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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 system
Climate System Computer


Climate modeling goals
Climate Modeling Goals Computer

  • 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 Computer

IPCC TAR 2001


Greenhouse gases
Greenhouse Gases Computer

  • Energy production

  • Bovine flatulence

  • Presidential campaigning



Polar and thc
Polar and THC Computer


State of the art
State of the Art Computer

  • 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 Computer

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
Performance Computer


Performance portability
Performance Portability Computer

  • 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 Computer

  • 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 Computer

Many century-scale simulations (>2500yrs)

@~5yrs/day

Cycle vampires:

Many dedicated cycles at computer centers


Attribution
Attribution Computer

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


The annual mean change Computer

of temperature (map) and

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


The annual mean change Computer

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… Computer

  • 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 Computer

(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 Computer

Obs

2 deg

0.28 deg

0.1 deg


Only decades… Computer


Chemistry biogeochemistry
Chemistry, Biogeochemistry Computer

  • 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 Computer

  • 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 Computer

  • 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 Computer

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 (10 Computer10-1012)

  • 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 Computer

  • 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 Computer

  • Algorithms

    • 50% of past improvements

    • Tracer-friendly algorithms (inc remap advect)

    • Subgrid schemes

    • Implicit or other methods


Remapping advection
Remapping Advection Computer

  • monotone

  • multiple tracers free

  • 2nd order


Subgrid orography scheme
Subgrid Orography Scheme Computer

  • Reproduces orographic signature without increasing dynamic resolution

  • Realisitic precipitation, snowcover, runoff

  • Month of March simulated with CCSM


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

(a)

(b)

Feb 20, 1987

(d)

(c)

Feb 26, 1987


Beyond moore s law1
Beyond Moore’s Law Computer

  • New architectures

    • Improved single-processor performance

    • Scaling vs. throughput


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