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   Kircher, Kevin J.; Zhang, K. Max (March 2021, Applied Energy)
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   https://doi.org/10.1063/5.0069551  

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3. Underappreciated plant vulnerabilities to heat waves

   https://doi.org/10.1111/nph.17348  

   Breshears, David D.; Fontaine, Joseph B.; Ruthrof, Katinka X.; Field, Jason P.; Feng, Xiao; Burger, Joseph R.; Law, Darin J.; Kala, Jatin; Hardy, Giles E. St. J. (April 2021, New Phytologist)

   Summary With climate change, heat waves are becoming increasingly frequent, intense and broader in spatial extent. However, while the lethal effects of heat waves on humans are well documented, the impacts on flora are less well understood, perhaps except for crops. We summarize recent findings related to heat wave impacts including: sublethal and lethal effects at leaf and plant scales, secondary ecosystem effects, and more complex impacts such as increased heat wave frequency across all seasons, and interactions with other disturbances. We propose generalizable practical trials to quantify the critical bounding conditions of vulnerability to heat waves. Collectively, plant vulnerabilities to heat waves appear to be underappreciated and understudied, particularly with respect to understanding heat wave driven plant die‐off and ecosystem tipping points. 

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4. Chlorophyll Production in the Amundsen Sea Boosts Heat Flux to Atmosphere and Weakens Heat Flux to Ice Shelves

   https://doi.org/10.1029/2024JC021121  

   Twelves, A_G; Goldberg, D_N; Holland, P_R; Henley, S_F; Mazloff, M_R; Jones, D_C (September 2024, Journal of Geophysical Research: Oceans)

   Abstract The Amundsen Sea in West Antarctica features rapidly thinning ice shelves, large polynyas, and sizable spring phytoplankton blooms. Although considerable effort has gone into characterizing heat fluxes between the Amundsen Sea, its associated ice shelves, and the overlying atmosphere, the effect of the phytoplankton blooms on the distribution of heat remains poorly understood. In this modeling study, we implement a feedback from biogeochemistry onto physics into MITgcm‐BLING and use it to show that high levels of chlorophyll—concentrated in the Amundsen Sea Polynya and the Pine Island Polynya—have the potential to increase springtime surface warming in polynyas by steepening the attenuation profile of solar radiation with depth. The chlorophyll‐associated warm anomaly (on average between +0.2C and +0.3C) at the surface is quickly dissipated to the atmosphere, by increases in longwave, latent and sensible heat loss from open water areas. Outside of the coastal polynyas, the summertime warm anomaly leads to an average sea ice thinning of 1.7 cm across the region, and stimulates up to 20% additional seasonal melting near the fronts of ice shelves. The accompanying cold anomaly, caused by shading of deeper waters, persists year‐round and affects a decrease in the volume of Circumpolar Deep Water on the continental shelf. This cooling ultimately leads to an average sea ice thickening of 3.5 cm and, together with associated changes to circulation, reduces basal melting of Amundsen Sea ice shelves by approximately 7% relative to the model scenario with no phytoplankton bloom. 

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5. Machine learning assisted inverse heat transfer problem to find heat flux in ablative materials

   https://doi.org/10.1016/j.mtcomm.2025.112337  

   Islam, Md Shariful; Dutta, Prashanta (April 2025, Materials Today Communications)

   Thermal ablation of materials is a complex phenomenon that involves physical and chemical processes for the thermal protection of systems. However, due to the extreme thermal conditions and moving boundaries, predicting temperature and heat flux at the ablative material is quite challenging. A physics-informed neural network is a promising technique for many such inverse problems, including the prediction of unsteady heat flux. However, traditional physics-informed machine learning algorithms struggle with heat flux predictions in thermal ablation problems due to moving boundary conditions and lack of temperature data in the inaccessible domain. This study presents a hybrid approach, where an artificial neural network (ANN) is used for the accessible domain of the material and a physics-based numerical solution (PNS) technique is used in the inaccessible domain of the material, to find heat flux at the ablative surface. Temperature data at the accessible sensor points are used to train the ANN model. The heat flux at the ablative boundary was iteratively obtained from the numerical solution of the energy equation in the inaccessible domain by matching the ANN-predicted temperature at the last accessible sensor point. Our results indicate that this hybrid methodology significantly outperforms traditional physics-informed machine learning techniques, achieving excellent accuracy in predicting the temperature profiles and heat fluxes under complex conditions for both constant and variable heat flux and properties. By addressing the limitations of conventional physics-informed machine learning methods, our approach provides a robust and reliable solution for modeling the intricate dynamics of ablative processes. 

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