Earth System Complexity Challenges: Predicting Climate Change

1. Challenges for ENES Guy P. Brasseur National Center for Atmospheric Research Boulder, CO

2. The First Grand Challenge: Numerical Weather Prediction • The deterministic laws offluid mechanics should apply to the atmosphere: weather can be predicted (V. Bjerknes) • The first numerical attempts were unsuccessful (Richardson) • With the development of electronic computers, the first successful numerical weather predictions are made (Charney and von Neumann, Smagorinsky) • Weather predictions are greatly improved through the use ofsatellite observationsand the development of data assimilation techniques. Bjerknes Richardson Smagorinsky

3. The Second Grand Challenge:Predicting Climate Change Fourier • Svante Arrhenius quantifies in 1896 the changes in surface temperature (approx. 5 C) to be expected from a doubling in CO2, based on the concept of ”glass bowl”effect introduced in 1824 by Joseph Fourier. • Norman Phillips develops the firstglobal atmospheric GCM, and the first climate models are being developed by many (Manabe, Mintz and Arakawa, Washington, etc.) Arrhenius Manabe Arakawa Washington

4. ESSL - The The Conceptual Framework

5. Future Global Warming (IPCC Projections 2007)

6. Towards Comprehensive Earth System Models 1970 1997 2000 1975 1985 1992 Atmosphere Atmosphere Atmosphere Atmosphere Atmosphere Atmosphere Land surface Land surface Land surface Land surface Land surface Ocean & sea-ice Ocean & sea-ice Ocean & sea-ice Ocean & sea-ice Sulphate aerosol Sulphate aerosol Sulphate aerosol Non-sulphate aerosol Non-sulphate aerosol Carbon cycle Carbon cycle Atmospheric chemistry Off-line model development Strengthening colours denote improvements in models Sulphur cycle model Non-sulphate aerosols Ocean & sea-ice model Land carbon cycle model Carbon cycle model Ocean carbon cycle model The Met.OfficeHadley Centre Atmospheric chemistry Atmospheric chemistry

7. Advances in the last decade • Better understanding of the drivers (i.e. cause and effect) • Better understanding and parameterization of scale interactions • Better understanding of systemic interactions and feedbacks • Improvedglobal datasets (climate, atmosphere, land and oceans) and historic coverage • Integrationnatural and human processes: a wealth of global change scenarios was developed • (understanding a system is being able to predict its behavior)

8. Recently, the direction of our climate change research program dramatically changed. • WAS: Is anthropogenic climate change occurring? • NOW: What will be the of impact of anthropogenic climate change on coupled human and natural systems? • Magnitude and speed? • Direct and indirect impacts? • Adaptation vs mitigation • What are our options & limits? • Addressing these new, much more complex, questions requires • new approaches & priorities, • new science capabilities, • new collaborators/partners Image courtesy of Canada DND

9. Attribute sources of historical warming Project range of possible non-mitigated future warming from SRES scenarios Quantify Climate Change Commitment • Project adaptation needs under various mitigation scenarios • Time-evolving regional climate change on short and long-term timeframes • Quantify carbon cycle feedbacks Climate Change Epochs Before IPCCAR4 After Conclusion: With the wide public acceptance of the IPCC AR4 findings, the climate science community is now facing the new challenge of quantifying time evolving regional climate change that human societies will have to adapt to under several possible mitigation scenarios, as well as addressing the size of carbon cycle feedbacks with more comprehensive Earth System Models

10. The Third Grand Challenge: Understanding the Earth as a Complex Nonlinear System • TheLorenzattractors: the story of predictability. • The Vostock Ice core (Oeschger, Lorius) • The Dansgaard/Oeschgercycles • The CLAW hypothesis (R. Charlson, M. Andreae,et al.) • The realization of the importance of the carbon cycle (B. Bolin, R. Revelle) R. Revelle Ed Lorenz

11. Intellectual Challenges: Scientific and Engineering • Toimproveweather forecast, climate predictions, air pollution simulations, etc. and to better understand and predict the functioning of the Earth System at theglobal and regional scales, current weather-climate models need to better simulate dynamical, physical, chemical, biological and social processes. • In addition,new scientific questions require a better understanding of complex interactions and feedbacks that affect the Earth system level. • Society requires new types of information on the impactsof weather events and climate change, specifically on the water system, the health of ecosystems, agriculture, human health, fisheries, etc. Stakeholders must be involved. • Policymakers require objective information to design mitigationand adaptationmeasures.

12. ESSL - The The Conceptual Framework Climate change Air pollution Climate change Air pollution

13. ESSL - The Earth System Framework Socio-economic Models Climate change Air pollution Climate change Air pollution Dynamic Global Vegetation Models Biophysical and Biogeochemical Models General Circulation Models Regional Atmospheric Models Chemical Transport Models Ocean General Circulation Models Ocean Biogeochemical Models Terrestrial Hydrology Models

14. Coupled Ocean-Land-Atmosphere Model The Next-Generation Weather-Climate Model (2015)Global non-hydrostatic atmospheric cloud-resolving model with coupled eddy-resolving ocean model and landscape-resolving land component: ~1 km x ~1 km (cloud-resolving) 100 levels, whole atmosphere Unstructured, adaptive grids ~100 m 10 levels Landscape-resolving ~10 km x ~10 km (eddy-resolving) 100 levels Unstructured, adaptive grids Assumption: Computing power enhancement by a factor of 104-106 100,000 processors? And new algorithms

15. Some challenges • What are challenging scientific questions at the earth system level that the community needs to address? • What are thebest tools to address these questions? • Whattype of information should we provide in support of mitigation and adaptation strategies? • What kind ofpartnerships should we establish? • What are themethodologiesthat need to be develop to link the natural and the social systems?

16. HPC dimensions of Climate Prediction • New Science • Better Science Data Assimilation • (new processes/interactions not previously included) • (parameterization → explicit model) • Spatial • Resolution • Timescale • (simulate finer details, regions & transients) • (Length of simulations • * time step) • Ensemble size • (decadal prediction/ initial value forecasts) • (quantify statistical properties of simulation) Lawrence Buja (NCAR) / Tim Palmer (ECMWF)

17. 70 10 10 10 10 10 10 10 10 HPC dimensions of Climate Prediction • New Science • Better Science ESM+multiscale GCRM Code Rewrite Earth System Model • Spatial • Resolution • (x*y*z) Climate Model • Timescale • (Years*timestep) Regular10000 ? 400 0.2° 22km 1.4° 160km 1Km 100yr* 20min 1000yr* 3min 1000yr * ? AMR 1000 5 Today Terascale Cost Multiplier 2010 Petascale 50 500 2015 Exascale Data Assimilation • Ensemble size

18. From: Earth System Grid Center for Enabling Technologies: (ESG-CET) DATA: Earth System Grid Center for Enabling Technologies (ESG-CET) Current ESG Sites ESG Goals • Petabyte-scale data volumes • Globally federated sites • “Virtual Datasets” created through • subsetting and aggregation • Metadata-based search and discovery • Bulk data access • Web-based and analysis tool access • Increased flexibility and robustness http://www.earthsystemgrid.org http://www-pcmdi.llnl.gov For AR5, ESG will be expanded to form a global virtual data center!

19. Next Generation Physical Models

20. New Algorithms for Dynamical Core • New dynamical core which represents the multi-scale nature of the earth system and which is computationally efficient on future hardware architectures. • Choice of basic grid that avoids singularities • Accurate representation of topography, i.e. computational mesh generation. • Efficient down- (or up-) scaling within the same model. i.e. mesh adaptation. • Formulation of the governing equations to be valid at all scales. • Solution of these equations on the discrete space defined by the generated meshes.

21. Next Generation Dynamical Core • Fully compressible non-hydrostatic equations • Mass conserving • Scalar mass conserving, consistent. • Positive-definite (PD) transport for PD scalars • Local refinement capability • Regional modeling capability • Monotonic transport options • Horizontal grid uniformity (little variation in cell area) • Horizontal grid isotropy (dx ~ dy) • Energy conservation? • Efficient (cost for a given accuracy level) • The grid should be invisible

22. Scale Interactions and Dynamical Modes Courtesy of Julia Slingo

23. Including Atmospheric Chemistry

24. Challenges for the Future Based on P. Cox, 2004 CLIMATE Direct and Indirect Effects / Feedbacks on natural sources Greenhouse Effect Heat island effect GREENHOUSE GASES Human Emissions AEROSOLS CH4, O3, N2O, CFC Fires: soot Mineral dust Oxidants: OH, H2O2 HO2,O3 Human Emissions CO2 N deposition 03, UV radiation (Gas-phase) CHEMISTRY ECOSYSTEMS Biogenic Emissions:CH4,DMS,VOC’s Dry deposition: stomatal conductance Land-use Change, Fires Human Emissions LAND WATER / CITIES Damming / Irrigation / Emission of heat The future: a full treatment of climate-chemistry-ecosystem-land surface feedbacks

25. size distribution AEROSOLS HAM: The Aerosol Model Component • Resolves aerosol distribution by seven log-normal modes • Components: • Sulfate, Black Carbon, Organic Carbon, Sea Salt, Dust composition mixing state

26. HAM - Aerosol Representation • Considered Compounds: • Sulfate Black Organic Sea Salt Mineral Dust Carbon Carbon • Resolve aerosol size-distribution by 7 log-normal modes • Three modes are composed of solely one aerosol component • Four modes are internal mixtures of several components

27. Aerosol-Cloud interactions Glaciation Indirect Aerosol Effect Warm Indirect Aerosol Effects Cloud albedo + _ Cloud cover and lifetime Lohmann et al. (1999) Lohmann (2002) + - _ Precipitation _ + + + Cloud droplets Mixed particles Ice crystals + + Cloud nuclei Aerosols Ice nuclei + + + Aerosol Microphysics Chemistry Emissions Aerosol Dynamics

28. Challenges for the Future • Address fundamental uncertainties in our understanding of aerosol microphysics, chemical composition of aerosols, aerosol-cloud interactions, indirect climate effects, etc. • Assess in particular the role of organic aerosols. • Develop appropriate field campaigns, laboratory experiments, and physical models to provide the basic knowledge required to study the aerosol climate interactions. • Investigate the role of aerosols in the earth system: impact on the biosphere, on the ocean, the carbon cycle, etc.

29. StratosphericOzone Lightning (NOx) Chemistry Transport NO2 OH VOC RO2 NO HO2 Emissions (NOx, VOC, CO, CH4) Deposition (O3, HNO3, NOx, ...) Feedbacks in tropospheric chemistry aerosol Tropospheric Ozone Winds,Temperature Humidity Climate Change

30. %-change Year 2100 (A2), climate as in year 2000 Climate – Chemistry Feedbacks %-change Year 2100 (A2) Climate of year 2100 No climate change Surface ozone changes 2000-2100 (A2) With climate change

31. Challenges for the Future • Better quantify the role of the changing oxidizing power of the atmosphere on the climate system. • Better determine through which processes climate change could affect air quality. • Better assess the role of the biosphere and of the UTLS region in the chemistry-climate interactions. • Make full use of space observations to better understand the processes affecting chemical compounds in the atmosphere, and to better quantify their budget.

32. Introducing Life in Earth System Models

33. Introducing Life into Earth System Models • To develop a modelling system for the biosphere, in its broadest terms, which can represent in functional form how it is influenced by, and itself influences, human activities and the climate system • To establish a modelling framework that allows such a modelling system to be fully coupled with the physical system.

34. Atmospheric CO2 concentration at Mauna Loa

35. Pre-industrial carbon fluxes (positive upward) January July uptake release [gC/day m2]

36. Difference in carbon uptake between experiments (with minus without carbon cycle - climate feedback) [kgC / m2] 2100 positive feedback negative feedback

37. Enhanced atmospheric CO2 due to Carbon-Climate Feedback Atmospheric CO2 Feedback C4MIP (IGBP/AIMES)

38. Carbon cycle Nitrogen cycle Atm CO2 Internal (fast) External (slow) photosynthesis denitrification Plant N deposition assimilation respiration litterfall & mortality Soil Mineral N Litter / CWD N fixation decomposition mineralization Soil Organic Matter N leaching

39. Methane allocation and transport O2  E M I S S I O N  aerobic horizon o x i d a t i o n water table level vascular transport micro-aerobic horizon ebullition diffusion root oxidation rootexudation entrapped gas bubbles anaerobic horizon dissolved CH4 gaseous CH4 m e t h a n o - g e n e s i s acetate, CO2, H2 decomposition of dead organic matter

40. Challenges for the Future • Better quantify the different processes affecting the global carbon cycle. • Study biogeochemistry of the land-atmosphere interface, and couple it to the hydrological cycle, human perturbations, and climate changes • Couple the carbon cycle with other biogeochemical cycles (e.g., nitrogen). • Consider a potential positive methane climate feedback

41. Introducing the Human Dimension

42. The Fourth Grand Challenge: Including Social feedbacks Human perturbation Earth System Model Human impact

43. The Fourth Grand Challenge: Including Social feedbacks Human perturbation Earth System Model Human impact Land use Water use Energy production and consumption Population growth Economic growth Structure of the economy Human health

44. Part 3:Research for Tomorrow

45. Hot Topics for Future Research • Interfaces between components of the Earth System. • Global water and biogeochemical cycles • Hot spots and teleconnections in the Earth System • Integrated interdisciplinary regional studies (inc. social systems) • Integration of scales: from nano-processes to global evolution. • Research towards operational systems for monitoring, and predicting the evolution of the Earth System on different timescales (data assimilation).

46. Geo-engineering strategies • Space mirrors, (Wood, Angel) • High Altitude Sulphur injections • Seeding stratocumulus clouds to brighten clouds • Sequestration of CO2 • Iron Fertilization, ... We are not proposing that geo-engineering be carried out! We are proposing that the implications should be carefully explored. Phil Rasch NCAR

47. NCAR Add sulfate at a rate of 0.5 Pinatubo/yr

48. Seamless Prediction: from Weather Processes to Climate Projections • Weather forecast: scale of days, deterministic time evolution of individual synoptic systems must be predicted as an initial value problem. • Climate projection: Changes in radiative forcing and coupled interactions and feedbacks are critical. • Theseamless predictionexplicitly recognizes the importance and potential benefit of • better representation of weather-climate link: • initialization of the climate system (ocean, soil moisture).

49. Scientific Basis for Decadal Prediction Perturbed ensemble members evolve coherently for two decades Courtesy of Tom Delworth

50. The Need for a Systems Approach to Climate Observations The imperative is to build an observing and information system to better plan for the future. • A climate information system • Observations: forcings, atmosphere, ocean, land • Analysis: comprehensive, integrated, products • Assimilation: model based, initialization • Attribution: understanding, causes • Assessment: global, regions, impacts, planning • Predictions: multiple time scales • Decision Making: impacts, adaptation Trenberth et al. (2002; 2006)