Correction to: Future changes in Atlantic hurricanes with the rotated-stretched ARPEGE-Climat at very high resolution

Climate Dynamics - The original article can be found online.     

An adaptive finite element model for wellbore breakout simulation

A finite element formulation is proposed and implemented for analysing the stability of excavated wells using the DiMaggio-Sandler constitutive elastoplastic model with a typical carbonate reservoir configuration. The quality of the finite element approximation is ensured by applying smooth curved elements adapted to the wellbore geometry, and h − p adaptive finite element meshes in the plastic zone. General purpose procedures are defined to transfer the elastoplastic deformation history to newly created integration points. A breakout damage criterion is proposed based on the second invariant of the deviatoric plastic deformation tensor. This damage criterion is used to apply a mesh movement algorithm to represent material collapse. The automatic successive application of the breakout damage criterion results in elliptical realistically looking geometries obtained in experiments reported in the literature.     

Development of a rain‐on‐snow detection algorithm using passive microwave radiometry

Currently observed climate warming in the Arctic has numerous consequences. Of particular relevance, the precipitation regime is modified where mixed and liquid precipitation can occur during the winter season leading to rain‐on‐snow (ROS) events. This phenomenon is responsible for ice crust formation, which has a significant impact on ecosystems (such as biological, hydrological, ecological and physical processes). The spatially and temporally sporadic nature of ROS events makes the phenomenon difficult to monitor using meteorological observations. This paper focuses on the detection of ROS events using passive microwave (PMW) data from a modified brightness temperature (T_B) gradient approach at 19 and 37 GHz. The approach presented here was developed empirically for observed ROS events with coincident ground‐based PMW measurements in Sherbrooke, Quebec, Canada. It was then tested in Nunavik, Quebec, with the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR‐E). We obtained a detection accuracy of 57, 71 and 89% for ROS detection for three AMSR‐E grid cells with a maximum error of 7% when considering all omissions and commissions with regard to the total number of AMSR‐E passes throughout the winter period. Copyright © 2016 John Wiley & Sons, Ltd.     

Simulation of site-scale water fluxes in desert and natural oasis ecosystems of the arid region in Northwest China

The study of water fluxes is important to better understand hydrological cycles in arid regions. Data-driven machine learning models have been recently applied to water flux simulation. Previous studies have built site-scale simulation models of water fluxes for individual sites separately, requiring a large amount of data from each site and significant computation time. For arid areas, there is no consensus as to the optimal model and variable selection method to simulate water fluxes. Using data from seven flux observation sites in the arid region of Northwest China, this study compared the performance of random forest (RF), support vector machine (SVM), back propagation neural network (BPNN), and multiple linear regression (MLR) models in simulating water fluxes. Additionally, the study investigated inter-annual and seasonal variation in water fluxes and the dominant drivers of this variation at different sites. A universal simulation model for water flux was built using the RF approach and key variables as determined by MLR, incorporating data from all sites. Model performance of the SVM algorithm (R² = 0.25–0.90) was slightly worse than that of the RF algorithm (R² = 0.41–0.91); the BPNN algorithm performed poorly in most cases (R² = 0.15–0.88). Similarly, the MLR results were limited and unreliable (R² = 0.00–0.66). Using the universal RF model, annual water fluxes were found to be much higher than the precipitation received at each site, and natural oases showed higher fluxes than desert ecosystems. Water fluxes were highest during the growing season (May–September) and lowest during the non-growing season (October–April). Furthermore, the dominant drivers of water flux variation were various among different sites, but the normalized difference vegetation index (NDVI), soil moisture and soil temperature were important at most sites. This study provides useful insights for simulating water fluxes in desert and oasis ecosystems, understanding patterns of variation and the underlying mechanisms. Besides, these results can make a contribution as the decision-making basis to the water management in desert and oasis ecosystems.     

Occurrence,durée et intensité des précipitations simulées par deux modèles régionaux canadiens du climat sur la région du Maghreb

[Translated by the editorial staff] Simulating the precipitation regime of Northern Africa is challenging for regional climate models, particularly because of the strong spatial and temporal variability of rain events in the region. In this study we evaluate simulations conducted with two recent versions of regional climate models (RCM) developed in Canada: the CRCM5 and CanRCM4. Both are also used in the COordinated Regional Climate Downscaling EXperiment (CORDEX)-Africa. The assessment is based on the occurrence, duration, and intensity indices of daily precipitation in Maghreb during the fall and spring seasons from 1998 to 2008. We also examine the links between the North-Atlantic Oscillation (NAO) index, weather systems, and the precipitation regime over the region. During the rainy season (September to February), the CRCM5 reproduces the frequency and intensity of extreme precipitation adequately, as well as the occurrence of days with rain, while the CanRCM4 underestimates precipitation extremes. The study of links between weather systems and the precipitation regime shows that, along the Atlantic coast, precipitation (occurrence, intensity, and wet sequences) increases significantly with storm frequency in the fall. In winter, these links grow stronger going east, from the Atlantic coast to the Mediterranean coast. The negative phases of the NAO index are statistically associated with the increase in rain intensity, extremes, and accumulation along the Atlantic coast in the fall. However, the link weakens in winter over these regions and strengthens along the Mediterranean coast as the precipitation frequency rises during negative phases of the NAO. Both RCMs generally reproduce the links between the NAO and the precipitation regime well, regardless of location.