Overview of Earth System Modeling and Fluid Dynamical Issue

1. Overview of Earth System Modeling and Fluid Dynamical Issue Model and Data Hierarchies for Simulating and Understanding Climate Marco A. Giorgetta

2. Overview • The Earth System – and Earth System Models (ESMs) • Research with ESMs • A GCM study on emission pathways to climate stabilization • Fluid dynamical issues in the development of ESMs

3. 1. The Earth System – and Earth System Models (ESMs)

4. The Earth System In general terms: The Earth and everything gravitationally bound to it • Earth interior • Oceans with sea ice • Land surfaces: soil, ice shields, glaciers • Atmosphere up to ~100 km • Life in all compartments • Land vegetation and soil organism • Marine biota • Humans!

5. The Earth System In climate science: A relatively new term, chosen to describe: • The physical climate system … • … and geo-bio-chemical processes … • … as necessary to understand the climate of the past … • … and to “predict” the future climate of the next ~100 years … • … where climate = [T, wind, q, precipitation] Explicitly account for the interaction of bio-geo-chemical processes with climate, and anthropogenic influences.

6. Key for understanding climate: Energy transfer • Radiation + heat fluxes and storage in A, O, and L • Distributions of T, q and wind, • Hydrological cycle Globally averaged vertical energy transfer in the atmosphere Source:IPCC AR4 WG1 Rep., Ch. 1, FAQ Fig.1

7. Components of the climate system, interactions, and changes (Source: IPCC AR4 WG1 Ch.1, FAQ 1.2, Figure 1)

8. Earth System Models (ESMs) • Simplified/idealized descriptions of the ES[Cf. “Model” in architecture, fashion, engineering, …] • Test understanding of the functioning of the ES • Explain observed features • Formal description, allowing for computational experiments  What if … • Turbulent mixing in oceans was stronger • “Major” volcanic eruptions happened? • … • Highly complex models within the model hierarchy • Fortran code of ~105 lines

9. The Earth System History of “Type II” models • General circulation models of atmosphere or ocean weather, seasonal cycle, … • Coupled atmosphere ocean models = “climate model” El Niño/La Niña, “small” climate change, … • Earth system model = “climate model” + • Land and ocean bio-geo-chemistry • Clouds/aerosols/chemistry in the atmosphere • Cryosphere: Glaciers, ice shields, shelf ice  Climate of other periods, “large” climate change  ESMs are most complex

10. Schematic view of the ES HealthWealthFood etc. Atmosphere Substance cyclesH2O, C N S P … EnergyMomentum Society Land Ocean Use & management of the environment

11. Construction of ESMs 1. Decide on spatial and temporal scales, and on processes, which are scientifically relevant and practically feasible ( model hierarchies) • Length of simulations ~102 years • Required turnover rate ~102 years/week • ~200 km horizontal resolution 2. Equations for the dynamics of atmosph., ocean, and ice • 200 km  Primitive equations • Numerical methods  discretized, i.e. computable, equations • “Dynamical core”  Christiane’s talk

12. Construction of ESMs (cont.) • Transport scheme for the advection of vapor, cloud particles, … / salt, plankton, … • “Physics package” for the physical, biological, chemical and unresolved dynamical processes; atmosphere: • Radiation • Turbulent vertical fluxes (“vertical diffusion”) of heat, momentum, tracers • Surface (snow cover, albedo, evaporation, transpiration, lateral water flows) • Microphysics • Convection • Cloudiness • Sub-grid-scale orographic effects • Non-orographic gravity wave drag

13. Construction of ESMs (cont.) Parameterizations rely on assumptions, e.g.: • Radiation • Grid scale << Earth radius  plane parallel assumption • Grid scale >> layer thickness  neglect fluxes trough lateral boundaries • Local thermal equilibrium  valid up to ~70 km in the atmosphere of Earth • Gas = air + small variations  valid for the atmosphere of Earth • …

14. 2. Research with ESMs

15. A GCM study on emission pathways to climate stabilization E. Roeckner, M. Giorgetta, T. Crüger, M. Esch, and J. Pongratz Submitted to Climatic Change

16. Motivation • United Nations Framework on Climate Change: • Article 2: ‘... to achieve stabilization of greenhouse gas concentrations ... that would prevent dangerous anthropogenic interference with the climate system‘ • Questions • For a given CO2 concentration pathway into the future: • What is the climate change? • What anthropogenic CO2 emissions are allowable? • What fraction of anthrop. carbon remains in the atmosphere? • What is the role of feedbacks between climate change and the C-cycle?

17. Use Earth system model including the carbon cycle • simulate the carbon flux between atmosphere, and ocean or land • Use two scenarios for the future until 2100: • “SRES A1B” scenario • No mitigation • “E1” scenario developed for ENSEMBLES (Van Vuuren et al., 2007) • Agressive mitigation scenario E1 • Limit global change in surface air temperature to 2° • (implies stablization of CO2 concentration in 22nd century at ~450 ppmv • European ENSEMBLES project • Other models  multi model ensemble

18. Policies Story lines Impacts (Mitigation) Scenario Surface temperature Emissions Concentrations 2B 1 2A Carbon cycle - climate model Methodology Method proposed for the future CMIP5 experiments, i.e. experiments for the 5th IPCC assessment of climate change (Hibbard et al., 2007):

19. 1860 1900 1950 2000 2050 2100 Experiments Control“1860”1000 yr Historic1860-2005 SRES A1B Ensembles of 5 realizations E1 450 ppm : full coupling; : C-cycle decoupled

20. Scenarios for CO2 concentration CO2 concentration in ppmv • 1860-2005: observations • 2005-2100: scenarios Others: CH4, N2O, CFCs

21. … and of the model used here X (no feedback) A: ECHAM Substance cyclesH2O, C EnergyMomentum Society L: JSBACH O: MPIOM + HAMOCC Prescribed BCs fromobservations+scenarios

22. Pre-industrial control simulation Global annual mean surface air temperature (°C) and CO2 concentration (ppmv) Pre-industrial conditions, thick lines: 11-year running means • Climate of undisturbed system stable over 1000 years • Surface air temperature(left scale, °C) • Atmospheric CO2 concentration (right scale, ppmv)

23. Global mean surface air temperature Global annual mean surface air temperature anomalies w.r.t. 1860-1880 (°C)5 year running means simulated (5 realizations) observed (Brohan et al., 2006) • Simulated surface air temperature less variable than observed. • Natural sources of variability like volcanic forcing or the 11 year solar cycle are excluded from the experiment. • Simulated warming in 2005 slightly underestimated.

24. Global mean CO2 emissions 1860 to 2005 CO2 emissions from fossil fuel combustion and cement production (GtC/yr)Global annual mean; 11-year running means • Model allows for relatively higher emissions before 1930. • Minimum in 1940s • Similar emissions in 2000. Implied emissions from simulations Observed (Marland et al., 2006)

25. Simulated carbon uptake 1860 to 2005 Simulated carbon uptake (GtC/yr)11-year running means • Ocean carbon uptake very similar to land uptake • Reduced uptake in 1950s Simulated ocean uptake Simulated land uptake

26. Carbon uptake by ocean and land Fraction of simulated fossil fuel emissions (%) Remaining in the atmosphere Absorbed by ocean Aborbed by land • 50% of simulated fossil fuel emissons remain in the atmosphere • In 2000: simulated ocean uptake = ~2 x simulated land uptake

27. Global surface air temperature anomalies Global annual mean surface air temperature anomalies w.r.t. 1860-1880 (°C) Historic 1950-2000 A1B 2001 – 2100 E1 2001 – 2100 • Initially stronger warming in E1 than in A1B because of faster reduction in sulfate aerosol loading, hence less cooling. • Reduce warming in E1 after 2040 • Warming in 2100: ~4°C in A1B and ~2°C in E1 • Climate – carbon cycle feedback differs after 2050

28. without feedback with feedback Implied CO2 emissions 1950 to 2100 Implied CO2 emissions with and without climate – carbon cycle feedback (GtC/yr) Historic 1950 – 2000 A1B 2001 – 2100 E1 2001 – 2100 • Implied CO2 emissions of E1 scenario drop sharply after ~2015 (unlike emissions for A1B scenario) • Implied emissions are reduced by feedbackIn 2100: -2 GtC/yr in E1 and -4.5 GtC/yr in A1B • Implied emissions of E1 close to 0 in 2100.

29. Accumulated C emissions: Coupled – Uncoupled Reduction in accumulated C emissions by climate – carbon cycle coupling (GtC)(11-year running means) • Climate – carbon cycle feedback reduces implied carbon emissions until 2100 by 180 (E1) to 280 (A1B) GtC. Historic 1860 – 2000 A1B 2001 – 2100 E1 2001 – 2100

30. Conclusions • The E1 scenario fulfills the EU climate policy goal of limiting the global temperature increase to a maximum of 2°C. • In the 2050s (2090s) the allowable CO2 emissions for E1 are about 65% (17%) of those of the 1990’s. • As in previous studies, a positive climate-carbon cycle feedback is simulated. • Climate warming reduces the ability of both land and ocean to take up anthropogenic carbon. • Climate – carbon cycle feedback reduces the allowable emissions by about 2 GtC/yr in the E1 scenario.

31. 3. Fluid dynamical issues in the development of ESMs

32. Conservation properties of numerical models • The discretized system shall have the same conservation properties as the underlying continuous system • Mass and tracer mass – consistent continuity and transport eq. • Momentum – “Radiation upper boundary condition” • Energy – Energy conversion due to wave dissipation

33. Adaptivity • Grid refinement • static or dynamic? • Redistribute grid points or create/destroy grid points? • 2d or 3d? • Single time integration scheme or recursive schemes? • Conservation properties? • Dynamical core • Adjust scheme to expected errors ( FE schemes) • Parameterizations: • Submodels: embedded dynamical models – “super-parameterizations” • Cost function • How to predict the need for refinement, and what for? • How to confine cost?

34. High performance computing • Parallelization: • From ~102 cores to 105 cores • Model integration, data handling, post processing • Hardware and software reliability • Data • Storage capacity grows less than computing power • Limited bandwidth for data access

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