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Understanding the climate dynamics at the inner edge of the habitable zone (HZ) is crucial for predicting the habitability of rocky exoplanets. Previous studies using Global Climate Models (GCMs) have indicated that planets receiving high stellar flux can exhibit climate bifurcations, leading to bistability between a cold (temperate) and a hot (runaway) climate. However, the mechanism causing this bistability has not been fully explained, in part due to the difficulty associated with inferring mechanisms from small numbers of expensive numerical simulations in GCMs. In this study, we employ a two-column (dayside and nightside), two-layer climate model to investigate the physical mechanisms driving this bistability. Through mechanism-denial experiments, we demonstrate that the runaway greenhouse effect, coupled with a cloud feedback on either the dayside or nightside, leads to climate bistability. We also map out the parameters that control the location of the bifurcations and size of the bistability. This work identifies which mechanisms and GCM parameters control the stellar flux at which rocky planets are likely to retain a hot, thick atmosphere if they experience a hot start. This is critical for the prioritization of targets and interpretation of observations by the James Webb Space Telescope (JWST). Furthermore, our modeling framework can be extended to planets with different condensable species and cloud types., Comment: 4 figures, submitted to ApJL

In the present tropical atmosphere, precipitation typically exhibits noisy, small-amplitude fluctuations about an average. However, recent cloud-resolving simulations show that in a hothouse climate, precipitation can shift to a regime characterized by nonlinear oscillations. In this regime, intense precipitation events are separated by several dry days. This raises questions about what triggers the shift from a quasi-equilibrium state of precipitation to nonlinear precipitation oscillations and what factors determine the characteristics of these oscillations. To address these questions, we present a low-order model that includes two nonlinear ordinary differential equations, one for precipitation and the other for convective inhibition (CIN). Three processes govern the development of precipitation: a convective trigger that enhances precipitation, a self-limiting mechanism that reduces intense precipitation, and the effect of CIN in suppressing precipitation. On the other hand, CIN increases due to compensating subsidence from convection and decays exponentially over time due to radiation. A critical parameter in our model is the time-mean CIN (CIN*). As CIN* gradually increases, precipitation transitions from a quasi-equilibrium state to a nonlinear oscillation via a supercritical Hopf bifurcation. In the high CIN* limit, our model reduces to predator-prey dynamics, with CIN as the predator and precipitation as the prey. Here, the nonlinear oscillation's amplitude (maximum precipitation) grows with CIN*, and its period increases with the oscillation amplitude. A suite of cloud-resolving simulations corroborates these predictions from our low-order model, highlighting the effect of convective triggering and inhibition in regulating precipitation variability.

Potentially habitable exoplanets are targets of great interest for the James Webb Space Telescope and upcoming mission concepts such as the Habitable Worlds Observatory. Clouds strongly affect climate and habitability, but predicting their properties is difficult. In Global Climate Models (GCMs), especially those aiming at simulating Earth, cloud microphysics is often crudely approximated by assuming that all cloud particles have a single, constant size or a prescribed size distribution and that all clouds in a grid cell are identical. For exoplanets that range over a large phase space of planetary properties, this method could result in large errors. In this work, our goal is to determine how cloud microphysics on terrestrial exoplanets, whose condensable is mainly water vapor, depend on aerosol properties and planetary parameters such as surface pressure, surface gravity, and incident stellar radiation. We use the Community Aerosol and Radiation Model for Atmospheres as a 1D microphysical model to simulate the formation and evolution of clouds including the processes of nucleation, condensation, evaporation, coagulation, and vertical transfer. In these 1D idealized experiments, we find that the parameters that determine the macrophysical thermal structure of the atmospheres, including surface pressure and stellar flux, impact cloud radiative effect (CRE) most significantly. Parameters such as gravity and number density of aerosols working as cloud condensation nuclei affect the microphysical processes of cloud formation, including activation and vertical transfer. They also have a significant, though weaker effect on CRE. This work motivates the development of more accurate GCM cloud schemes and should aid in the interpretation of future observations., Comment: 17 pages, 10 figures

Blocking events are an important cause of extreme weather, especially long-lasting blocking events that trap weather systems in place. The duration of blocking events is, however, underestimated in climate models. Explainable Artificial Intelligence are a class of data analysis methods that can help identify physical causes of prolonged blocking events and diagnose model deficiencies. We demonstrate this approach on an idealized quasigeostrophic model developed by Marshall and Molteni (1993). We train a convolutional neural network (CNN), and subsequently, build a sparse predictive model for the persistence of Atlantic blocking, conditioned on an initial high-pressure anomaly. Shapley Additive ExPlanation (SHAP) analysis reveals that high-pressure anomalies in the American Southeast and North Atlantic, separated by a trough over Atlantic Canada, contribute significantly to prediction of sustained blocking events in the Atlantic region. This agrees with previous work that identified precursors in the same regions via wave train analysis. When we apply the same CNN to blockings in the ERA5 atmospheric reanalysis, there is insufficient data to accurately predict persistent blocks. We partially overcome this limitation by pre-training the CNN on the plentiful data of the Marshall-Molteni model, and then using Transfer Learning to achieve better predictions than direct training. SHAP analysis before and after transfer learning allows a comparison between the predictive features in the reanalysis and the quasigeostrophic model, quantifying dynamical biases in the idealized model. This work demonstrates the potential for machine learning methods to extract meaningful precursors of extreme weather events and achieve better prediction using limited observational data., Comment: 29 pages, 10 figures

Rare event schemes require an approximation of the probability of the rare event as a function of system state. Finding an appropriate reaction coordinate is typically the most challenging aspect of applying a rare event scheme. Here we develop an artificial intelligence (AI) based reaction coordinate that effectively predicts which of a limited number of simulations of the Solar System will go unstable using a convolutional neural network classifier. The performance of the algorithm does not degrade significantly even 3.5 billion years before the instability. We overcome the class imbalance intrinsic to rare event problems using a combination of minority class oversampling, increased minority class weighting, and pulling multiple non-overlapping training sequences from simulations. Our success suggests that AI may provide a promising avenue for developing reaction coordinates without detailed theoretical knowledge of the system., Comment: 3 pages, 1 figure

Seeley and Wordsworth (2021) showed that in small-domain cloud-resolving simulations the pattern of precipitation transforms in extremely hot climates ($\ge$ 320 K) from quasi-steady to organized episodic deluges, with outbursts of heavy rain alternating with several dry days. They proposed a mechanism for this transition involving increased water vapor absorption of solar radiation leading to net lower-tropospheric radiative heating. This heating inhibits lower-tropospheric convection and decouples the boundary layer from the upper troposphere during the dry phase, allowing lower-tropospheric moist static energy to build until it discharges, resulting in a deluge. We perform cloud-resolving simulations in polar night and show that the same transition occurs, implying that some revision of their mechanism is necessary. We show that episodic deluges can occur even if the lower-tropospheric radiative heating rate is negative, as long as the magnitude of the upper-tropospheric radiative cooling is about twice as large. We find that in the episodic deluge regime the mean precipitation can be inferred from the atmospheric column energy budget and the period can be predicted from the time for radiation and reevaporation to cool the lower atmosphere.

Mercury's orbit can destabilize, generally resulting in a collision with either Venus or the Sun. Chaotic evolution can cause g1 to decrease to the approximately constant value of g5 and create a resonance. Previous work has approximated the variation in g1 as stochastic diffusion, which leads to a phenomological model that can reproduce the Mercury instability statistics of secular and N-body models on timescales longer than 10 Gyr. Here we show that the diffusive model underpredicts the Mercury instability probability by a factor of 3-10,000 on timescales less than 5 Gyr, the remaining lifespan of the Solar System. This is because g1 exhibits larger variations on short timescales than the diffusive model would suggest. To better model the variations on short timescales, we build a new subdiffusive phenomological model for g1. Subdiffusion is similar to diffusion but exhibits larger displacements on short timescales and smaller displacements on long timescales. We choose model parameters based on the behavior of the g1 trajectories in the N-body simulations, leading to a tuned model that can reproduce Mercury instability statistics from 1-40 Gyr. This work motivates fundamental questions in Solar System dynamics: Why does subdiffusion better approximate the variation in g1 than standard diffusion? Why is there an upper bound on g1, but not a lower bound that would prevent it from reaching g5?, Comment: accepted at ApJ

The long-term stability of the Solar System is an issue of significant scientific and philosophical interest. The mechanism leading to instability is Mercury's eccentricity being pumped up so high that Mercury either collides with Venus or is scattered into the Sun. Previously, only three five-billion-year $N$-body ensembles of the Solar System with thousands of simulations have been run to assess long-term stability. We generate two additional ensembles, each with 2750 members, and make them publicly available at \texttt{https://archive.org/details/@dorianabbot}. We find that accurate Mercury instability statistics can be obtained by (1) including only the Sun and the 8 planets, (2) using a simple Wisdom-Holman scheme without correctors, (3) using a basic representation of general relativity, and (4) using a time step of 3.16 days. By combining our Solar System ensembles with previous ensembles we form a 9,601-member ensemble of ensembles. In this ensemble of ensembles, the logarithm of the frequency of a Mercury instability event increases linearly with time between 1.3 and 5 Gyr, suggesting that a single mechanism is responsible for Mercury instabilities in this time range and that this mechanism becomes more active as time progresses. Our work provides a robust estimate of Mercury instability statistics over the next five billion years, outlines methodologies that may be useful for exoplanet system investigations, and provides two large ensembles of publicly available Solar System integrations that can serve as testbeds for theoretical ideas as well as training sets for artificial intelligence schemes., Comment: accepted at ApJ

The climate of a planet can be strongly affected by its eccentricity due to variations in the stellar flux. There are two limits for the dependence of the inner habitable zone boundary (IHZ) on eccentricity: (1) the mean-stellar flux approximation ($S_{\mbox{IHZ}} \propto \sqrt{1-e^2}$), in which the temperature is approximately constant throughout the orbit, and (2) the maximum-stellar flux approximation ($S_{\mbox{IHZ}} \propto (1-e)^2$), in which the temperature adjusts instantaneously to the stellar flux. Which limit is appropriate is determined by the dimensionless parameter $\Pi = \frac{C}{BP}$, where $C$ is the heat capacity of the planet, $P$ is the orbital period, and $B=\frac{\partial \Omega}{\partial T_s}$, where $\Omega$ is the outgoing longwave radiation and $T_s$ is the surface temperature. We use the Buckingham $\Pi$ theorem to derive an analytical function for the IHZ in terms of eccentricity and $\Pi$. We then build a time-dependent energy balance model to resolve the surface temperature evolution and constrain our analytical result. We find that $\Pi$ must be greater than about $\sim 1$ for the mean-stellar flux approximation to be nearly exact and less than about $\sim 0.01$ for the maximum-stellar flux approximation to be nearly exact. In addition to assuming a constant heat capacity, we also consider the effective heat capacity including latent heat (evaporation and precipitation). We find that for planets with an Earth-like ocean, the IHZ should follow the mean-stellar flux limit for all eccentricities. This work will aid in the prioritization of potentially habitable exoplanets with non-zero eccentricity for follow-up characterization., Comment: Accepted to ApJL

Extreme weather events have significant consequences, dominating the impact of climate on society. While high-resolution weather models can forecast many types of extreme events on synoptic timescales, long-term climatological risk assessment is an altogether different problem. A once-in-a-century event takes, on average, 100 years of simulation time to appear just once, far beyond the typical integration length of a weather forecast model. Therefore, this task is left to cheaper, but less accurate, low-resolution or statistical models. But there is untapped potential in weather model output: despite being short in duration, weather forecast ensembles are produced multiple times a week. Integrations are launched with independent perturbations, causing them to spread apart over time and broadly sample phase space. Collectively, these integrations add up to thousands of years of data. We establish methods to extract climatological information from these short weather simulations. Using ensemble hindcasts by the European Center for Medium-range Weather Forecasting (ECMWF) archived in the subseasonal-to-seasonal (S2S) database, we characterize sudden stratospheric warming (SSW) events with multi-centennial return times. Consistent results are found between alternative methods, including basic counting strategies and Markov state modeling. By carefully combining trajectories together, we obtain estimates of SSW frequencies and their seasonal distributions that are consistent with reanalysis-derived estimates for moderately rare events, but with much tighter uncertainty bounds, and which can be extended to events of unprecedented severity that have not yet been observed historically. These methods hold potential for assessing extreme events throughout the climate system, beyond this example of stratospheric extremes., Comment: 28 pages, 6 figures. Resubmitted for publication

The influence of atmospheric composition on the climates of present-day and early Earth has been studied extensively, but the role of ocean composition has received less attention. We use the ROCKE-3D ocean-atmosphere general circulation model to investigate the response of Earth's present-day and Archean climate system to low vs. high ocean salinity. We find that saltier oceans yield warmer climates in large part due to changes in ocean dynamics. Increasing ocean salinity from 20 g/kg to 50 g/kg results in a 71% reduction in sea ice cover in our present-day Earth scenario. This same salinity change also halves the pCO$_2$ threshold at which Snowball glaciation occurs in our Archean scenarios. In combination with higher levels of greenhouse gases such as CO$_2$ and CH$_4$, a saltier ocean may allow for a warm Archean Earth with only seasonal ice at the poles despite receiving 20% less energy from the Sun., Comment: Accepted for publication in Geophysical Research Letters 04/19/22

During the summer, vast regions of Arctic sea ice are covered by meltwater ponds that significantly lower the ice reflectivity and accelerate melting. Ponds develop over the melt season through an initial rapid growth stage followed by drainage through macroscopic holes. Recent analysis of melt pond photographs indicates that late-summer ponds exist near the critical percolation threshold, a special pond coverage fraction below which the ponds are largely disconnected and above which they are highly connected. Here, we show that the percolation threshold, a statistical property of ice topography, constrains pond evolution due to pond drainage through macroscopic holes. We show that it sets the approximate upper limit and scales pond coverage throughout its evolution after the beginning of drainage. Furthermore, we show that the rescaled pond coverage during drainage is a universal function of a single non-dimensional parameter, $\eta$, roughly interpreted as the number of drainage holes per characteristic area of the surface. This universal curve allows us to formulate an equation for pond coverage time-evolution during and after pond drainage that captures the dependence on environmental parameters and is supported by observations on undeformed first-year ice. This equation reveals that pond coverage is highly sensitive to environmental parameters, suggesting that modeling uncertainties could be reduced by more directly parameterizing the ponds' natural parameter, $\eta$. Our work uncovers previously unrecognized constraints on melt pond physics and places ponds within a broader context of phase transitions and critical phenomena. Therefore, it holds promise for improving ice-albedo parameterizations in large-scale models.

Our ability to predict the future of Arctic sea ice is limited by ice's sensitivity to detailed surface conditions such as the distribution of snow and melt ponds. Snow on top of the ice decreases ice's thermal conductivity, increases its reflectivity (albedo), and provides a source of meltwater for melt ponds during summer that decrease the ice's albedo. In this paper, we develop a simple model of pre-melt snow topography that accurately describes snow cover of flat, undeformed Arctic sea ice on several study sites for which data was available. The model considers a surface that is a sum of randomly sized and placed "snow dunes" represented as Gaussian mounds. This model generalizes the "void model" of Popovi\'c et al. (2018) and, as such, accurately describes the statistics of melt pond geometry. We test this model against detailed LiDAR measurements of the pre-melt snow topography. We show that the model snow-depth distribution is statistically indistinguishable from the measurements on flat ice, while small disagreement exists if the ice is deformed. We then use this model to determine analytic expressions for the conductive heat flux through the ice and for melt pond coverage evolution during an early stage of pond formation. We also formulate a criterion for ice to remain pond-free throughout the summer. Results from our model could be directly included in large-scale models, thereby improving our understanding of energy balance on sea ice and allowing for more reliable predictions of Arctic sea ice in a future climate.