Challenges for ENES Guy P Brasseur National Center

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

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

Fourier The Second Grand Challenge: Predicting Climate Change Arrhenius Manabe Svante Arrhenius quantifies in 1896 the changes in surface temperature (approx. 5 C) to be expected from a doubling in CO 2, based on the concept of ”glass bowl” effect introduced in 1824 by Joseph Fourier. Norman Phillips develops the first global atmospheric GCM, and the first climate models are being developed by many (Manabe, Mintz and Arakawa, Washington, etc. ) Arakawa Washington

The Conceptual Framework ESSL - The

Future Global Warming (IPCC Projections 2007)

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

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 Improved global datasets (climate, atmosphere, land oceans) and historic coverage Integration natural and human processes: a wealth of global change scenarios was developed (understanding a system is being able to predict its behavior)

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

Climate Change Epochs Before Attribute sources of historical warming Project range of possible non-mitigated future warming from SRES scenarios Quantify Climate Change Commitment IPCC AR 4 After • Project adaptation needs under various mitigation scenarios • Time-evolving regional climate change on and long-term timeframes • Quantify carbon cycle feedbacks short Conclusion: With the wide public acceptance of the IPCC AR 4 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

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

Intellectual Challenges: Scientific and Engineering To improve weather forecast, climate predictions, air pollution simulations, etc. and to better understand predict the functioning of the Earth System at the global 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 impacts of 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 mitigation and adaptation measures.

The Conceptual Framework ESSL - The Climate change Air pollution

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

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

Some challenges What are challenging scientific questions at the earth system level that the community needs to address? What are the best tools to address these questions? What type of information should we provide in support of mitigation and adaptation strategies? What kind of partnerships should we establish? What are the methodologies that need to be develop to link the natural and the social systems?

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

HPC dimensions of Climate Prediction New Science 10 Spatial Resolution (x*y*z) 1 Km 1000 Climate Model 10 400 Regular AMR 10 Earth System Model 10 10000 Better Science ESM+multiscale GCRM Code Rewrite ? 70 0. 2° 1. 4° 100 yr* 1000 yr* 22 km 160 km 20 min 3 min 500 Ensemble size 10 2010 Petascale 1000 yr * ? Cost Multiplier 10 10 (Years*timestep) Today Terascale 5 50 Timescale 2015 Exascale 10 Data Assimilation

DATA: Earth System Grid Center for Enabling Technologies (ESGCET) ESG Goals Current ESG Sites • Petabyte-scale data volumes • Globally federated sites • “Virtual Datasets” created through subsetting and aggregation • Metadata-based search and discovery • Bulk data access • Web-based analysis tool http: //www. earthsystemgrid. org access http: //www-pcmdi. llnl. gov • Increased flexibility and robustness For AR 5, ESG will be expanded to form a global virtual data center! From: Earth System Grid Center for Enabling Technologies: (ESG-CET)

Next Generation Physical Models

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. o o o 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.

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

Scale Interactions and Dynamical Modes Courtesy of Julia Slingo

Including Atmospheric Chemistry

Challenges for the Future Based on P. Cox, 2004 CLIMATE Direct and Indirect Effects / Feedbacks on natural sources Human Emissions Greenhouse Effect Heat island effect GREENHOUSE GASES AEROSOLS Oxidants: OH, H 2 O 2 HO 2, O 3 (Gas-phase) CHEMISTRY Human Emissions Fires: soot Mineral dust CH 4, O 3, N 2 O, CFC CO 2 Human Emissions N deposition 03, UV radiation ECOSYSTEMS Biogenic Emissions: CH 4, DMS, VOC’s Dry deposition: stomatal conductance LAND WATER / CITIES Damming / Irrigation / Emission of heat Land-use Change, Fires The future: a full treatment of climate-chemistry-ecosystem-land surface feedbacks

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 size distribution

HAM - Aerosol Representation Considered Compounds: Sulfate Black Carbon Organic Carbon Sea Salt Mineral Dust 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

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

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.

Feedbacks in tropospheric chemistry Stratospheric Ozone aerosol Chemistry VOC RO 2 OH HO 2 NO Lightning (NOx) Tropospheric Ozone Transport Winds, Temperature Humidity Emissions (NOx, VOC, CO, CH 4) Deposition (O 3, HNO 3, NOx, . . . ) Climate Change

%-change Year 2100 (A 2), climate as in year 2000 Climate – Chemistry Feedbacks %-change Year 2100 (A 2) Climate of year 2100 No climate change Surface ozone changes 2000 -2100 (A 2) With climate change

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.

Introducing Life in Earth System Models

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.

Atmospheric CO 2 concentration at Mauna Loa

Pre-industrial carbon fluxes (positive upward) January uptake July release [g. C/day m 2]

Difference in carbon uptake between experiments (with minus without carbon cycle - climate feedback) [kg. C / m 2] 2100 positive feedback negative feedback

Enhanced atmospheric CO 2 due to Carbon-Climate Feedback Atmospheric CO 2 Feedback C 4 MIP (IGBP/AIMES)

Carbon cycle Nitrogen cycle Atm CO 2 Internal (fast) photosynthesis External (slow) denitrification N deposition Plant assimilation respiration litterfall & mortality Litter / CWD decomposition Soil Organic Matter Soil Mineral N N fixation mineralization N leaching

Methane allocation and transport O 2 root exudation anaerobic horizon dissolved CH 4 acetate, CO 2, H 2 m e t h a n o - ebullition micro-aerobic horizon root oxidation diffusion aerobic horizon vascular transport E M I S S I O N water table level entrapped gas bubbles gaseous CH 4 genesis decomposition of dead organic matter

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

Introducing the Human Dimension

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

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

Part 3: Research for Tomorrow

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).

Geo-engineering strategies Space mirrors, (Wood, Angel) High Altitude Sulphur injections Seeding stratocumulus clouds brighten clouds to Sequestration of CO 2 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

NCAR Add sulfate at a rate of 0. 5 Pinatubo/yr

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. • The seamless prediction explicitly recognizes the importance and potential benefit of • better representation of weather-climate link: • initialization of the climate system (ocean, soil moisture).

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

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

Earth System Modeling

Thank You