2,338 research outputs found
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Linking the anomaly initialization approach to the mapping paradigm: a proof-of-concept study
No full textSeasonal-to-decadal predictions are initialized using observations of the present climatic state in full field initialization (FFI). Such model integrations undergo a drift toward the model attractor due to model deficiencies that incur a bias in the model. The anomaly initialization (AI) approach reduces the drift by adding an estimate of the bias onto the observations at the expense of a larger initial error.
In this study FFI is associated with the fidelity paradigm, and AI is associated with an instance of the mapping paradigm, in which the initial conditions are mapped onto the imperfect model attractor by adding a fixed error term; the mapped state on the model attractor should correspond to the nature state. Two diagnosis tools assess how well AI conforms to its own paradigm under various circumstances of model error: the degree of approximation of the model attractor is measured by calculating the overlap of the AI initial conditions PDF with the model PDF; and the sensitivity to random error in the initial conditions reveals how well the selected initial conditions on the model attractor correspond to the nature states. As a useful reference, the initial conditions of FFI are subjected to the same analysis.
Conducting hindcast experiments using a hierarchy of low-order coupled climate models, it is shown that the initial conditions generated using AI approximate the model attractor only under certain conditions: differences in higher-than-first-order moments between the model and nature PDFs must be negligible. Where such conditions fail, FFI is likely to perform better
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Reliability of regional climate model trends
No full textA necessary condition for a good probabilistic forecast is that the forecast system is shown to be reliable: forecast probabilities should equal observed probabilities verified over a large number of cases. As climate change trends are now emerging from the natural variability, we can apply this concept to climate predictions and compute the reliability of simulated local and regional temperature and precipitation trends (1950–2011) in a recent multi-model ensemble of climate model simulations prepared for the Intergovernmental Panel on Climate Change (IPCC) fifth assessment report (AR5). With only a single verification time, the verification is over the spatial dimension. The local temperature trends appear to be reliable. However, when the global mean climate response is factored out, the ensemble is overconfident: the observed trend is outside the range of modelled trends in many more regions than would be expected by the model estimate of natural variability and model spread. Precipitation trends are overconfident for all trend definitions. This implies that for near-term local climate forecasts the CMIP5 ensemble cannot simply be used as a reliable probabilistic forecast
Full-field and anomaly initialization using a low-order climate model: A comparison and proposals for advanced formulations
No full textInitialization 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. © Author(s) 2014. CC Attribution 3.0 License
Predictability of the tropospheric circulation in the southern hemisphere from CHFP models
No full textAn assessment of the predictability and prediction skill of the tropospheric circulation in the Southern Hemisphere was done. The analysis is based on seasonal forecasts of geopotential heights at 200, 500 and 850 hPa, for austral summer and winter from 11 models participating in the Climate Historical Forecast Project. It is found that predictability (signal-to-variance ratio) and prediction skill (anomaly correlation) in the tropics is higher than in the extratropics and is also higher in summer than in winter. Both predictability and skill are higher at high than at low altitudes. Modest values of predictability and skill are found at polar latitudes in the Bellinghausen-Amundsen Seas. The analysis of the changes in predictability and prediction skill in ENSO events reveals that both are slightly higher in the El Niño-Southern Oscillation (ENSO) years than in all years, while the spatial patterns of maxima and minima remain unchanged. Changes in signal-to-noise ratio observed are mainly due to signal changes rather than changes in noise. Composites of geopotential heights anomalies for El Niño and La Niña years are in agreement with observations.Fil: Osman, Marisol. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; ArgentinaFil: Vera, Carolina Susana. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; ArgentinaFil: Doblas Reyes, F.J.. Institucio Catalana de Recerca i Estudis Avançats; España. Institut Català de Ciències del Clima; España. Barcelona Supercomputing Center - Centro Nacional de Supercomputacion; Españ
Sources of skill in near-term climate prediction: generating initial conditions
No full textThis study investigates the role of different areas of the ocean in driving the climate variability. The impact of both global and regional ocean nudging on the climate reconstruction obtained with the climate model EC-Earth v2.3 is studied over the period 1960–2012. Ocean temperature and salinity below the mixed layer are relaxed toward the monthly averages from the ORAS4 ocean reanalysis. Three coupled ocean–atmosphere simulations are considered: (1) global ocean nudging, (2) nudging in the global upper ocean (above 2000 m) and (3) nudging in the mid-latitude ocean and at full ocean depth. The experimental setup allows for identifying local and remote effects of nudging on different geographical areas. The validation is based on the correlation coefficients and the root mean square error skill score and concerns the following variables: ocean heat content, ocean barotropic streamfunction, intensity of the ocean gyres and indexes of convection, sea ice extension, near-surface air and sea surface temperature, and El Niño–Southern Oscillation 3.4 index. The results can be summarized as follows: (1) the positive impact on the reconstruction of the ocean state is found almost everywhere and for most of the analyzed variables, including unconstrained variables and/or regions, (2) deep-ocean nudging shows low impact on sea-surface temperature but a significant impact on the ocean circulation, (3) mid-latitude ocean nudging shows systematically the worst performance pointing at the importance of the poles and tropics in reconstructing the global ocean
Skilful forecasting of global fire activity using seasonal climate predictions
No full textSocietal exposure to large fires has been increasing in recent years. Estimating the expected fire activity a few months in advance would allow reducing environmental and socio-economic impacts through short-term adaptation and response to climate variability and change. However, seasonal prediction of climate-driven fires is still in its infancy. Here, we discuss a strategy for seasonally forecasting burned area anomalies linking seasonal climate predictions with parsimonious empirical climate–fire models using the standardized precipitation index as the climate predictor for burned area. Assuming near-perfect climate predictions, we obtained skilful predictions of fire activity over a substantial portion of the global burnable area (~60%). Using currently available operational seasonal climate predictions, the skill of fire seasonal forecasts remains high and significant in a large fraction of the burnable area (~40%). These findings reveal an untapped and useful burned area predictive ability using seasonal climate forecasts, which can play a crucial role in fire management strategies and minimise the impact of adverse climate conditions.This work was partially funded by the EU H2020 Project 641762 “ECOPOTENTIAL:
Improving Future Ecosystem Benefits through Earth Observations” and the SERVFORFIRE
project of the ERA-NET for Climate Services, ERA4CS. M. Turco was supported
by the Spanish Juan de la Cierva Programme (IJCI-2015-26953). F.J. Doblas-
Reyes was supported by the H2020 IMPREX (GA 641811) and EUCP (GA 776613)
projects. A.A. was partially supported by the National Oceanic and Atmospheric
Administration (NOAA) award NA14OAR4310222, National Aeronautics and Space
Administration (NASA) award NNX15AC27G, and National Science Foundation (NSF)
INFEWS grant EAR 1639318. Special thanks to Esteve Canyameras and Xavier Castro for
helpful discussions on the study.Peer Reviewe
The limit case of the Cesàro-α convergence of the ergodic averages and the ergodic Hilbert transform
No full textRecently, Sarrión and the authors gave a sufficient condition on invertible Lamperti operators on Lp which guarantees the convergence in the Cesàro-α sense of the ergodic averages and the ergodic Hilbert transform for all f ∈ Lp with p > 1/(1 + α) and −1 < α ≤ 0. The result does not hold for the space L1/(1 + α). In this paper we give a positive result for the smaller Lorentz space L1/(1 + α),1.Fil: Bernardis, Ana Lucia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; ArgentinaFil: Martín Reyes, F.J.. Universidad de Málaga; Españ
Dispelling the Myths Behind First-author Citation Counts
No full textWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Weighted inequalities for the two-dimensional one-sided Hardy-Littlewood maximal function
No full textIn this work we characterize the pairs of weights (w, v) such that the one-sided Hardy-Littlewood maximal function in dimension two is of weak-type (p, p), 1 ≤ p ≤ ∞, with respect to the pair (w, v). As an application of this result we obtain a generalization of the classic Dunford-Schwartz Ergodic Maximal Theorem for bi-parameter flows of null-preserving transformations.Fil: Forzani, Liliana Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; ArgentinaFil: Martín-Reyes, F.J.. Universidad de Málaga; EspañaFil: Ombrosi, Sheldy Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentin
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