Full-field and anomaly initialization using a low-order climate model: a comparison and proposals for advanced formulations

Initialization techniques for seasonal-to-decadal climate predictions fall into two main categories; namely full-field initialization (FFI) and anomaly initialization (AI). In the FFI case the initial model state is replaced by the best possible available estimate of the real state. By doing so the initial error is efficiently reduced but, due to the unavoidable presence of model deficiencies, once the model is let free to run a prediction, its trajectory drifts away from the observations no matter how small the initial error is. This problem is partly overcome with AI where the aim is to forecast future anomalies by assimilating observed anomalies on an estimate of the model climate. The large variety of experimental setups, models and observational networks adopted worldwide make it difficult to draw firm conclusions on the respective advantages and drawbacks of FFI and AI, or to identify distinctive lines for improvement. The lack of a unified mathematical framework adds an additional difficulty toward the design of adequate initialization strategies that fit the desired forecast horizon, observational network and model at hand. Here we compare FFI and AI using a low-order climate model of nine ordinary differential equations and use the notation and concepts of data assimilation theory to highlight their error scaling properties. This analysis suggests better performances using FFI when a good observational network is available and reveals the direct relation of its skill with the observational accuracy. The skill of AI appears, however, mostly related to the model quality and clear increases of skill can only be expected in coincidence with model upgrades. We have compared FFI and AI in experiments in which either the full system or the atmosphere and ocean were independently initialized. In the former case FFI shows better and longer-lasting improvements, with skillful predictions until month 30. In the initialization of single compartments, the best performance is obtained when the stabler component of the model (the ocean) is initialized, but with FFI it is possible to have some predictive skill even when the most unstable compartment (the extratropical atmosphere) is observed. Two advanced formulations, least-square initialization (LSI) and exploring parameter uncertainty (EPU), are introduced. Using LSI the initialization makes use of model statistics to propagate information from observation locations to the entire model domain. Numerical results show that LSI improves the performance of FFI in all the situations when only a portion of the system's state is observed. EPU is an online drift correction method in which the drift caused by the parametric error is estimated using a short-time evolution law and is then removed during the forecast run. Its implementation in conjunction with FFI allows us to improve the prediction skill within the first forecast year. Finally, the application of these results in the context of realistic climate models is discussed

Electron microscopy of nanoparticle superlattice formation at a solid-liquid interface in nonpolar liquids

Nanoparticle superlattice films form at the solid-liquid interface and are important for mesoscale materials, but are notoriously difficult to analyze before they are fully dried. Here, the early stages of nanoparticle assembly were studied at solid-liquid interfaces using liquid-phase electron microscopy. Oleylamine-stabilized gold nanoparticles spontaneously formed thin layers on a silicon nitride (SiN) membrane window of the liquid enclosure. Dense packings of hexagonal symmetry were obtained for the first monolayer independent of the nonpolar solvent type. The second layer, however, exhibited geometries ranging from dense packing in a hexagonal honeycomb structure to quasi-crystalline particle arrangements depending on the dielectric constant of the liquid. The complex structures formed by the weaker interactions in the second particle layer were preserved, while the surface remained immersed in liquid. Fine-tuning the properties of the involved materials can thus be used to control the three-dimensional geometry of a superlattice including quasi-crystals

Northern Hemisphere blocking simulation in current climate models: evaluating progress from the Climate Model Intercomparison Project Phase 5 to 6 and sensitivity to resolution

Global climate models (GCMs) are known to suffer from biases in the simulation of atmospheric blocking, and this study provides an assessment of how blocking is represented by the latest generation of GCMs. It is evaluated (i) how historical CMIP6 (Climate Model Intercomparison Project Phase 6) simulations perform compared to CMIP5 simulations and (ii) how horizontal model resolution affects the simulation of blocking in the CMIP6-HighResMIP (PRIMAVERA – PRocess-based climate sIMulation: AdVances in high-resolution modelling and European climate Risk Assessment) model ensemble, which is designed to address this type of question. Two blocking indices are used to evaluate the simulated mean blocking frequency and blocking persistence for the Euro-Atlantic and Pacific regions in winter and summer against the corresponding estimates from atmospheric reanalysis data. There is robust evidence that CMIP6 models simulate blocking frequency and persistence better than CMIP5 models in the Atlantic and Pacific and during winter and summer. This improvement is sizeable so that, for example, winter blocking frequency in the median CMIP5 model in a large Euro-Atlantic domain is underestimated by 33 % using the absolute geopotential height (AGP) blocking index, whereas the same number is 18 % for the median CMIP6 model. As for the sensitivity of simulated blocking to resolution, it is found that the resolution increase, from typically 100 to 20 km grid spacing, in most of the PRIMAVERA models, which are not re-tuned at the higher resolutions, benefits the mean blocking frequency in the Atlantic in winter and summer and in the Pacific in summer. Simulated blocking persistence, however, is not seen to improve with resolution. Our results are consistent with previous studies suggesting that resolution is one of a number of interacting factors necessary for an adequate simulation of blocking in GCMs. The improvements reported in this study hold promise for further reductions in blocking biases as model development continues

La franja aurífera de Xallas (Galicia occidental, España): Las mineralizaciones de Albores dentro de un contexto tectónico y metalogénico evolutivo.

Las mineralizaciones de oro de Albores (Galicia occidental) se encuadran dentro de una franja metalogénica N-S a NE de edad hercínica-tardihercínica (franja aurífera de Xallas). Dicha franja comprende una serie de indicios auríferos caracterizados por la presencia de una fase sulfurada rica en arsenopirita y otra metálica con oro/electrum. Estas mineralizaciones encajan generalmente en estructuras frágiles desarrolladas en zonas de cizalla de componente horizontal y vertical, que incluyen mílonitas, ultramilonitas, cataclasitas y brechas.Las mineralizaciones de Albores incluyen una paragénesis compleja consistente en arsenopirita, oro/electrum, pirrotina, marcasita, pirita, bismuto, bismutina, esfalerita, galena y calcopirita. Estudios de inclusionesfluidas en cuarzos acompañantes de la mineralización sugieren que ésta fue depositada por fluidos acuoso-carbónicos complejos de moderada baja salinidad (<56-9 por 100 eq. NaCI; salinidades máximas) operando en un rango de temperaturas epi a mesotermal (Th: 18Cl°-36Cl° CJ. Estas mineralizaciones se formaron durante un régimen extensional desarrollado durante el período comprendido entre la fase deformativa 3 hercínica (D3; Carbonífero Medio) y el episodio frágil tardihercínico (Carbonífero Superior/Pérmico Inferior}. La etapa extensional se caracterizó por el desarrollo de corredores miloníticos principalmente extensionales, de bajo ángulo (zona de cizalla de Xallas) con superposición de estructuras frágiles sobre la fábrica dúctil, estructuras antiformes, y fallas normales de gran ángulo de carácter frágil

Predicción climática decadal global con el modelo EC-EARTH: avanzando hacia una predicción operativa en tiempo real [Presentación]

Presentación realizada en el VI Simposio Nacional de Predicción "Memorial Antonio Mestre", celebrado en la sede central de AEMET en Madrid del 17 al 19 de septiembre de 2018

Multi-model assessment of the impact of soil moisture initialization on mid-latitude summer predictability

Land surface initial conditions have been recognized as a potential source of predictability in sub-seasonal to seasonal forecast systems, at least for near-surface air temperature prediction over the mid-latitude continents. Yet, few studies have systematically explored such an influence over a sufficient hindcast period and in a multi-model framework to produce a robust quantitative assessment. Here, a dedicated set of twin experiments has been carried out with boreal summer retrospective forecasts over the 1992–2010 period performed by five different global coupled ocean–atmosphere models. The impact of a realistic versus climatological soil moisture initialization is assessed in two regions with high potential previously identified as hotspots of land–atmosphere coupling, namely the North American Great Plains and South-Eastern Europe. Over the latter region, temperature predictions show a significant improvement, especially over the Balkans. Forecast systems better simulate the warmest summers if they follow pronounced dry initial anomalies. It is hypothesized that models manage to capture a positive feedback between high temperature and low soil moisture content prone to dominate over other processes during the warmest summers in this region. Over the Great Plains, however, improving the soil moisture initialization does not lead to any robust gain of forecast quality for near-surface temperature. It is suggested that models biases prevent the forecast systems from making the most of the improved initial conditions.The authors thank Jeff Knight (Met Office Hadley Centre) for his constructive comments on earlier versions of this manuscript. The research leading to these results received funding from the European Union Seventh Framework Programme (FP7/2007–2013) SPECS project (Grant Agreement Number 308378) and H2020 Framework Programme IMPREX project (Grant Agreement Number 641811). Constantin Ardilouze was also supported by the BSC Centro de Excelencia Severo Ochoa Programme.Peer ReviewedPostprint (author's final draft

Impact of Atlantic multidecadal variability on rainfall intensity distribution and timing of the West African monsoon

Previous studies agree on an impact of the Atlantic multidecadal variability (AMV) on the total seasonal rainfall amounts over the Sahel. However, whether and how the AMV affects the distribution of rainfall or the timing of the West African monsoon is not well known. Here we seek to explore these impacts by analyzing daily rainfall outputs from climate model simulations with an idealized AMV forcing imposed in the North Atlantic, which is representative of the observed one. The setup follows a protocol largely consistent with the one proposed by the Component C of the Decadal Climate Prediction Project (DCPP-C). We start by evaluating model's performance in simulating precipitation, showing that models underestimate it over the Sahel, where the mean intensity is consistently smaller than observations. Conversely, models overestimate precipitation over the Guinea coast, where too many rainy days are simulated. In addition, most models underestimate the average length of the rainy season over the Sahel; some are due to a monsoon onset that is too late and others due to a cessation that is too early. In response to a persistent positive AMV pattern, models show an enhancement in total summer rainfall over continental West Africa, including the Sahel. Under a positive AMV phase, the number of wet days and the intensity of daily rainfall events are also enhanced over the Sahel. The former explains most of the changes in seasonal rainfall in the northern fringe, while the latter is more relevant in the southern region, where higher rainfall anomalies occur. This dominance is connected to the changes in the number of days per type of event; the frequency of both moderate and heavy events increases over the Sahel's northern fringe. Conversely, over the southern limit, it is mostly the frequency of heavy events which is enhanced, thus affecting the mean rainfall intensity there. Extreme rainfall events are also enhanced over the whole Sahel in response to a positive phase of the AMV. Over the Sahel, models with stronger negative biases in rainfall amounts compared to observations show weaker changes in response to AMV, suggesting that systematic biases could affect the simulated responses. The monsoon onset over the Sahel shows no clear response to AMV, while the demise tends to be delayed, and the overall length of the monsoon season enhanced between 2 and 5 d with the positive AMV pattern. The effect of AMV on the seasonality of the monsoon is more consistent to the west of 10∘ W, with all models showing a statistically significant earlier onset, later demise, and enhanced monsoon season with the positive phase of the AMV. Our results suggest a potential for the decadal prediction of changes in the intraseasonal characteristics of rainfall over the Sahel, including the occurrence of extreme events.</p

Kinetic Control over Self-Assembly of Semiconductor Nanoplatelets

Semiconductor nanoplatelets exhibit spectrally pure, directional fluorescence. To make polarized light emission accessible and the charge transport effective, nanoplatelets have to be collectively oriented in the solid state. We discovered that the collective nanoplatelets orientation in monolayers can be controlled kinetically by exploiting the solvent evaporation rate in self-assembly at liquid interfaces. Our method avoids insulating additives such as surfactants, making it ideally suited for optoelectronics. The monolayer films with controlled nanoplatelets orientation (edge-up or face-down) exhibit long-range ordering of transition dipole moments and macroscopically polarized light emission. Furthermore, we unveil that the substantial in-plane electronic coupling between nanoplatelets enables charge transport through a single nanoplatelets monolayer, with an efficiency that strongly depends on the orientation of the nanoplatelets. The ability to kinetically control the assembly of nanoplatelets into ordered monolayers with tunable optical and electronic properties paves the way for new applications in optoelectronic devices

Kinetic Control over Self-Assembly of Semiconductor Nanoplatelets

Semiconductor nanoplatelets exhibit spectrally pure, directional fluorescence. To make polarized light emission accessible and the charge transport effective, nanoplatelets have to be collectively oriented in the solid state. We discovered that the collective nanoplatelets orientation in monolayers can be controlled kinetically by exploiting the solvent evaporation rate in self-assembly at liquid interfaces. Our method avoids insulating additives such as surfactants, making it ideally suited for optoelectronics. The monolayer films with controlled nanoplatelets orientation (edge-up or face-down) exhibit long-range ordering of transition dipole moments and macroscopically polarized light emission. Furthermore, we unveil that the substantial in-plane electronic coupling between nanoplatelets enables charge transport through a single nanoplatelets monolayer, with an efficiency that strongly depends on the orientation of the nanoplatelets. The ability to kinetically control the assembly of nanoplatelets into ordered monolayers with tunable optical and electronic properties paves the way for new applications in optoelectronic devices

Bias adjustment and ensemble recalibration methods for seasonal forecasting: a comprehensive intercomparison using the C3S dataset

This work presents a comprehensive intercomparison of diferent alternatives for the calibration of seasonal forecasts, ranging from simple bias adjustment (BA)-e.g. quantile mapping-to more sophisticated ensemble recalibration (RC) methods- e.g. non-homogeneous Gaussian regression, which build on the temporal correspondence between the climate model and the corresponding observations to generate reliable predictions. To be as critical as possible, we validate the raw model and the calibrated forecasts in terms of a number of metrics which take into account diferent aspects of forecast quality (association, accuracy, discrimination and reliability). We focus on one-month lead forecasts of precipitation and temperature from four state-of-the-art seasonal forecasting systems, three of them included in the Copernicus Climate Change Service dataset (ECMWF-SEAS5, UK Met Ofce-GloSea5 and Météo France-System5) for boreal winter and summer over two illustrative regions with diferent skill characteristics (Europe and Southeast Asia). Our results indicate that both BA and RC methods efectively correct the large raw model biases, which is of paramount importance for users, particularly when directly using the climate model outputs to run impact models, or when computing climate indices depending on absolute values/thresholds. However, except for particular regions and/or seasons (typically with high skill), there is only marginal added value-with respect to the raw model outputs-beyond this bias removal. For those cases, RC methods can outperform BA ones, mostly due to an improvement in reliability. Finally, we also show that whereas an increase in the number of members only modestly afects the results obtained from calibration, longer hindcast periods lead to improved forecast quality, particularly for RC methods.This work has been funded by the C3S activity on Evaluation and Quality Control for seasonal forecasts. JMG was partially supported by the project MULTI-SDM (CGL2015-66583-R, MINECO/FEDER). FJDR was partially funded by the H2020 EUCP project (GA 776613)