Abstract
This paper evaluates the simulated Arctic land snow cover duration, snow water equivalent, snow cover fraction, surface albedo, and land surface temperature in the regional climate model HIRHAM5 during 2008–2010, compared with various satellite and reanalysis data and one further regional climate model (COSMO-CLM). HIRHAM5 shows a general agreement in the spatial patterns and annual course of these variables, although distinct biases for specific regions and months are obvious. The most prominent biases occur for east Siberian deciduous forest albedo, which is overestimated in the simulation for snow covered conditions in spring. This may be caused by the simplified albedo parameterization (e.g., nonconsideration of different forest types and neglecting the effect of fallen leaves and branches on snow for deciduous tree forest). The land surface temperature biases mirror the albedo biases in their spatial and temporal structures. The snow cover fraction and albedo biases can explain the simulated land surface temperature bias of ca. −3°C over the Siberian forest area in spring.
Highlights
The Arctic belongs to the key regions in the global climate system, evidenced by many rapid environmental changes (e.g., [1, 2])
We evaluate the simulated land surface temperature (LST) of the Regional climate models (RCMs) HIRHAM5 against ERA-Interim reanalysis data with respect to the following questions: is the model able to reproduce observed LSTs? Can biases in the modeled LST be explained with biases in snow cover characteristics? Does the influence of albedo on LST play a key role? To answer these questions, we analyze the observed and modeled three snow cover characteristics (SWE, snow cover fraction (SCF), and snow cover duration (SCD))
HIRHAM5 modeled Arctic land snow cover; albedo and LST are evaluated in spring and autumn using various satellite and reanalysis data
Summary
The Arctic belongs to the key regions in the global climate system, evidenced by many rapid environmental changes (e.g., [1, 2]). In this context, the land surface temperature (LST) is an important variable, because it reflects the changes in the surface energy budget, the energy exchange between land and atmosphere and feedbacks with cryospheric variables like snow cover and frozen ground. The isolating snow effect leads to less cooling of the ground under snow and reduced upward longwave radiative fluxes and to increased cooling of snow surface. The occurrence of a snow pack manifests in modified sensible and latent surface heat fluxes
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