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Doble M.J.,CNRS Oceanography Laboratory of Villefranche | De Carolis G.,CNR Institute for Electromagnetic Sensing of the Environment | Meylan M.H.,University of Newcastle | Bidlot J.-R.,European Center for Medium Range Weather Forecasting | Wadhams P.,University of Cambridge
Geophysical Research Letters | Year: 2015

Wave attenuation coefficients (α, m-1) were calculated from in situ data transmitted by custom wave buoys deployed into the advancing pancake ice region of the Weddell Sea. Data cover a 12 day period as the buoy array was first compressed and then dilated under the influence of a passing low-pressure system. Attenuation was found to vary over more than 2 orders of magnitude and to be far higher than that observed in broken-floe marginal ice zones. A clear linear relation between α and ice thickness was demonstrated, using ice thickness from a novel dynamic/thermodynamic model. A simple expression for α in terms of wave period and ice thickness was derived, for application in research and operational models. The variation of α was further investigated with a two-layer viscous model, and a linear relation was found between eddy viscosity in the sub-ice boundary layer and ice thickness. ©2015. American Geophysical Union. All Rights Reserved. Source


Seidel D.J.,National Oceanic and Atmospheric Administration | Zhang Y.,Nanjing University of Information Science and Technology | Beljaars A.,European Center for Medium Range Weather Forecasting | Golaz J.-C.,National Oceanic and Atmospheric Administration | And 2 more authors.
Journal of Geophysical Research: Atmospheres | Year: 2012

Although boundary layer processes are important in climate, weather and air quality, boundary layer climatology has received little attention, partly for lack of observational data sets. We analyze boundary layer climatology over Europe and the continental U.S. using a measure of boundary layer height based on the bulk Richardson number. Seasonal and diurnal variations during 1981-2005 are estimated from radiosonde observations, a reanalysis that assimilates observations, and two contemporary climate models that do not. Data limitations in vertical profiles introduce height uncertainties that can exceed 50% for shallow boundary layers (<1 km) but are generally <20% for deeper boundary layers. Climatological heights are typically <1 km during daytime and <0.5 km at night over both regions. Seasonal patterns for daytime and nighttime differ; daytime heights are larger in summer than winter, but nighttime heights are larger in winter. The four data sets show similar patterns of spatial and seasonal variability but with biases that vary spatially, seasonally, and diurnally. Compared with radiosonde observations, the reanalysis and the climate models produce deeper layers due to difficulty simulating stable conditions. The higher-time-resolution reanalysis reveals the diurnal cycle in height, with maxima in the afternoon, and with amplitudes that vary seasonally (larger in summer) and regionally (larger over western U.S. and southern Europe). The lower-time-resolution radiosonde data and climate model simulations capture diurnal variations better over Europe than over the U.S., due to differences in local sampling times. © 2012. American Geophysical Union. All Rights Reserved. Source


Wetterhall F.,Swedish Meteorological and Hydrological Institute | Wetterhall F.,Kings College London | Wetterhall F.,European Center for Medium Range Weather Forecasting | Graham L.P.,Swedish Meteorological and Hydrological Institute | And 3 more authors.
Natural Hazards and Earth System Sciences | Year: 2011

Assessing hydrological effects of global climate change at local scales is important for evaluating future hazards to society. However, applying climate model projections to local impact models can be difficult as outcomes can vary considerably between different climate models, and including results from many models is demanding. This study combines multiple climate model outputs with hydrological impact modelling through the use of response surfaces. Response surfaces represent the sensitivity of the impact model to incremental changes in climate variables and show probabilies for reaching a priori determined thresholds. Response surfaces were calculated using the HBV hydrological model for three basins in Sweden. An ensemble of future climate projections was then superimposed onto each response surface, producing a probability estimate for exceeding the threshold being evaluated. Site specific impacts thresholds were used where applicable. Probabilistic trends for future change in hazards or potential can be shown and evaluated. It is particularly useful for visualising the range of probable outcomes from climate models and can easily be updated with new results as they are made available. © 2012 Author(s). Source


Boone A.A.,Meteo - France | Poccard-Leclercq I.,University of Nantes | Xue Y.,University of California at Los Angeles | Feng J.,University of California at Los Angeles | de Rosnay P.,European Center for Medium Range Weather Forecasting
Climate Dynamics | Year: 2010

The West African monsoon (WAM) circulation and intensity have been shown to be influenced by the land surface in numerous numerical studies using regional scale and global scale atmospheric climate models (RCMs and GCMs, respectively) over the last several decades. The atmosphere-land surface interactions are modulated by the magnitude of the north-south gradient of the low level moist static energy, which is highly correlated with the steep latitudinal gradients of the vegetation characteristics and coverage, land use, and soil properties over this zone. The African Multidisciplinary Monsoon Analysis (AMMA) has organised comprehensive activities in data collection and modelling to further investigate the significance land-atmosphere feedbacks. Surface energy fluxes simulated by an ensemble of land surface models from AMMA Land-surface Model Intercomparison Project (ALMIP) have been used as a proxy for the best estimate of the "real world" values in order to evaluate GCM and RCM simulations under the auspices of the West African Monsoon Modelling Experiment (WAMME) project, since such large-scale observations do not exist. The ALMIP models have been forced in off-line mode using forcing based on a mixture of satellite, observational, and numerical weather prediction data. The ALMIP models were found to agree well over the region where land-atmosphere coupling is deemed to be most important (notably the Sahel), with a high signal to noise ratio (generally from 0.7 to 0.9) in the ensemble and a inter-model coefficient of variation between 5 and 15%. Most of the WAMME models simulated spatially averaged net radiation values over West Africa which were consistent with the ALMIP estimates, however, the partitioning of this energy between sensible and latent heat fluxes was significantly different: WAMME models tended to simulate larger (by nearly a factor of two) monthly latent heat fluxes than ALMIP. This results due to a positive precipitation bias in the WAMME models and a northward displacement of the monsoon in most of the GCMs and RCMs. Another key feature not found in the WAMME models is peak seasonal latent heat fluxes during the monsoon retreat (approximately a month after the peak precipitation rates) from soil water stores. This is likely related to the WAMME northward bias of the latent heat flux gradient during the WAM onset. © 2009 Springer-Verlag. Source


Slingo J.,UK Met Office | Palmer T.,European Center for Medium Range Weather Forecasting | Palmer T.,University of Oxford
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences | Year: 2011

Following Lorenz's seminal work on chaos theory in the 1960s, probabilistic approaches to prediction have come to dominate the science of weather and climate forecasting. This paper gives a perspective on Lorenz's work and how it has influenced the ways in which we seek to represent uncertainty in forecasts on all lead times from hours to decades. It looks at how model uncertainty has been represented in probabilistic prediction systems and considers the challenges posed by a changing climate. Finally, the paper considers how the uncertainty in projections of climate change can be addressed to deliver more reliable and confident assessments that support decision-making on adaptation and mitigation. This journal is © 2011 The Royal Society. Source

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