Berkeley Earth Temperature Averaging Process

By R. Rohde, J. Curry, D. Groom, Robert Jacobsen, R. Muller, S. Perlmutter, A. Rosenfeld, C. Wickham, J. Wurtele. 2013

423 citations recorded by the source corpus.

Subfields: Atmospheric Science, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/8dc8ca34582360ab4f9cffc8619956058ac80979

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. Sandrine Bony
2. J. Dufresne
3. Reto Knutti
4. J. Vial
5. L. Brunner
6. R. Lorenz
7. A. Merrifield
8. Eric Guilyardi

In Environmental Engineering

1. Jiacan Yuan
2. J. Vialard
3. J. Grandpeix
4. Frederic Hourdin
5. Fabienne Maignan
6. M. Mann
7. Robert E Kopp
8. M. Menne

In Global and Planetary Change

1. Sandrine Bony
2. J. Dufresne
3. Reto Knutti
4. J. Vial
5. L. Brunner
6. R. Lorenz
7. A. Merrifield
8. J. Vialard

Citations in the corpus

• Robust comparison of climate models with observations using blended land air and ocean sea surface temperatures (2015, background): “Temperature records may be based on spatially incomplete data [Morice et al., 2012; Vose et al., 2012] or on data that have been infilled to provide an estimate of the global mean temperature [Hansen et al., 2010; Rohde et al., 2013; Cowtan and Way, 2014].”
• Valuing the Global Mortality Consequences of Climate Change Accounting for Adaptation Costs and Benefits (2019, positive): “In particular, the spatial averaging method uses close neighbors of a station to identify discontinuities in a particular time series that may be due to instrumental change or re-positioning, and decreases the influence of these changes in the spatially averaged grid (Rohde et al., 2013).”
• An investigation of weighting schemes suitable for incorporating large ensembles into multi-model ensembles (2020, methodology): “BEST was created to be an independent estimate of global temperature obtained through the spatiotemporal interpolation of in situ temperature measurements (Rohde et al., 2013).”
• Improvements in the GISTEMP Uncertainty Model (2019, methodology): “Notably, with the introduction of the Hadley Centre sea surface temperature (SST) analysis HadSST3 (Kennedy et al., 2011a, 2011b), Berkeley Earth (Rohde et al., 2013a), and the joint Hadley Centre and University of East Anglia's Climatic Research Unit Hadley Centre/Climatic Research Unit 4…”
• Evaluation of CMIP6 DECK Experiments With CNRM‐CM6‐1 (2019, methodology): “Land near surface temperature reference is taken as the average of BEST (Muller et al., 2013; Rohde et al., 2013), CRU‐TS4‐00 (Harris et al., 2014), and GHCN‐CAMS (Fan & van den Dool, 2008).”
• Presentation and Evaluation of the IPSL‐CM6A‐LR Climate Model (2020, methodology): “We compare this anomaly to both the Cowtan and Way (2014) and (Rohde et al., 2013a, 2013b) observational data sets.”
• Global urban population exposure to extreme heat (2021, methodology): “By combining cloud-screened harmonized geostationary satellite thermal infrared observations with ∼15,000 in situ station observations from Berkeley Earth (62), CHIRTSmax is the most accurate (R 2 = 0.8 to 0.9) high-resolution, monthly maximum temperature dataset with global coverage (24).”