Tuesday, 24 January 2017: 9:00 AM
Conference Center: Skagit 3 (Washington State Convention Center )
Sea ice simulations from the current generation of global climate models show
significant spread in their simulation of sea ice for the 20th century as well as in
their projections of sea ice for the 21st century. In order to better assess these
model simulations and to develop and test new sea ice model features and
parameterizations, sea ice observations are crucial. For the assessment of global
climate models, gridded large scale observational datasets for long periods of
time are needed, while in-situ observations from individual years and specific
locations can be used for sea ice model parameterization development. In the
following, I will focus on observational needs for global climate model simulations
of sea ice, and illustrate challenges with model-observations sea ice
comparisons in the light of internal variability. Furthermore, I will highlight other
challenges in model-observation comparisons for sea ice, due to differences in
variable definitions. For CMIP6, a new data request for sea ice models has been
compiled by SIMIP (Sea Ice Model Intercomparison Project), which addresses
many of these variable definition challenges, and will allow more process-based
model evaluations compared to observations.
significant spread in their simulation of sea ice for the 20th century as well as in
their projections of sea ice for the 21st century. In order to better assess these
model simulations and to develop and test new sea ice model features and
parameterizations, sea ice observations are crucial. For the assessment of global
climate models, gridded large scale observational datasets for long periods of
time are needed, while in-situ observations from individual years and specific
locations can be used for sea ice model parameterization development. In the
following, I will focus on observational needs for global climate model simulations
of sea ice, and illustrate challenges with model-observations sea ice
comparisons in the light of internal variability. Furthermore, I will highlight other
challenges in model-observation comparisons for sea ice, due to differences in
variable definitions. For CMIP6, a new data request for sea ice models has been
compiled by SIMIP (Sea Ice Model Intercomparison Project), which addresses
many of these variable definition challenges, and will allow more process-based
model evaluations compared to observations.
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