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Observed Interannual Variability (observed + interannual_variability)

Distribution by Scientific Domains
Earth and Environmental Science75%

Selected Abstracts

Modelling the interannual variability of net ecosystem CO2 exchange at a subarctic sedge fen

GLOBAL CHANGE BIOLOGY, Issue 5 2001
Timothy J. Griffis
Abstract This paper presents an empirical model of net ecosystem CO2 exchange (NEE) developed for a subarctic fen near Churchill, Manitoba. The model with observed data helps explain the interannual variability in growing season NEE. Five years of tower-flux data are used to test and examine the seasonal behaviour of the model simulations. Processes controlling the observed interannual variability of CO2 exchange at the fen are examined by exploring the sensitivity of the model to changes in air temperature, precipitation and leaf area index. Results indicate that the sensitivity of NEE to changing environmental controls is complex and varies interannually depending on the initial conditions of the wetland. Changes in air temperature and the timing of precipitation events have a strong influence on NEE, which is largely manifest in gross ecosystem photosynthesis (GEP). Climate change scenarios indicate that warmer air temperatures will increase carbon acquisition during wet years but may act to reduce wetland carbon storage in years that experience a large water deficit early in the growing season. Model simulations for this subarctic sedge fen indicate that carbon acquisition is greatest during wet and warm conditions. This suggests therefore that carbon accumulation was greatest at this subarctic fen during its early developmental stages when hydroclimatic conditions were relatively wet and warm at approximately 2500 years before present. [source]

Dynamical versus statistical downscaling methods for ocean wave heights

INTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 3 2010
Xiaolan L. Wang
Abstract In this study, dynamical and statistical downscaling methods for estimating seasonal statistics of significant wave heights (SWH) were intercompared, with the downscaling results being evaluated against the ERA40 wave data in terms of climatological characteristics and interannual variability. It was also shown that biases in climate-model-simulated climate and variability of the atmospheric circulation (or predictors in general) can result in large biases in the estimated climate and variability of SWH (or the predictand in general), and that such biases can be effectively diminished by using standardized predictor quantities in statistical downscaling models. In dynamical downscaling, however, model variability biases remain to be dealt with, whereas the effects of model climate biases can be reduced to some extent by replacing the climate-model-simulated wind climate with the observed one. Therefore, the dynamical approach was found to be not as good as the statistical methods in terms of reproducing the observed climate and interannual variability of the predictand, although it bears substantial similarity to the statistical methods in terms of projected possible future changes. Also, it was shown that the observed interannual variability of seasonal statistics (including extremes) can be better reproduced by using 12-hourly, rather than seasonal, data in statistical downscaling. This stresses the importance of availability of higher-resolution data from climate model outputs. Nevertheless, a non-stationary extreme value model with covariates was found to be the best in reproducing the observed climate of extremes. All the statistical downscaling methods and the intercomparison results are applicable to other climate variables (not limited to ocean wave heights). Copyright © 2009 Crown in the right of Canada. Published by John Wiley & Sons, Ltd. [source]

Simulations of observed interannual variability of tropical cyclone formation east of Australia

ATMOSPHERIC SCIENCE LETTERS, Issue 1-4 2003
Kevin J.E. Walsh
Abstract A modelling system comprising a regional climate model nested within a GCM is used to simulate the observed interannual variability of tropical cyclone formation off the east coast of Australia. The model's interannual variability of cyclone formation is weaker than that observed, with shortcomings in the model's simulation of vertical wind shear the likely cause. Copyright © 2003 Royal Meteorological Society. Published by Elsevier Ltd. All rights reserved. [source]