Abstract
Abstract. The observational uncertainty in sea ice concentration estimates from remotely sensed passive microwave brightness temperatures is a challenge for reliable climate model evaluation and initialization. To address this challenge, we introduce a new tool: the Arctic Ocean Observation Operator (ARC3O). ARC3O allows us to simulate brightness temperatures at 6.9 GHz at vertical polarization from standard output of an Earth System Model. To evaluate sources of uncertainties when applying ARC3O, we compare brightness temperatures simulated by applying ARC3O on three assimilation runs of the MPI Earth System Model (MPI-ESM), assimilated with three different sea ice concentration products, with brightness temperatures measured by the Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) from space. We find that the simulated and observed brightness temperatures differ up to 10 K in the period between October and June, depending on the region and the assimilation run. We show that these discrepancies between simulated and observed brightness temperature can be attributed mainly to the underlying observational uncertainty in sea ice concentration and, to a lesser extent, to the data assimilation process, rather than to biases in ARC3O itself. In summer, the discrepancies between simulated and observed brightness temperatures are larger than in winter and locally reach up to 20 K. This is caused by the very large observational uncertainty in summer sea ice concentration and the melt pond parametrization in MPI-ESM, which is not necessarily realistic. ARC3O is therefore capable of realistically translating the simulated Arctic Ocean climate state into one observable quantity for a more comprehensive climate model evaluation and initialization.
Highlights
The diversity in sea ice concentration observational estimates affects our understanding of past and future sea ice evolution as it inhibits reliable climate model evaluation (Notz et al, 2013) and initialization (Bunzel et al, 2016)
We do so by comparing brightness temperatures simulated by ARC3O based on Max Planck Institute Earth System Model (MPI-ESM) output from assimilation experiments, i.e., experiments where the model is regularly nudged towards observations
The observations used in the data assimilation are reanalysis data for the atmosphere and ocean and retrieved sea ice concentration products for the sea ice
Summary
The diversity in sea ice concentration observational estimates affects our understanding of past and future sea ice evolution as it inhibits reliable climate model evaluation (Notz et al, 2013) and initialization (Bunzel et al, 2016). It limits our ability to fully exploit relationships between the evolution of sea ice and other climate variables, such as globalmean surface temperature (Niederdrenk and Notz, 2018) and CO2 emissions (Notz and Stroeve, 2016) To address these issues, we construct an observation operator for the Arctic Ocean at the frequency of 6.9 GHz. To address these issues, we construct an observation operator for the Arctic Ocean at the frequency of 6.9 GHz This operator provides an alternative approach for climate model evaluation and initialization with satellite observations. A variety of algorithms have been developed to retrieve an estimate of sea ice concentration from the measured brightness temperatures These retrieval algorithms take advantage of the fact that the relative influence of the individual physical variables on the brightness temperature depends on the frequency and polarization of the radiation. We evaluate the brightness temperatures simulated based on assimilation runs against brightness temperatures observed by satellites and investigate potential uncertainty sources in the brightness temperature simulation
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