mean T2m based on clear sky satellite ice surface skin temperatures. The daily satellite derived T2m product includes estimated uncertainties and covers clear sky snow and ice surfaces in the Arctic region during the period 2000–2009, provided on a 0.25 degree regular latitude-longitude grid. Comparisons with independent in situ measured T2m show average biases of 0.30 °C and 0.35 °C and average root mean square errors of 3.47 °C and 3.20 °C for land ice and sea ice, respectively. The associated uncertainties are verified to be very realistic for both land ice and sea ice, using in situ observations. The reconstruction provides a much better spatial coverage than the sparse in situ observations of T2m in the Arctic, is independent of numerical weather prediction model input and it therefore provides an important supplement to simulated air temperatures to be used for assimilation or global surface temperature reconstructions. A comparison between in situ T2m versus T2m derived from satellite and ERA-Interim/ERA5 estimates shows that the T2m derived from satellite observations validate similar or better than ERA-Interim/ERA5 in the Arctic.

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The EUSTACE Project: Delivering Global, Daily Information on Surface Air Temperature

Abstract: Capsule The main goals and activities of the EUSTACE project are discussed along with some key results, including a global, multi-decadal daily air temperature record from satellite and in situ measurements.

Cited by 21 publications

References 60 publications

An Updated Assessment of Near‐Surface Temperature Change From 1850: The HadCRUT5 Data Set

An Updated Assessment of Near‐Surface Temperature Change From 1850: The HadCRUT5 Data Set

"Apples and Oranges": On comparing simulated historic near‐surface temperature changes with observations

Deriving Arctic 2 m air temperatures over snow and ice from satellite surface temperature measurements

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