Low-frequency variability of the Arctic climate: The role of oceanic and atmospheric heat transport variations

Changes in meridional heat transports, carried either by the atmosphere (HTRA) or by the ocean (HTRO), have been proposed to explain the decadal to multidecadal climate variations in the Arctic. On the other hand, model simulations indicate that, at high northern latitudes, variations in HTRA and HTRO are strongly coupled and may even compensate each other. A multi-century control integration with the Max Planck Institute global atmosphere-ocean model is analyzed to investigate the relative role of the HTRO and HTRA variations in shaping the Arctic climate and the consequences of their possible compensation. In the simulation, ocean heat transport anomalies modulate sea ice cover and surface heat fluxes mainly in the Barents Sea/Kara Sea region and the atmosphere responds with a modified pressure field. In response to positive HTRO anomalies there are negative HTRA anomalies associated with an export of relatively warm air southward to Western Siberia and a reduced inflow of heat over Alaska and northern Canada. While the compensation mechanism is prominent in this model, its dominating role is not constant over long time scales. The presence or absence of the compensation is determined mainly by the atmospheric circulation in the Pacific sector of the Arctic where the two leading large-scale atmospheric circulation patterns determine the lateral fluxes with varying contributions. The degree of compensation also determines the heat available to modulate the large-scale Arctic climate. The combined effect of atmospheric and oceanic contributions has to be considered to explain decadal-scale warming or cooling trends

Arctic rapid sea ice loss events in regional coupled climate scenario experiments

Rapid sea ice loss events (RILEs) in a mini-ensemble of regional Arctic coupled climate model scenario experiments are analyzed. Mechanisms of sudden ice loss are strongly related to atmospheric circulation conditions and preconditioning by sea ice thinning during the seasons and years before the event. Clustering of events in time suggests a strong control by large-scale atmospheric circulation. Anomalous atmospheric circulation is providing warm air anomalies of up to 5 K and is forcing ice flow, affecting winter ice growth. Even without a seasonal preconditioning during winter, ice drop events can be initiated by anomalous inflow of warm air during summer. It is shown that RILEs can be generated based on atmospheric circulation changes as a major driving force without major competing mechanisms, other than occasional longwave effects during spring and summer. Other anomalous seasonal radiative forcing or short-lived forcers (e.g., soot) play minor roles or no role at all in our model. RILEs initiated by ocean forcing do not occur in the model, although cannot be ruled out due to model limitations. Mechanisms found are qualitatively in line with observations of the 2007 RILE

Tundra shrubification and tree-line advance amplify arctic climate warming:results from an individual-based dynamic vegetation model

One major challenge to the improvement of regional climate scenarios for the northern high latitudes is to understand land surface feedbacks associated with vegetation shifts and ecosystem biogeochemical cycling. We employed a customized, Arctic version of the individual-based dynamic vegetation model LPJ-GUESS to simulate the dynamics of upland and wetland ecosystems under a regional climate model-downscaled future climate projection for the Arctic and Subarctic. The simulated vegetation distribution (1961-1990) agreed well with a composite map of actual arctic vegetation. In the future (2051-2080), a poleward advance of the forest-tundra boundary, an expansion of tall shrub tundra, and a dominance shift from deciduous to evergreen boreal conifer forest over northern Eurasia were simulated. Ecosystems continued to sink carbon for the next few decades, although the size of these sinks diminished by the late 21st century. Hot spots of increased CH4 emission were identified in the peatlands near Hudson Bay and western Siberia. In terms of their net impact on regional climate forcing, positive feedbacks associated with the negative effects of tree-line, shrub cover and forest phenology changes on snow-season albedo, as well as the larger sources of CH4, may potentially dominate over negative feedbacks due to increased carbon sequestration and increased latent heat flux

Seasonal to interannual Arctic sea-ice predictability in current GCMs

We establish the first inter-model comparison of seasonal to interannual predictability of present-day Arctic climate by performing coordinated sets of idealized ensemble predictions with four state-of-the-art global climate models. For Arctic sea-ice extent and volume, there is potential predictive skill for lead times of up to three years, and potential prediction errors have similar growth rates and magnitudes across the models. Spatial patterns of potential prediction errors differ substantially between the models, but some features are robust. Sea-ice concentration errors are largest in the marginal ice zone, and in winter they are almost zero away from the ice edge. Sea-ice thickness errors are amplified along the coasts of the Arctic Ocean, an effect that is dominated by sea-ice advection. These results give an upper bound on the ability of current global climate models to predict important aspects of Arctic climate

Will Arctic sea ice thickness initialization improve seasonal forecast skill?

Arctic sea ice thickness is thought to be an important predictor of Arctic sea ice extent. However, coupled seasonal forecast systems do not generally use sea ice thickness observations in their initialization and are therefore missing a potentially important source of additional skill. To investigate how large this source is, a set of ensemble potential predictability experiments with a global climate model, initialized with and without knowledge of the sea ice thickness initial state, have been run. These experiments show that accurate knowledge of the sea ice thickness field is crucially important for sea ice concentration and extent forecasts up to 8 months ahead, especially in summer. Perturbing sea ice thickness also has a significant impact on the forecast error in Arctic 2 m temperature a few months ahead. These results suggest that advancing capabilities to observe and assimilate sea ice thickness into coupled forecast systems could significantly increase skill

Re-examining the roles of surface heat flux and latent heat release in a “hurricane-like” polar low over the Barents Sea

Polar lows are intense mesoscale cyclones that occur at high latitudes in both hemispheres during winter. Their sometimes evidently convective nature, fueled by strong surface fluxes and with cloud-free centers, have led to some polar lows being referred to as “arctic hurricanes.” Idealized studies have shown that intensification by hurricane development mechanisms is theoretically possible in polar winter atmospheres, but the lack of observations and realistic simulations of actual polar lows have made it difficult to ascertain if this occurs in reality. Here the roles of surface heat fluxes and latent heat release in the development of a Barents Sea polar low, which in its cloud structures showed some similarities to hurricanes, are studied with an ensemble of sensitivity experiments, where latent heating and/or surface fluxes of sensible and latent heat were switched off before the polar low peaked in intensity. To ensure that the polar lows in the sensitivity runs did not track too far away from the actual environmental conditions, a technique known as spectral nudging was applied. This was shown to be crucial for enabling comparisons between the different model runs. The results presented here show that (1) no intensification occurred during the mature, postbaroclinic stage of the simulated polar low; (2) surface heat fluxes, i.e., air-sea interaction, were crucial processes both in order to attain the polar low's peak intensity during the baroclinic stage and to maintain its strength in the mature stage; and (3) latent heat release played a less important role than surface fluxes in both stages

Deep mixed ocean volume in the Labrador Sea in HighResMIP models

Simulations from seven global coupled climate models performed at high and standard resolution as part of the high resolution model intercomparison project (HighResMIP) are analyzed to study deep ocean mixing in the Labrador Sea and the impact of increased horizontal resolution. The representation of convection varies strongly among models. Compared to observations from ARGO-floats and the EN4 data set, most models substantially overestimate deep convection in the Labrador Sea. In four out of five models, all four using the NEMO-ocean model, increasing the ocean resolution from 1° to 1/4° leads to increased deep mixing in the Labrador Sea. Increasing the atmospheric resolution has a smaller effect than increasing the ocean resolution. Simulated convection in the Labrador Sea is mainly governed by the release of heat from the ocean to the atmosphere and by the vertical stratification of the water masses in the Labrador Sea in late autumn. Models with stronger sub-polar gyre circulation have generally higher surface salinity in the Labrador Sea and a deeper convection. While the high-resolution models show more realistic ocean stratification in the Labrador Sea than the standard resolution models, they generally overestimate the convection. The results indicate that the representation of sub-grid scale mixing processes might be imperfect in the models and contribute to the biases in deep convection. Since in more than half of the models, the Labrador Sea convection is important for the Atlantic Meridional Overturning Circulation (AMOC), this raises questions about the future behavior of the AMOC in the models

Impact of higher spatial atmospheric resolution on precipitation extremes over land in global climate models

Finer grids in global climate models could lead to an improvement in the simulation of precipitation extremes. We assess the influence on model performance of increasing spatial resolution by evaluating pairs of high‐ and low‐resolution forced atmospheric simulations from six global climate models (generally the latest CMIP6 version) on a common 1° × 1° grid. The differences in tuning between the lower and higher resolution versions are as limited as possible, which allows the influence of higher resolution to be assessed exclusively. We focus on the 1985–2014 climatology of annual extremes of daily precipitation over global land, and models are compared to observations from different sources (i.e., in situ‐based and satellite‐based) to enable consideration of observational uncertainty. Finally, we address regional features of model performance based on four indices characterizing different aspects of precipitation extremes. Our analysis highlights good agreement between models that precipitation extremes are more intense at higher resolution. We find that the spread among observations is substantial and can be as large as intermodel differences, which makes the quantitative evaluation of model performance difficult. However, consistently across the four precipitation extremes indices that we investigate, models often show lower skill at higher resolution compared to their corresponding lower resolution version. Our findings suggest that increasing spatial resolution alone is not sufficient to obtain a systematic improvement in the simulation of precipitation extremes, and other improvements (e.g., physics and tuning) may be required