REPRESENTING MODEL UNCERTAINTY IN WEATHER AND CLIMATE PREDICTION

@article{citeulike:2959603, abstract = {Abstract Weather and climate predictions are uncertain, because both forecast initial conditions and the computational representation of the known equations of motion are uncertain. Ensemble prediction systems provide the means to estimate the flow-dependent growth of uncertainty during a forecast. Sources of uncertainty must therefore be represented in such systems. In this paper, methods used to represent model uncertainty are discussed. It is argued that multimodel and related ensembles are vastly superior to corresponding single-model ensembles, but do not provide a comprehensive representation of model uncertainty. A relatively new paradigm is discussed, whereby unresolved processes are represented by computationally efficient stochastic-dynamic schemes.}, author = {Palmer, T. N. and Shutts, G. J. and Hagedorn, R. and Reyes, Doblas F. J. and Jung, T. and Leutbecher, M. }, citeulike-article-id = {2959603}, doi = {10.1146/annurev.earth.33.092203.122552}, journal = {Annual Review of Earth and Planetary Sciences}, keywords = {climate, dynamics, model, prediction, skill, stochastic, uncertainty, weather}, number = {1}, pages = {163--193}, posted-at = {2008-07-03 15:47:54}, priority = {2}, title = {REPRESENTING MODEL UNCERTAINTY IN WEATHER AND CLIMATE PREDICTION}, url = {http://arjournals.annualreviews.org/doi/abs/10.1146/annurev.earth.33.092203.122552}, volume = {33}, year = {2005} }

TY - JOUR ID - citeulike:2959603 L3 - citeulike-article-id:2959603 TI - REPRESENTING MODEL UNCERTAINTY IN WEATHER AND CLIMATE PREDICTION JF - Annual Review of Earth and Planetary Sciences VL - 33 IS - 1 SP - 163 EP - 193 N2 - Abstract Weather and climate predictions are uncertain, because both forecast initial conditions and the computational representation of the known equations of motion are uncertain. Ensemble prediction systems provide the means to estimate the flow-dependent growth of uncertainty during a forecast. Sources of uncertainty must therefore be represented in such systems. In this paper, methods used to represent model uncertainty are discussed. It is argued that multimodel and related ensembles are vastly superior to corresponding single-model ensembles, but do not provide a comprehensive representation of model uncertainty. A relatively new paradigm is discussed, whereby unresolved processes are represented by computationally efficient stochastic-dynamic schemes. KW - climate KW - dynamics KW - model KW - prediction KW - skill KW - stochastic KW - uncertainty KW - weather AU - Palmer, TN AU - Shutts, GJ AU - Hagedorn, R AU - Reyes, Doblas AU - Jung, T AU - Leutbecher, M PY - 2005/// UR - http://arjournals.annualreviews.org/doi/abs/10.1146/annurev.earth.33.092203.122552 ER -