Novel estimates of global mean temperature from land- vs. ocean-based records reveal high consistency except for early 20th century ocean cold anomaly

Global-mean surface air temperature (GSAT) is a key diagnostic of climate change and a key metric for climate policies. Yet, global temperature datasets (1) are usually based on blending the sea surface temperature (SST) record with land surface air temperature (LSAT) data, and (2) contain a large number of missing values due to incomplete coverage, particularly in the early record. These issues are usually accounted for in model-observation comparisons via a similar processing of models and/or statistical infilling, but an apple-to-apple comparison between LSAT and SST records in their contribution to GSAT estimates, and also in their spatial consistency, remains difficult. 

 

Here, we present a set of novel GSAT estimates based separately on either the LSAT or SST record, and climate model information. The method is based on regularized linear regression models that are trained on climate model simulations to optimally predict GSAT from the climate model’s LSAT or SST predictors, respectively, which are masked to match the observational coverage at any given time step. In a second step, the derived statistical models are used to predict GSAT from the HadSST4 (SST) and CRUTEM5 (LSAT) observational data, respectively, and for any observational coverage from January 1850 up to December 2020. In addition, we generate a variant of these estimates that explicitly take into account the estimated errors as well as bias realizations for HadSST4 and CRUTEM5 data in the GSAT prediction. 

 

We show that the resulting independent, land- and ocean based GSAT estimates are remarkably consistent since around 1950: the squared correlation between the land- and ocean GSAT estimates is 0.98 for annual and 0.92 for monthly data, whereas it is only 0.94 (annual) and 0.77 (monthly), for the original CRUTEM5 and HadSST4 global land and ocean  time series. In addition, the 1950-2020 long-term trends in GSAT estimates are virtually identical when inferred independently from land- or ocean data (1.14°C or 1.17°C warming per 71 years, respectively), and GSAT of the past decade (2011-2020) increased by 1.18°C (LSAT-based) and 1.15°C (SST-based) relative to an early period (1850-1880).

 

However, the GSAT estimates show a pronounced period of disagreement in the early 20th century (1900 up to around 1920), when the SST-based GSAT estimates appear up to about 0.5°C (0.3°C on average) colder than the LSAT-based estimate, with important implications for the magnitude of the early 20th century warming. This finding is consistent with concerns about biased observed estimates raised in the literature, and is potentially related to instrumental cold biases in SST measurements, but overall reasons for the disagreement remain largely unclear. We show several lines of evidence, based on statistical analysis, physical reasoning and comparison with climate models, which indicate that the ocean data may indeed be implausibly cold. However, the early 20th century ocean cold anomaly, as well as the associated strong early 20th century ocean warming, require further study.