Recent warming on Spitsbergen - influence of atmospheric circulation and sea ice cover

Spitsbergen has experienced some of the most severe temperature changes in the Arctic during the last three decades. This study relates the recent warming to variations in large-scale atmospheric circulation (AC), air mass characteristics, and sea ice concentration (SIC), both regionally around Spitsbergen and locally in three fjords. We find substantial warming for all AC patterns for all seasons, with greatest temperature increase in winter. A major part of the warming can be attributed to changes in air mass characteristics associated with situations of both cyclonic and anticyclonic air advection from north and east and situations with a nonadvectional anticyclonic ridge. In total, six specific AC types (out of 21), which occur on average 41% of days in a year, contribute approximately 80% of the recent warming. The relationship between the land-based surface air temperature (SAT) and local and regional SIC was highly significant, particularly for the most contributing AC types. The high correlation between SAT and SIC for air masses from east and north of Spitsbergen suggests that a major part of the atmospheric warming observed in Spitsbergen is driven by heat exchange from the larger open water area in the Barents Sea and region north of Spitsbergen. Finally, our results show that changes in frequencies of AC play a minor role to the total recent surface warming. Thus, the strong warming in Spitsbergen in the latest decades is not driven by increased frequencies of “warm” AC types but rather from sea ice decline, higher sea surface temperatures, and a general background warming

Applying machine learning to improve simulations of a chaotic dynamical system using empirical error correction

Dynamical weather and climate prediction models underpin many studies of the Earth system and hold the promise of being able to make robust projections of future climate change based on physical laws. However, simulations from these models still show many differences compared with observations. Machine learning has been applied to solve certain prediction problems with great success, and recently it's been proposed that this could replace the role of physically-derived dynamical weather and climate models to give better quality simulations. Here, instead, a framework using machine learning together with physically-derived models is tested, in which it is learnt how to correct the errors of the latter from timestep to timestep. This maintains the physical understanding built into the models, whilst allowing performance improvements, and also requires much simpler algorithms and less training data. This is tested in the context of simulating the chaotic Lorenz '96 system, and it is shown that the approach yields models that are stable and that give both improved skill in initialised predictions and better long-term climate statistics. Improvements in long-term statistics are smaller than for single time-step tendencies, however, indicating that it would be valuable to develop methods that target improvements on longer time scales. Future strategies for the development of this approach and possible applications to making progress on important scientific problems are discussed.Comment: 26p, 7 figures To be published in Journal of Advances in Modeling Earth System

Optimal parameters for the ocean's nutrient, carbon, and oxygen cycles compensate for circulation biases but replumb the biological pump

Accurate predictive modelling of the ocean's global carbon and oxygen cycles is challenging because of uncertainties in both biogeochemistry and ocean circulation. Advances over the last decade have made parameter optimization feasible, allowing models to better match observed biogeochemical fields. However, does fitting a biogeochemical model to observed tracers using a circulation with known biases robustly capture the inner workings of the biological pump? Here we embed a mechanistic model of the ocean's coupled nutrient, carbon, and oxygen cycles into two circulations for the current climate. To assess the effects of biases, one circulation (ACCESS-M) is derived from a climate model and the other from data assimilation of observations (OCIM2). We find that parameter optimization compensates for circulation biases at the expense of altering how the biological pump operates. Tracer observations constrain pump strength and regenerated inventories for both circulations, but ACCESS-M export production optimizes to twice that of OCIM2 to compensate for ACCESS-M having lower sequestration efficiencies driven by less efficient particle transfer and shorter residence times. Idealized simulations forcing complete Southern Ocean nutrient utilization show that the response of the optimized system is sensitive to the embedding circulation. In ACCESS-M, Southern Ocean nutrient and DIC trapping is partially short-circuited by unrealistically deep mixed layers. For both circulations, intense Southern Ocean production deoxygenates Southern-Ocean-sourced deep waters, muting the imprint of circulation biases on oxygen. Our findings highlight that the biological pump's plumbing needs careful assessment to predict the biogeochemical response to environmental changes, even when optimally matching observations.</p

Using urine to diagnose large-scale mtDNA deletions in adult patients

Objective: The aim of this study was to evaluate if urinary sediment cells offered a robust alternative to muscle biopsy for the diagnosis of single mtDNA deletions. Methods: Eleven adult patients with progressive external ophthalmoplegia and a known single mtDNA deletion were investigated. Urinary sediment cells were used to isolate DNA, which was then subjected to long-range polymerase chain reaction. Where available, the patient's muscle DNA was studied in parallel. Breakpoint and thus deletion size were identified using both Sanger sequencing and next generation sequencing. The level of heteroplasmy was determined using quantitative polymerase chain reaction. Results: We identified the deletion in urine in 9 of 11 cases giving a sensitivity of 80%. Breakpoints and deletion size were readily detectable in DNA extracted from urine. Mean heteroplasmy level in urine was 38% +/- 26 (range 8 - 84%), and 57% +/- 28 (range 12 - 94%) in muscle. While the heteroplasmy level in urinary sediment cells differed from that in muscle, we did find a statistically significant correlation between these two levels (R = 0.714, P = 0.031(Pearson correlation)). Interpretation: Our findings suggest that urine can be used to screen patients suspected clinically of having a single mtDNA deletion. Based on our data, the use of urine could considerably reduce the need for muscle biopsy in this patient group.Peer reviewe