Understanding Local & Remote Forcing of Interannual Variability of Equatorial Eastern Indian Ocean Upwelling 20 January 2016

Understanding Local & Remote Forcing of Interannual Variability of Equatorial Eastern Indian Ocean Upwelling

A recently published article in the Journal of Physical Oceanography written by Chen et al., and supported by CPO’s Climate Variability and Predictability (CVP) program, explores remote and local forcing that drives the interannual variability of EIO upwelling by analyzing observations and performing experiments in the HYCOM ocean model.
Sea ice loss predicted to slow in the Atlantic, says new CVP-funded research 28 December 2015

Sea ice loss predicted to slow in the Atlantic, says new CVP-funded research

“There is little doubt that we will see a decline in Arctic sea ice cover in this century in response to anthropogenic warming, and yet internal climate variations and other external forcings could generate considerable spread in Arctic sea ice trends on decadal timescales,” begins a newly released article by Yeager et al., in Geophysical Research Letters.

New CVP-supported research reports on AMOC experiments for CORE-II 1 December 2015

New CVP-supported research reports on AMOC experiments for CORE-II

CORE-II is the Coordinated Ocean-ice Reference Experiments are a CLIVAR model intercomparison effort that examines a group of global ocean-sea ice models under a common atmospheric state to facilitate improved understanding and modeling of the ocean.
Sea level feedback lowers projections of future Antarctic Ice-Sheet mass loss, says CPO-funded research 1 December 2015

Sea level feedback lowers projections of future Antarctic Ice-Sheet mass loss, says CPO-funded research

Research supported by CPO’s MAPP and CVP programs evaluated the influence of the feedback mechanism between sea-level fall and ice sheets on future AIS retreat on centennial and millennial timescales for different emission scenarios, using a coupled ice sheet-sea-level model.
Novel data science approaches could drive advances in seasonal to sub-seasonal predictions of precipitation 25 November 2015

Novel data science approaches could drive advances in seasonal to sub-seasonal predictions of precipitation

Predictions at the seasonal to sub-seasonal scale are important for planning and decision-making in a variety of disciplines, and improving understanding and model skill at this timescale is a key research priority. An as yet underexplored approach to sub-seasonal prediction using data science and graph theory methods that are increasingly common to other fields outside of meteorology and climate science shows potential to improve predictions at this challenging timescale.

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