Abstract
Abstract. Melt ponds are key elements in the energy balance of Arctic sea ice. Observing their temporal evolution is crucial for understanding melt processes and predicting sea ice evolution. Remote sensing is the only technique that enables large-scale observations of Arctic sea ice. However, monitoring melt pond deepening in this way is challenging because most of the optical signal reflected by a pond is defined by the scattering characteristics of the underlying ice. Without knowing the influence of meltwater on the reflected signal, the water depth cannot be determined. To solve the problem, we simulated the way meltwater changes the reflected spectra of bare ice. We developed a model based on the slope of the log-scaled remote sensing reflectance at 710 nm as a function of depth that is widely independent from the bottom albedo and accounts for the influence of varying solar zenith angles. We validated the model using 49 in situ melt pond spectra and corresponding depths from shallow ponds on dark and bright ice. Retrieved pond depths are accurate (root mean square error, RMSE=2.81 cm; nRMSE=16 %) and highly correlated with in situ measurements (r=0.89; p=4.34×10-17). The model further explains a large portion of the variation in pond depth (R2=0.74). Our results indicate that our model enables the accurate retrieval of pond depth on Arctic sea ice from optical data under clear sky conditions without having to consider pond bottom albedo. This technique is potentially transferrable to hyperspectral remote sensors on unmanned aerial vehicles, aircraft and satellites.
Highlights
Melt ponds on sea ice are key elements for the Arctic energy budget
We validated the model with the in situ melt pond dataset from dark and bright ponds (Sect. 2.1.2) and observed a strong linear and statistically significant correlation (r = 0.86; p = 2.36 × 10−15; R2 = 0.65; root mean square error (RMSE) = 3.29 cm and normalized RMSE (nRMSE) = 19 %)
If we further correct for the offset, R2 increases to 0.74 and RMSE improves to 2.81 cm
Summary
Melt ponds on sea ice are key elements for the Arctic energy budget. They are a main driver of the ice–albedo feedback mechanism (Curry et al, 1995) and affect the mass and heat balance of sea ice (e.g., Flocco et al, 2012; Perovich et al, 2009). In the context of climate change, it is important to increase our understanding of how melt ponds on sea ice change (Lee et al, 2012). Melt pond depth is a parameter in the Los Alamos sea ice model CICE (Flocco et al, 2012; Hunke et al, 2013) and the ECHAM5 general circulation model (Pedersen et al, 2009). Holland et al (2012) related pond water volume to surface meltwater fluxes in the community climate system model, version 4, and Palmer et al (2014) used melt pond depths to model primary production below sea ice. Liu et al (2015) point out that climate models and forecast systems that account for realistic melt pond evolution “seem to be a worthy area of expanded research and development” (Liu et al, 2015) and question the suitability of statistical forecasting methods in the context of the changing
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