Arctic Melt Ponds Research Articles

Experimental investigation of the partitioning of radiation in the melt pond–ice–ocean system

To study the partitioning of solar radiation over the melting Arctic sea ice and address the lack of systematic measurements of the optical properties of melt ponds and the underlying ice, four experiments in non-Arctic regions were designed to explore the distribution of solar irradiance on pond surface, pond bottom, and underlying ice. These experiments also investigated the influence of different pond depths (Hp), underlying ice thicknesses (Hi), and melt pond sidewall conditions (i.e., the black sidewalls, ice sidewalls, and pond horizon size) on the apparent optical properties (AOPs) of melt ponds. The black sidewalls with a small pond radius (barrel experiment) showed the greatest influence on the AOPs, significantly lowering the albedo and transmittance, and increasing the energy absorbed by the pond (Ψp) and underlying ice (Ψi). Different sidewall types, such as the black and ice sidewalls, did not affect the AOPs' trend, but affected their values. The AOPs were also influenced by pond size. The albedo linearly decreased and Ψp linearly increased with the increase of pond radius. The albedo and Ψp were almost independent of pond radius as pond radius larger than 0.58 m and 1.25, respectively. The black sidewalls experiment, in which the pond radius was 2.5 m, and the pond size experiment showed that solar energy was mainly absorbed by the melt pond. Ψp increased from 19% to 62%, which corresponded to Hp increasing from 5 cm to 35 cm in the pond size experiment. The results of the four experiments were consistent with the values obtained from numerical simulations and in situ measurements in the Arctic. The experiments conducted in this study were shown to be effective tools to complement and verify the optical measurements of Arctic melt ponds. They allow us to obtain the key information in Arctic pond measurements, which is difficult to achieve due to the severe weather conditions and marine environment.

Improvement of sea ice thermodynamics with variable sea ice salinity and melt pond parameterizations in an OGCM

Enhanced representation of sea ice processes in ocean modeling studies is required for advancing our understanding and prediction of the climate variability. In the present study, we improved the sea ice thermodynamics in an OGCM by introducing processes for variable sea ice salinity and melt ponds on sea ice. The former affects the latent heat and conductivity of sea ice as well as the salt budget in the ocean–sea ice system. We formulated salinification and desalination processes based on previous observational studies. Melt ponds enhance summertime surface melting by reducing the surface albedo. In our formulation of the melt pond thermodynamics, the shortwave radiation absorbed in the pond was partly released to the air and partly conducted to the underlying sea ice. By adopting these parameterizations, seasonal cycles of the sea ice salinity distributions in both hemispheres were generally reproduced. Simulated seasonal and interannual variations in the Arctic melt pond area and associated surface albedo reductions were basically consistent with observational estimates. The melt pond effect reduced the Arctic sea ice volume by about 1.5 × 103 km3 in the summer; half of this impact remained throughout the year. Our results suggest that the new parameterizations have the potential to improve the seasonal evolution of the sea ice thickness and concentration distributions, which would contribute to prediction studies.

Effect of melt ponds fraction on sea ice anomalies in the Arctic Ocean

We used deep learning networks to establish a relationship model among MODIS daily surface reflectance product (MOD09GA) and Arctic melt ponds fraction (MPF), ice fraction (IF), and open water fraction (OWF). We applied this model to MODIS 8-day surface reflectance (MOD09A1) to derive Arctic 8-day MPF and SIF (SIF as the sum of IF and MPF). The results demonstrate that our model improved MPF estimation accuracy to an RMSE of 3.7%, compared with previous models. The characteristics of MPF spatiotemporal changes seen in early summer (May-July) indicate that MPF increased first from May-June, reaching its peak around early July, and then decreased. In addition, early summer MPF was significantly negatively correlated with sea ice extent (SIE) in September. We also found that early summer MPF caused sea ice in the Beaufort Sea, the Chukchi Sea, and the East Siberian Sea to move to warm water. Moreover, the movement of sea ice from the marginal sea to the center of the Arctic was shown to be conducive to the reduction of SIE in September. Early summer MPF was also related to Arctic oscillation (AO) during June to July, and significantly positively related to air temperature in the East Siberian and Chukchi Seas in September. As a consequence, these areas produced more open water and absorbed more heat, reducing the extent of sea ice in September, while increasing their air temperatures. The results also show that early summer MPF has a high negative correlation with air temperature in northern China, and MPF can be used to predict air temperature in northern China. These new findings should be investigated in future studies with additional data collection and field observations.

Ising model for melt ponds on Arctic sea ice

Perhaps the most iconic feature of melting Arctic sea ice is the distinctive ponds that form on its surface. The geometrical patterns describing how melt water is distributed over the surface largely determine the solar reflectance and transmittance of the sea ice cover, which are key parameters in climate modeling and upper ocean ecology. In order to help develop a predictive theoretical approach to studying melting sea ice, and the resulting patterns of light and dark regions on its surface in particular, we look to the statistical mechanics of phase transitions and introduce a two-dimensional random field Ising model which accounts for only the most basic physics in the system. The ponds are identified as metastable states in the model, where the binary spin variable corresponds to the presence of melt water or ice on the sea ice surface. With the lattice spacing determined by snow topography data as the only measured parameter input into the model, energy minimization drives the system toward realistic pond configurations from an initially random state. The model captures the essential mechanism of pattern formation of Arctic melt ponds, with predictions that agree very closely with observed scaling of pond sizes and transition in pond fractal dimension.

Dissolved and particulate trace elements in late summer Arctic melt ponds

Melt ponds are a prominent feature of Arctic sea ice during the summer and play a role in the complex interface between the atmosphere, cryosphere and surface ocean. During melt pond formation and development, micronutrient and contaminant trace elements (TEs) from seasonally accumulated atmospheric deposition are mixed with entrained sedimentary and marine-derived material before being released to the surface ocean during sea ice melting. Here we present particulate and size-fractionated dissolved (truly soluble and colloidal) TE data from five melt ponds sampled in late summer 2015, during the US Arctic GEOTRACES (GN01) cruise. Analyses of salinity, δ18O, and 7Be indicate variable contributions to the melt ponds from snowmelt, melting sea ice, and surface seawater. Our data highlight the complex TE biogeochemistry of late summer Arctic melt ponds and the variable importance of different sources for specific TEs. Dissolved TE concentrations indicate a strong influence from seawater intrusion for V, Ni, Cu, Cd, and Ba. Ultrafiltration methods reveal dissolved Fe, Zn, and Pb to be mostly colloidal (0.003–0.2 μm), while Mn, Co, Ni, Cu, and Cd are dominated by a truly soluble (<0.003 μm) fraction. Isotopically light dissolved Fe in some melt ponds suggests that photochemical and/or biologically driven redox cycling also takes place. Comparisons of particulate TE/Al ratios to mean crustal values indicate influences from lithogenic sources, including natural aerosols and/or sedimentary material, with significant enrichments for some elements, including Ni, Cu, Zn, Cd and Pb, that may result from anthropogenic aerosols, biogenic material, and/or in situ scavenging of dissolved TEs. Our results indicate that melt ponds represent a transitional environment in which some atmospherically-derived TEs undergo physical and/or chemical changes before their release to the surface ocean. As a result, the ongoing changes in sea ice areal extent, thickness, and melt season length are likely to influence the bioavailability of atmospheric TE input to the surface Arctic Ocean, with material released from snow and sea ice via melt ponds earlier in the summer and with more extensive direct deposition to the ocean surface.

Simple Rules Govern the Patterns of Arctic Sea Ice Melt Ponds.

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.

Diazotroph Diversity in the Sea Ice, Melt Ponds, and Surface Waters of the Eurasian Basin of the Central Arctic Ocean.

The Eurasian basin of the Central Arctic Ocean is nitrogen limited, but little is known about the presence and role of nitrogen-fixing bacteria. Recent studies have indicated the occurrence of diazotrophs in Arctic coastal waters potentially of riverine origin. Here, we investigated the presence of diazotrophs in ice and surface waters of the Central Arctic Ocean in the summer of 2012. We identified diverse communities of putative diazotrophs through targeted analysis of the nifH gene, which encodes the iron protein of the nitrogenase enzyme. We amplified 529 nifH sequences from 26 samples of Arctic melt ponds, sea ice and surface waters. These sequences resolved into 43 clusters at 92% amino acid sequence identity, most of which were non-cyanobacterial phylotypes from sea ice and water samples. One cyanobacterial phylotype related to Nodularia sp. was retrieved from sea ice, suggesting that this important functional group is rare in the Central Arctic Ocean. The diazotrophic community in sea-ice environments appear distinct from other cold-adapted diazotrophic communities, such as those present in the coastal Canadian Arctic, the Arctic tundra and glacial Antarctic lakes. Molecular fingerprinting of nifH and the intergenic spacer region of the rRNA operon revealed differences between the communities from river-influenced Laptev Sea waters and those from ice-related environments pointing toward a marine origin for sea-ice diazotrophs. Our results provide the first record of diazotrophs in the Central Arctic and suggest that microbial nitrogen fixation may occur north of 77°N. To assess the significance of nitrogen fixation for the nitrogen budget of the Arctic Ocean and to identify the active nitrogen fixers, further biogeochemical and molecular biological studies are needed.

Pond fractals in a tidal flat.

Studies over the past decade have reported power-law distributions for the areas of terrestrial lakes and Arctic melt ponds, as well as fractal relationships between their areas and coastlines. Here we report similar fractal structure of ponds in a tidal flat, thereby extending the spatial and temporal scales on which such phenomena have been observed in geophysical systems. Images taken during low tide of a tidal flat in Damariscotta, Maine, reveal a well-resolved power-law distribution of pond sizes over three orders of magnitude with a consistent fractal area-perimeter relationship. The data are consistent with the predictions of percolation theory for unscreened perimeters and scale-free cluster size distributions and are robust to alterations of the image processing procedure. The small spatial and temporal scales of these data suggest this easily observable system may serve as a useful model for investigating the evolution of pond geometries, while emphasizing the generality of fractal behavior in geophysical surfaces.

Production rate estimation of mycosporine-like amino acids in two Arctic melt ponds by stable isotope probing with NAH(13) CO3.

The net carbon uptake rate and net production rate of mycosporine-like amino acids (MAAs) were measured in phytoplankton from 2 different melt ponds (MPs; closed and open type pond) in the western Arctic Ocean using a (13) C stable isotope tracer technique. The Research Vessel Araon visited ice-covered western-central basins situated at 82°N and 173°E in the summer of 2012, when Arctic sea ice declined to a record minimum. The average net carbon uptake rate of the phytoplankton in polycarbonate (PC) bottles in the closed MP was 3.24 mg C · m(-3) · h(-1) (SD = ±1.12 mg C · m(-3) · h(-1) ), while that in the open MP was 1.3 mg C · m(-3) · h(-1) (SD = ±0.05 mg C · m(-3) · h(-1) ). The net production rate of total MAAs in incubated PC bottles was highest (1.44 (SD = ±0.24) ng C · L(-1) · h(-1) ) in the open MP and lowest (0.05 (SD = ±0.003) ng C · L(-1) · h(-1) ) in the closed MP. The net production rate of shinorine and palythine in incubated PC bottles at the open MP presented significantly high values 0.76 (SD = ±0.12) ng C · L(-1) · h(-1) and 0.53 (SD = ±0.06) ng C · L(-1) · h(-1) . Our results showed that high net production rate of MAAs in the open MP was enhanced by a combination of osmotic and UVR stress and that in situ net production rates of individual MAA can be determined using (13) C tracer in MPs in Arctic sea ice.

Arctic melt ponds and bifurcations in the climate system

Understanding how sea ice melts is critical to climate projections. In the Arctic, melt ponds that develop on the surface of sea ice floes during the late spring and summer largely determine their albedo – a key parameter in climate modeling. Here we explore the possibility of a conceptual sea ice climate model passing through a bifurcation point – an irreversible critical threshold as the system warms, by incorporating geometric information about melt pond evolution. This study is based on a bifurcation analysis of the energy balance climate model with ice-albedo feedback as the key mechanism driving the system to bifurcation points.

Transition in the fractal geometry of Arctic melt ponds

Abstract. During the Arctic melt season, the sea ice surface undergoes a remarkable transformation from vast expanses of snow covered ice to complex mosaics of ice and melt ponds. Sea ice albedo, a key parameter in climate modeling, is determined by the complex evolution of melt pond configurations. In fact, ice–albedo feedback has played a major role in the recent declines of the summer Arctic sea ice pack. However, understanding melt pond evolution remains a significant challenge to improving climate projections. By analyzing area–perimeter data from hundreds of thousands of melt ponds, we find here an unexpected separation of scales, where pond fractal dimension D transitions from 1 to 2 around a critical length scale of 100 m2 in area. Pond complexity increases rapidly through the transition as smaller ponds coalesce to form large connected regions, and reaches a maximum for ponds larger than 1000 m2, whose boundaries resemble space-filling curves, with D ≈ 2. These universal features of Arctic melt pond evolution are similar to phase transitions in statistical physics. The results impact sea ice albedo, the transmitted radiation fields under melting sea ice, the heat balance of sea ice and the upper ocean, and biological productivity such as under ice phytoplankton blooms.

A concept for autonomous and continuous observation of melt pond morphology: Instrument design and test trail during the 4th CHINARE-Arctic in 2010

Accelerated decline of summer and winter Arctic sea ice has been demonstrated progressively. Meltponds play a key role in enhancing the feedback of solar radiation in the ice/ocean-atmosphere system, andhave thus been a focus of researchers and modelers. A new melt pond investigation system was designed todetermine morphologic and hydrologic features, and their evolution. This system consists of three major parts:Temperature-salinity measuring, surface morphology monitoring, and water depth monitoring units. The setupwas deployed during the ice camp period of the fourth Chinese National Arctic Research Expedition in summer2010. The evolution of a typical Arctic melt pond was documented in terms of pond depth, shape and surfacecondition. These datasets are presented to scientifically reveal how involved parameters change, contributing tobetter understanding of the evolution mechanism of the melt pond. The main advantage of this system is itssuitability for autonomous and long-term observation, over and within a melt pond. Further, the setup is portableand robust. It can be easily and quickly installed, which is most valuable for deployment under harsh conditions.

A model of the three‐dimensional evolution of Arctic melt ponds on first‐year and multiyear sea ice

During winter the ocean surface in polar regions freezes over to form sea ice. In the summer the upper layers of sea ice and snow melts producing meltwater that accumulates in Arctic melt ponds on the surface of sea ice. An accurate estimate of the fraction of the sea ice surface covered in melt ponds is essential for a realistic estimate of the albedo for global climate models. We present a melt‐pond–sea‐ice model that simulates the three‐dimensional evolution of melt ponds on an Arctic sea ice surface. The advancements of this model compared to previous models are the inclusion of snow topography; meltwater transport rates are calculated from hydraulic gradients and ice permeability; and the incorporation of a detailed one‐dimensional, thermodynamic radiative balance. Results of model runs simulating first‐year and multiyear sea ice are presented. Model results show good agreement with observations, with duration of pond coverage, pond area, and ice ablation comparing well for both the first‐year ice and multiyear ice cases. We investigate the sensitivity of the melt pond cover to changes in ice topography, snow topography, and vertical ice permeability. Snow was found to have an important impact mainly at the start of the melt season, whereas initial ice topography strongly controlled pond size and pond fraction throughout the melt season. A reduction in ice permeability allowed surface flooding of relatively flat, first‐year ice but had little impact on the pond coverage of rougher, multiyear ice. We discuss our results, including model shortcomings and areas of experimental uncertainty.