* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download ppt - Zettaflops.org
Climate change denial wikipedia , lookup
Fred Singer wikipedia , lookup
Effects of global warming on human health wikipedia , lookup
Global warming hiatus wikipedia , lookup
Numerical weather prediction wikipedia , lookup
Climate change adaptation wikipedia , lookup
Climate governance wikipedia , lookup
Citizens' Climate Lobby wikipedia , lookup
Instrumental temperature record wikipedia , lookup
Politics of global warming wikipedia , lookup
Climate engineering wikipedia , lookup
Climate change in Tuvalu wikipedia , lookup
Effects of global warming on oceans wikipedia , lookup
Climate change and agriculture wikipedia , lookup
Economics of global warming wikipedia , lookup
Media coverage of global warming wikipedia , lookup
Climate sensitivity wikipedia , lookup
Atmospheric model wikipedia , lookup
Climate change in Canada wikipedia , lookup
Carbon Pollution Reduction Scheme wikipedia , lookup
Ocean acidification wikipedia , lookup
Global warming wikipedia , lookup
Climate change in the United States wikipedia , lookup
Scientific opinion on climate change wikipedia , lookup
Physical impacts of climate change wikipedia , lookup
Public opinion on global warming wikipedia , lookup
Climate change feedback wikipedia , lookup
Effects of global warming wikipedia , lookup
Attribution of recent climate change wikipedia , lookup
Effects of global warming on humans wikipedia , lookup
Surveys of scientists' views on climate change wikipedia , lookup
Climate change and poverty wikipedia , lookup
Effects of global warming on Australia wikipedia , lookup
Climate change, industry and society wikipedia , lookup
Solar radiation management wikipedia , lookup
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 • • • 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 IPCC TAR 2001 Greenhouse Gases • Energy production • Bovine flatulence • Presidential campaigning Rapid Climate Change Polar and THC 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 Land Atmosphere CAM 7 States 10 Fluxes 150km NSF/DOE Physical Models (No biogeochem) LSM/CLM 6 States 6 Fluxes Once per per Flux Coupler hour 6 States 6 Fluxes 4 States 3 Fluxes Once 7 States 9 Fluxes 6 States 13 Fluxes day per Once Once 6 Fluxes Ocean POP 100km hour 11 States 10 Fluxes per hour Ice CICE/CSIM Performance 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 • Atmosphere – Dynamics (spectral or FV), comms – Physics, flops • Ocean – Baroclinic, 3d explicit, flops/comms – Barotropic, 2d implicit, comms • All – timestep Prediction and Assessment Many century-scale simulations (>2500yrs) @~5yrs/day Cycle vampires: Many dedicated cycles at computer centers Attribution “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 Stott et al, Science 2000 The annual mean change of temperature (map) and the regional seasonal change (upper box: DJF; lower box: JJA) for the scenarios A2 and B2 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… • High resolution – Cloud resolving atmosphere (10km) – Eddy-resolving ocean (<10km) – Regional prediction • Fully coupled biogeochemistry – Source-based scenarios • More scenarios, more ensembles – Uncertainty quantification Towards Regional Prediction 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 Obs 2 deg 0.28 deg 0.1 deg Only decades… 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) Aerosol Uncertainty Atmospheric Chemistry • • • Gas-phase chemistry with emissions, deposition, transport and photochemical 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 •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 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 (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 • 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 • Algorithms – – – – 50% of past improvements Tracer-friendly algorithms (inc remap advect) Subgrid schemes Implicit or other methods Remapping Advection • monotone • multiple tracers free • 2nd order Subgrid Orography Scheme • • • 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 ‘old’ (b,d) models (a) (b) Feb 20, 1987 (c) (d) Feb 26, 1987 Beyond Moore’s Law • New architectures – Improved single-processor performance – Scaling vs. throughput