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he Arctic has been warming faster than elsewhere, especially during the cold season. According to the leading theory, ice‐albedo feedback warms the Arctic Ocean during the summer, and the heat gained by the ocean is released during the winter, causing the cold‐season warming. Screen and Simmonds (2010; SS10) concluded that the theory is correct by comparing trend patterns in surface air temperature (SAT), surface turbulence heat flux (HF), and net surface infrared radiation (IR). However, in this comparison, downward IR is more appropriate to use. By analyzing the same data used in SS10 using the surface energy budget, it is shown here that over most of the Arctic the skin temperature trend, which closely resembles the SAT trend, is largely accounted for by the downward IR, not the HF, trend.
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The Maximum Entropy Production (MEP) Method for Modeling Surface Energy Budget – Theory and Applications to Arctic Regions
The Maximum Entropy Production (MEP) method for modeling surface energy budget has been developed and validated at local, regional and global scale including the Arctic regions. The MEP model has solid theoretical foundation built on the Bayesian probability theory, information theory, non-equilibrium thermodynamics and boundary layer turbulence theory. Its formulation has advantageous features including closing energy budget at any space-time scales, independence of moisture and temperature gradient, wind speed and surface roughness, and free of tunable empirical parameters. Application of the MEP model has been covering all types of land covers including Arctic permafrost tundra, sea ice and snow surfaces. Recent tests using field experimental observations suggest that the MEP model using fewer input data and model parameters is able to simulate surface energy budget accurately. It is a more efficient alternative to the classical Penman-Monteith model of potential evapotranspiration. The MEP method has potential to influence the study of Arctic water-energy cycles and climate change.
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- Award ID(s):
- 1724633
- PAR ID:
- 10184229
- Date Published:
- Journal Name:
- Agu
- ISSN:
- 2346-6855
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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