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

Assimilation of sea-ice concentration in a global climate model — physical and statistical aspects

We investigate the initialisation of Northern Hemisphere sea ice in the global climate model ECHAM5/MPI-OM by assimilating sea-ice concentration data. The analysis updates for concentration are given by Newtonian relaxation, and we discuss different ways of specifying the analysis updates for mean thickness. Because the conservation of mean ice thickness or actual ice thickness in the analysis updates leads to poor assimilation performance, we introduce a proportional dependence between concentration and mean thickness analysis updates. Assimilation with these proportional mean-thickness analysis updates leads to good assimilation performance for sea-ice concentration and thickness, both in identical-twin experiments and when assimilating sea-ice observations. The simulation of other Arctic surface fields in the coupled model is, however, not significantly improved by the assimilation. To understand the physical aspects of assimilation errors, we construct a simple prognostic model of the sea-ice thermodynamics, and analyse its response to the assimilation. We find that an adjustment of mean ice thickness in the analysis update is essential to arrive at plausible state estimates. To understand the statistical aspects of assimilation errors, we study the model background error covariance between ice concentration and ice thickness. We find that the spatial structure of covariances is best represented by the proportional mean-thickness analysis updates. Both physical and statistical evidence supports the experimental finding that assimilation with proportional mean-thickness updates outperforms the other two methods considered. The method described here is very simple to implement, and gives results that are sufficiently good to be used for initialising sea ice in a global climate model for seasonal to decadal predictions

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

Predictability of the Arctic sea ice edge

Skillful sea ice forecasts from days to years ahead are becoming increasingly important for the operation and planning of human activities in the Arctic. Here we analyze the potential predictability of the Arctic sea ice edge in six climate models. We introduce the integrated ice-edge error (IIEE), a user-relevant verification metric defined as the area where the forecast and the “truth” disagree on the ice concentration being above or below 15%. The IIEE lends itself to decomposition into an absolute extent error, corresponding to the common sea ice extent error, and a misplacement error. We find that the often-neglected misplacement error makes up more than half of the climatological IIEE. In idealized forecast ensembles initialized on 1 July, the IIEE grows faster than the absolute extent error. This means that the Arctic sea ice edge is less predictable than sea ice extent, particularly in September, with implications for the potential skill of end-user relevant forecasts

On the existence of stable seasonally varying Arctic sea ice in simple models

Within the framework of lower order thermodynamic theories for the climatic evolution of Arctic sea ice we isolate the conditions required for the existence of stable seasonally-varying solutions, in which ice forms each winter and melts away each summer. This is done by constructing a two-season model from the continuously evolving theory of Eisenman and Wettlaufer (2009) and showing that seasonally-varying states are unstable under constant annual average short-wave radiative forcing. However, dividing the summer season into two intervals (ice covered and ice free) provides sufficient freedom to stabilize seasonal ice. Simple perturbation theory shows that the condition for stability is determined by when the ice vanishes in summer and hence the relative magnitudes of the summer heat flux over the ocean versus over the ice. This scenario is examined within the context of greenhouse gas warming, as a function of which stability conditions are discerned.Comment: 11 pages, 6 figures, 1 tabl

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

The Arctic predictability and prediction on seasonal-to-interannual timescales (APPOSITE) data set version 1

This is the final version of the article. Available from the publisher via the DOI in this record. Discussion paper (published on 15 Oct 2015)Recent decades have seen significant developments in seasonal-to-interannual timescale climate prediction capabilities. However, until recently the potential of such systems to predict Arctic climate had not been assessed. This paper describes a multi- 5 model predictability experiment which was run as part of the Arctic Predictability and Prediction On Seasonal to Inter-annual Timescales (APPOSITE) project. The main goal of APPOSITE was to quantify the timescales on which Arctic climate is predictable. In order to achieve this, a coordinated set of idealised initial-value predictability experiments, with seven general circulation models, was conducted. This was the first model 10 intercomparison project designed to quantify the predictability of Arctic climate on seasonal to inter-annual timescales. Here we present a description of the archived data set (which is available at the British Atmospheric Data Centre) and an update of the project's results. Although designed to address Arctic predictability, this data set could also be used to assess the predictability of other regions and modes of climate vari15 ability on these timescales, such as the El Niño Southern Oscillation.This work was supported by the Natural Environment Research Council (grant NE/I029447/1). Helge Goessling was supported by a fellowship of the German Research Foundation (DFG grant GO 2464/1-1). Data storage and processing capacity was kindly provided by the British Atmospheric Data Centre (BADC). Thanks to Yanjun Jiao (CCCma) for his assistance with the CanCM4 simulations and to Bill Merryfield for his comments on a draft of the pape

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

Autologous stem cell transplantation improves quality of life in economically challenged, Brazilian multiple myeloma patients

OBJECTIVES: 1) To characterize the impact of multiple myeloma on the quality of life of patients treated in two public institutions in São Paulo State, Brazil, using a generic Short Form 36 Health Survey and a questionnaire specific for oncologic patients (QLQ-C30) upon diagnosis, after the clinical treatment, and at day +100 after autologous stem cell transplantation; 2) to evaluate whether autologous stem cell transplantation can improve the quality of life of our economically challenged population aside from providing a clinical benefit and disease control. METHODS: We evaluated 49 patients with multiple myeloma (a total of 70 interviews) using the two questionnaires. The scores upon diagnosis, post-treatment/pre-autologous stem cell transplantation, and at D+100 were compared using ANOVA (a comparison of the three groups), post hoc tests (two-by-two comparisons of the three groups), and paired t-tests (the same case at two different times). RESULTS: Of the included patients, 87.8% had a family budget under US $600 (economic class C, D, or E) per month. The generic Short Form 36 Health Survey questionnaire demonstrated that physical function, role-physical, and bodily pain indices were statistically different across all three groups, favoring the D+100 autologous stem cell transplantation group (ANOVA). The questionnaire specific for oncologic patients, the QLQ-C30 questionnaire, confirmed what had been demonstrated by the Short Form 36 Health Survey with respect to physical function and bodily pain, with improvements in role functioning, fatigue, and lack of appetite and constipation, favoring the D+100 autologous stem cell transplant group (ANOVA). The post hoc tests and paired t-tests confirmed a better outcome after autologous stem cell transplantation CONCLUSION: The questionnaire specific for cancer patients seems to be more informative than the generic Short Form 36 Health Survey questionnaire and reflects the real benefit of autologous stem cell transplantation in the quality of life of multiple myeloma patients in two public Brazilian institutions that provide assistance for economically challenged patients.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Universidade Federal de São Paulo (UNIFESP)Santa Casa de Misericórdia de São Paulo Faculdade de Ciências MédicasUniversidade Federal de São Paulo (UNIFESP) Departamento de MedicinaCedars-Sinai Outpatient Cancer CenterUNIFESP, Depto. de MedicinaSciEL