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1. Past climates inform our future

   https://doi.org/10.1126/science.aay3701  

   Tierney, Jessica E.; Poulsen, Christopher J.; Montañez, Isabel P.; Bhattacharya, Tripti; Feng, Ran; Ford, Heather L.; Hönisch, Bärbel; Inglis, Gordon N.; Petersen, Sierra V.; Sagoo, Navjit; et al (November 2020, Science)

   null (Ed.)

   As the world warms, there is a profound need to improve projections of climate change. Although the latest Earth system models offer an unprecedented number of features, fundamental uncertainties continue to cloud our view of the future. Past climates provide the only opportunity to observe how the Earth system responds to high carbon dioxide, underlining a fundamental role for paleoclimatology in constraining future climate change. Here, we review the relevancy of paleoclimate information for climate prediction and discuss the prospects for emerging methodologies to further insights gained from past climates. Advances in proxy methods and interpretations pave the way for the use of past climates for model evaluation—a practice that we argue should be widely adopted. 

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2. A 485-million-year history of Earth’s surface temperature

   https://doi.org/10.1126/science.adk3705  

   Judd, Emily J; Tierney, Jessica E; Lunt, Daniel J; Montañez, Isabel P; Huber, Brian T; Wing, Scott L; Valdes, Paul J (September 2024, Science)

   A long-term record of global mean surface temperature (GMST) provides critical insight into the dynamical limits of Earth’s climate and the complex feedbacks between temperature and the broader Earth system. Here, we present PhanDA, a reconstruction of GMST over the past 485 million years, generated by statistically integrating proxy data with climate model simulations. PhanDA exhibits a large range of GMST, spanning 11° to 36°C. Partitioning the reconstruction into climate states indicates that more time was spent in warmer rather than colder climates and reveals consistent latitudinal temperature gradients within each state. There is a strong correlation between atmospheric carbon dioxide (CO₂) concentrations and GMST, identifying CO₂as the dominant control on variations in Phanerozoic global climate and suggesting an apparent Earth system sensitivity of ~8°C. 

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3. The latitudinal temperature gradient and its climate dependence as inferred from foraminiferal δ ¹⁸ O over the past 95 million years

   https://doi.org/10.1073/pnas.2111332119  

   Gaskell, Daniel E.; Huber, Matthew; O’Brien, Charlotte L.; Inglis, Gordon N.; Acosta, R. Paul; Poulsen, Christopher J.; Hull, Pincelli M. (March 2022, Proceedings of the National Academy of Sciences)

   The latitudinal temperature gradient is a fundamental state parameter of the climate system tied to the dynamics of heat transport and radiative transfer. Thus, it is a primary target for temperature proxy reconstructions and global climate models. However, reconstructing the latitudinal temperature gradient in past climates remains challenging due to the scarcity of appropriate proxy records and large proxy–model disagreements. Here, we develop methods leveraging an extensive compilation of planktonic foraminifera δ 18 O to reconstruct a continuous record of the latitudinal sea-surface temperature (SST) gradient over the last 95 million years (My). We find that latitudinal SST gradients ranged from 26.5 to 15.3 °C over a mean global SST range of 15.3 to 32.5 °C, with the highest gradients during the coldest intervals of time. From this relationship, we calculate a polar amplification factor (PAF; the ratio of change in >60° S SST to change in global mean SST) of 1.44 ± 0.15. Our results are closer to model predictions than previous proxy-based estimates, primarily because δ 18 O-based high-latitude SST estimates more closely track benthic temperatures, yielding higher gradients. The consistent covariance of δ 18 O values in low- and high-latitude planktonic foraminifera and in benthic foraminifera, across numerous climate states, suggests a fundamental constraint on multiple aspects of the climate system, linking deep-sea temperatures, the latitudinal SST gradient, and global mean SSTs across large changes in atmospheric CO 2 , continental configuration, oceanic gateways, and the extent of continental ice sheets. This implies an important underlying, internally driven predictability of the climate system in vastly different background states. 

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4. Greenland temperature and precipitation over the last 20 000 years using data assimilation

   https://doi.org/10.5194/cp-16-1325-2020  

   Badgeley, Jessica A.; Steig, Eric J.; Hakim, Gregory J.; Fudge, Tyler J. (January 2020, Climate of the Past)

   Abstract. Reconstructions of past temperature and precipitation are fundamental to modeling the Greenland Ice Sheet and assessing its sensitivity to climate. Paleoclimate information is sourced from proxy records and climate-model simulations; however, the former are spatially incomplete while the latter are sensitive to model dynamics and boundary conditions. Efforts to combine these sources of information to reconstruct spatial patterns of Greenland climate over glacial–interglacial cycles have been limited by assumptions of fixed spatial patterns and a restricted use of proxy data. We avoid these limitations by using paleoclimate data assimilation to create independent reconstructions of mean-annual temperature and precipitation for the last 20 000 years. Our method uses oxygen isotope ratios of ice and accumulation rates from long ice-core records and extends this information to all locations across Greenland using spatial relationships derived from a transient climate-model simulation. Standard evaluation metrics for this method show that our results capture climate at locations without ice-core records. Our results differ from previous work in the reconstructed spatial pattern of temperature change during abrupt climate transitions; this indicates a need for additional proxy data and additional transient climate-model simulations. We investigate the relationship between precipitation and temperature, finding that it is frequency dependent and spatially variable, suggesting that thermodynamic scaling methods commonly used in ice-sheet modeling are overly simplistic. Our results demonstrate that paleoclimate data assimilation is a useful tool for reconstructing the spatial and temporal patterns of past climate on timescales relevant to ice sheets. 

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5. Climate-invariant machine learning

   https://doi.org/10.1126/sciadv.adj7250  

   Beucler, Tom; Gentine, Pierre; Yuval, Janni; Gupta, Ankitesh; Peng, Liran; Lin, Jerry; Yu, Sungduk; Rasp, Stephan; Ahmed, Fiaz; O’Gorman, Paul A.; et al (February 2024, Science Advances)

   Projecting climate change is a generalization problem: We extrapolate the recent past using physical models across past, present, and future climates. Current climate models require representations of processes that occur at scales smaller than model grid size, which have been the main source of model projection uncertainty. Recent machine learning (ML) algorithms hold promise to improve such process representations but tend to extrapolate poorly to climate regimes that they were not trained on. To get the best of the physical and statistical worlds, we propose a framework, termed “climate-invariant” ML, incorporating knowledge of climate processes into ML algorithms, and show that it can maintain high offline accuracy across a wide range of climate conditions and configurations in three distinct atmospheric models. Our results suggest that explicitly incorporating physical knowledge into data-driven models of Earth system processes can improve their consistency, data efficiency, and generalizability across climate regimes. 

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