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Effective climate sensitivity (EffCS), commonly estimated from model simulations with abrupt 4×CO2 for 150 years, has been shown to depend on the CO2 forcing level. To understand this dependency systematically, we performed a series of simulations with a range of abrupt CO2 forcing in two climate models. Our results indicate that normalized EffCS values in these simulations are a non-monotonic function of the CO2 forcing, decreasing between 3× and 4×CO2 in CESM1-LE (2× and 3×CO2 in GISS-E2.1-G) and increasing at higher CO2 levels. The minimum EffCS value, caused by anomalously negative radiative feedbacks, arises mainly from sea-surface temperature (SST) relative cooling in the tropical and subtropical North Atlantic. This cooling is associated with the formation of the North Atlantic Warming Hole and Atlantic Meridional Overturning Circulation collapse under CO2 forcing. Our findings imply that understanding changes in North Atlantic SST patterns is important for constraining near-future and equilibrium global warming.

The Brewer–Dobson circulation (BDC) is a key feature of the stratosphere that models need to accurately represent in order to simulate surface climate variability and change adequately. For the first time, the Climate Model Intercomparison Project includes in its phase 6 (CMIP6) a set of diagnostics that allow for careful evaluation of the BDC. Here, the BDC is evaluated against observations and reanalyses using historical simulations. CMIP6 results confirm the well-known inconsistency in the sign of BDC trends between observations and models in the middle and upper stratosphere. Nevertheless, the large uncertainty in the observational trend estimates opens the door to compatibility. In particular, when accounting for the limited sampling of the observations, model and observational trend error bars overlap in 40 % of the simulations with available output. The increasing CO2 simulations feature an acceleration of the BDC but reveal a large spread in the middle-to-upper stratospheric trends, possibly related to the parameterized gravity wave forcing. The very close connection between the shallow branch of the residual circulation and surface temperature is highlighted, which is absent in the deep branch. The trends in mean age of air are shown to be more robust throughout the stratosphere than those in the residual circulation.

Apparent hydrological sensitivity (ηa), the change in the global mean precipitation per degree K of global surface warming, is a key aspect of the climate system's response to increasing CO2 forcing. To determine whether ηa depends on the forcing amplitude we analyze idealized experiments over a broad range of abrupt CO2 forcing, from 2× to 8× preindustrial values, with two distinct climate models. We find little change in ηa between 2× and 4×CO2, and almost no change beyond 5×CO2. We validate this finding under transient CO2 forcing at 1%‐per‐year, up to 8×CO2. We further corroborate this result by analyzing the 1%‐per‐year output of more than 15 CMIP5/6 models. Lastly, we examine the 1,000‐year long LongrunMIP model output, and again find little change in ηa. This wealth of results demonstrates that ηa is a very weak function of CO2 forcing. Plain Language Summary Hydrological sensitivity (HS) is defined as the change in globally‐averaged precipitation per degree K of surface temperature increase caused by increasing concentrations of greenhouse gasses, such as CO2. It is important to understand how HS changes with different levels of CO2 in the atmosphere. To do this we analyzed model experiments with varying increases of CO2. We find little change in HS between 2× to 4× the pre‐industrial levels of CO2, and almost no change beyond 5×CO2. Additionally, we analyze model experiments where CO2 concentrations increase by 1% per year and see similar results. Finally, we validate this finding with models with significantly longer run times. We thus conclude that HS is independent of the level of CO2 in the atmosphere. Key Points We examine the dependence of apparent hydrological sensitivity (ηa, defined as ΔP/ΔT) on the magnitude of CO2 forcing We find little change in ηa in abrupt 2× to 8×CO2 experiments and a transient 1%/year experiment up to 8×CO2 Little change in ηa, notably at large CO2, is also found in most CMIP5, CMIP6, and Longrun‐MIP models

One of the key questions in the air quality and climate sciences is how tropospheric ozone concentrations will change in the future. This will depend on two factors: changes in stratosphere-to-troposphere transport (STT) and changes in tropospheric chemistry. Here we aim to identify robust changes in STT using simulations from the Chemistry Climate Model Initiative (CCMI) under a common climate change scenario (RCP6.0). We use two idealized stratospheric tracers to isolate changes in transport: stratospheric ozone (O3S), which is exactly like ozone but has no chemical sources in the troposphere, and st80, a passive tracer with fixed volume mixing ratio in the stratosphere. We find a robust increase in the tropospheric columns of these two tracers across the models. In particular, stratospheric ozone in the troposphere is projected to increase 10 %–16 % by the end of the 21st century in the RCP6.0 scenario. Future STT is enhanced in the subtropics due to the strengthening of the shallow branch of the Brewer–Dobson circulation (BDC) in the lower stratosphere and of the upper part of the Hadley cell in the upper troposphere. The acceleration of the deep branch of the BDC in the Northern Hemisphere (NH) and changes in eddy transport contribute to increased STT at high latitudes. These STT trends are caused by greenhouse gas (GHG) increases, while phasing out of ozone-depleting substances (ODS) does not lead to robust transport changes. Nevertheless, the decline of ODS increases the reservoir of ozone in the lower stratosphere, which results in enhanced STT of O3S at middle and high latitudes. A higher emission scenario (RCP8.5) produces stronger STT trends, with increases in tropospheric column O3S more than 3 times larger than those in the RCP6.0 scenario by the end of the 21st century.

The enhanced warming of the Arctic, relative to other parts of the Earth, a phenomenon known as Arctic amplification, is one of the most striking features of climate change, and has important climatic impacts for the entire Northern Hemisphere. Several mechanisms are believed to be responsible for Arctic amplification; however, a quantitative understanding of their relative importance is still missing. Here, using ensembles of model integrations, we quantify the contribution of ocean coupling, both its thermodynamic and dynamic components, to Arctic amplification over the 20th and 21st centuries. We show that ocean coupling accounts for ~80% of the amplification by 2100. In particular, we show that thermodynamic coupling is responsible for future amplification and sea-ice loss as it overcomes the effect of dynamic coupling which reduces the amplification and sea-ice loss by ~35%. Our results demonstrate the utility of targeted numerical experiments to quantify the role of specific mechanisms in Arctic amplification, for better constraining climate projections.

The North Atlantic Oscillation (NAO) is the principal mode of atmospheric variability over the North Atlantic, modulating the weather and climate of neighboring regions in both winter and summer. While Earth System Models generally project a more positive NAO under 21st century high-emission scenarios, uncertainties persist as to the precise response of the NAO to increased CO 2 levels, owing to large internal variability. In this study we investigate the response of the NAO to a wide range of CO 2 forcings, from two to eight times the preindustrial values. Analyzing a large sample of present-generation climate models, we find that the NAO likely becomes more positive with increasing CO 2 concentrations. Moreover, we find a reduction in NAO variability. This leads to a smaller increase in the likelihood of extremely positive NAO events than would be expected based solely on the shift in the mean. On the other hand, we also find a reduction in extremely negative NAO events, which is attributable to both the shift toward more positive values and the decrease in variance. Finally, our analysis reveals that the distribution of the NAO response at high CO 2 forcing is negatively skewed. This fact partially offsets the decrease in extremely positive NAO events associated with reduced variability. Ultimately, our results suggest a greater increase in positive NAO events compared to the decrease in extremely negative NAO events at higher CO 2 forcing.

The distribution of transit times from the Northern Hemisphere (NH) midlatitude surface is a fundamental property of tropospheric transport. Here, the authors present an analysis of the transit-time distribution (TTD) since air last contacted the NH midlatitude surface, as simulated by the NASA Global Modeling Initiative Chemistry Transport Model. Throughout the troposphere, the TTD is characterized by young modes and long tails. This results in mean transit times or “mean ages” Γ that are significantly larger than their corresponding modal transit times or “modal ages” τmode, especially in the NH, where Γ ≈ 0.5 yr, while τmode < 20 days. In addition, the shape of the TTD changes throughout the troposphere as the ratio of the spectral width Δ—the second temporal moment of the TTD—to the mean age decreases sharply in the NH from ~2.5 at NH high latitudes to ~0.7 in the Southern Hemisphere (SH). Decreases in Δ/Γ in the SH reflect a narrowing of the TTD relative to its mean and physically correspond to changes in the contributions of fast transport paths relative to slow eddy-diffusive recirculations. It is shown that fast transport paths control the patterns and seasonal cycles of idealized 5- and 50-day loss tracers in the Arctic and the tropics, respectively. The relationship between different TTD time scales and the idealized loss tracers, therefore, is conditional on the shape of the TTD.

Accurately modeling the large‐scale transport of trace gases and aerosols is critical for interpreting past (and projecting future) changes in atmospheric composition. Simulations of the stratospheric mean age‐of‐air continue to show persistent biases in chemistry climate models, although the drivers of these biases are not well understood. Here we identify one driver of simulated stratospheric transport differences among various NASA Global Earth Observing System (GEOS) candidate model versions under consideration for the upcoming GEOS Retrospective analysis for the 21st$21\\text{st}$Century (GEOS‐R21C). In particular, we show that the simulated age‐of‐air values are sensitive to the so‐called “remapping” algorithm used within the finite‐volume dynamical core, which controls how individual material surfaces are vertically interpolated back to standard pressure levels after each horizontal advection time step. Differences in the age‐of‐air resulting from changes within the remapping algorithm approach ∼${\\sim} $ 1 year over the high latitude middle stratosphere—or about 30% climatological mean values—and imprint on several trace gases, including methane (CH4${\\text{CH}}_{4}$ ) and nitrous oxide (N2${\\mathrm{N}}_{2}$ O). These transport sensitivities reflect, to first order, changes in the strength of tropical upwelling in the lower stratosphere (70–100 hPa) which are driven by changes in resolved wave convergence over northern midlatitudes as (critical lines of) wave propagation shift in latitude. Our results strongly support continued examination of the role of numerics in contributing to transport biases in composition modeling. Plain Language Summary Large‐scale transport plays a crucial role in distributing climatically important trace constituents in the atmosphere, especially in the stratosphere where transport largely determines the chemical lifetimes of trace gases. One summary of transport in the stratosphere is the “mean age” or the mean transit time since air at a point in the stratosphere was last in the troposphere. Current models used for simulating stratospheric composition produce a range of simulated ages, although these differences are poorly understood. Among other factors, model numerics play a critical role in transport, but few studies have explored the sensitivity of the mean age to the choice of numerical scheme employed within different dynamical cores. Here we use one model to show that the mean age is sensitive to the so‐called “remapping” algorithm used within the finite‐volume dynamical core that controls how individual material surfaces are vertically interpolated back to standard pressure levels after each horizontal advection time step. This reflects sensitivities in the representation of how waves propagate from the troposphere into the stratosphere. This work suggests that model numerics can be an important factor in contributing to differences in simulated transport among models. Key Points The stratospheric mean age‐of‐air simulated in NASA Global Earth Observing System is sensitive to the remapping scheme used within the finite‐volume dynamical core This sensitivity in the age‐of‐air is large (∼30%) and imprints on the simulated distributions of long‐lived trace gases (e.g., N2O, CH4) The age‐of‐air sensitivities primarily reflect changes in resolved wave convergence over the NH extratropical stratosphere

Offline chemical transport models (CTMs) have traditionally been used to perform studies of atmospheric chemistry in a fixed dynamical environment. An alternative to using CTMs is to constrain the flow in a general circulation model using winds from meteorological analyses. The Goddard Earth Observing System (GEOS) “replay” approach involves reading in analyzed fields every 6 h and recomputing the analysis increments, which are applied as a forcing to the meteorology at every model time step. Unlike in CTM, all of the subgrid‐scale processes are recalculated online so that they are consistent with the large‐scale analysis fields, similar in spirit to “nudged” simulations, in which the online meteorology is relaxed to the analysis. Here we compare the transport of idealized tracers in different replay simulations constrained with meteorological fields taken from The Modern‐Era Retrospective Analysis for Research and Applications, Version 2 (MERRA‐2). We show that there are substantial differences in their large‐scale stratospheric transport, depending on whether analysis fields or assimilated fields are used. Replay simulations constrained with the instantaneous analysis fields produce stratospheric mean age values that are up to 30% too young relative to observations; by comparison, simulations constrained with the time‐averaged assimilated fields produce more credible stratospheric transport. Our study indicates that care should be taken to correctly configure the model when the replay technique is used to simulate stratospheric composition. Key Points GEOS replay simulations produce credible large‐scale stratospheric and tropospheric transport for use in studies of atmospheric composition Simulations constrained with analysis fields produce stratospheric mean ages that are too young, versus when assimilated fields are used By comparison, large‐scale tropospheric transport properties are relatively insensitive to whether analysis or assimilated fields are used

We provide an overview of the REF-C1SD specified-dynamics experiment that was conducted as part of phase 1 of the Chemistry-Climate Model Initiative (CCMI). The REF-C1SD experiment, which consisted of mainly nudged general circulation models (GCMs) constrained with (re)analysis fields, was designed to examine the influence of the large-scale circulation on past trends in atmospheric composition. The REF-C1SD simulations were produced across various model frameworks and are evaluated in terms of how well they represent different measures of the dynamical and transport circulations. In the troposphere there are large (~40 %) differences in the climatological mean distributions, seasonal cycle amplitude, and trends of the meridional and vertical winds. In the stratosphere there are similarly large (~50 %) differences in the magnitude, trends and seasonal cycle amplitude of the transformed Eulerian mean circulation and among various chemical and idealized tracers. At the same time, interannual variations in nearly all quantities are very well represented, compared to the underlying reanalyses. We show that the differences in magnitude, trends and seasonal cycle are not related to the use of different reanalysis products; rather, we show they are associated with how the simulations were implemented, by which we refer both to how the large-scale flow was prescribed and to biases in the underlying free-running models. In most cases these differences are shown to be as large or even larger than the differences exhibited by free-running simulations produced using the exact same models, which are also shown to be more dynamically consistent. Overall, our results suggest that care must be taken when using specified-dynamics simulations to examine the influence of large-scale dynamics on composition.