rock lobster fishery. During non-El Niño years,
they found that coastal sea level is high and that
recruitment of lobster pueruli to the near-shore
nursery grounds is high. They suggest that in nonEl Niño years high coastal sea levels are indicative
of a large cross-shelf sea-level slope and a strong
Leeuwin Current over the continental slope. During El Niño years, coastal sea level falls and the
inferred transport of the Leeuwin Current is less.
Hydrographic data indicate that during these
periods of low sea level, shelf waters are cooler and
more saline, consistent with a weaker southward
transport of tropical waters by the Leeuwin Current. The exact mechanism linking oceanographic
conditions to recruitment is unclear but the origin
appears to be in the physical environment. In
European waters there has been a demonstrated
link between the offshore transports in the Northeast Atlantic since 1975 and changes in the distribution and abundance of zooplankton species in the
North Sea as revealed by the continuous plankton
recorder surveys (Reid et al., 1998; Holliday and
Reid, 2001). A link with large-scale offshore temperature changes has also been deduced.
As a result of multiple forcing factors in the shelf
region, interannual/decadal variability is often
obscured by high-frequency events. Thus, determining the impact of climatic variability or change
on the shelf (where much of our use of the marine
environment and where most marine environmental degradation occurs) is a challenging task given
our generally sparse ocean data sets. Understanding
the response of the shelf circulation and its productivity to climate variability and change will require
coupled models of the shelf, at the appropriate vertical and horizontal resolution, and the open ocean,
as proposed by Smith (Chapter 7.4).
1.2.8 Conclusion
During the planning phase of WOCE, many of the
features of the ocean circulation were known only
qualitatively. Despite the ocean’s profound importance for predicting long-term climate variability
and change, we are only now gaining a quantitative understanding of the oceans’ mean state and
of several aspects of ocean variability. This quantitative understanding has come about through
coordinated high-quality in-situ observational
efforts (e.g. King et al., Chapter 3.1), development
of new in-situ (e.g. Davis and Zenk, Chapter 3.2;
Schlosser et al., Chapter 5.8) and satellite (Fu,
Chapter 3.3; Liu and Katsaros, Chapter 3.4) observational techniques, the widespread distribution of
this data (Lindstrom and Legler, Chapter 3.5) and
rapid development of computer models (Boening
and Semtner, Chapter 2.2; Wood and Bryan,
Chapter 2.3).
At present our quantitative understanding of
ocean variability is strongest for the relatively
short-lived ENSO events in the heavily sampled
equatorial Pacific and for these we have developed
a predictive skill. We now are beginning to explore
other, longer-period, aspects of ocean variability
(Dickson, Chapter 7.3) and establish sustained
observing systems (Smith, Chapter 7.4). Through
analyses of these data we might reasonably expect
to discover other modes of ocean variability and
understand their links to the atmosphere. Better
climate predictions will require our understanding
of ocean variability and its interaction with the
atmosphere on a broad range of spatial and
temporal scales to improve. Overcoming the predictability barrier will require the continued development of in-situ and satellite observing systems
(Smith, Chapter 7.4), improved numerical models
(Willebrand and Haidvogel, Chapter 7.2) and the
assimilation of the data in models (Talley et al.,
Chapter 7.1), and the closer linking of the physical
system to the important biogeochemical aspects of
the ocean and climate.
Acknowledgements
JC acknowledges the support of the Australian
Antarctic Cooperative Research Centre and
CSIRO. This article is a contribution to the
CSIRO Climate Change Research Program.
SECTION 1 THE OCEAN AND CLIMATE
30
they found that coastal sea level is high and that
recruitment of lobster pueruli to the near-shore
nursery grounds is high. They suggest that in nonEl Niño years high coastal sea levels are indicative
of a large cross-shelf sea-level slope and a strong
Leeuwin Current over the continental slope. During El Niño years, coastal sea level falls and the
inferred transport of the Leeuwin Current is less.
Hydrographic data indicate that during these
periods of low sea level, shelf waters are cooler and
more saline, consistent with a weaker southward
transport of tropical waters by the Leeuwin Current. The exact mechanism linking oceanographic
conditions to recruitment is unclear but the origin
appears to be in the physical environment. In
European waters there has been a demonstrated
link between the offshore transports in the Northeast Atlantic since 1975 and changes in the distribution and abundance of zooplankton species in the
North Sea as revealed by the continuous plankton
recorder surveys (Reid et al., 1998; Holliday and
Reid, 2001). A link with large-scale offshore temperature changes has also been deduced.
As a result of multiple forcing factors in the shelf
region, interannual/decadal variability is often
obscured by high-frequency events. Thus, determining the impact of climatic variability or change
on the shelf (where much of our use of the marine
environment and where most marine environmental degradation occurs) is a challenging task given
our generally sparse ocean data sets. Understanding
the response of the shelf circulation and its productivity to climate variability and change will require
coupled models of the shelf, at the appropriate vertical and horizontal resolution, and the open ocean,
as proposed by Smith (Chapter 7.4).
1.2.8 Conclusion
During the planning phase of WOCE, many of the
features of the ocean circulation were known only
qualitatively. Despite the ocean’s profound importance for predicting long-term climate variability
and change, we are only now gaining a quantitative understanding of the oceans’ mean state and
of several aspects of ocean variability. This quantitative understanding has come about through
coordinated high-quality in-situ observational
efforts (e.g. King et al., Chapter 3.1), development
of new in-situ (e.g. Davis and Zenk, Chapter 3.2;
Schlosser et al., Chapter 5.8) and satellite (Fu,
Chapter 3.3; Liu and Katsaros, Chapter 3.4) observational techniques, the widespread distribution of
this data (Lindstrom and Legler, Chapter 3.5) and
rapid development of computer models (Boening
and Semtner, Chapter 2.2; Wood and Bryan,
Chapter 2.3).
At present our quantitative understanding of
ocean variability is strongest for the relatively
short-lived ENSO events in the heavily sampled
equatorial Pacific and for these we have developed
a predictive skill. We now are beginning to explore
other, longer-period, aspects of ocean variability
(Dickson, Chapter 7.3) and establish sustained
observing systems (Smith, Chapter 7.4). Through
analyses of these data we might reasonably expect
to discover other modes of ocean variability and
understand their links to the atmosphere. Better
climate predictions will require our understanding
of ocean variability and its interaction with the
atmosphere on a broad range of spatial and
temporal scales to improve. Overcoming the predictability barrier will require the continued development of in-situ and satellite observing systems
(Smith, Chapter 7.4), improved numerical models
(Willebrand and Haidvogel, Chapter 7.2) and the
assimilation of the data in models (Talley et al.,
Chapter 7.1), and the closer linking of the physical
system to the important biogeochemical aspects of
the ocean and climate.
Acknowledgements
JC acknowledges the support of the Australian
Antarctic Cooperative Research Centre and
CSIRO. This article is a contribution to the
CSIRO Climate Change Research Program.
SECTION 1 THE OCEAN AND CLIMATE
30