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Why Climate Modelers Think We Need a Really, Really Big Computer. Phil Jones Climate, Ocean and Sea Ice Modeling (COSIM) Climate Change Prediction Program Co-PI SciDAC CCSM Collaboration. Climate System. Climate Modeling Goals.

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why climate modelers think we need a really really big computer

Why Climate Modelers Think We Need a Really, Really Big Computer

Phil Jones

Climate, Ocean and Sea Ice Modeling (COSIM)

Climate Change Prediction Program

Co-PI SciDAC CCSM Collaboration

climate modeling goals
Climate Modeling Goals
  • Understanding processes and how they interact (only one on-going experiment)
  • Attribution of causes of observed climate change
  • Prediction
    • Natural variability (ENSO, PDO, NAO)
    • Anthropogenic climate change (alarmist fearmongering) – IPCC assessments
    • Rapid climate change
  • Input on energy policy
climate change
Climate Change

IPCC TAR 2001

greenhouse gases
Greenhouse Gases
  • Energy production
  • Bovine flatulence
  • Presidential campaigning
state of the art
State of the Art
  • T85 Atmosphere (150km)
  • Land on same
  • 1 degree ocean (100km)
  • Sea ice on same
  • Physical models only – no biogeochemistry
  • 5-20 simulated years per CPU day
    • Limited number of scenarios
community climate system model
Community Climate System Model

Land

LSM/CLM

Atmosphere

CAM

NSF/DOE

Physical Models

(No biogeochem)

7 States

10 Fluxes

6 States

6 Fluxes

150km

Once

hour

per

per

Flux Coupler

hour

Once

6 States

6 Fluxes

7 States

9 Fluxes

4 States

3 Fluxes

6 States

13 Fluxes

day

per

Once

per

Once

hour

6 Fluxes

11 States

10 Fluxes

Ocean

POP

Ice

CICE/CSIM

100km

performance portability
Performance Portability
  • Vectorization
    • POP easy (forefront of retro fashion)
    • CAM, CICE, CLM
  • Blocked/chunked decomposition
    • Sized for vector/cache
    • Load balanced distribution of blocks/chunks
    • Hybrid MPI/OpenMP
    • Land elimination
performance limitations
Performance Limitations
  • Atmosphere
    • Dynamics (spectral or FV), comms
    • Physics, flops
  • Ocean
    • Baroclinic, 3d explicit, flops/comms
    • Barotropic, 2d implicit, comms
  • All
    • timestep
prediction and assessment
Prediction and Assessment

Many century-scale simulations (>2500yrs)

@~5yrs/day

Cycle vampires:

Many dedicated cycles at computer centers

attribution
Attribution

Stott et al, Science 2000

“Simulations of the response to natural forcings alone … do not explain the warming in the second half of the century”

“..model estimates that take into account both greenhouse gases and sulphate aerosols are consistent with observations over this*period” - IPCC 2001

slide15

The annual mean change

of temperature (map) and

the regional seasonal change (upper box: DJF; lower box: JJA) for the scenarios A2 and B2

slide16

The annual mean change

of precipitation (map) and

the regional seasonal change (upper box: DJF; lower box: JJA) for the scenarios A2 and B2

if elected we plan
If elected, we plan…
  • High resolution
    • Cloud resolving atmosphere (10km)
    • Eddy-resolving ocean (<10km)
    • Regional prediction
  • Fully coupled biogeochemistry
    • Source-based scenarios
  • More scenarios, more ensembles
    • Uncertainty quantification
resolution and precipitation
Resolution and Precipitation

(DJF) precipitation in the California region in 5 simulations, plus observations. The 5 simulations are: CCM3 at T42 (300 km), CCM3 at T85 (150 km) , CCM3 at T170 (75 km), CCM3 at T239 (50 km), and CAM2 with FV dycore at 0.4 x 0.5 deg.

CCM3 extreme precipitation events depend on model resolution. Here we are using as a measure of extreme precipitation events the 99th percentile daily precipitation amount. Increasing resolution helps the CCM3 reproduce this measure of extreme daily precipitation events.

eddy resolving ocean
Eddy-Resolving Ocean

Obs

2 deg

0.28 deg

0.1 deg

chemistry biogeochemistry
Chemistry, Biogeochemistry
  • Atmospheric chemistry
    • Aerosols, ozone, GHG
  • Ocean biogeochemistry
    • Phytoplankton, zooplankton, bacteria, elemental cycling, trace gases, yada, yada…
  • Land Model
    • Carbon, nitrogen cycling, dynamic vegetation
  • Source-based scenarios
    • Specify emissions rather than concentrations
  • Sequestration strategies (land and ocean)
atmospheric chemistry
Atmospheric Chemistry
  • Gas-phase chemistry with emissions, deposition, transport and photo-chemical reactions for 89 species.
  • Experiments performed with 4x5 degree Fvcore – ozone concentration at 800hPa for selected stations (ppmv)
  • Mechanism development with IMPACT
    • A)    Small mechanism (TS4), using the ozone field it generates for photolysis rates.
    • B)     Small mechanism (TS4), using an ozone climatology for photolysis rates.
    • C)    Full mechanism (TS2), using the ozone field it generates for photolysis rates.

Zonal mean

Ozone, Ratio A/C

Zonal mean

Ozone, Ratio B/C

ocean biogeochemistry
Ocean Biogeochemistry
  • Iron Enrichment in the Parallel Ocean Program
  • Surface chlorophyll distributions in POP
  • for 1996 La Niña and 1997 El Niño
global dms flux from the ocean using pop
Global DMS Flux from the Ocean using POP

The global flux of DMS from the ocean to the atmosphere is shown as an annual mean. The globally integrated flux of DMS from the ocean to the atmosphere is 23.8 Tg S yr-1 .

increasing the deficit 10 10 10 12
Increasing the deficit (1010-1012)
  • Resolution (103-105)
    • x100 horiz, x10 timestep, x5-10 vert
  • Completeness (102)
    • Biogeochem (30-100 tracers)
  • Fidelity (102)
    • Better cloud processes, dynamic land, others
  • Increase length/number of runs(103)
    • Run length (x100)
    • Number of scenarios/ensembles (x10)
storage
Storage
  • Atmosphere
    • T85 29 GB/sim-yr, 0.08 GB/tracer
    • T170 110 GB/sim-yr, 0.3 GB/tracer
  • Ocean
    • 1 1.7 GB/sim-yr, 0.2 GB/tracer
    • 0.1 120 GB/sim-yr, 17 GB/tracer
beyond moore s law
Beyond Moore’s Law
  • Algorithms
    • 50% of past improvements
    • Tracer-friendly algorithms (inc remap advect)
    • Subgrid schemes
    • Implicit or other methods
remapping advection
Remapping Advection
  • monotone
  • multiple tracers free
  • 2nd order
subgrid orography scheme
Subgrid Orography Scheme
  • Reproduces orographic signature without increasing dynamic resolution
  • Realisitic precipitation, snowcover, runoff
  • Month of March simulated with CCSM
slide34

Comparison of sea ice shear (%/day) from CICE (a,c) and ‘old’ (b,d) models

(a)

(b)

Feb 20, 1987

(d)

(c)

Feb 26, 1987

beyond moore s law1
Beyond Moore’s Law
  • New architectures
    • Improved single-processor performance
    • Scaling vs. throughput
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