5 resultados para sub-seasonal prediction

em BORIS: Bern Open Repository and Information System - Berna - Suiça


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Accumulation and delta O-18 data from Alpine ice cores provide information on past temperature and precipitation. However, their correlation with seasonal or annual mean temperature and precipitation at nearby sites is often low. This is partly due to the irregular sampling of the atmosphere by the ice core (i.e. ice cores almost only record precipitation events and not dry periods) and the possible incongruity between annual layers and calendar years. Using daily meteorological data from a nearby station and reanalyses, we replicate the ice core from the Grenzgletscher (Switzerland, 4200m a.s.l.) on a sample-by-sample basis by calculating precipitation-weighted temperature (PWT) over short intervals. Over the last 15 yr of the ice core record, accumulation and delta O-18 variations can be well reproduced on a sub-seasonal scale. This allows a wiggle-matching approach for defining quasi-annual layers, resulting in high correlations between measured quasi-annual delta O-18 and PWT. Further back in time, the agreement deteriorates. Nevertheless, we find significant correlations over the entire length of the record (1938-1993) of ice core delta O-18 with PWT, but not with annual mean temperature. This is due to the low correlations between PWT and annual mean temperature, a characteristic which in ERA-Interim reanalysis is also found for many other continental mid-to-high-latitude regions. The fact that meteorologically very different years can lead to similar combinations of PWT and accumulation poses limitations to the use of delta O-18 from Alpine ice cores for temperature reconstructions. Rather than for reconstructing annual mean temperature, delta O-18 from Alpine ice cores should be used to reconstruct PWT over quasi-annual periods. This variable is reproducible in reanalysis or climate model data and could thus be assimilated into conventional climate models.

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Recent focus on early detection and intervention in psychosis has renewed interest in subtle psychopathology beyond positive and negative symptoms. Such self-experienced sub-clinical disturbances are described in detail by the basic symptom concept. This review will give an introduction into the concept of basic symptoms and describe the development of the current instruments for their assessment, the Schizophrenia Proneness Instrument, Adult (SPI-A) and Child and Youth version (SPI-CY), as well as of the two at-risk criteria: the at-risk criterion Cognitive-Perceptive Basic Symptoms (COPER) and the high-risk criterion Cognitive Disturbances (COGDIS). Further, an overview of prospective studies using both or either basic symptom criteria and transition rates related to these will be given, and the potential benefit of combining ultra-high risk criteria, particularly attenuated psychotic symptoms, and basic symptom criteria will be discussed. Finally, their prevalence in psychosis patients, i.e. the sensitivity, as well as in general population samples will be described. It is concluded that both COPER and COGDIS are able to identify subjects at a high risk of developing psychosis. Further, they appear to be sufficiently frequent prior to onset of the first psychotic episode as well as sufficiently rare in persons of general population to be considered as valuable for an early detection of psychosis.

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There is a need for accurate predictions of ecosystem carbon (C) and water fluxes in field conditions. Previous research has shown that ecosystem properties can be predicted from community abundance-weighted means (CWM) of plant functional traits and measures of trait variability within a community (FDvar). The capacity for traits to predict carbon (C) and water fluxes, and the seasonal dependency of these trait-function relationships has not been fully explored. Here we measured daytime C and water fluxes over four seasons in grasslands of a range of successional ages in southern England. In a model selection procedure, we related these fluxes to environmental covariates and plant biomass measures before adding CWM and FDvar plant trait measures that were scaled up from measures of individual plants grown in greenhouse conditions. Models describing fluxes in periods of low biological activity contained few predictors, which were usually abiotic factors. In more biologically active periods, models contained more predictors, including plant trait measures. Field-based plant biomass measures were generally better predictors of fluxes than CWM and FDvar traits. However, when these measures were used in combination traits accounted for additional variation. Where traits were significant predictors their identity often reflected seasonal vegetation dynamics. These results suggest that database derived trait measures can improve the prediction of ecosystem C and water fluxes. Controlled studies and those involving more detailed flux measurements are required to validate and explore these findings, a worthwhile effort given the potential for using simple vegetation measures to help predict landscape-scale fluxes.