The Arctic climate has changed significantly in the last decades, experiencing a dramatic loss of sea ice and stronger than global warming. The Arctic surface temperature and the growth or melt of sea ice is determined by the local surface energy budget. In this context, clouds are of essential importance as they strongly interact with the radiative fluxes and modulate the surface energy budget depending on their properties, the surface types, and atmospheric thermodynamics. For the quantification of changes in the radiative energy budget (REB) associated with the presence or absence of clouds, the concept of cloud radiative forcing (CRF) is commonly used. This concept is defined as the differences between the REB in cloudy and cloud-free conditions, two atmospheric states which can not be observed at the same location and time. Consequently, either radiative transfer simulations or observations in both states have to be related, both of which complicate the derivation of CRF. A review of available studies and their approaches to derive the CRF reveals conceptual differences as well as deficiencies in the handling of radiative processes related to the surface albedo. These findings call into question the current state of CRF assessment in the Arctic based on the few available studies, but also their comparability. By combining atmospheric radiative transfer simulations with a snow albedo model, two processes that control the surface albedo during the transition from cloud-free to cloudy conditions and their role in the derivation of CRF are discussed. The broadband surface albedo of snow surfaces typically increases in the presence of clouds due to a spectral weighting of downward irradiance toward shorter wavelengths. For more absorbing surface types such as white ice and melt ponds, which are common in summer, there is a strong shift between the albedo of direct and diffuse illuminated surface, which diminishes the surface albedo depending on the cloud optical thickness and solar zenith angle. In this thesis, a hypothesis on the impact of those surface-albedo--cloud interactions on the annual cycle of shortwave CRF is discussed, but an application to inner Arctic conditions remains an open issue. An improved method to derive the shortwave CRF is proposed and an application to two airborne campaigns in the marginal sea ice zone northwest of Svalbard (Norway) illustrates the role of surface-albedo--cloud interactions in the Arctic in spring and early summer. For the longwave CRF, conceptual differences and the general interpretation of the different CRF estimates are discussed and illustrated for a case study. Radiative transfer simulations of a rarely observed annual cycle of thermodynamic profiles in the inner Arctic are used to study both longwave CRF approaches and the impact of thermodynamic profiles on the longwave CRF. Making use of airborne low-level flights in the MIZ and other available datasets, common seasonal radiative states on sea ice and case studies of warm air intrusions and cold air outbreaks are illustrated. The CRF is analyzed as a function of the observed cloud/surface regime, which is extended by radiative transfer simulations characterizing the conditions in this region and seasons.

Land ice is losing mass to the worlds oceans at an accelerated rate. The worlds glaciers contain much less ice than the ice sheets but contribute equally to eustatic sea level rise and are expected to continue to do so over the coming centuries if global temperatures continue to rise. It is therefore important to characterize the mass balance of these glaciers and its relationship to climate trends and variability. In the Canadian High Arctic, analysis of long-term surface mass balance records shows a shift to more negative mass balances after 1987 and is coincident with a change in the mean location of the July circumpolar vortex, a mid-troposphere cyclonic feature known to have a strong influence on Arctic summer climate. Since 1987 the occurrence of July vortices centered in the Eastern Hemisphere have increased significantly. This change is associated with an increased frequency of tropospheric ridging over the Canadian High Arctic, higher surface air temperatures, and more negative glacier mass balance. However, regional scale mass balance modeling is needed to determine whether or not the long-term mass balance measurements in this region accurately reflect the mass balance of the entire Canadian High Arctic. The Canadian High Arctic is characterized by high relief and complex terrain that result in steep horizontal gradients in surface mass balance, which can only be resolved if models are run at high spatial resolutions. For such runs, models often require input fields such as air temperature that are derived by downscaling of output from climate models or reanalyses. Downscaling is often performed using a specified relationship between temperature and elevation (a lapse rate). Although a constant lapse rate is often assumed, this is not well justified by observations. To improve upon this assumption, near-surface temperature lapse rates during the summer ablation season were derived from surface measurements on 4 Arctic glaciers. Near-surface lapse rates vary systematically with free-air temperatures and are less steep than the free-air lapse rates that have often been used in mass balance modeling. Available observations were used to derive a new variable temperature downscaling method based on temperature dependent daily lapse rates. This method was implemented in a temperature index mass balance model, and results were compared with those derived from a constant linear lapse rate. Compared with other approaches, model estimates of surface mass balance fit observations much better when variable, temperature dependent lapse rates are used. To better account for glacier-climate feedbacks within mass balance models, more physically explicit representations of snow and ice processes must be used. Since absorption of shortwave radiation is often the single largest source of energy for melt, one of the most important parameters to model correctly is surface albedo. To move beyond the limitations of empirical snow and ice albedo parameterizations often used in surface mass balance models, a computationally simple, theoretically-based parameterization for snow and ice albedo was developed. Unlike previous parameterizations, it provides a single set of equations for the estimation of both snow and ice albedo. The parameterization also produces accurate results for a much wider range of snow, ice, and atmospheric conditions.

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