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Seasonal Time Scales (seasonal + time_scale)
Selected AbstractsChanges in the sub-decadal covariability between Northern Hemisphere snow cover and the general circulation of the atmosphereINTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 1 2004Kazuyuki Saito Abstract Details of the sub-decadal covariability relationship between continental snow cover extent anomalies and the dominant mode of atmospheric variability, referred to as the Arctic oscillation (AO) or North Atlantic oscillation (NAO), for the period 1971,2001 are explored. On the seasonal time scale, the winter AO is found to be significantly correlated with the preceding autumn Eurasian snow cover (SNCEUR) throughout the period observed. Consistent with this finding, SNCEUR variability led the AO variability on the sub-decadal time scale in the early half of the record. However, starting in the mid 1980s, the AO and SNCEUR vary in phase. Analyses of the seasonal relationship and persistence of snow and atmospheric variables illustrate a phase shift in the sub-decadal variability between the AO and SNCEUR due to the loss of autumn,winter SNCEUR autocorrelation replaced by a significant winter,spring persistence and the emergence of a concurrent SNCEUR,AO connection in winter and spring. Similar analysis shows that the sub-decadal NAO variation is mostly described by the fluctuation in summer North American snow cover. Copyright © 2004 Royal Meteorological Society [source] Recalibration of general circulation model output to austral summer rainfall over southern AfricaINTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 12 2003A. G. Bartman Abstract Empirical techniques are developed to adjust dynamic model forecasts on the seasonal time scale for southern African summer rainfall. The techniques, perfect prognosis and model output statistics (MOS), are utilized to ,recalibrate' the CSIRO 9 general circulation model (GCM) large-scale fields statistically to three equi-probable rainfall categories for December to February. The recalibration is applied to a GCM experiment where simultaneously observed sea-surface temperature fields serve as the lower boundary forcing. An optimal canonical correlation analysis model is designed for MOS and perfect prognosis and the 700 hPa geopotential height field is selected as the single predictor field in the two sets of statistical equations that are subsequently used to produce recalibrated rainfall simulations over a 10 year independent test period. MOS produced the higher forecast skill for southern Africa over the independent test period. Copyright © 2003 Royal Meteorological Society [source] Estimating diurnal to annual ecosystem parameters by synthesis of a carbon flux model with eddy covariance net ecosystem exchange observationsGLOBAL CHANGE BIOLOGY, Issue 2 2005Bobby H. Braswell Abstract We performed a synthetic analysis of Harvard Forest net ecosystem exchange of CO2 (NEE) time series and a simple ecosystem carbon flux model, the simplified Photosynthesis and Evapo-Transpiration model (SIPNET). SIPNET runs at a half-daily time step, and has two vegetation carbon pools, a single aggregated soil carbon pool, and a simple soil moisture sub-model. We used a stochastic Bayesian parameter estimation technique that provided posterior distributions of the model parameters, conditioned on the observed fluxes and the model equations. In this analysis, we estimated the values of all quantities that govern model behavior, including both rate constants and initial conditions for carbon pools. The purpose of this analysis was not to calibrate the model to make predictions about future fluxes but rather to understand how much information about process controls can be derived directly from the NEE observations. A wavelet decomposition enabled us to assess model performance at multiple time scales from diurnal to decadal. The model parameters are most highly constrained by eddy flux data at daily to seasonal time scales, suggesting that this approach is not useful for calculating annual integrals. However, the ability of the model to fit both the diurnal and seasonal variability patterns in the data simultaneously, using the same parameter set, indicates the effectiveness of this parameter estimation method. Our results quantify the extent to which the eddy covariance data contain information about the ecosystem process parameters represented in the model, and suggest several next steps in model development and observations for improved synthesis of models with flux observations. [source] The influence of the tropical and subtropical Atlantic and Pacific Oceans on precipitation variability over Southern Central South America on seasonal time scalesINTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 4 2004Guillermo J. Berri Abstract This paper studies the temporal and spatial patterns of precipitation anomalies over southern central South America (SCSA; 22,40°S and 54,70°W), and their relationship with the sea-surface temperature (SST) variability over the surrounding tropical and subtropical Atlantic and Pacific Oceans. The data include monthly precipitation from 68 weather stations in central,northern Argentina and neighbouring Brazil, Paraguay and Uruguay, and monthly SSTs from the NOAA dataset with a 2° resolution for the period 1961,93. We use the method of canonical correlation analysis (CCA) to study the simultaneous relationship between bi-monthly precipitation and SST variability. Before applying the CCA procedure, standardized anomalies are calculated and a prefiltering is applied by means of an empirical orthogonal function (EOF) analysis. Thus, the CCA input consists of 10 EOF modes of SST and between 9 and 11 modes for precipitation and their corresponding principal components, which are the minimum number of modes necessary to explain at least 80% of the variance of the corresponding field. The results show that November,December presents the most robust association between the SST and SCSA precipitation variability, especially in northeastern Argentina and southern Brazil, followed by March,April and May,June. The period January,February, in contrast, displays a weak relationship with the oceans and represents a temporal minimum of oceanic influence during the summer semester. Based on the CCA maps, we identify the different oceanic and SCSA regions, the regional averages of SST and precipitation are calculated, and linear correlation analysis are conducted. The periods with greater association between the oceans and SCSA precipitation are November,December and May,June. During November,December, every selected region over SCSA reflects the influence of several oceanic regions, whereas during May,June only a few regions show a direct association with the oceans. The Pacific Ocean regions have a greater influence and are more widespread over SCSA; the Atlantic Ocean regions have an influence only over the northwestern and the southeastern parts of SCSA. In general, the relationship with the equatorial and tropical Atlantic and Pacific Oceans is of the type warm,wet/cold,dry, whereas the subtropical regions of both oceans show the opposite relationship, i.e. warm,dry/cold,wet. Copyright © 2004 Royal Meteorological Society [source] Relationship between snow cover variability and Arctic oscillation index on a hierarchy of time scalesINTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 2 2003A. S. Bamzai Abstract Based on satellite-derived global snow cover data on weekly time scales, the climatology and interannual variability of snow onset day-of-year, snowmelt day-of-year and number of snow-free days in a year are presented. Trends for snow onset day-of-year, snowmelt day-of-year and number of snow-free days in a year indicate that there has been an increase in number of snow-free days in recent decades. The relationship between snow cover and the Arctic oscillation (AO) index is examined on a hierarchy of time scales using lagged correlation and composite analysis. On weekly time scales, composite snow extent anomalies are maximum when AO leads snow cover by 1 week. These composite differences are maintained several weeks thereafter, particularly in the negative phase of the AO. Maps of composite snow cover anomalies when AO leads snow cover by 1 week delineate the spatial structure of these snow anomalies. On monthly time scales, lead,lag correlation between monthly snow cover and AO index indicates that the AO index during January, February and March is significantly correlated with snow cover in concurrent and subsequent spring months, particularly over Eurasia. Finally, on seasonal time scales, it is shown that winter season AO and winter/spring season snow cover are significantly correlated. Copyright © 2003 Royal Meteorological Society. [source] Hydrological seasonal forecast over France: feasibility and prospectsATMOSPHERIC SCIENCE LETTERS, Issue 2 2010J.-P. Céron Abstract This article presents a first evaluation of a hydrological forecasting suite at seasonal time scales over France. The hydrometeorological model SAFRAN-ISBA-MODCOU is forced by seasonal forecasts from the DEMETER project for the March,April,May period. Despite a simple downscaling method, the atmospheric forcings are reasonably well represented at the finest scale. The computed soil moisture shows some predictability with large regions of correlation above 0.3. Probabilistic scores for soil moisture and river flows for four different catchments are higher than that for atmospheric variables. These results suggest to go further for building an operational hydrological seasonal forecast system. Copyright © 2010 Royal Meteorological Society [source] Climate and respiratory disease in Auckland, New ZealandAUSTRALIAN AND NEW ZEALAND JOURNAL OF PUBLIC HEALTH, Issue 6 2009Ashmita Gosai Abstract Objective: Increases in the incidence of diseases are often observed during the cold winter months, particularly in cities in temperate climates. The study aim is to describe daily, monthly and seasonal trends in respiratory hospital admissions with climate in Auckland, New Zealand. Methods: Daily hospital admissions for total respiratory infections or inflammations (RII), total bronchitis and asthma (BA), and total whooping cough and acute bronchitis (TWCAB) for various age groups and ethnicities were obtained for the Auckland Region and compared with climate parameters on daily, monthly and seasonal time scales. Results: Seasonal and monthly relationships with minimum temperature were very strong (p<0.001) for RII over all age groups, for BA in the older age groups (14-64, 65+) and for TWCAB in the <1 year old age group. European, NZ M,ori and Pacific Islanders all showed increases in admissions as temperatures decreased. Pacific Islanders were particularly susceptible to RII. There was a lag in admissions of three to seven days after a temperature event. Conclusions and Implications: Results show that increases in respiratory admissions are strongly linked to minimum temperatures during winter, typical of cities with temperate climates and poorly-insulated houses. There are implications for hospital bed and staffing planning in Auckland hospitals. [source] | ||||||||||