European temperatures in CMIP5: origins of present-day biases and future uncertainties

European temperatures and their projected changes under the 8.5 W/m² Representative Concentration Pathway scenario are evaluated in an ensemble of 33 global climate models participating in the fifth phase of the Coupled Model Intercomparison Project (CMIP5). Respective contributions of large-scale dynamics and local processes to both biases and changes in temperatures, and to the inter-model spread, are then investigated from a recently proposed methodology based on weather regimes. On average, CMIP5 models exhibit a cold bias in winter, especially in Northern Europe. They overestimate summer temperatures in Central Europe, in association with a greater diurnal range than observed. The projected temperature increase is stronger in summer than in winter, with the highest summer warming occurring over Mediterranean regions. Links between biases and sensitivities are evidenced in winter, suggesting a potential influence of snow cover biases on the projected surface warming. A brief analysis of daily temperature extremes suggests that the intra-seasonal variability is projected to decrease (slightly increase) in winter (summer). Then, in order to understand model discrepancies in both present-day and future climates, we disentangle effects of large-scale atmospheric dynamics and regional physical processes. In particular, in winter, CMIP5 models simulate a stronger North-Atlantic jet stream than observed and, in contrast with CMIP3 results, the majority of them suggests an increased frequency of the negative phase of the North-Atlantic Oscillation under future warming. While large-scale circulation only has a minor contribution to ensemble-mean biases or changes, which are primarily dominated by non-dynamical processes, it substantially affects the inter-model spread. Finally, other sources of uncertainties, including the North-Atlantic warming and local radiative feedbacks related to snow cover and clouds, are briefly discussed.     

The magnetic structure of convection-driven numerical dynamos

The generation of a magnetic field in numerical simulations of the geodynamo is an intrinsically 3-D and time-dependent phenomenon. The concept of magnetic field lines and the frozen-flux approximation can provide insight into such systems, but a suitable visualization method is required. This paper presents results obtained using the Dynamical Magnetic Field line Imaging (DMFI) technique, which is a representation of magnetic field lines accounting for their local magnetic energy, together with an algorithm for the time evolution of their anchor points. The DMFI illustrations are consistent with previously published dynamo mechanisms, and allow further investigation of spatially and temporally complex systems. We highlight three types of magnetic structures: (i) magnetic cyclones and (ii) magnetic anticyclones are expelled by, but corotate with axial flow vortices; (iii) magnetic upwellings are amplified by stretching and advection within flow upwellings, and show structural similarity with helical plumes found in rotating hydrodynamic experiments. While magnetic anticyclones are responsible for the regeneration of a stable axial dipole, here we show that excursions and reversals of the dipole axis are caused by the emergence of magnetic upwellings, which amplify and transport a generally multipolar magnetic field from the inner to the outer boundary of the models. Geomagnetic observations suggest the presence of magnetic structures similar to those found in our models; thus, we discuss how our results may pertain to Earth's core dynamo processes. In order to make DMFI a standard tool for numerical dynamo studies, a public software package is available upon request to the authors (supplementary material is available at: http://www.ipgp.jussieu.fr/~aubert/DMFI.html ).     

EUREC<Superscript>4</Superscript>A: A Field Campaign to Elucidate the Couplings Between Clouds,Convection and Circulation

Trade-wind cumuli constitute the cloud type with the highest frequency of occurrence on Earth, and it has been shown that their sensitivity to changing environmental conditions will critically influence the magnitude and pace of future global warming. Research over the last decade has pointed out the importance of the interplay between clouds, convection and circulation in controling this sensitivity. Numerical models represent this interplay in diverse ways, which translates into different responses of trade-cumuli to climate perturbations. Climate models predict that the area covered by shallow cumuli at cloud base is very sensitive to changes in environmental conditions, while process models suggest the opposite. To understand and resolve this contradiction, we propose to organize a field campaign aimed at quantifying the physical properties of trade-cumuli (e.g., cloud fraction and water content) as a function of the large-scale environment. Beyond a better understanding of clouds-circulation coupling processes, the campaign will provide a reference data set that may be used as a benchmark for advancing the modelling and the satellite remote sensing of clouds and circulation. It will also be an opportunity for complementary investigations such as evaluating model convective parameterizations or studying the role of ocean mesoscale eddies in air–sea interactions and convective organization.     

A statistical�Cdynamical scheme for reconstructing ocean forcing in the Atlantic. Part II: methodology, validation and application to high-resolution ocean models

A novel statistical?Cdynamical scheme has been developed to reconstruct the sea surface atmospheric variables necessary to force an ocean model. Multiple linear regressions are first built over a so-called learning period and over the entire Atlantic basin from the observed relationship between the surface wind conditions, or predictands, and the anomalous large scale atmospheric circulations, or predictors. The latter are estimated in the extratropics by 500?hPa geopotential height weather regimes and in the tropics by low-level wind classes. The transfer function further combined to an analog step is then used to reconstruct all the surface variables fields over 1958?C2002. We show that the proposed hybrid scheme is very skillful in reproducing the mean state, the seasonal cycle and the temporal evolution of all the surface ocean variables at interannual timescale. Deficiencies are found in the level of variance especially in the tropics. It is underestimated for 2-m temperature and humidity as well as for surface radiative fluxes in the interannual frequency band while it is slightly overestimated at higher frequency. Decomposition in empirical orthogonal function (EOF) shows that the spatial and temporal coherence of the forcing fields is however very well captured by the reconstruction method. For dynamical downscaling purposes, reconstructed fields are then interpolated and used to carry out a high-resolution oceanic simulation using the NATL4 (1/4°) model integrated over 1979?C2001. This simulation is compared to a reference experiment where the original observed forcing fields are prescribed instead. Mean states between the two experiments are virtually undistinguishable both in terms of surface fluxes and ocean dynamics estimated by the barotropic and the meridional overturning streamfunctions. The 3-dimensional variance of the simulated ocean is well preserved at interannual timescale both for temperature and salinity except in the tropics where it is underestimated. The main modes of interannual variability assessed through EOF are correctly reproduced for sea surface temperature, barotropic streamfunction and mixed layer depth both in terms of spatial structure and temporal evolution. Collectively, our results provide evidence that the statistical?Cdynamical scheme presented in this two-part study is an efficient and promising tool to infer oceanic changes (in particular those related to the wind-driven circulation) due to modifications in the large-scale atmospheric circulation. As a prerequisite, we have here validated the method for present-day climate; we encourage its use for climate change studies with some adaptations though.     

Quantification of cloud water interception in the canopy vegetation from fog gauge measurements

With changes in climate looming, quantifying often‐overlooked components of the canopy water budget, such as cloud water interception (CWI), is increasingly important. Commonly, CWI quantification requires detailed continuous measurements, which is extremely challenging, especially when throughfall is included. In this study, we propose a simplified approach to estimate CWI using the Rutter‐type interception model, where CWI inputs in the canopy vegetation are proportional to fog interception measured by an artificial fog gauge. The model requires the continuous acquisition of meteorological variables as input and calibration datasets. Throughfall measurements below the forest are used only for calibration and validation of the model; thus, CWI estimates can be provided even after the cessation of throughfall monitoring. This approach provides an indirect and undemanding way to quantify CWI by vegetation and allows the identification of its controlling factors, which could be useful to the comparison of CWI in contrasting land covers. The method is applied on a 2‐year dataset collected in an endemic highland forest of San Cristobal Island (Galapagos). Our results show that CWI reaches 21% ± 6% of the total water input during the first year, and 9% ± 2% during the second one. These values represent 32% ± 10% and 17% ± 5% of water inputs during the cool foggy season of the first and second year, respectively. The difference between seasons is attributed to a lower fog liquid water during the second season.     

Chemical weathering and CO2 consumption rate in a multilayered‐aquifer dominated watershed under intensive farming: The Orgeval Critical Zone Observatory,France

The impact of intensive farming on chemical weathering in the Critical Zone is still an open question. Extensively instrumented and monitored over the last 50 years, the Orgeval Critical Zone Observatory (CZO) in France is an observation site impacted by intensive farming since the 1960s. The Orgeval observatory represents an ideal place to study the response and resilience capability of the Critical Zone under agricultural stress. This paper investigates the chemical composition of different water bodies in two nested catchments of the Orgeval CZO, including rainfall, springs, rivers, and rocks, over one and half hydrological year. We show that elemental and strontium isotopic ratios are powerful to constrain the origin of the elements. The results show that the river chemistry at the outlet of the two nested catchments is dominated by rain inputs (particularly atmospheric dust dissolution) and the chemical weathering of limestone and gypsum. Fertilizer input is clearly visible, although the distinction between gypsum dissolution and fertilizer inputs needs more investigation. The mixtures of water masses inferred from our data are in good agreement with the hydrological context of the watershed, that is, a multilayered aquifer structure. At the main outlet of the CZO, we estimate that the input of ocean‐derived solutes through rainfall represents 7 t km^?2 year^?1, on the same order of magnitude as the net fertilizer input (10 t km^?2 year^?1), and that rock weathering releases 50 t km^?2 year^?1. Including previously published physical erosion rates, we estimate that the total denudation rate (physical and chemical) of the Orgeval CZO is 20 mm (1,000 year)^?1, which, along with the entire Seine watershed, is among the lowest chemical denudation rates for carbonate terrains under temperate climate. Chemical denudation is about 10 times higher than physical erosion in the Orgeval CZO. The consumption of CO₂ by rock weathering is estimated to be between 265.10³ and 360.10³ molC km² year^?1, similar for the two nested catchments. Compared with the rivers, the springs show a higher CO₂ consumption rate that suggests, as pointed out earlier, a enhancement of carbonate dissolution linked to nitrification and thus fertilizer application. The hyporheic zone appears to be a hot spot in the carbon cycle at the Orgeval CZO. This study sheds light on the complex, anthropocenic, interplay between geology, climate, and human activities that characterize and that take place in intensive agriculture regions.     

An active wave generating–absorbing boundary condition for VOF type numerical model

The objective of the present work is to discuss the implementation of an active wave generating–absorbing boundary condition for a numerical model based on the Volume Of Fluid (VOF) method for tracking free surfaces. First an overview of the development of VOF type models with special emphasis in the field of coastal engineering is given. A new type of numerical boundary condition for combined wave generation and absorption in the numerical model VOFbreak² is presented. The numerical boundary condition is based on an active wave absorption system that was first developed in the context of physical wave flume experiments, using a wave paddle. The method applies to regular and irregular waves. Velocities are measured at one location inside the computational domain. The reflected wave train is separated from the incident wave field in front of a structure by means of digital filtering and subsequent superposition of the measured velocity signals. The incident wave signal is corrected, so that the reflected wave is effectively absorbed at the boundary. The digital filters are derived theoretically and their practical design is discussed. The practical use of this numerical boundary condition is compared to the use of the absorption system in a physical wave flume. The effectiveness of the active wave generating–absorbing boundary condition finally is proved using analytical tests and numerical simulations with VOFbreak².     

The Integration of Scene Understanding within a Geographic Information System: A Prototype Approach for Agricultural Applications

This paper describes progress towards engineering a combined GIS and image understanding system, designed to analyse remotely-sensed imagery in a task-oriented manner, with the task and contextual parameters being supplied by the GIS. The overall aim is to evaluate some of the more recent developments in the field of computer vision and to show how they may be integrated (along with more traditional methods of image interpretation) to improve system performance in terms of object recognition and extraction. Three aspects are reported: (1) Development of a formal notation as the basis of describing the process of transforming data from low level image representations to high level object representations; (2) Design of collaborating low and high level image processes used in this abstraction process and their control via Process Networks encapsulated into frames ; and (3) Use of Dempster-Shafer Decision Theory as the basis for combining different types of evidence to improve the recognition of objects in the scene. Some sample results are presented to illustrate the issues raised and mechanisms for the co-operative processing of data are suggested.