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1. Correlation Between Sea‐Level Rise and Aspects of Future Tropical Cyclone Activity in CMIP6 Models

   https://doi.org/10.1029/2021EF002462  

   Lockwood, Joseph W.; Oppenheimer, Michael; Lin, Ning; Kopp, Robert E.; Vecchi, Gabriel A.; Gori, Avantika (April 2022, Earth's Future)

   Abstract Future coastal flood hazard at many locations will be impacted by both tropical cyclone (TC) change and relative sea‐level rise (SLR). Despite sea level and TC activity being influenced by common thermodynamic and dynamic climate variables, their future changes are generally considered independently. Here, we investigate correlations between SLR and TC change derived from simulations of 26 Coupled Model Intercomparison Project Phase 6 models. We first explore correlations between SLR and TC activity by inference from two large‐scale factors known to modulate TC activity: potential intensity (PI) and vertical wind shear. Under the high emissions SSP5‐8.5, SLR is strongly correlated with PI change (positively) and vertical wind shear change (negatively) over much of the western North Atlantic and North West Pacific, with global mean surface air temperature (GSAT) modulating the co‐variability. To explore the impact of the joint changes on flood hazard, we conduct climatological–hydrodynamic modeling at five sites along the US East and Gulf Coasts. Positive correlations between SLR and TC change alter flood hazard projections, particularly at Wilmington, Charleston and New Orleans. For example, if positive correlations between SLR and TC changes are ignored in estimating flood hazard at Wilmington, the average projected change to the historical 100 years storm tide event is under‐estimated by 12%. Our results suggest that flood hazard assessments that neglect the joint influence of these factors and that do not reflect the full distribution of GSAT change may not accurately represent future flood hazard. 

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2. Hurricane-induced power outage risk under climate change is primarily driven by the uncertainty in projections of future hurricane frequency

   https://doi.org/10.1038/s41598-020-72207-z  

   Alemazkoor, Negin; Rachunok, Benjamin; Chavas, Daniel R.; Staid, Andrea; Louhghalam, Arghavan; Nateghi, Roshanak; Tootkaboni, Mazdak (September 2020, Scientific Reports)

   Abstract Nine in ten major outages in the US have been caused by hurricanes. Long-term outage risk is a function of climate change-triggered shifts in hurricane frequency and intensity; yet projections of both remain highly uncertain. However, outage risk models do not account for the epistemic uncertainties in physics-based hurricane projections under climate change, largely due to the extreme computational complexity. Instead they use simple probabilistic assumptions to model such uncertainties. Here, we propose a transparent and efficient framework to, for the first time, bridge the physics-based hurricane projections and intricate outage risk models. We find that uncertainty in projections of the frequency of weaker storms explains over 95% of the uncertainty in outage projections; thus, reducing this uncertainty will greatly improve outage risk management. We also show that the expected annual fraction of affected customers exhibits large variances, warranting the adoption of robust resilience investment strategies and climate-informed regulatory frameworks. 

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3. Evaluation of Arctic warming from the CMIP6 models

   https://doi.org/https://doi.org/10.1175/JCLI-D-20-0791.1  

   Cai, Z. (June 2021, Journal of climate)

   null (Ed.)

   The Arctic has experienced a warming rate higher than the global mean in the past decades, but previous studies show that there are large uncertainties associated with future Arctic temperature projections. In this study, near- surface mean temperatures in the Arctic are analyzed from 22 models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Compared with the ERA5 reanalysis, most CMIP6 models underestimate the observed mean temperature in the Arctic during 1979–2014. The largest cold biases are found over the Greenland Sea the Barents Sea, and the Kara Sea. Under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, the multimodel ensemble mean of 22 CMIP6 models exhibits significant Arctic warming in the future and the warming rate is more than twice that of the global/Northern Hemisphere mean. Model spread is the largest contributor to the overall uncertainty in projections, which accounts for 55.4% of the total uncertainty at the start of projections in 2015 and remains at 32.9% at the end of projections in 2095. Internal variability uncertainty accounts for 39.3% of the total uncertainty at the start of projections but decreases to 6.5% at the end of the twenty-first century, while scenario uncertainty rapidly increases from 5.3% to 60.7% over the period from 2015 to 2095. It is found that the largest model uncertainties are consistent cold bias in the oceanic regions in the models, which is connected with excessive sea ice area caused by the weak Atlantic poleward heat transport. These results suggest that large intermodel spread and uncertainties exist in the CMIP6 models’ simulation and projection of the Arctic near- surface temperature and that there are different responses over the ocean and land in the Arctic to greenhouse gas forcing. Future research needs to pay more attention to the different characteristics and mechanisms of Arctic Ocean and land warming to reduce the spread. 

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4. Drivers of uncertainty in future projections of Madden–Julian Oscillation teleconnections

   https://doi.org/10.5194/wcd-2-653-2021  

   Jenney, Andrea M.; Randall, David A.; Barnes, Elizabeth A. (January 2021, Weather and Climate Dynamics)

   Abstract. Teleconnections from the Madden–Julian Oscillation (MJO) are a key source of predictability of weather on the extended timescale of about 10–40 d. The MJO teleconnection is sensitive to a number of factors, including the mean dry static stability, the mean flow, and the propagation and intensity characteristics of the MJO, which are traditionally difficult to separate across models. Each of these factors may evolve in response to increasing greenhouse gas emissions, which will impact MJO teleconnections and potentially impact predictability on extended timescales. Current state-of-the-art climate models do not agree on how MJO teleconnections over central and eastern North America will change in a future climate. Here, we use results from the Coupled Model Intercomparison Project Phase 6 (CMIP6) historical and SSP585 experiments in concert with a linear baroclinic model (LBM) to separate and investigate alternate mechanisms explaining why and how boreal winter (January) MJO teleconnections over the North Pacific and North America may change in a future climate and to identify key sources of inter-model uncertainty. LBM simulations suggest that a weakening teleconnection due to increases in tropical dry static stability alone is robust across CMIP6 models and that uncertainty in mean state winds is a key driver of uncertainty in future MJO teleconnections. Uncertainty in future changes to the MJO's intensity, eastward propagation speed, zonal wavenumber, and eastward propagation extent are other important sources of uncertainty in future MJO teleconnections. We find no systematic relationship between future changes in the Rossby wave source and the MJO teleconnection or between changes to the zonal wind or stationary Rossby wave number and the MJO teleconnection over the North Pacific and North America. LBM simulations suggest a reduction of the boreal winter MJO teleconnection over the North Pacific and an uncertain change over North America, with large spread over both regions that lends to weak confidence in the overall outlook. While quantitatively determining the relative importance of MJO versus mean state uncertainties in determining future teleconnections remains a challenge, the LBM simulations suggest that uncertainty in the mean state winds is a larger contributor to the uncertainty in future projections of the MJO teleconnection than the MJO. 

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5. Projecting Changes in the Drivers of Compound Flooding in Europe Using CMIP6 Models

   https://doi.org/10.1029/2023EF004188  

   Hermans, Tim_H J; Busecke, Julius_J M; Wahl, Thomas; Malagón‐Santos, Víctor; Tadesse, Michael G; Jane, Robert A; van_de_Wal, Roderik_S W (May 2024, Earth's Future)

   Abstract When different flooding drivers co‐occur, they can cause compound floods. Despite the potential impact of compound flooding, few studies have projected how the joint probability of flooding drivers may change. Furthermore, existing projections may not be very robust, as they are based on only 5 to 6 climate model simulations. Here, we use a large ensemble of simulations from the Coupled Model Intercomparison Project 6 (CMIP6) to project changes in the joint probability of extreme storm surges and precipitation at European tide gauges under a medium and high emissions scenario, enabled by data‐proximate cloud computing and statistical storm surge modeling. We find that the joint probability will increase in the northwest and decrease in most of the southwest of Europe. Averaged over Europe, the absolute magnitude of these changes is 36%–49% by 2080, depending on the scenario. The large‐scale changes in the joint probability of extreme storm surges and precipitation are similar to those in the joint probability of extreme wind speeds and precipitation, but locally, differences can exceed the changes themselves. Due to internal climate variability and inter‐model differences, projections based on simulations of only 5 to 6 randomly chosen CMIP6 models have a probability of higher than 10% to differ qualitatively from projections based on all CMIP6 simulations in multiple regions, especially under the medium emissions scenario and earlier in the twenty‐first century. Therefore, our results provide a more robust and less uncertain representation of changes in the potential for compound flooding in Europe than previous projections. 

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