The NOAA CPO Modeling, Analysis, Predictions, and Projections (MAPP) program hosted a webinar on the topic of Seasonal Prediction of High Water Levels on Wednesday, March 23, 2016. The announcement is provided below.
NOAA Climate Prediction Center
Potential sources for oceanic predictability
Variability in the oceans exists on multiple time-scales that can range from monthly to decadal and longer. Sources of predictability can be attributed to persistence of initial oceanic anomalies and their decay towards climatology; ocean dynamical process; or could be due to coupled air-sea interactions that can result in a modulation of oceanic predictability associated ocean dynamics. Another source of oceanic predictability on very long time-scales can be attributed to changes in external forcings, e.g., changes in atmospheric constituents, or due to changes in orbital forcings. In this talk a brief overview of various sources of oceanic predictability in the context of prediction of sea level will be given.
NOAA Geophysical Fluid Dynamics Laboratory
USA East Coast sea level, the AMOC and the NAO
We summarize recent observational and modeling studies that connect sea level variations along the USA east coast to the AMOC and NAO. A reduction in AMOC strength is correlated to increases in sea level along the eastern US seaboard. Coastal sea level increases also arise when the atmospheric surface pressure reduces near the coast, as during a negative phase of the NAO. Mechanisms for these sea level changes are reviewed.
Bureau of Meteorology, Melbourne, Australia
Prediction of seasonal sea level anomalies within the Bureau of Meteorology
Sea level rise as a result of human caused climate change poses a severe threat to Pacific Island Countries. Sea level rise in the Western Pacific region has been well above the global average (3.2 mm/year) over the last two to three decades, with impacts already evident through coastal erosion, damage to coastal infrastructure, contamination of ground water and salt water intrusion affecting agricultural land. In recognising that it is through natural variability that the early effects of climate change are most acutely felt, the Pacific-Australia Climate Change Science Adaptation Planning Program (PACCSAP) sought to assess the relationships between seasonal variability, regional sea-level and its predictability at a seasonal timescale. This study was the first attempt to quantitatively evaluate seasonal sea level anomaly (SLA) forecasts over the globe from the Australian Bureau of Meteorology’s dynamical seasonal coupled ocean–atmosphere multi-model system (POAMA). POAMA calculates SLA using a rigid lid ocean model (MOM2) that determines sea surface height based on temperature, salinity and wind gradients. As a dynamical model, POAMA has a distinct advantage over statistical models in being able to predict SLA under unprecedented changes to current physical forcings, such as those from climate change. The skill of POAMA SLA deterministic and probabilistic forecasts was assessed using satellite altimeter data over the period 1993–2010 and tide gauge records. These results were used to develop prototype seasonal forecast products and are available online. As global warming is likely to increase the frequency and severity of extreme SLA events the development of such products is crucial to combat problems due to climate change in the near future.
NOAA National Ocean Service
Projections of “nuisance” tidal flooding
Recurrent tidal flooding is now a serious problem in many U.S. coastal communities. Measured by NOAA tide gauges, annual frequencies of “nuisance” level impacts are today 300-900% greater than 50 years ago. Largely due to increasing mean sea level, the trends in tidal flooding are also compounded on an annual basis by El Nino Southern Oscillation (ENSO) effects. The 2015 meteorological year (May 2015 – April 2016) was projected to be (and will be) a record-breaking year, with El Nino-related increases in daily flood frequencies ranging from 33% to 200% above local trends on the West and East Coasts based upon a bivariate statistical model approach.