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Dorian S. Abbot

Abstract

Recent general circulation model (GCM) simulations have challenged the idea that a snowball Earth would be nearly entirely cloudless. This is important because clouds would provide a strong warming to a high-albedo snowball Earth. GCM results suggest that clouds could lower the threshold CO2 needed to deglaciate a snowball by a factor of 10–100, enough to allow consistency with geochemical data. Here a cloud-resolving model is used to investigate cloud and convection behavior in a snowball Earth climate. The model produces convection that extends vertically to a similar temperature as modern tropical convection. This convection produces clouds that resemble stratocumulus clouds under an inversion on modern Earth, which slowly dissipate by sedimentation of cloud ice. There is enough cloud ice for the clouds to be optically thick in the longwave, and the resulting cloud radiative forcing is similar to that produced in GCMs run in snowball conditions. This result is robust to large changes in the cloud microphysics scheme because the cloud longwave forcing, which dominates the total forcing, is relatively insensitive to cloud amount and particle size. The cloud-resolving model results are therefore consistent with the idea that clouds would provide a large warming to a snowball Earth, helping to allow snowball deglaciation.

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Dorian S. Abbot and Eli Tziperman

Abstract

Previous work has shown that a convective cloud feedback can greatly increase high-latitude surface temperature upon the removal of sea ice and can keep sea ice from forming throughout polar night. This feedback activates at increased greenhouse gas concentrations. It may help to explain the warm “equable climates” of the late Cretaceous and early Paleogene eras (∼100 to ∼35 million years ago) and may be relevant for future climate under global warming. Here, the factors that determine the critical threshold CO2 concentration at which this feedback is active and the magnitude of the warming caused by the feedback are analyzed using both a highly idealized model and NCAR’s single-column atmospheric model (SCAM) run under Arctic-like conditions. The critical CO2 is particularly important because it helps to establish the relevance of the feedback for past and future climates.

Both models agree that increased heat flux into the high latitudes at low altitudes generally decreases the critical CO2. Increases in oceanic heat transport and in solar radiation absorbed during the summer should cause a sharp decrease in the critical CO2, but the effect of increases in atmospheric heat transport depends on its vertical distribution. It is furthermore found (i) that if the onset of convection produces more clouds and moisture, the critical CO2 should decrease, and the maximum temperature increase caused by the convective cloud feedback should increase and (ii) that reducing the depth of convection reduces the critical CO2 but has little effect on the maximum temperature increase caused by the convective cloud feedback. These results should help with interpretation of the strength and onset of the convective cloud feedback as found, for example, in Intergovernmental Panel on Climate Change (IPCC) coupled ocean–atmosphere models with different cloud and convection schemes.

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Dorian S. Abbot and Itay Halevy

Abstract

Most previous global climate model simulations could only produce the termination of Snowball Earth episodes at CO2 partial pressures of several tenths of a bar, which is roughly an order of magnitude higher than recent estimates of CO2 levels during and shortly after Snowball events. These simulations have neglected the impact of dust aerosols on radiative transfer, which is an assumption of potentially grave importance. In this paper it is argued, using the Dust Entrainment and Deposition (DEAD) box model driven by GCM results, that atmospheric dust aerosol concentrations may have been one to two orders of magnitude higher during a Snowball Earth event than today. It is furthermore asserted on the basis of calculations using NCAR’s Single Column Atmospheric Model (SCAM)—a radiative–convective model with sophisticated aerosol, cloud, and radiative parameterizations—that when the surface albedo is high, such increases in dust aerosol loading can produce several times more surface warming than an increase in the partial pressure of CO2 from 10−4 to 10−1 bar. Therefore the conclusion is reached that including dust aerosols in simulations may reconcile the CO2 levels required for Snowball termination in climate models with observations.

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Dorian S. Abbot and Kerry A. Emanuel

Abstract

A two-column atmospheric model on a land–sea interface is studied. The model has sophisticated convection, cloud, and radiation schemes, a mixed layer ocean, and a bucket model to simulate land hydrology. A self-sustained oscillation in soil moisture with a period on the order of months is found. This oscillation is strongest when the model is run with parameters chosen to correspond to the arid subtropics. The effect of changing model parameters on the oscillation is explored. The existence and qualitative behavior of the oscillation are relatively robust to changes in model parameters.

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Daniel D. B. Koll and Dorian S. Abbot

Abstract

Previous studies have shown that increases in poleward ocean heat transport (OHT) do not strongly affect tropical SST. The goal of this paper is to explain this observation. To do so, the authors force two atmospheric global climate models (GCMs) in aquaplanet configuration with a variety of prescribed OHTs. It is found that increased OHT weakens the Hadley circulation, which decreases equatorial cloud cover and shortwave reflection, as well as reduces surface winds and evaporation, which both limit changes in tropical SST. The authors also modify one of the GCMs by alternatively setting the radiative effect of clouds to zero and disabling wind-driven evaporation changes to show that the cloud feedback is more important than the wind–evaporation feedback for maintaining constant equatorial SST as OHT changes. This work highlights the fact that OHT can reduce the meridional SST gradient without affecting tropical SST and could therefore serve as an additional degree of freedom for explaining past warm climates.

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Justin Finkel, Dorian S. Abbot, and Jonathan Weare

Abstract

Many rare weather events, including hurricanes, droughts, and floods, dramatically impact human life. To accurately forecast these events and characterize their climatology requires specialized mathematical techniques to fully leverage the limited data that are available. Here we describe transition path theory (TPT), a framework originally developed for molecular simulation, and argue that it is a useful paradigm for developing mechanistic understanding of rare climate events. TPT provides a method to calculate statistical properties of the paths into the event. As an initial demonstration of the utility of TPT, we analyze a low-order model of sudden stratospheric warming (SSW), a dramatic disturbance to the polar vortex that can induce extreme cold spells at the surface in the midlatitudes. SSW events pose a major challenge for seasonal weather prediction because of their rapid, complex onset and development. Climate models struggle to capture the long-term statistics of SSW, owing to their diversity and intermittent nature. We use a stochastically forced Holton–Mass-type model with two stable states, corresponding to radiative equilibrium and a vacillating SSW-like regime. In this stochastic bistable setting, from certain probabilistic forecasts TPT facilitates estimation of dominant transition pathways and return times of transitions. These “dynamical statistics” are obtained by solving partial differential equations in the model’s phase space. With future application to more complex models, TPT and its constituent quantities promise to improve the predictability of extreme weather events through both generation and principled evaluation of forecasts.

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Jonah Bloch-Johnson, Maria Rugenstein, and Dorian S. Abbot

Abstract

The sensitivity of the climate to CO2 forcing depends on spatially varying radiative feedbacks that act both locally and nonlocally. We assess whether a method employing multiple regression can be used to estimate local and nonlocal radiative feedbacks from internal variability. We test this method on millennial-length simulations performed with six coupled atmosphere–ocean general circulation models (AOGCMs). Given the spatial pattern of warming, the method does quite well at recreating the top-of-atmosphere flux response for most regions of Earth, except over the Southern Ocean where it consistently overestimates the change, leading to an overestimate of the sensitivity. For five of the six models, the method finds that local feedbacks are positive due to cloud processes, balanced by negative nonlocal shortwave cloud feedbacks associated with regions of tropical convection. For four of these models, the magnitudes of both are comparable to the Planck feedback, so that changes in the ratio between them could lead to large changes in climate sensitivity. The positive local feedback explains why observational studies that estimate spatial feedbacks using only local regressions predict an unstable climate. The method implies that sensitivity in these AOGCMs increases over time due to a reduction in the share of warming occurring in tropical convecting regions and the resulting weakening of associated shortwave cloud and longwave clear-sky feedbacks. Our results provide a step toward an observational estimate of time-varying climate sensitivity by demonstrating that many aspects of spatial feedbacks appear to be the same between internal variability and the forced response.

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Dorian S. Abbot, Ian Eisenman, and Raymond T. Pierrehumbert

Abstract

Sea ice schemes with a few vertical levels are typically used to simulate the thermodynamic evolution of sea ice in global climate models. Here it is shown that these schemes overestimate the magnitude of the diurnal surface temperature cycle by a factor of 2–3 when they are used to simulate tropical ice in a Snowball earth event. This could strongly influence our understanding of Snowball termination, which occurs in global climate models when the midday surface temperature in the tropics reaches the melting point. A hierarchy of models is used to show that accurate simulation of surface temperature variation on a given time scale requires that a sea ice model resolve the e-folding depth to which a periodic signal on that time scale penetrates. This is used to suggest modifications to the sea ice schemes used in global climate models that would allow more accurate simulation of Snowball deglaciation.

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Dorian S. Abbot, Chris C. Walker, and Eli Tziperman

Abstract

Winter sea ice dramatically cools the Arctic climate during the coldest months of the year and may have remote effects on global climate as well. Accurate forecasting of winter sea ice has significant social and economic benefits. Such forecasting requires the identification and understanding of all of the feedbacks that can affect sea ice.

A convective cloud feedback has recently been proposed in the context of explaining equable climates, for example, the climate of the Eocene, which might be important for determining future winter sea ice. In this feedback, CO2-initiated warming leads to sea ice reduction, which allows increased heat and moisture fluxes from the ocean surface, which in turn destabilizes the atmosphere and leads to atmospheric convection. This atmospheric convection produces optically thick convective clouds and increases high-altitude moisture levels, both of which trap outgoing longwave radiation and therefore result in further warming and sea ice loss.

Here it is shown that this convective cloud feedback is active at high CO2 during polar night in the coupled ocean–sea ice–land–atmosphere global climate models used for the 1% yr−1 CO2 increase to the quadrupling (1120 ppm) scenario of the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report. At quadrupled CO2, model forecasts of maximum seasonal (March) sea ice volume are found to be correlated with polar winter cloud radiative forcing, which the convective cloud feedback increases. In contrast, sea ice volume is entirely uncorrelated with model global climate sensitivity. It is then shown that the convective cloud feedback plays an essential role in the elimination of March sea ice at quadrupled CO2 in NCAR’s Community Climate System Model (CCSM), one of the IPCC models that loses sea ice year-round at this CO2 concentration. A new method is developed to disable the convective cloud feedback in the Community Atmosphere Model (CAM), the atmospheric component of CCSM, and to show that March sea ice cannot be eliminated in CCSM at CO2 = 1120 ppm without the aide of the convective cloud feedback.

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