A review on Arctic sea ice predictability and prediction on seasonal-to-decadal timescales

Sea ice plays a crucial role in the earth's energy and water budget and substantially impacts local and remote atmospheric and oceanic circulations. Predictions of Arctic sea ice conditions a few months to a few years in advance could be of interest for stakeholders. This article presents a review of the potential sources of Arctic sea ice predictability on these timescales. Predictability mainly originates from persistence or advection of sea ice anomalies, interactions with the ocean and atmosphere and changes in radiative forcing. After estimating the inherent potential predictability limit with state-of-the-art models, current sea ice forecast systems are described, together with their performance. Finally, some challenges and issues in sea ice forecasting are presented, along with suggestions for future research priorities.

Pan-Arctic and regional sea ice predictability: initialization month dependence

Seasonal-to-interannual predictions of Arctic sea ice may be important for Arctic communities and industries alike. Previous studies have suggested that Arctic sea ice is potentially predictable but that the skill of predictions of the September extent minimum, initialized in early summer, may be low. The authors demonstrate that a melt season “predictability barrier” and two predictability reemergence mechanisms, suggested by a previous study, are robust features of five global climate models. Analysis of idealized predictions with one of these models [Hadley Centre Global Environment Model, version 1.2 (HadGEM1.2)], initialized in January, May and July, demonstrates that this predictability barrier exists in initialized forecasts as well. As a result, the skill of sea ice extent and volume forecasts are strongly start date dependent and those that are initialized in May lose skill much faster than those initialized in January or July. Thus, in an operational setting, initializing predictions of extent and volume in July has strong advantages for the prediction of the September minimum when compared to predictions initialized in May. Furthermore, a regional analysis of sea ice predictability indicates that extent is predictable for longer in the seasonal ice zones of the North Atlantic and North Pacific than in the regions dominated by perennial ice in the central Arctic and marginal seas. In a number of the Eurasian shelf seas, which are important for Arctic shipping, only the forecasts initialized in July have continuous skill during the first summer. In contrast, predictability of ice volume persists for over 2 yr in the central Arctic but less in other regions.

Arctic warming, atmospheric blocking and cold European winters in CMIP5 models

Amplified Arctic warming is expected to have a significant longterm influence on the midlatitude atmospheric circulation by the latter half of the 21st century. Potential influences of recent and near future Arctic changes on shorter timescales are much less clear, despite having received much recent attention in the literature. In this letter, climate models from the recent CMIP5 experiment are analysed for evidence of an influence of Arctic temperatures on midlatitude blocking and cold European winters in particular. The focus is on the variability of these features in detrended data and, in contrast to other studies, limited evidence of an influence is found. The occurrence of cold European winters is found to be largely independent of the temperature variability in the key Barents–Kara Sea region. Positive correlations of the Barents–Kara temperatures with Eurasian blocking are found in some models, but significant correlations are limited.

The ocean's role in polar climate change: asymmetric Arctic and Antarctic responses to greenhouse gas and ozone forcing

In recent decades, the Arctic has been warming and sea ice disappearing. By contrast, the Southern Ocean around Antarctica has been (mainly) cooling and sea-ice extent growing. We argue here that interhemispheric asymmetries in the mean ocean circulation, with sinking in the northern North Atlantic and upwelling around Antarctica, strongly influence the sea-surface temperature (SST) response to anthropogenic greenhouse gas (GHG) forcing, accelerating warming in the Arctic while delaying it in the Antarctic. Furthermore, while the amplitude of GHG forcing has been similar at the poles, significant ozone depletion only occurs over Antarctica. We suggest that the initial response of SST around Antarctica to ozone depletion is one of cooling and only later adds to the GHG-induced warming trend as upwelling of sub-surface warm water associated with stronger surface westerlies impacts surface properties. We organize our discussion around ‘climate response functions’ (CRFs), i.e. the response of the climate to ‘step’ changes in anthropogenic forcing in which GHG and/or ozone-hole forcing is abruptly turned on and the transient response of the climate revealed and studied. Convolutions of known or postulated GHG and ozone-hole forcing functions with their respective CRFs then yield the transient forced SST response (implied by linear response theory), providing a context for discussion of the differing warming/cooling trends in the Arctic and Antarctic. We speculate that the period through which we are now passing may be one in which the delayed warming of SST associated with GHG forcing around Antarctica is largely cancelled by the cooling effects associated with the ozone hole. By mid-century, however, ozone-hole effects may instead be adding to GHG warming around Antarctica but with diminished amplitude as the ozone hole heals. The Arctic, meanwhile, responding to GHG forcing but in a manner amplified by ocean heat transport, may continue to warm at an accelerating rate.

An investigation into the impact of using various techniques to estimate Arctic surface air temperature anomalies

Time series of global and regional mean Surface Air Temperature (SAT) anomalies are a common metric used to estimate recent climate change. Various techniques can be used to create these time series from meteorological station data. The degree of difference arising from using five different techniques, based on existing temperature anomaly dataset techniques, to estimate Arctic SAT anomalies over land and sea ice were investigated using reanalysis data as a testbed. Techniques which interpolated anomalies were found to result in smaller errors than non-interpolating techniques relative to the reanalysis reference. Kriging techniques provided the smallest errors in estimates of Arctic anomalies and Simple Kriging was often the best kriging method in this study, especially over sea ice. A linear interpolation technique had, on average, Root Mean Square Errors (RMSEs) up to 0.55 K larger than the two kriging techniques tested. Non-interpolating techniques provided the least representative anomaly estimates. Nonetheless, they serve as useful checks for confirming whether estimates from interpolating techniques are reasonable. The interaction of meteorological station coverage with estimation techniques between 1850 and 2011 was simulated using an ensemble dataset comprising repeated individual years (1979-2011). All techniques were found to have larger RMSEs for earlier station coverages. This supports calls for increased data sharing and data rescue, especially in sparsely observed regions such as the Arctic.

Sensitivity of simulated regional Arctic climate to the choice of coupled model domain

The climate over the Arctic has undergone changes in recent decades. In order to evaluate the coupled response of the Arctic system to external and internal forcing, our study focuses on the estimation of regional climate variability and its dependence on large-scale atmospheric and regional ocean circulations. A global ocean–sea ice model with regionally high horizontal resolution is coupled to an atmospheric regional model and global terrestrial hydrology model. This way of coupling divides the global ocean model setup into two different domains: one coupled, where the ocean and the atmosphere are interacting, and one uncoupled, where the ocean model is driven by prescribed atmospheric forcing and runs in a so-called stand-alone mode. Therefore, selecting a specific area for the regional atmosphere implies that the ocean–atmosphere system can develop ‘freely’ in that area, whereas for the rest of the global ocean, the circulation is driven by prescribed atmospheric forcing without any feedbacks. Five different coupled setups are chosen for ensemble simulations. The choice of the coupled domains was done to estimate the influences of the Subtropical Atlantic, Eurasian and North Pacific regions on northern North Atlantic and Arctic climate. Our simulations show that the regional coupled ocean–atmosphere model is sensitive to the choice of the modelled area. The different model configurations reproduce differently both the mean climate and its variability. Only two out of five model setups were able to reproduce the Arctic climate as observed under recent climate conditions (ERA-40 Reanalysis). Evidence is found that the main source of uncertainty for Arctic climate variability and its predictability is the North Pacific. The prescription of North Pacific conditions in the regional model leads to significant correlation with observations, even if the whole North Atlantic is within the coupled model domain. However, the inclusion of the North Pacific area into the coupled system drastically changes the Arctic climate variability to a point where the Arctic Oscillation becomes an ‘internal mode’ of variability and correlations of year-to-year variability with observational data vanish. In line with previous studies, our simulations provide evidence that Arctic sea ice export is mainly due to ‘internal variability’ within the Arctic region. We conclude that the choice of model domains should be based on physical knowledge of the atmospheric and oceanic processes and not on ‘geographic’ reasons. This is particularly the case for areas like the Arctic, which has very complex feedbacks between components of the regional climate system.

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.

An AeroCom assessment of black carbon in Arctic snow and sea ice

Though many global aerosols models prognose surface deposition, only a few models have been used to directly simulate the radiative effect from black carbon (BC) deposition to snow and sea ice. Here, we apply aerosol deposition fields from 25 models contributing to two phases of the Aerosol Comparisons between Observations and Models (AeroCom) project to simulate and evaluate within-snow BC concentrations and radiative effect in the Arctic. We accomplish this by driving the offline land and sea ice components of the Community Earth System Model with different deposition fields and meteorological conditions from 2004 to 2009, during which an extensive field campaign of BC measurements in Arctic snow occurred. We find that models generally underestimate BC concentrations in snow in northern Russia and Norway, while overestimating BC amounts elsewhere in the Arctic. Although simulated BC distributions in snow are poorly correlated with measurements, mean values are reasonable. The multi-model mean (range) bias in BC concentrations, sampled over the same grid cells, snow depths, and months of measurements, are −4.4 (−13.2 to +10.7) ng g−1 for an earlier phase of AeroCom models (phase I), and +4.1 (−13.0 to +21.4) ng g−1 for a more recent phase of AeroCom models (phase II), compared to the observational mean of 19.2 ng g−1. Factors determining model BC concentrations in Arctic snow include Arctic BC emissions, transport of extra-Arctic aerosols, precipitation, deposition efficiency of aerosols within the Arctic, and meltwater removal of particles in snow. Sensitivity studies show that the model–measurement evaluation is only weakly affected by meltwater scavenging efficiency because most measurements were conducted in non-melting snow. The Arctic (60–90° N) atmospheric residence time for BC in phase II models ranges from 3.7 to 23.2 days, implying large inter-model variation in local BC deposition efficiency. Combined with the fact that most Arctic BC deposition originates from extra-Arctic emissions, these results suggest that aerosol removal processes are a leading source of variation in model performance. The multi-model mean (full range) of Arctic radiative effect from BC in snow is 0.15 (0.07–0.25) W m−2 and 0.18 (0.06–0.28) W m−2 in phase I and phase II models, respectively. After correcting for model biases relative to observed BC concentrations in different regions of the Arctic, we obtain a multi-model mean Arctic radiative effect of 0.17 W m−2 for the combined AeroCom ensembles. Finally, there is a high correlation between modeled BC concentrations sampled over the observational sites and the Arctic as a whole, indicating that the field campaign provided a reasonable sample of the Arctic.

Atmospheric response in summer linked to recent Arctic sea ice loss

Since 2007 a large decline in Arctic sea ice has been observed. The large-scale atmospheric circulation response to this decline is investigated in ERA-Interim reanalyses and HadGEM3 climate model experiments. In winter, post-2007 observed circulation anomalies over the Arctic, North Atlantic and Eurasia are small compared to interannual variability. In summer, the post-2007 observed circulation is dominated by an anticyclonic anomaly over Greenland which has a large signal-to-noise ratio. Climate model experiments driven by observed SST and sea ice anomalies are able to capture the summertime pattern of observed circulation anomalies, although the magnitude is a third of that observed. The experiments suggest high SSTs and reduced sea ice in the Labrador Sea lead to positive temperature anomalies in the lower troposphere which weaken the westerlies over North America through thermal wind balance. The experiments also capture cyclonic anomalies over Northwest Europe, which are consistent with downstream Rossby wave propagation

Arctic amplification dominated by temperature feedbacks in contemporary climate models

Climate change is amplified in the Arctic region. Arctic amplification has been found in past warm1 and glacial2 periods, as well as in historical observations3, 4 and climate model experiments5, 6. Feedback effects associated with temperature, water vapour and clouds have been suggested to contribute to amplified warming in the Arctic, but the surface albedo feedback—the increase in surface absorption of solar radiation when snow and ice retreat—is often cited as the main contributor7, 8, 9, 10. However, Arctic amplification is also found in models without changes in snow and ice cover11, 12. Here we analyse climate model simulations from the Coupled Model Intercomparison Project Phase 5 archive to quantify the contributions of the various feedbacks. We find that in the simulations, the largest contribution to Arctic amplification comes from a temperature feedbacks: as the surface warms, more energy is radiated back to space in low latitudes, compared with the Arctic. This effect can be attributed to both the different vertical structure of the warming in high and low latitudes, and a smaller increase in emitted blackbody radiation per unit warming at colder temperatures. We find that the surface albedo feedback is the second main contributor to Arctic amplification and that other contributions are substantially smaller or even opposeArctic amplification.

Comments on 'Current GCMs' Unrealistic Negative Feedback in the Arctic'

In contrast to prior studies showing a positive lapse-rate feedback associated with the Arctic inversion, Boé et al. reported that strong present-day Arctic temperature inversions are associated with stronger negative longwave feedbacks and thus reduced Arctic amplification in the model ensemble from phase 3 of the Coupled Model Intercomparison Project (CMIP3). A permutation test reveals that the relation between longwave feedbacks and inversion strength is an artifact of statistical self-correlation and that shortwave feedbacks have a stronger correlation with intermodel spread. The present comment concludes that the conventional understanding of a positive lapse-rate feedback associated with the Arctic inversion is consistent with the CMIP3 model ensemble.

Aspects of designing and evaluating seasonal-to-interannual Arctic sea-ice prediction systems

Using lessons from idealised predictability experiments, we discuss some issues and perspectives on the design of operational seasonal to inter-annual Arctic sea-ice prediction systems. We first review the opportunities to use a hierarchy of different types of experiment to learn about the predictability of Arctic climate. We also examine key issues for ensemble system design, such as: measuring skill, the role of ensemble size and generation of ensemble members. When assessing the potential skill of a set of prediction experiments, using more than one metric is essential as different choices can significantly alter conclusions about the presence or lack of skill. We find that increasing both the number of hindcasts and ensemble size is important for reliably assessing the correlation and expected error in forecasts. For other metrics, such as dispersion, increasing ensemble size is most important. Probabilistic measures of skill can also provide useful information about the reliability of forecasts. In addition, various methods for generating the different ensemble members are tested. The range of techniques can produce surprisingly different ensemble spread characteristics. The lessons learnt should help inform the design of future operational prediction systems.