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Expanding shrubs in the Arctic trap blowing snow, increasing snow height and accelerating permafrost warming. Topography also affects snow height as snow accumulates in hollows. The respective roles of topography and erect vegetation in snow accumulation were investigated using a UAV-borne lidar at two nearby contrasted sites in northern Quebec, Canada. The North site featured tall vegetation up to 2.5 m high, moderate snow height, and smooth topography. The South site featured lower vegetation, greater snow height, and rougher topography. There was little correlation between topography and vegetation height at both sites. Vegetation lower than snow height had very little effect on snow height. When vegetation protruded above the snow, snow height was well correlated with vegetation height. The topographic position index (TPI) was well correlated with snow height when it was not masked by the effect of protruding vegetation. The North site with taller vegetation therefore showed a good correlation between vegetation height and snow height, R2 = 0.37, versus R2 = 0.04 at the South site. Regarding topography, the reverse was observed between TPI and snow height, with R2 = 0.29 at the North site and R2 = 0.67 at the South site. The combination of vegetation height and TPI improved the prediction of snow height at the North site (R2 = 0.59) but not at the South site because vegetation height has little influence there. Vegetation was therefore the main factor determining snow height when it protruded above the snow. When it did not protrude, snow height was mostly determined by topography. Significance Statement: Wind-induced snow drifting is a major snow redistribution process in the Arctic. Shrubs trap drifting snow, and drifting snow accumulates in hollows. Determining the respective roles of both these processes in snow accumulation is required to predict permafrost temperature and its emission of greenhouse gases, because thicker snow limits permafrost winter cooling. Using a UAV-borne lidar, we have determined snow height distribution over two contrasted sites in the Canadian low Arctic, with varied vegetation height and topography. When snow height exceeds vegetation height, topography is a good predictor of snow height, with negligible effect of buried vegetation. When vegetation protrudes above the snow, combining both topography and vegetation height is required for a good prediction of snow height. [ABSTRACT FROM AUTHOR]

The noise forcing underlying the variability in the Arctic ice cover has a wide range of principally unknown origins. For this reason, the analytical and numerical solutions of a stochastic Arctic sea ice model are analyzed with both additive and multiplicative noise over a wide range of external heat fluxes Δ F0, corresponding to greenhouse gas forcing. The stochastic variability fundamentally influences the nature of the deterministic steady-state solutions corresponding to perennial and seasonal ice and ice-free states. Thus, the results are particularly relevant for the interpretation of the state of the system as the ice cover thins with Δ F0, allowing a thorough examination of the differing effects of additive versus multiplicative noise. In the perennial ice regime, the principal stochastic moments are calculated and compared to those determined from a stochastic perturbation theory described previously. As Δ F0 increases, the competing contributions to the variability of the destabilizing sea ice-albedo feedback and the stabilizing longwave radiative loss are examined in detail. At the end of summer the variability of the stochastic paths shows a clear maximum, which is due to the combination of the increasing influence of the albedo feedback and an associated 'memory effect,' in which fluctuations accumulate from early spring to late summer. This is counterbalanced by the stabilization of the ice cover resulting from the longwave loss of energy from the ice surface, which is enhanced during winter, thereby focusing the stochastic paths and decreasing the variability. Finally, common examples in stochastic dynamics with multiplicative noise are discussed wherein the choice of the stochastic calculus (Itô or Stratonovich) is not necessarily determinable a priori from observations alone, which is why both calculi are treated on equal footing herein. [ABSTRACT FROM AUTHOR]

Century-scale global near-surface temperature trends in response to rising greenhouse gas concentrations in climate models vary by almost a factor of 2, with greatest intermodel spread in the Arctic region where sea ice is a key climate component. Three factors contribute to the intermodel spread: 1) model formulation, 2) control climate state, and 3) internal climate variability. This study focuses on the influence of Arctic sea ice in the control climate on the intermodel spread in warming, using idealized 1% yr−1 CO2 increase simulations of 33 state-of-the-art global climate models, and combining sea ice-temperature relations on local to large spatial scales. On the Arctic mean scale, the spread in temperature trends is only weakly related to ice volume or area in the control climate, and is probably not dominated by internal variability. This suggests that other processes, such as ocean heat transport and meteorological conditions, play a more important role in the spread of long-term Arctic warming than control sea ice conditions. However, on a local scale, sea ice-warming relations show that in regions with more sea ice, models generally simulate more warming in winter and less warming in summer. The local winter warming is clearly related to control sea ice and universal among models, whereas summer sea ice-warming relations are more diverse, and are probably dominated by differences in model formulation. To obtain a more realistic representation of Arctic warming, it is recommended to simulate control sea ice conditions in climate models so that the spatial pattern is correct. [ABSTRACT FROM AUTHOR]

Internal variability in twenty-first-century summer Arctic sea ice loss and its relationship to the large-scale atmospheric circulation is investigated in a 39-member Community Climate System Model, version 3 (CCSM3) ensemble for the period 2000-61. Each member is subject to an identical greenhouse gas emissions scenario and differs only in the atmospheric model component's initial condition. September Arctic sea ice extent trends during 2020-59 range from −2.0 × 106 to −5.7 × 106 km2 across the 39 ensemble members, indicating a substantial role for internal variability in future Arctic sea ice loss projections. A similar nearly threefold range (from −7.0 × 103 to −19 × 103 km3) is found for summer sea ice volume trends. Higher rates of summer Arctic sea ice loss in CCSM3 are associated with enhanced transpolar drift and Fram Strait ice export driven by surface wind and sea level pressure patterns. Over the Arctic, the covarying atmospheric circulation patterns resemble the so-called Arctic dipole, with maximum amplitude between April and July. Outside the Arctic, an atmospheric Rossby wave train over the Pacific sector is associated with internal ice loss variability. Interannual covariability patterns between sea ice and atmospheric circulation are similar to those based on trends, suggesting that similar processes govern internal variability over a broad range of time scales. Interannual patterns of CCSM3 ice-atmosphere covariability compare well with those in nature and in the newer CCSM4 version of the model, lending confidence to the results. Atmospheric teleconnection patterns in CCSM3 suggest that the tropical Pacific modulates Arctic sea ice variability via the aforementioned Rossby wave train. Large ensembles with other coupled models are needed to corroborate these CCSM3-based findings. [ABSTRACT FROM AUTHOR]

This study diagnoses the changes in Arctic clouds simulated by the Community Climate System Model version 3 (CCSM3) in a transient 2 × CO2 simulation. Four experiments-one fully coupled and three with prescribed SSTs and/or sea ice cover-are used to identify the mechanisms responsible for the projected cloud changes. The target simulation uses a T42 version of the CCSM3, in which the atmosphere is coupled to a dynamical ocean with mobile sea ice. This simulation is approximated by a T42 atmosphere-only integration using CCSM3's atmospheric component [the Community Atmosphere Model version 3 (CAM3)] forced at its lower boundary with the changes in both SSTs and sea ice concentration from CCSM3's 2 × CO2 run. The authors decompose the combined effect of the higher SSTs and reduced sea ice concentration on the Arctic cloud response in this experiment by running two additional CAM3 simulations: one forced with modern SSTs and the projected sea ice cover changes in CCSM3 and the other forced with modern sea ice coverage and the projected changes in SSTs in CCSM3. The results suggest that future increases in Arctic cloudiness simulated by CCSM3 are mostly attributable to two separate processes. Low cloud gains are primarily initiated locally by enhanced evaporation within the Arctic due to reduced sea ice, whereas cloud increases at middle and high levels are mostly driven remotely via greater meridional moisture transport from lower latitudes in a more humid global atmosphere. The enhanced low cloudiness attributable to sea ice loss causes large increases in cloud radiative forcing during the coldest months and therefore promotes even greater surface warming. Because CCSM3's Arctic cloud response to greenhouse forcing is similar to other GCMs, the driving mechanisms identified here may be applicable to other models and could help to advance our understanding of likely changes in the vertical structure of polar clouds. [ABSTRACT FROM AUTHOR]

Climate projections at regional scales are in increased demand from management agencies and other stakeholders. While global atmosphere--ocean climate models provide credible quantitative estimates of future climate at continental scales and above, individual model performance varies for different regions, variables, and evaluation metrics--a less than satisfying situation. Using the high-latitude Northern Hemisphere as a focus, the authors assess strategies for providing regional projections based on global climate models. Starting with a set of model results obtained from an ''ensemble of opportunity,'' the core of this procedure is to retain a subset of models through comparisons of model simulations with observations at both continental and regional scales. The exercise is more one of model culling than model selection. The continental-scale evaluation is a check on the large-scale climate physics of the models, and the regional-scale evaluation emphasizes variables of ecological or societal relevance. An additional consideration is given to the comprehensiveness of processes included in the models. In many but not all applications, different results are obtained from a reduced set of models compared to relying on the simple mean of all available models. For example, in the Arctic the top-performing models tend to be more sensitive to greenhouse forcing than the poorer-performing models. Because of the mostly unexplained inconsistencies in model performance under different selection criteria, simple and transparent evaluation methods are favored. The use of a single model is not recommended. For some applications, no model may be able to provide a suitable regional projection. The use of model evaluation strategies, as opposed to relying on simple averages of ensembles of opportunity, should be part of future synthesis activities such as the upcoming Fifth Assessment Report of the Intergovernmental Panel on Climate Change. [ABSTRACT FROM AUTHOR]

The article focuses on the history of arctic aerosols. Arctic is considered as the head of contemporary climate change which deeply modified by the distant emissions of the green gases. The first discovery of Arctic aerosol pollution happened between 1940 and 1950. The disturbance of aerosols from midlatitudes in the Arctic climate has been considered as the prevailing anthropogenic climate signature. The examination for the future development of aerosol contributions in the Arctic needs a long-term maintenance of network observation.

Associations between cloudiness, radiative fluxes, and surface air temperature in the central Arctic are evaluated from 1) measurements made at Russian drifting ice stations, and 2) atmospheric reanalyses of the National Centers for Environmental Prediction (NCEP) and the European Centre for Medium-Range Weather Forecasts (ECMWF). In the ice station data, cloudiness is associated with an increase of downward longwave radiation in all months and an increase of net (downward minus upward) total radiation from September through March. The surface air temperatures under overcast skies are 6°--9°C higher than under clear skies during September-- March, and the differences are even larger when the observations are stratified by wind as well as cloudiness. The warming by the radiative flux enhancement after a transition from clear skies to overcast has a 1--2-day timescale, while the cooling after the transition to clear skies has a somewhat shorter timescale. The NCEP reanalysis exaggerates slightly the association between cloudiness and surface air temperature, while the ECMWF reanalysis shows a considerably weaker association. The maximum cloud-radiative forcing (MCRF), defined as the difference between the ice station measurements of net surface radiation under cloudy and clear skies, ranges from 259 W m 22 in June to positive values of 20--30 W m 22 in September--March. The annual mean is small but positive,3 W m 22 , despite the approximately three-month summer period of substantially negative MCRF. These findings are consistent with the conventional cloud-radiative forcing obtained in earlier studies using satellite data and one-dimensional models of the Arctic atmosphere and sea ice. Neither reanalysis captures the seasonality of the observationally deduced effects of clouds on surface radiation. The NCEP reanalysis does not capture the seasonality of the actual cloudiness (as defined by the reported cloud fractions), while the ECMWF... [ABSTRACT FROM AUTHOR]