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1. An increase in marine heatwaves without significant changes in surface ocean temperature variability

   https://doi.org/10.1038/s41467-022-34934-x  

   Xu, Tongtong; Newman, Matthew; Capotondi, Antonietta; Stevenson, Samantha; Di Lorenzo, Emanuele; Alexander, Michael A. (December 2022, Nature Communications)

   Abstract Marine heatwaves (MHWs)—extremely warm, persistent sea surface temperature (SST) anomalies causing substantial ecological and economic consequences—have increased worldwide in recent decades. Concurrent increases in global temperatures suggest that climate change impacted MHW occurrences, beyond random changes arising from natural internal variability. Moreover, the long-term SST warming trend was not constant but instead had more rapid warming in recent decades. Here we show that this nonlinear trend can—on its own—appear to increase SST variance and hence MHW frequency. Using a Linear Inverse Model to separate climate change contributions to SST means and internal variability, both in observations and CMIP6 historical simulations, we find that most MHW increases resulted from regional mean climate trends that alone increased the probability of SSTs exceeding a MHW threshold. Our results suggest the need to carefully attribute global warming-induced changes in climate extremes, which may not always reflect underlying changes in variability. 

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2. CMIP6 Models Underestimate Arctic Sea Ice Loss during the Early Twentieth-Century Warming, despite Simulating Large Low-Frequency Sea Ice Variability

   https://doi.org/10.1175/JCLI-D-23-0647.1  

   Bianco, Elena; Blanchard-Wrigglesworth, Edward; Materia, Stefano; Ruggieri, Paolo; Iovino, Doroteaciro; Masina, Simona (December 2024, Journal of Climate)

   Abstract The variability of Arctic sea ice extent (SIE) on interannual and multidecadal time scales is examined in 29 models with historical forcing participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6) and in twentieth-century sea ice reconstructions. Results show that during the historical period with low external forcing (1850–1919), CMIP6 models display relatively good agreement in their representation of interannual sea ice variability (IVSIE) but exhibit pronounced intermodel spread in multidecadal sea ice variability (MVSIE), which is overestimated with respect to sea ice reconstructions and is dominated by model uncertainty in sea ice simulation in the subpolar North Atlantic. We find that this is associated with differences in models’ sensitivity to Northern Hemispheric sea surface temperatures (SSTs). Additionally, we show that while CMIP6 models are generally capable of simulating multidecadal changes in Arctic sea ice from the mid-twentieth century to present day, they tend to underestimate the observed sea ice decline during the early twentieth-century warming (ETCW; 1915–45). These results suggest the need for an improved characterization of the sea ice response to multidecadal climate variability in order to address the sources of model bias and reduce the uncertainty in future projections arising from intermodel spread. Significance StatementThe credibility of Arctic sea ice predictions depends on whether climate models are capable of reproducing changes in the past climate, including patterns of sea ice variability which can mask or amplify the response to global warming. This study aims to better understand how latest-generation global climate models simulate interannual and multidecadal variability of Arctic sea ice relative to available observations. We find that models differ in their representation of multidecadal sea ice variability, which is overall larger than in observations. Additionally, models underestimate the sea ice decline during the period of observed warming between 1915 and 1945. Our results suggest that, to achieve better predictions of Arctic sea ice, the realism of low-frequency sea ice variability in models should be improved. 

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3. Local Regions Associated With Interdecadal Global Temperature Variability in the Last Millennium Reanalysis and CMIP5 Models

   https://doi.org/10.1029/2019JD030426  

   Parsons, L. A.; Hakim, G. J. (September 2019, Journal of Geophysical Research: Atmospheres)

   Abstract Despite the importance of interdecadal climate variability, we have a limited understanding of which geographic regions are associated with global temperature variability at these timescales. The instrumental record tends to be too short to develop sample statistics to study interdecadal climate variability, and Coupled Model Intercomparison Project, Phase 5 (CMIP5) climate models tend to disagree about which locations most strongly influence global mean interdecadal temperature variability. Here we use a new paleoclimate data assimilation product, the Last Millennium Reanalysis (LMR), to examine where local variability is associated with global mean temperature variability at interdecadal timescales. The LMR framework uses an ensemble Kalman filter data assimilation approach to combine the latest paleoclimate data and state‐of‐the‐art model data to generate annually resolved field reconstructions of surface temperature, which allow us to explore the timing and dynamics of preinstrumental climate variability in new ways. The LMR consistently shows that the middle‐ to high‐latitude north Pacific and the high‐latitude North Atlantic tend to lead global temperature variability on interdecadal timescales. These findings have important implications for understanding the dynamics of low‐frequency climate variability in the preindustrial era. 

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4. Intensification of El Niño Rainfall Variability Over the Tropical Pacific in the Slow Oceanic Response to Global Warming

   https://doi.org/10.1029/2018GL081414  

   Zheng, Xiao‐Tong; Hui, Chang; Xie, Shang‐Ping; Cai, Wenju; Long, Shang‐Min (February 2019, Geophysical Research Letters)

   Abstract Changes in rainfall variability of El Niño–Southern Oscillation (ENSO) are investigated under scenarios where the greenhouse gases increase and then stabilize. During the period of increasing greenhouse forcing, the ocean mixed layer warms rapidly. After the forcing stabilizes, the deeper ocean continues to warm the surface (the slow response). We show that ENSO rainfall variability over the tropical Pacific intensifies in both periods but the rate of increase per degree global mean surface temperature (GMST) warming is larger for the slow response because of greater relative warming in the base state as the mean upwelling changes from a damping to a driver of the surface warming. Our results have important implications for climate extremes under GMST stabilization that the Paris Agreement calls for. To stabilize GMST, the fast surface cooling offsets the slow warming from the prior greenhouse gas increase, while ENSO rainfall variability would continue to increase. 

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5. Biased Estimates of Equilibrium Climate Sensitivity and Transient Climate Response Derived From Historical CMIP6 Simulations

   https://doi.org/10.1029/2021GL095778  

   Dong, Yue; Armour, Kyle C.; Proistosescu, Cristian; Andrews, Timothy; Battisti, David S.; Forster, Piers M.; Paynter, David; Smith, Christopher J.; Shiogama, Hideo (December 2021, Geophysical Research Letters)

   Abstract This study assesses the effective climate sensitivity (EffCS) and transient climate response (TCR) derived from global energy budget constraints within historical simulations of eight CMIP6 global climate models (GCMs). These calculations are enabled by use of the Radiative Forcing Model Intercomparison Project (RFMIP) simulations, which permit accurate quantification of the radiative forcing. Long‐term historical energy budget constraints generally underestimate EffCS from CO₂quadrupling and TCR from CO₂ramping, owing to changes in radiative feedbacks and changes in ocean heat uptake efficiency. Atmospheric GCMs forced by observed warming patterns produce lower values of EffCS that are more in line with those inferred from observed historical energy budget changes. The differences in the EffCS estimates from historical energy budget constraints of models and observations are traced to discrepancies between modeled and observed historical surface warming patterns. 

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