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Abstract Atmospheric heat transport (AHT) is an important piece of our climate system but has primarily been studied at monthly or longer time scales. We introduce a new method for calculating zonal-mean meridional AHT using instantaneous atmospheric fields. When time averaged, our calculations closely reproduce the climatological AHT used elsewhere in the literature to understand AHT and its trends on long time scales. In the extratropics, AHT convergence and atmospheric heating are strongly temporally correlated suggesting that AHT drives the vast majority of zonal-mean atmospheric temperature variability. Our AHT methodology separates AHT into two components (eddies and the mean meridional circulation) which we find are negatively correlated throughout most of the mid- to high latitudes. This negative correlation reduces the variance in the total AHT compared to eddy AHT. Last, we find that the temporal distribution of the total AHT at any given latitude is approximately symmetric. 

Abstract The observed partitioning of poleward heat transport between atmospheric and oceanic heat transports (AHT and OHT) is compared to that in coupled climate models. Model ensemble mean poleward OHT is biased low in both hemispheres, with the largest biases in the Southern Hemisphere extratropics. Poleward AHT is biased high in the Northern Hemisphere, especially in the vicinity of the peak AHT near 40°N. The significant model biases are persistent across three model generations (CMIP3, CMIP5, CMIP6) and are insensitive to the satellite radiation and atmospheric reanalyzes products used to derive observational estimates of AHT and OHT. Model biases in heat transport partitioning are consistent with biases in the spatial structure of energy input to the ocean and atmosphere. Specifically, larger than observed model evaporation in the tropics adds excess energy to the atmosphere that drives enhanced poleward AHT at the expense of weaker OHT. 

Abstract Given the key role that atmospheric heat transport plays in Earth's climate system, efforts to document its changes over the satellite era are valuable. Clark et al. (2022,https://doi.org/10.1029/2022GL098822) calculated trends in atmospheric heat transport among four reanalysis data sets and found substantial disagreements between data sets. However, after accounting for the lack of mass‐conservation in reanalysis data sets, we find much smaller magnitude trends, with much better agreement among reanalyses. This highlights the importance of mass corrections when calculating atmospheric heat transport. 

Abstract We investigate the linear trends in meridional atmospheric heat transport (AHT) since 1980 in atmospheric reanalysis datasets, coupled climate models, and atmosphere-only climate models forced with historical sea surface temperatures. Trends in AHT are decomposed into contributions from three components of circulation: (i) transient eddies, (ii) stationary eddies, and (iii) the mean meridional circulation. All reanalyses and models agree on the pattern of AHT trends in the Southern Ocean, providing confidence in the trends in this region. There are robust increases in transient-eddy AHT magnitude in the Southern Ocean in the reanalyses, which are well replicated by the atmosphere-only models, while coupled models show smaller magnitude trends. This suggests that the pattern of sea surface temperature trends contributes to the transient-eddy AHT trends in this region. In the tropics, we find large differences between mean-meridional circulation AHT trends in models and the reanalyses, which we connect to discrepancies in tropical precipitation trends. In the Northern Hemisphere, we find less evidence of large-scale trends and more uncertainty, but note several regions with mismatches between models and the reanalyses that have dynamical explanations. Throughout this work we find strong compensation between the different components of AHT, most notably in the Southern Ocean where transient-eddy AHT trends are well compensated by trends in the mean-meridional circulation AHT, resulting in relatively small total AHT trends. This highlights the importance of considering AHT changes holistically, rather than each AHT component individually. 

Abstract An unprecedented heatwave impacted East Antarctica in March 2022, peaking at 39°C above climatology, the largest temperature anomaly ever recorded globally. We investigate the causes of the heatwave, the impact of climate change, and a climate model's ability in simulating such an event. The heatwave, which was skillfully forecast, resulted from a highly anomalous large‐scale circulation pattern that advected an Australian airmass to East Antarctica in 4 days and produced record atmospheric heat fluxes. Southern Ocean sea surface temperatures anomalies had a minimal impact on the heatwave's amplitude. Simulations from a climate model fail to simulate such a large temperature anomaly mostly due to biases in its large‐scale circulation variability, showcasing a pathway for future model improvement in simulating extreme heatwaves. The heatwave was made 2°C warmer by climate change, and end of 21st century heatwaves may be an additional 5–6°C warmer, raising the prospect of near‐melting temperatures over the interior of East Antarctica. 

Abstract Arctic warming under increased CO₂peaks in winter, but is influenced by summer forcing via seasonal ocean heat storage. Yet changes in atmospheric heat transport into the Arctic have mainly been investigated in the annual mean or winter, with limited focus on other seasons. We investigate the full seasonal cycle of poleward heat transport modeled with increased CO₂or with individually applied Arctic sea‐ice loss and global sea‐surface warming. We find that a winter reduction in dry heat transport is driven by Arctic sea‐ice loss and warming, while a summer increase in moist heat transport is driven by sub‐Arctic warming and moistening. Intermodel spread in Arctic warming controls spread in seasonal poleward heat transport. These seasonal changes and their intermodel spread are well‐captured by down‐gradient diffusive heat transport. While changes in moist and dry heat transport compensate in the annual‐mean, their opposite seasonality may support non‐compensating effects on Arctic warming.