Monteiro, Pedro MSVan der Plas, AK2007-08-062007-08-062006-09Monteiro, PMS and Van der Plas, AK. 2006. Low Oxygen Water (LOW) variability in the Benguela system: key processes and forcing scales relevant to forecasting. In: Benguela: predicting a large marine ecosystem, Vol. 14, 19p.http://hdl.handle.net/10204/1085Copyright: 2006 Elsevier B.VLOW variability in the Benguela is governed by varying scales of remote and local forcing linked to both Equatorial and Cape Basin systems. The nature of these nonlinear interactions is not clearly understood because scales are large and their elucidation through observational programmes alone is not cost effective. Models are required to characterise the complexity of the most important forcing and response scales in both time and space. It will be necessary to approach this as a multi-phase process, beginning with a diagnostic emphasis which evolves to a forecasting system through hindcasting focussed specifically on large scale events of the past. It is clear that not all the variability scales are amenable to forecasting either because the driving process scales are too uncertain or because they are of little management of policy interest. Two scales were defined as being of interest to both these criteria: Short term (7 day) scale related to forecasting conditions leading to the walkout or mortality of rock lobster in the southern Benguela; Medium term (2 month) forecasting of the intensification of the remote forcing of ETSA derived LOW which has a bearing on the Namibian hake fishery These two scales are discussed in detail in the companion Chapter 13.enMarine resourcesFisheries habitatsBenguela shelfRemote forcingBook ChapterMonteiro, P. M., & Van der Plas, A. (2006). Low Oxygen Water (LOW) variability in the Benguela system: Key processes and forcing scales relevant to forecasting., <i></i> Elsevier B.V. http://hdl.handle.net/10204/1085Monteiro, Pedro MS, and AK Van der Plas. "Low Oxygen Water (LOW) variability in the Benguela system: key processes and forcing scales relevant to forecasting" In <i></i>, n.p.: Elsevier B.V. 2006. http://hdl.handle.net/10204/1085.Monteiro PM, Van der Plas A. Low Oxygen Water (LOW) variability in the Benguela system: key processes and forcing scales relevant to forecasting. [place unknown]: Elsevier B.V; 2006. [cited yyyy month dd]. http://hdl.handle.net/10204/1085.TY - Book Chapter AU - Monteiro, Pedro MS AU - Van der Plas, AK AB - LOW variability in the Benguela is governed by varying scales of remote and local forcing linked to both Equatorial and Cape Basin systems. The nature of these nonlinear interactions is not clearly understood because scales are large and their elucidation through observational programmes alone is not cost effective. Models are required to characterise the complexity of the most important forcing and response scales in both time and space. It will be necessary to approach this as a multi-phase process, beginning with a diagnostic emphasis which evolves to a forecasting system through hindcasting focussed specifically on large scale events of the past. It is clear that not all the variability scales are amenable to forecasting either because the driving process scales are too uncertain or because they are of little management of policy interest. Two scales were defined as being of interest to both these criteria: Short term (7 day) scale related to forecasting conditions leading to the walkout or mortality of rock lobster in the southern Benguela; Medium term (2 month) forecasting of the intensification of the remote forcing of ETSA derived LOW which has a bearing on the Namibian hake fishery These two scales are discussed in detail in the companion Chapter 13. DA - 2006-09 DB - ResearchSpace DP - CSIR KW - Marine resources KW - Fisheries habitats KW - Benguela shelf KW - Remote forcing LK - https://researchspace.csir.co.za PY - 2006 T1 - Low Oxygen Water (LOW) variability in the Benguela system: key processes and forcing scales relevant to forecasting TI - Low Oxygen Water (LOW) variability in the Benguela system: key processes and forcing scales relevant to forecasting UR - http://hdl.handle.net/10204/1085 ER -