Climate change, amplified in the far north, has led to rapid sea ice decline in recent years. In the summer, melt ponds form on the surface of Arctic sea ice, significantly lowering the ice reflectivity (albedo) and thereby accelerating ice melt. Pond geometry controls the details of this crucial feedback; however, a reliable model of pond geometry does not currently exist. Here we show that a simple model of voids surrounding randomly sized and placed overlapping circles reproduces the essential features of pond patterns. The only two model parameters, characteristic circle radius and coverage fraction, are chosen by comparing, between the model and the aerial photographs of the ponds, two correlation functions which determine the typical pond size and their connectedness. Using these parameters, the void model robustly reproduces the ponds' area-perimeter and area-abundance relationships over more than 6 orders of magnitude. By analyzing the correlation functions of ponds on several dates, we also find that the pond scale and the connectedness are surprisingly constant across different years and ice types. Moreover, we find that ponds resemble percolation clusters near the percolation threshold. These results demonstrate that the geometry and abundance of Arctic melt ponds can be simply described, which can be exploited in future models of Arctic melt ponds that would improve predictions of the response of sea ice to Arctic warming.
A critical question in astrobiology is whether exo-Earth candidates (EECs) are Earth-like, in that they originate life that progressively oxygenates their atmospheres similarly to Earth. We propose answering this question statistically by searching for O2 and O3 on EECs with missions such as HabEx or LUVOIR. We explore the ability of these missions to constrain the fraction, f E, of EECs that are Earth-like in the event of a null detection of O2 or O3 on all observed EECs. We use the Planetary Spectrum Generator to simulate observations of EECs with O2 and O3 levels based on Earth’s history. We consider four instrument designs—LUVOIR-A (15 m), LUVOIR-B (8 m), HabEx with a starshade (4 m, “HabEx/SS”), and HabEx without a starshade (4 m, “HabEx/no-SS”)—as well as three estimates of the occurrence rate of EECs (η earth): 24%, 5%, and 0.5%. In the case of a null detection, we find that for η earth = 24%, LUVOIR-A, LUVOIR-B, and HabEx/SS would constrain f E to ≤0.094, ≤0.18, and ≤0.56, respectively. This also indicates that if f E is greater than these upper limits, we are likely to detect O3 on at least one EEC. Conversely, we find that HabEx/no-SS cannot constrain f E, due to the lack of a coronagraph ultraviolet channel. For η earth = 5%, only LUVOIR-A and LUVOIR-B would be able to constrain f E, to ≤0.45 and ≤0.85, respectively. For η earth = 0.5%, none of the missions would allow us to constrain f E, due to the low number of detectable EECs. We conclude that the ability to constrain f E is more robust to uncertainties in η earth for missions with larger aperture mirrors. However, all missions are susceptible to an inconclusive null detection if η earth is sufficiently low.
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, η, 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, η. 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.
The ice-albedo feedback on rapidly-rotating terrestrial planets in the habitable zone can lead to abrupt transitions (bifurcations) between a warm and a snowball (ice-covered) state, bistability between these states, and hysteresis in planetary climate. This is important for planetary habitability because snowball events may trigger rises in the complexity of life, but could also endanger complex life that already exists. Recent work has shown that planets tidally locked in synchronous rotation states will transition smoothly into the snowball state rather than experiencing bifurcations. Here we investigate the structure of snowball bifurcations on planets that are tidally influenced, but not synchronously rotating, so that they experience long solar days. We use PlaSIM, an intermediatecomplexity global climate model, with a thermodynamic mixed layer ocean and the Sun's spectrum. We find that the amount of hysteresis (range in stellar flux for which there is bistability in climate) is significantly reduced for solar days with lengths of tens of Earth days, and disappears for solar days of hundreds of Earth days. These results suggest that tidally influenced planets orbiting M and K-stars that are not synchronously rotating could have much less hysteresis associated with the snowball bifurcations than they would if they were rapidly rotating. This implies that the amount of time it takes them to escape a snowball state via CO 2 outgassing would be greatly reduced, as would the period of cycling between the warm and snowball state if they have a low CO 2 outgassing rate.
Abstract. As the melt season progresses, sea ice in the Arctic often becomes permeable enough to allow for nearly complete drainage of meltwater that has collected on the ice surface. Melt ponds that remain after drainage are hydraulically connected to the ocean and correspond to regions of sea ice whose surface is below sea level. We present a simple model for the evolution of melt pond coverage on such permeable sea ice floes in which we allow for spatially varying ice melt rates and assume the whole floe is in hydrostatic balance. The model is represented by two simple ordinary differential equations, where the rate of change of pond coverage depends on the pond coverage. All the physical parameters of the system are summarized by four strengths that control the relative importance of the terms in the equations. The model both fits observations and allows us to understand the behavior of melt ponds in a way that is often not possible with more complex models. Examples of insights we can gain from the model are that (1) the pond growth rate is more sensitive to changes in bare sea ice albedo than changes in pond albedo, (2) ponds grow slower on smoother ice, and (3) ponds respond strongest to freeboard sinking on first-year ice and sidewall melting on multiyear ice. We also show that under a global warming scenario, pond coverage would increase, decreasing the overall ice albedo and leading to ice thinning that is likely comparable to thinning due to direct forcing. Since melt pond coverage is one of the key parameters controlling the albedo of sea ice, understanding the mechanisms that control the distribution of pond coverage will help improve large-scale model parameterizations and sea ice forecasts in a warming climate.
Ponds that form on sea ice can cause it to thin or break-up, which can promote calving from an adjacent ice shelf. Studies of sea ice ponds have predominantly focused on Arctic ponds formed by in situ melting/ponding. Our study documents another mechanism for the formation of sea ice ponds. Using Landsat 8 and Sentinel-2 images from the 2015–16 to 2018–19 austral summers, we analyze the evolution of sea ice ponds that form adjacent to the McMurdo Ice Shelf, Antarctica. We find that each summer, meltwater flows from the ice shelf onto the sea ice and forms large (up to 9 km2) ponds. These ponds decrease the sea ice's albedo, thinning it. We suggest the added mass of runoff causes the ice to flex, potentially promoting sea-ice instability by the ice-shelf front. As surface melting on ice shelves increases, we suggest that ice-shelf surface hydrology will have a greater effect on sea-ice stability.
<p>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, and provides a source of meltwater for melt ponds during summer that decrease the ice's albedo. Here, we develop a simple model of pre-melt ice surface topography that accurately describes snow cover on flat, undeformed ice. 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 Popovic 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.&#160;</p>
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 premelt snow topography that accurately describes snow cover of flat, undeformed Arctic sea ice on several study sites for which data were 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 Popović et al. (2018, https://doi.org/10.1103/PhysRevLett.120.148701) and, as such, accurately describes the statistics of melt pond geometry. We test this model against detailed LiDAR measurements of the premelt 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.
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