Dramatic interannual changes of perennial Arctic sea ice linked to abnormal summer storm activity

Copyright © 2011 American Geophysical UnionThe perennial (September) Arctic sea ice cover exhibits large interannual variability, with changes of over a million square kilometers from one year to the next. Here we explore the role of changes in Arctic cyclone activity, and related factors, in driving these pronounced year-to-year changes in perennial sea ice cover. Strong relationships are revealed between the September sea ice changes and the number of cyclones in the preceding late spring and early summer. In particular, fewer cyclones over the central Arctic Ocean during the months of May, June, and July appear to favor a low sea ice area at the end of the melt season. Years with large losses of sea ice are characterized by abnormal cyclone distributions and tracks: they lack the normal maximum in cyclone activity over the central Arctic Ocean, and cyclones that track from Eurasia into the central Arctic are largely absent. Fewer storms are associated with above-average mean sea level pressure, strengthened anticyclonic winds, an intensification of the transpolar drift stream, and reduced cloud cover, all of which favor ice melt. It is also shown that a strengthening of the central Arctic cyclone maximum helps preserve the ice cover, although the association is weaker than that between low cyclone activity and reduced sea ice. The results suggest that changes in cyclone occurrence during late spring and early summer have preconditioning effects on the sea ice cover and exert a strong influence on the amount of sea ice that survives the melt season

Approximating the Solution of Surface Wave Propagation Using Deep Neural Networks

Partial differential equations formalise the understanding of the behaviour of the physical world that humans acquire through experience and observation. Through their numerical solution, such equations are used to model and predict the evolution of dynamical systems. However, such techniques require extensive computational resources and assume the physics are prescribed \textit{a priori}. Here, we propose a neural network capable of predicting the evolution of a specific physical phenomenon: propagation of surface waves enclosed in a tank, which, mathematically, can be described by the Saint-Venant equations. The existence of reflections and interference makes this problem non-trivial. Forecasting of future states (i.e. spatial patterns of rendered wave amplitude) is achieved from a relatively small set of initial observations. Using a network to make approximate but rapid predictions would enable the active, real-time control of physical systems, often required for engineering design. We used a deep neural network comprising of three main blocks: an encoder, a propagator with three parallel Long Short-Term Memory layers, and a decoder. Results on a novel, custom dataset of simulated sequences produced by a numerical solver show reasonable predictions for as long as 80 time steps into the future on a hold-out dataset. Furthermore, we show that the network is capable of generalising to two other initial conditions that are qualitatively different from those seen at training time

A link between reduced Barents-Kara sea ice and cold winter extremes over northern continents

The recent overall Northern Hemisphere warming was accompanied by several severe northern continental winters, as for example, extremely cold winter 2005/2006 in Europe and northern Asia. Here we show that anomalous decrease of wintertime sea ice concentration in the Barents-Kara (B-K) Seas could bring about extreme cold events like winter 2005/2006. Our simulations with the ECHAM5 general circulation model demonstrate that lower-troposphere heating over the B-K Seas in the Eastern Arctic caused by the sea ice reduction may result in strong anti-cyclonic anomaly over the Polar Ocean and anomalous easterly advection over northern continents. This causes a continental-scale winter cooling reaching -1.5°C, with more than three times increased probability of cold winter extremes over large areas including Europe. Our results imply that several recent severe winters do not conflict the global warming picture but rather supplement it, being in qualitative agreement with the simulated large-scale atmospheric circulation realignment. Furthermore, our results suggest that high-latitude atmospheric circulation response to the B-K sea ice decrease is highly nonlinear and characterized by transition from anomalous cyclonic circulation to anticyclonic one and then again back to cyclonic type of circulation as the B-K sea ice concentration gradually reduces from 100% to ice free conditions. We present a conceptual model which may explain the nonlinear local atmospheric response in the B-K Seas region by counter play between convection over the surface heat source and baroclinic effect due to modified temperature gradients in the vicinity of the heating area

Assessment of wind–damage relations for Norway using 36 years of daily insurance data

Extreme winds are by far the largest contributor to Norway’s insurance claims related to natural hazards. The predictive skills of four different damage functions are assessed for Norway at the municipality and national levels on daily and annual temporal scales using municipality-level insurance data and the high-resolution Norwegian hindcast (NORA3) wind speed data for the period 1985–2020. Special attention is given to extreme damaging events and occurrence probabilities of wind-speed-induced damage. Because of the complex topography of Norway and the resulting high heterogeneity of the population density, the wind speed is weighted with the population. The largest per capita losses and severe damage occur most frequently in the western municipalities of Norway, which are more exposed to incoming storms from the North Atlantic, whilst there are seldom any large losses further inland. There is no single damage function that outperforms others. However, a good agreement between the observed and estimated losses at municipality and national levels for a combination of damage functions suggests their usability in estimating severe damage associated with windstorms. Furthermore, the damage functions are able to successfully reconstruct the geographical pattern of losses caused by extreme windstorms with a high degree of correlation. From event occurrence probabilities, the present study devises a damage classifier that exhibits some skill at distinguishing between daily damaging and non-damaging events at the municipality level. While large-loss events are well captured, the skewness and zero inflation of the loss data greatly reduce the quality of both the damage functions and the classifier for moderate- and weak-loss events.</p

Sensitivity of SWAT simulated streamflow to climatic changes within the Eastern Nile River basin

Abstract. The hydrological model SWAT was run with daily station based precipitation and temperature data for the whole Eastern Nile basin including the three subbasins: the Abbay (Blue Nile), BaroAkobo and Tekeze. The daily and monthly streamflows were calibrated and validated at six outlets with station-based streamflow data in the three different subbasins. The model performed very well in simulating the monthly variability while the validation against daily data revealed a more diverse performance. The simulations indicated that around 60% of the average annual rainfalls of the subbasins were lost through evaporation while the estimated runoff coefficients were 0.24, 0.30 and 0.18 for Abbay, BaroAkobo and Tekeze subbasins, respectively. About half to two-thirds of the runoff could be attributed to surface runoff while the other contributions came from groundwater. Twenty hypothetical climate change scenarios (perturbed temperatures and precipitation) were conducted to test the sensitivity of SWAT simulated annual streamflow. The result revealed that the annual streamflow sensitivity to changes in precipitation and temperature differed among the basins and the dependence of the response on the strength of the changes was not linear. On average the annual streamflow responses to a change in precipitation with no temperature change were 19%, 17%, and 26% per 10% change in precipitation while the average annual streamflow responses to a change in temperature and no precipitation change were −4.4% K−1, −6.4% K−1, and −1.3% K−1 for Abbay, BaroAkobo and Tekeze river basins, respectively. 47 temperature and precipitation scenarios from 19 AOGCMs participating inCMIP3 were used to estimate future changes in streamflow due to climate changes. The climate models disagreed on both the strength and the direction of future precipitation changes. Thus, no clear conclusions could be made about future changes in the Eastern Nile streamflow. However, such types of assessment are important as they emphasise the need to use several an ensemble of AOGCMs as the results strongly dependent on the choice of climate models. </jats:p

Characterization of unknown genetic modifications using high throughput sequencing and computational subtraction

<p>Abstract</p> <p>Background</p> <p>When generating a genetically modified organism (GMO), the primary goal is to give a target organism one or several novel traits by using biotechnology techniques. A GMO will differ from its parental strain in that its pool of transcripts will be altered. Currently, there are no methods that are reliably able to determine if an organism has been genetically altered if the nature of the modification is unknown.</p> <p>Results</p> <p>We show that the concept of computational subtraction can be used to identify transgenic cDNA sequences from genetically modified plants. Our datasets include 454-type sequences from a transgenic line of <it>Arabidopsis thaliana </it>and published EST datasets from commercially relevant species (rice and papaya).</p> <p>Conclusion</p> <p>We believe that computational subtraction represents a powerful new strategy for determining if an organism has been genetically modified as well as to define the nature of the modification. Fewer assumptions have to be made compared to methods currently in use and this is an advantage particularly when working with unknown GMOs.</p

Predicting the Propagation of Acoustic Waves using Deep Convolutional Neural Networks

A novel approach for numerically propagating acoustic waves in two-dimensional quiescent media has been developed through a fully convolutional multi-scale neural network. This data-driven method managed to produce accurate results for long simulation times with a database of Lattice Boltzmann temporal simulations of propagating Gaussian Pulses, even in the case of initial conditions unseen during training time, such as the plane wave configuration or the two initial Gaussian pulses of opposed amplitudes. Two different choices of optimization objectives are compared, resulting in an improved prediction accuracy when adding the spatial gradient difference error to the traditional mean squared error loss function. Further accuracy gains are observed when performing an a posteriori correction on the neural network prediction based on the conservation of acoustic energy, indicating the benefit of including physical information in data-driven methods