Badge available
The global mean surface temperature record —combining sea surface and near-surface air data— is central to understanding climate variability and change. Understanding the past record also helps constrain uncertainty in future climate projections. In my talk, I will present a recent study (Sippel et al., 2024, Nature, doi:10.1038/s41586-024-08230-1) that refines our view of the historical record and explore its implications for near-future climate risk.
Past temperature record: The early temperature record (before ~1950) remains uncertain due to evolving methods, limited documentation, and sparse coverage. Independent reconstructions show that historical ocean temperatures were likely measured too cold —by about 0.26 °C compared to land estimates— despite strong agreement in other periods. This cold bias cannot be explained by natural variability; multiple lines of evidence (climate attribution, timescale analysis, coastal data, palaeoclimate records) support a substantial cold bias in early ocean records. While overall warming since the mid-19th century is unchanged, correcting the bias reduces early-20th-century warming trends, lowers global decadal variability, and brings models and observations into closer alignment.
Constraining climate risk: I will close my talk by discussing how these findings sharpen near-future temperature projections and our understanding of climate risk; and furthermore how new AI methods may provide an even clearer picture of past climate and near-future climate risk.
Learning Objectives:
Describe the historical global mean surface temperature record and identify the key sources of uncertainty in early ocean and land temperature measurements.
Examine evidence supporting the cold bias in pre-1950 ocean temperature records, including climate attribution studies, timescale analysis, coastal data, and palaeoclimate reconstructions.
Assess how correcting historical temperature biases influences early-20th-century warming trends, decadal variability, and alignment between climate models and observations.
Discuss the implications of refined historical temperature records for projecting near-future climate risk.
Explore potential applications of AI methods to improve reconstructions of past climate and enhance near-future climate risk assessment.
Recommended Mastery Level / Prerequisites:
Recommended Mastery Level: Intermediate to Advanced – suitable for graduate students, early career researchers, and professionals in climate science, oceanography, or environmental data science.
Prerequisites:
Basic understanding of climate science and global temperature records.
Familiarity with statistical concepts in time series analysis is helpful but not required.
General knowledge of climate modeling and observational datasets.
