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The Snow, Ice, and Aerosol Radiative (SNICAR) model has been used in various capacities over the last 15 years to model the spectral albedo of snow with light-absorbing constituents (LACs). Recent studies have extended the model to include an adding-doubling two-stream solver and representations of non-spherical ice particles; carbon dioxide snow; snow algae; and new types of mineral dust, volcanic ash, and brown carbon. New options also exist for ice refractive indices and solar-zenith-angle-dependent surface spectral irradiances used to derive broadband albedo. The model spectral range was also extended deeper into the ultraviolet for studies of extraterrestrial and high-altitude cryospheric surfaces. Until now, however, these improvements and capabilities have not been merged into a unified code base. Here, we document the formulation and evaluation of the publicly available SNICAR-ADv3 source code, web-based model, and accompanying library of constituent optical properties. The use of non-spherical ice grains, which scatter less strongly into the forward direction, reduces the simulated albedo perturbations from LACs by ∼9%–31%, depending on which of the three available non-spherical shapes are applied. The model compares very well against measurements of snow albedo from seven studies, though key properties affecting snow albedo are not fully constrained with measurements, including ice effective grain size of the top sub-millimeter of the snowpack, mixing state of LACs with respect to ice grains, and site-specific LAC optical properties. The new default ice refractive indices produce extremely high pure snow albedo (>0.99) in the blue and ultraviolet part of the spectrum, with such values only measured in Antarctica so far. More work is needed particularly in the representation of snow algae, including experimental verification of how different pigment expressions and algal cell concentrations affect snow albedo. Representations and measurements of the influence of liquid water on spectral snow albedo are also needed.

We implement a set of new parameterizations into the widely used Snow, Ice, and Aerosol Radiative (SNICAR) model to account for effects of snow grain shape (spherical vs. nonspherical) and black carbon (BC)–snow mixing state (external vs. internal). We find that nonspherical snow grains lead to higher pure albedo but weaker BC-induced albedo reductions relative to spherical snow grains, while BC–snow internal mixing significantly enhances albedo reductions relative to external mixing. The combination of snow nonsphericity and internal mixing suggests an important interactive effect on BC-induced albedo reduction. Comparisons with observations of clean and BC-contaminated snow albedo show that model simulations accounting for both snow nonsphericity and BC–snow internal mixing perform better than those using the common assumption of spherical snow grains and external mixing. We further apply the updated SNICAR model with comprehensive in situ measurements of BC concentrations in the Tibetan Plateau snowpack to quantify the present-day (2000–2015) BC-induced snow albedo effects from a regional and seasonal perspective. The BC concentrations show distinct and substantial sub-regional and seasonal variations, with higher values in the non-monsoon season and low altitudes. As a result, the BC-induced regional mean snow albedo reductions and surface radiative effects vary by up to an order of magnitude across different sub-regions and seasons, with values of 0.7–30.7 and 1.4–58.4 W m−2 for BC externally mixed with fresh and aged snow spheres, respectively. The BC radiative effects are further complicated by uncertainty in snow grain shape and BC–snow mixing state. BC–snow internal mixing enhances the mean albedo effects over the plateau by 30–60 % relative to external mixing, while nonspherical snow grains decrease the mean albedo effects by up to 31 % relative to spherical grains. Based on this study, extensive measurements and improved model characterization of snow grain shape and aerosol–snow mixing state are urgently needed in order to precisely evaluate BC–snow albedo effects.

The concept of cloud radiative forcing (CRF) is commonly applied to quantify the impact of clouds on the surface radiative energy budget (REB). In the Arctic, specific radiative interactions between microphysical and macrophysical properties of clouds and the surface strongly modify the warming or cooling effect of clouds, complicating the estimate of CRF obtained from observations or models. Clouds tend to increase the broadband surface albedo over snow or sea ice surfaces compared to cloud-free conditions. However, this effect is not adequately considered in the derivation of CRF in the Arctic so far. Therefore, we have quantified the effects caused by surface-albedo–cloud interactions over highly reflective snow or sea ice surfaces on the CRF using radiative transfer simulations and below-cloud airborne observations above the heterogeneous springtime marginal sea ice zone (MIZ) during the Arctic CLoud Observations Using airborne measurements during polar Day (ACLOUD) campaign. The impact of a modified surface albedo in the presence of clouds, as compared to cloud-free conditions, and its dependence on cloud optical thickness is found to be relevant for the estimation of the shortwave CRF. A method is proposed to consider this surface albedo effect on CRF estimates by continuously retrieving the cloud-free surface albedo from observations under cloudy conditions, using an available snow and ice albedo parameterization. Using ACLOUD data reveals that the estimated average shortwave cooling by clouds almost doubles over snow- and ice-covered surfaces (−62 W m−2 instead of −32 W m−2), if surface-albedo–cloud interactions are considered. As a result, the observed total (shortwave plus longwave) CRF shifted from a warming effect to an almost neutral one. Concerning the seasonal cycle of the surface albedo, it is demonstrated that this effect enhances shortwave cooling in periods when snow dominates the surface and potentially weakens the cooling by optically thin clouds during the summertime melting season. These findings suggest that the surface-albedo–cloud interaction should be considered in global climate models and in long-term studies to obtain a realistic estimate of the shortwave CRF to quantify the role of clouds in Arctic amplification.

The aim of this work is to investigate whether snow albedo seasonality and trend under all sky conditions at Johnsons Glacier (Livingston Island, Antarctica) can be tracked using the Moderate Resolution Imaging Spectroradiometer (MODIS) snow albedo daily product MOD10A1. The time span is from December 2006 to February 2015. As the MOD10A1 snow albedo product has never been used in Antarctica before, we also assess the performance for the MOD10A1 cloud mask. The motivation for this work is the need for a description of snow albedo under all sky conditions (including overcast days) using satellite data with mid-spatial resolution. In-situ albedo was filtered with a 5-day windowed moving average, while the MOD10A1 data were filtered using a maximum filter. Both in-situ and MOD10A1 data follow an exponential decay during the melting season, with a maximum decay of 0.049/0.094 day−1 (in-situ/MOD10A1) for the 2006–2007 season and a minimum of 0.016/0.016 day−1 for the 2009–2010 season. The duration of the decay varies from 85 days (2007–2008) to 167 days (2013–2014). Regarding the albedo trend, both data sets exhibit a slight increase of albedo, which may be explained by an increase of snowfall along with a decrease of snowmelt in the study area. Annual albedo increases of 0.2% and 0.7% are obtained for in-situ and MOD10A1 data, respectively, which amount to respective increases of 2% and 6% in the period 2006–2015. We conclude that MOD10A1 can be used to characterize snow albedo seasonality and trend on Livingston Island when filtered with a maximum filter.

The second edition of the satellite-derived climate data record CLARA (The CM SAF Cloud, Albedo And Surface Radiation dataset from AVHRR data – second edition denoted as CLARA-A2) is described. The data record covers the 34-year period from 1982 until 2015 and consists of cloud, surface albedo and surface radiation budget products derived from the AVHRR (Advanced Very High Resolution Radiometer) sensor carried by polar-orbiting, operational meteorological satellites. The data record is produced by the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF) project as part of the operational ground segment. Its upgraded content and methodology improvements since edition 1 are described in detail, as are some major validation results. Some of the main improvements to the data record come from a major effort in cleaning and homogenizing the basic AVHRR level-1 radiance record and a systematic use of CALIPSO-CALIOP cloud information for development and validation purposes. Examples of applications studying decadal changes in Arctic summer surface albedo and cloud conditions are provided.

Regional climate model (RegCM-4.6.0) coupled with Community Land Model (CLM4.5), which includes SNow, ICe and Aerosol Radiation (SNICAR) model was used to investigate the effect of aerosol-induced snow albedo feedback on the regional radiation balance and atmospheric thermodynamics. During pre-monsoon (March–April–May) season, the deposition of absorbing aerosols such as dust and Black Carbon (BC) decreases the snow albedo of Himalayan–Tibetan region by 0.15 ± 0.13 causing a positive radiative effect of 14 ± 13 W m−2. In spite of the low absorption efficiency, the contribution of dust to the total radiative effect is found to be comparable to that due to BC over this region. The snow darkening due to aerosols increases the surface temperature by 1.33 ± 1.2 K, which results in the reduction of snow cover fraction by 7 ± 11%. The snow cover reduction is more than 20% in the mid-Himalayan region and northern Tibetan slopes due to its proximity to the major source regions like Indo-Gangetic Plain and Taklimakan desert respectively. Direct radiative effect (DRE, scattering and absorption of radiation) of atmospheric aerosols is smaller compared to the snow albedo effect (SAE) over the Himalayan region. DRE increase the mid-tropospheric temperature up to 1 K, whereas SAE effects are smaller and highly localized over the Himalayan region. Due to the large geographical extend of the forcing, the change in precipitation due to the direct effect is more prominent than that of the snow albedo effect. In general, change in snow cover fraction is dominated by the SAE and precipitation is more dominated by DRE. Present work demonstrates that the snow albedo feedback process over the Himalayan–Tibetan region plays a significant role in the regional climate of South Asia and therefore is crucial for the assessment of anthropogenic impacts.

The anthropogenically forced decline of Arctic sea ice is superimposed on strong internal variability. Possible drivers for this variability include fluctuations in surface albedo, clouds and water vapour, surface winds and poleward atmospheric and oceanic energy transport, but their relative contributions have not been quantified. By isolating the impact of the individual drivers in an Earth system model, we here demonstrate that internal variability of sea ice is primarily caused directly by atmospheric temperature fluctuations. The other drivers together explain only 25% of sea-ice variability. The dominating impact of atmospheric temperature fluctuations on sea ice is consistent across observations, reanalyses and simulations from global climate models. Such atmospheric temperature fluctuations occur due to variations in moist-static energy transport or local ocean heat release to the atmosphere. The fact that atmospheric temperature fluctuations are the key driver for sea-ice variability limits prospects of interannual predictions of sea ice, and suggests that observed record lows in Arctic sea-ice area are a direct response to an unusually warm atmosphere.Atmospheric temperature fluctuations are the main influence on Arctic sea-ice variability, whereas other factors explain only 25% of variability, according to an analysis of Earth system model simulations.

To explore the driving mechanisms of elevation-dependent warming (EDW) over the Tibetan Plateau (TP), the output from a suite of numerical experiments with different cumulus parameterization schemes (CPs) under the Coordinated Regional Climate Downscaling Experiments-East Asia (CORDEX-EA-II) project is examined. Results show that all experiments can broadly capture the observed temperature distributions over the TP with consistent cold biases, and the spread in temperature simulations commonly increases with elevation with the maximum located around 4000–5000 m. Such disagreements among the temperature simulations could to a large extent be explained by their spreads in the surface albedo feedback (SAF). All the experiments reproduce the observed EDW below 5000 m in winter but fail to capture the observed EDW above 4500 m in spring. Further analysis suggests that the simulated EDW during winter is mainly caused by the SAF, and the clear-sky downward longwave radiation (LW clr ) plays a secondary role in shaping EDW. The models’ inability in simulating EDW during spring is closely related to the SAF and the surface cloud radiative forcing (CRFs). Furthermore, the magnitude and structure of the simulated EDW are sensitive to the choice of CPs. Different CPs generate diverse snow cover fractions, which can modulate the simulated SAF and its effect on EDW. Also, the CPs show great influence on the LW clr via altering the low-level air temperature. Additionally, the mechanism for different temperature changes among the experiments varies with altitudes during summer and autumn, as the diverse temperature changes appear to be caused by the LW clr for the low altitudes while by the SAF for the middle-high altitudes.

Various studies reported an elevation dependent precipitation and temperature changes in mountainous regions of the world including the Himalayas. Various mechanisms are proposed to link the possible dependence of the precipitation and temperature on elevation with other variables, including, long- and short-wave radiation, albedo, clouds, humidity, etc. In the present study changes and trends of precipitation and temperature at different elevation ranges in the Indian Himalayan region (IHR) is assessed. Observations and modelling fields during the period 1970–2099 are used. Modelling simulations from the Coordinated Regional Climate Downscaling Experiment-South Asia experiments (CORDEX-SA) suites are considered. In addition, four seasons—winter (Dec, Jan, Feb: DJF), pre-monsoon (Mar, Apr, May: MAM), monsoon (Jun, Jul, Aug, Sep: JJAS) and post-monsoon (Oct, Nov: ON)—are considered to detect the possible seasonal response of elevation dependency. Firstly, precipitation and temperature fields, separately, as well as the diurnal temperature range (DTR) are assessed. Following, their long-term trends are investigated, if varying, at different elevational ranges in the IHR. To explain plausible physical mechanisms due to elevation dependency, trend of other variables viz., surface downward longwave radiation (DLR), total cloud faction, soil moisture, near surface specific humidity, surface snow melt and surface albedo, etc. are investigated. Results point towards an decreased (increased) precipitation in higher (lower) elevation. And amplified warming signals at higher elevations (above 3000 m), both in daytime and nighttime temperatures, during all seasons except the monsoon, are noticed. Increased DLR trends at higher elevation are also simulated well by the model and are likely the main elevation dependent driver in the IHR.