Precipitation characteristics in a matrix of atmospheric GCMs with increasing resolution

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Precipitation characteristics in a matrix of
atmospheric GCMs with increasing resolution

• M.-E. Demory, P.L. Vidale, J. Donners,
• M. Roberts, A. Clayton
• With thanks to Christoph Frei (MeteoSwiss)

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What is the role of resolution in climate
research ?

• Matrix of coupled models, with increasing
  resolution
• What is the impact of resolving eddies in the
  ocean ?
• What is the impact of resolving weather ?
• What are the crucial scales for proper coupling ?
• What are the emerging processes ?
• Vertical resolution is fixed 38 atmospheric
  levels, 40 oceanic levels
• Hundreds of years of simulations have been
  completed, including 25 years of AMIP2

?x 135 km
?x 90 km
?x 60 km
Flux coupler
Completed in 2007
1o - 1/3o ocean model
1/3o ocean model
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General study motivations the hydrological
cycle

• Role of resolution in representing components of
  the hydrological cycle over land precipitation,
  evaporation, runoff
• Do high resolution models improve our
  understanding of the hydrological cycle?
• Interactions with the global circulation and the
  weather systems (storms)?

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Global mean precipitation
Weighted average in kg/year/m2 (excess of
precipitation)

• Overestimation of precipitation (too much LH and
  net radiation)
• Very little improvement with higher resolution
  models

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Impact on precipitation over the Alps

• Do high resolution models represent precipitation
  distribution in a better way?
• Is the precipitation frequency-intensity changing
  with resolution?
• Is that due to spatial resolution (better
  resolved orography) or to better resolved weather
  systems?

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DJF-MAM mean precipitation
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JJA-SON mean precipitation
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• Show the winds in DJF and JJA?
• For DJF pbs with all precip on the northern side
  of the Alps
• For JJA pb with no precip in Eurasia

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High-impact precipitation QQ plot
Percentiles
Alpine dataset (C. Frei)
HadGAM
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95th percentile precipitation distribution
(mm/day) in DJF
Alpine dataset
HadGAM
HiGAM
NUGAM
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95th percentile precipitation distribution
(mm/day) in JJA
Alpine dataset
HadGAM
HiGAM
NUGAM
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Summary (1/2)

• Precipitation better represented in high
  resolution models
• But mean precipitation biases remain the same in
  all resolution models, especially in summer
• gt problem with the global circulation?
• Same biases in regional models (MERCURE and
  PRUDENCE)
• NUGAM shows better skills in representing high
  intensity events
• Better location, especially along coastlines
• gt impact on local river flows?

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European river flows Po
MAM
MAM
Outflow (1000kg/s) in Po river
MAM
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European river flows Rhine Danube
DJF
DJF
Rhine
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Summary (2/2)

• Improvement of river flow seasonal cycle around
  the Alps
• Due to more localized precipitation
• Better snowmelt timing
• Too dry in summer due to too much evaporation and
  too little rainfall

Snowmelt (mm/day)
HadGAM (MAM)
NUGAM (MAM)
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Future work

• High-impact precipitation better represented as
  the resolution increases
• Is that due to orography? gt Run a NUGAM test run
  with low resolution (N96) orography
• Is that due to better resolved weather systems?
  gt Storms analyses in Europe