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2023 Temperatures Reflect Steady Global Warming and Internal Sea Surface Temperature Variability

Earth Sciences

2023 Temperatures Reflect Steady Global Warming and Internal Sea Surface Temperature Variability

B. H. Samset, M. T. Lund, et al.

In 2023, temperatures soared to unprecedented levels, raising questions about accelerating warming and aerosol cooling. Research conducted by Bjørn H. Samset, Marianne T. Lund, Jan S. Fuglestvedt, and Laura J. Wilcox reveals that these extreme temperatures are closely tied to sea surface temperature influences and internal variability, highlighting the complex interplay between natural variability and human-induced climate change.

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Playback language: English
Introduction
Anthropogenic global warming, primarily driven by greenhouse gas emissions, has increased at a relatively steady rate of approximately 0.2 °C per decade since at least 1970. However, recent studies suggest a slight increase in the rate of global mean surface temperature anomaly (GSTA) increase around 1990, a continued rise in the global energy imbalance, and an acceleration in ocean heat content accumulation. These increases have been attributed to continued greenhouse gas buildup and the loss of aerosol-induced cooling due to emission cleanup efforts in China and the global shipping sector. The record surface temperature anomalies in 2023, exceeding previous records by a significant margin, were unexpected despite the apparent increase in warming rates and an ENSO-positive state in the Equatorial Pacific. This study addresses the central question of whether this anomaly aligns with internal variability and known decadal-scale regional climate forcing or signals a rapid change in the climate system or human influence. Several hypotheses have been proposed, including the impact of shipping emission cleanup, the 2021 Hunga Tonga volcanic eruption, and the potential unmasking of higher climate sensitivity due to aerosol changes. However, the possibility remains that the record 2023 GSTA was merely a combination of ongoing anthropogenic influences and a pattern of SSTs within the range of observed interannual and decadal variability. This study leverages a novel Green's function-based method to isolate the SST pattern's contribution to the monthly or annual GSTA, comparing the influence to that of previous record-warm years.
Literature Review
Several studies have documented a minor step-up in the rate of global mean surface temperature anomaly (GSTA) increase around 1990, a continued rise in the global energy imbalance, and an acceleration in the accumulation of ocean heat content. These studies have suggested various factors, including continued greenhouse gas buildup from anthropogenic emissions and loss of cooling from anthropogenic aerosols. Previous research has also shown that SST anomalies in different geographical locations have varying influences on global temperatures, a phenomenon known as the pattern effect. The influence of SST patterns on global temperatures can be consistently quantified using Earth System Models (ESMs) through simulations that independently perturb SSTs in multiple locations. This study builds upon these previous findings and utilizes a Green's function method to quantify the component of interannual (or monthly) GSTA variability that arises from the different ocean basins.
Methodology
This study employs a recently developed Green's function-based method to quantify the contribution of SST patterns to the global mean surface temperature anomaly (GSTA). The method utilizes a Green's function derived from the NCAR CESM1 Earth System Model. This Green's function relates an idealized increase in sea surface temperature at a given location to its influence on global mean surface temperature, allowing for the calculation of the modulation of global mean surface temperatures resulting from observed SST variability. The analysis begins by removing the long-term trend from the observed SST data using a 10-year boxcar smoothing at each grid point, relative to an 1850-1900 baseline. This process isolates the pattern of monthly internal temperature variability while removing the underlying global mean temperature increase and geographical patterns of global warming. The monthly temperature fields from the observational series are then regridded to the GF resolution. GSTA modulations are then calculated by multiplying the Green's function for each month with the detrended SST pattern from observations. The total modulation is the sum of the contributions from all ocean-dominated grid points. The study uses the HadCRUT5 gridded dataset as its primary dataset, with three other gridded surface temperature data products (GISTEMP v4, NOAA GlobalTemp, and Berkeley Earth) used for consistency checking. The analysis compares the SST pattern-induced corrections for 2023 to those of previous record-warm years to determine if the net influence is unprecedented or falls within the bounds of previous observations. The study also incorporates data from an ensemble of CMIP6 models to assess the extent to which the observed 2023 SST corrections are exceptional in the context of simulated internal variability. Ocean basin definitions are provided for the analysis of regional contributions.
Key Findings
The analysis of the margin by which record-breaking years surpassed previous records reveals that while 2023 set record temperatures, the margin was not unprecedented for strong El Niño years. The SST pattern-induced corrections for 2023, when applied to the GSTA, show that while 2023 remains the warmest year on record, it aligns better with recent long-term warming trends after accounting for the SST influence. The monthly corrections for 2023 fall within the historical spread for the 1950-2023 period, indicating that these corrections are not exceptional. While 2023 exhibits the strongest SST-induced GSTA corrections in the HadCRUT5 data among years on record, this difference is small when compared with other years of strong correction such as 1952, 1969, 1998, and 2016. The analysis across four major temperature reconstructions (HadCRUT5, NOAA, Berkeley Earth, and GISTEMP) yields consistent results. Comparing the 2023 correction to an ensemble of CMIP6 models indicates that it is in the upper 5th percentile, but not exceptionally unusual. Geographically, the SST corrections display similar patterns across the strong-correction years. All five years (1952, 1969, 1998, 2016, and 2023) experienced El Niño conditions in the Equatorial Pacific and anomalously warm conditions in the Tropical North Atlantic. The North Atlantic contributions were particularly substantial in 2023, with the Tropical North Atlantic (0–30°N) exhibiting the strongest contribution on record, though it was consistent with observations farther north. After the application of Green's function-based filtering, the warming rates in HadCRUT5 data for the last 10, 20, and 50 years were calculated as 0.29, 0.27, and 0.19 °C/decade respectively. This suggests a relatively constant warming rate over these timescales despite the exceptionally warm conditions in 2023.
Discussion
The findings suggest that the significant deviation of the 2023 GSTA from recent global warming trends is primarily attributable to the warm state of various ocean basins. Although none of the basins individually exhibited exceptionally anomalous conditions, the simultaneous warm anomalies in multiple basins contributed to the record temperature. This indicates that the record temperature in 2023 may not signal an acceleration in surface warming but rather a combination of steady anthropogenic warming and internal ocean temperature variability at interannual and decadal scales. The results support previous research highlighting the role of El Niño in the 2023 GSTA. The study acknowledges that the method does not identify the underlying reasons for the SST patterns, and other factors such as changes in global energy imbalance, aerosol cleanup, or cloudiness may have contributed to the 2023 temperatures.
Conclusion
The study concludes that the record-breaking temperatures of 2023 are largely explained by a combination of steady anthropogenic global warming and unusually warm conditions across multiple ocean basins, consistent with the range of observed interannual and decadal variability. The findings do not suggest an acceleration in global warming beyond that already known. Future research should investigate the causes of the observed SST patterns and explore other contributing factors, such as ocean heat uptake, aerosol emission changes, and the infrequent transition to El Niño conditions after a multi-year La Niña event.
Limitations
The study's results depend on the detailed response of the CESM1 model to localized SST perturbations. While previous studies show that the general spatial patterns of Green's functions are similar across models, differences in model responses might influence the quantification of SST contributions. Additionally, the method does not identify the underlying causes of the SST pattern observed in 2023, and other factors could still be contributing. The limited coverage of observational datasets in some regions, particularly the Southern Ocean, could also affect some regional results. Finally, the analysis focuses on SST influences, and other climatic factors could have played additional roles in 2023's record temperatures.
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