Nonstationary Teleconnection Between the Pacific Ocean and Arctic Sea Ice

Over the last 40 years observations show a teleconnection between summertime Pacific Ocean sea surface temperatures and September Arctic sea ice extent. However, the short satellite observation record has made it difficult to further examine this relationship. Here, we use 30 fully coupled general circulation models (GCMs) participating in Phase 5 of the Coupled Model Intercomparison Project to assess the ability of GCMs to simulate this teleconnection and analyze its stationarity over longer timescales. GCMs can temporarily simulate the teleconnection in continuous 40‐year segments but not over longer, centennial timescales. Each GCM exhibits considerable teleconnection variability on multidecadal timescales. Further analysis shows that the teleconnection depends on an equally nonstationary atmospheric bridge from the subequatorial Pacific Ocean to the upper Arctic troposphere. These findings indicate that the modulation of Arctic sea ice loss by subequatorial Pacific Ocean variability is not fixed in time, undermining the assumption of teleconnection stationarity as defined by the satellite record

Nonstationary Teleconnection Between the Pacific Ocean and Arctic Sea Ice

Over the last 40 years observations show a teleconnection between summertime Pacific Ocean sea surface temperatures and September Arctic sea ice extent. However, the short satellite observation record has made it difficult to further examine this relationship. Here, we use 30 fully coupled general circulation models (GCMs) participating in Phase 5 of the Coupled Model Intercomparison Project to assess the ability of GCMs to simulate this teleconnection and analyze its stationarity over longer timescales. GCMs can temporarily simulate the teleconnection in continuous 40‐year segments but not over longer, centennial timescales. Each GCM exhibits considerable teleconnection variability on multidecadal timescales. Further analysis shows that the teleconnection depends on an equally nonstationary atmospheric bridge from the subequatorial Pacific Ocean to the upper Arctic troposphere. These findings indicate that the modulation of Arctic sea ice loss by subequatorial Pacific Ocean variability is not fixed in time, undermining the assumption of teleconnection stationarity as defined by the satellite record

The reversibility of sea ice loss in a state-of-the-art climate model

Rapid Arctic sea ice retreat has fueled speculation about the possibility of threshold (or ‘tipping point’) behavior and irreversible loss of the sea ice cover. We test sea ice reversibility within a state-of-the-art atmosphere–ocean global climate model by increasing atmospheric carbon dioxide until the Arctic Ocean becomes ice-free throughout the year and subsequently decreasing it until the initial ice cover returns. Evidence for irreversibility in the form of hysteresis outside the envelope of natural variability is explored for the loss of summer and winter ice in both hemispheres. We find no evidence of irreversibility or multiple ice-cover states over the full range of simulated sea ice conditions between the modern climate and that with an annually ice-free Arctic Ocean. Summer sea ice area recovers as hemispheric temperature cools along a trajectory that is indistinguishable from the trajectory of summer sea ice loss, while the recovery of winter ice area appears to be slowed due to the long response times of the ocean near the modern winter ice edge. The results are discussed in the context of previous studies that assess the plausibility of sea ice tipping points by other methods. The findings serve as evidence against the existence of threshold behavior in the summer or winter ice cover in either hemisphere

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

Seasonal Arctic sea ice forecasting with probabilistic deep learning

Anthropogenic warming has led to an unprecedented year-round reduction in Arctic sea ice extent. This has far-reaching consequences for indigenous and local communities, polar ecosystems, and global climate, motivating the need for accurate seasonal sea ice forecasts. While physics-based dynamical models can successfully forecast sea ice concentration several weeks ahead, they struggle to outperform simple statistical benchmarks at longer lead times. We present a probabilistic, deep learning sea ice forecasting system, IceNet. The system has been trained on climate simulations and observational data to forecast the next 6 months of monthly-averaged sea ice concentration maps. We show that IceNet advances the range of accurate sea ice forecasts, outperforming a state-of-the-art dynamical model in seasonal forecasts of summer sea ice, particularly for extreme sea ice events. This step-change in sea ice forecasting ability brings us closer to conservation tools that mitigate risks associated with rapid sea ice loss

Towards seasonal Arctic shipping route predictions

The continuing decline in Arctic sea-ice will likely lead to increased human activity and opportunities for shipping in the region, suggesting that seasonal predictions of route openings will become ever more important. Here we present results from a set of 'perfect model' experiments to assess the predictability characteristics of the opening of Arctic sea routes. We find skilful predictions of the upcoming summer shipping season can be made from as early as January, although typically forecasts show lower skill before a May 'predictability barrier'. We demonstrate that in forecasts started from January, predictions of route opening date are twice as uncertain as predicting the closing date and that the Arctic shipping season is becoming longer due to climate change, with later closing dates mostly responsible. We find that predictive skill is state dependent with predictions for high or low ice years exhibiting greater skill than medium ice years. Forecasting the fastest open water route through the Arctic is accurate to within 200 km when predicted from July, a six-fold increase in accuracy compared to forecasts initialised from the previous November, which are typically no better than climatology. Finally we find that initialisation of accurate summer sea-ice thickness information is crucial to obtain skilful forecasts, further motivating investment into sea-ice thickness observations, climate models, and assimilation systems

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

Assessment of Satellite and Reanalysis Cold Season Snowfall Estimates Over Arctic Sea Ice

This work presents a systematic assessment of precipitation estimates from satellite and reanalysis products over Arctic sea ice by reconstructing snow depths from precipitation products and comparing them with snow depth observations from National Aeronautics and Space Administration (NASA)'s Operation IceBridge (OIB). Results show that the observed snow depth pattern is generally captured through reconstruction of snow depth using various precipitation products, but the use of passive microwave precipitation estimates results in significant underestimation of the snow depth. By using CloudSat monthly precipitation rate, to adjust the Global Precipitation Climatology Product (GPCP V1.3), the modified product (GPCP V1.3-mod) shows improved statistics over GPCP V1.3 as compared with OIB snow depth observations. Snow depth reconstructed from ERA-Int precipitation rate outperformed other products by showing the highest correlation coefficient and lowest root-mean-square error (RMSE). ERA5 shows largerRMSEthan ERA-Int, while MERRA-2 results in large overestimation of snow depth and largerRMSEcompared to GPCP and other reanalysis products.6 month embargo; first published online 08 July 2020This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]