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The sixth and latest IPCC assessment weights climate models according to how well they reproduce other evidence. Now the rest of the community should do the same. The sixth and latest IPCC assessment weights climate models according to how well they reproduce other evidence. Now the rest of the community should do the same.

Southern Ocean surface cooling and Antarctic sea ice expansion from 1979 through 2015 have been linked both to changing atmospheric circulation and melting of Antarctica's grounded ice and ice shelves. However, climate models have largely been unable to reproduce this behavior. Here we examine the contribution of observed wind variability and Antarctic meltwater to Southern Ocean sea surface temperature (SST) and Antarctic sea ice. The free‐running, CMIP6‐class GISS‐E2.1‐G climate model can simulate regional cooling and neutral sea ice trends due to internal variability, but they are unlikely. Constraining the model to observed winds and meltwater fluxes from 1990 through 2021 gives SST variability and trends consistent with observations. Meltwater and winds contribute a similar amount to the SST trend, and winds contribute more to the sea ice trend than meltwater. However, while the constrained model captures much of the observed sea ice variability, it only partially captures the post‐2015 sea ice reduction. Plain Language Summary While most of the globe has warmed in recent decades, the Southern Ocean around Antarctica cooled at the surface and its sea ice expanded from the beginning of satellite observations in 1979 through 2015. This unexpected behavior has been linked to changes in winds and to the addition of cold, fresh water from the melting of Antarctic's ice sheet and ice shelves. However, the importance of these two potential drivers has been unclear, partly because global climate models have often struggled to reproduce the observed changes. Here, we modify a climate model, constraining it to simulate observed winds and adding in realistic amounts of meltwater. With these changes, the model can simulate changes in SST and sea ice that are similar to observations. Winds and meltwater both play an important role. However, they cannot fully explain the large Antarctic sea ice reductions that were observed after 2015, suggesting that other factors may be at play. Key Points We nudge winds to observations and add estimates of observed freshwater from ice sheet and ice shelf melt in a coupled climate model Southern Ocean sea surface temperature trends and variability better match observations, with both winds and meltwater being important The constrained model simulates strong Antarctic sea ice expansion and only partially captures recent sea ice lows

Climate models projected stronger warming over the past 15 years than has been seen in observations. Conspiring factors of errors in volcanic and solar inputs, representations of aerosols, and El Niño evolution, may explain most of the discrepancy.

Recent mass loss from ice sheets and ice shelves is now persistent and prolonged enough that it impacts downstream oceanographic conditions. To demonstrate this, we use an ensemble of coupled GISS‐E2.1‐G simulations forced with historical estimates of anomalous freshwater, in addition to other climate forcings, from 1990 through 2019. There are detectable differences in zonal‐mean sea surface temperatures (SST) and sea ice in the Southern Ocean, and in regional sea level around Antarctica and in the western North Atlantic. These impacts mostly improve the model's representation of historical changes, including reversing the forced trends in Antarctic sea ice. The changes in SST may have implications for estimates of the SST pattern effect on climate sensitivity and for cloud feedbacks. We conclude that the changes are sufficiently large that model groups should strive to include more accurate estimates of these drivers in all‐forcing historical simulations in future coupled model intercomparisons. Plain Language Summary Simulations of recent historical periods are a key test of climate model reliability and skill. These model simulations require an accounting of all the drivers of climate change. We show that the impact of historical changes in freshwater fluxes from ice sheets and ice shelves on the ocean (through changes in salinity and stratification) are detectable in sea surface temperature and sea ice trends, and help improve the match between the modeled climate changes and observations. We recommend that more accurate estimates of these drivers be included in all climate simulations that do not explicitly model ice sheets and ice shelves. Key Points The response to anomalous meltwater from ice sheets and shelves is large enough for it to be a forcing in historical climate simulations When the GISS model includes these drivers, Southern Ocean SST and sea ice trends better match observations Steric and dynamic impacts on regional sea level in parts of the North Atlantic and coastal Antarctica are significant

Taking into account all known factors, the planet warmed 0.2 °C more last year than climate scientists expected. More and better data are urgently needed. Taking into account all known factors, the planet warmed 0.2 °C more last year than climate scientists expected. More and better data are urgently needed.

Ample physical evidence shows that carbon dioxide (CO₂) is the single most important climate-relevant greenhouse gas in Earth's atmosphere. This is because CO₂, like ozone, N₂O, CH₄, and chlorofluorocarbons, does not condense and precipitate from the atmosphere at current climate temperatures, whereas water vapor can and does. Noncondensing greenhouse gases, which account for 25% of the total terrestrial greenhouse effect, thus serve to provide the stable temperature structure that sustains the current levels of atmospheric water vapor and clouds via feedback processes that account for the remaining 75% of the greenhouse effect. Without the radiative forcing supplied by CO₂ and the other noncondensing greenhouse gases, the terrestrial greenhouse would collapse, plunging the global climate into an icebound Earth state.

Societal benefits from climate change mitigation accrue via multiple pathways. We examine the US impacts of emission changes on several factors that are affected by both climate and air quality responses. Nationwide benefits through midcentury stem primarily from air quality improvements, which are realized rapidly, and include human health, labor productivity, and crop yield benefits. Benefits from reduced heat exposure become large around 2060, thereafter often dominating over those from improved air quality. Monetized benefits are in the tens of trillions of dollars for avoided deaths and tens of billions for labor productivity and crop yield increases and reduced hospital expenditures. Total monetized benefits this century are dominated by health and are much larger than in previous analyses due to improved understanding of the human health impacts of exposure to both heat and air pollution. Benefit–cost ratios are therefore much larger than in prior studies, especially those that neglected clean air benefits. Specifically, benefits from clean air exceed costs in the first decade, whereas benefits from climate alone exceed costs in the latter half of the century. Furthermore, monetized US benefits largely stem from US emissions reductions. Increased emphasis on the localized, near-term air quality–related impacts would better align policies with societal benefits and, by reducing the mismatch between perception of climate as a risk distant in space and time and the need for rapid action to mitigate long-term climate change, might help increase acceptance of mitigation policies.

A 10-member ensemble simulation with the NASA GISS-E2-1-G climate model shows a clear bifurcation in the Atlantic meridional overturning circulation (AMOC) strength under the SSP2–4.5 extended scenario. At 26°N, the bifurcation leads to 8 strong AMOC and 2 much weaker AMOC states, while at 48°N, it leads to 8 stable AMOC-on and 2 nearly AMOC-off states, the latter lasting approximately 800 years. A variety of fully coupled models have demonstrated tipping points in AMOC through hosing experiments, i.e., prescribing sufficient freshwater inputs in the subpolar North Atlantic. In the GISS simulations, there are no external freshwater perturbations. The bifurcation arises freely in the coupled system and is the result of stochastic variability (noise-induced bifurcation) associated with sea ice transport and melting in the Irminger Sea after a slowing of the greenhouse gas forcing. While the AMOC strength follows the near shutdown of the Labrador Sea deep convection initially, the Irminger Sea salinity and deep mixing determine the timing of the AMOC recovery or near collapse at 48°N, which varies widely across the ensemble members. Other feedbacks such as ice-albedo, ice-evaporation, E − P , and the overturning salt-advection feedback play a secondary role that may enhance or reduce the primary mechanism which is ice melt. We believe this is the first time that a coupled climate model has shown such a bifurcation across an initial condition ensemble and might imply that there is a chance for significant and prolonged AMOC slow down due to internal variability of the system.

Scafetta (2022, https://doi.org/10.1029/2022gl097716) purports to test Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models through a comparison of temperature changes over three decades. Unfortunately, the paper contains numerous conceptual and statistical errors that undermine all of the conclusions. First, no uncertainty is given for the observational temperature difference, making it impossible to assess compatibility with any model result. Second, the CMIP6 data are the ensemble means for each model, but the metric being tested is sensitive to the internal variability and so the full ensemble for each model must be used. When this is corrected, the conclusion that “all models with ECS > 3.0°C overestimate the observed global surface warming” is not sustained. Third, the statistical test in Section 2 would reject all models even in a perfect model setup given sufficient ensemble members, thus the second conclusion “that spatial t-statistics rejects the data-model agreement” is also not sustainable.