Breakout Report Global Change Prediction for Disaster Prevention/Mitigation

Breakout ReportGlobal Change Prediction for Disaster Prevention/Mitigation James Hack Jack Fellows Dag Lohmann BudhuBhaduri Kate Evens Ben Preston (in spirit) Dr. Hirofumi Tomita Dr. Takemasa Miyoshi Dr. MichioKawamiya

Discussion • Substantial discussion of model development directions including higher resolution, improved physics, innovative applications of data assimilation techniques for deterministic forecasting, and mechanisms for sharing and distributing simulation data • Discussion of HPC plans in Japan and the United States and how this was pacing traditional modeling efforts • Discussion of special topics related to risk assessment and geospatial work to understand population pressures, movement, and human built infrastructure

Global Modeling Complexity has Evolved with Improvements in Computational Capabilities Computational Capability Regional climate variability, Sea Level Rise, Societal Interactions, Uncertainty Quantification Exascale Atmopsheric & Ocean Eddy Motion Field, Reduced Microphysical, Chemical, Biogeochemical Processes Petascale Chemistry, Vegetation Resolution/System Complexity Carbon Cycle, Simple Aerosols Terascale Sulphate forcing, dynamical ocean “Swamp” Ocean Gigascale Simple Land, Ice, Cloud Models Megascale Simplified hydrological cycle & CO2 2015 2025 2005 1985 1995 1975 Simulation Capabilities/Fidelity

Environmental Disasters Come in All Forms

New York Magazine

The challenge of motion/time scales • 12 orders of magnitude from planetary scale to micrometer scales of motion

Examples of climate change consequences • Water Resources • management and maintenance of existing water supply systems, development of flood control systems and drought plans • Agriculture and food security • Erosion control, dam construction (irrigation), optimizing planting/harvesting times, introduction of tolerant/resistant crops (to drought, insect/pests, etc.) • Human health • Public health management reform, improved urban and housing design, improved disease/vector surveillance and monitoring • Terrestrial ecosystems • Improvement of management systems (deforestation, reforestation,…), development/improvement of forest fire management plans • Coastal zones and marine ecosystems • Better integrated coastal zone planning and management • Human-engineered systems • Better planning for long-lived infrastructure investments

Emerging goal in “Big Data” Enable fundamentally new methods of scientific discovery by building stronger collaborations with experimental facilities as well as DOE offices that have large computation and data science challenges. • There is a growing class of large data science problems where the volume and velocity of the data require the computational and data resources only available at the LCF. • Coupling of simulation and experiment: The big data generated by large science experiments will be fed directly to the Leadership computer simulations and the output used to drive the experiment in a feedback loop that has the potential to revolutionize discovery. • Analysis and data exploration of huge volumes of disparate data from sensors, satellites, and experimental data will require LCF computers with the largest amounts of internal memory of any computers in the world. • As generators of big data from simulations, the LCF will be responsible for the protection and collaborate with the data creators to disseminate this data to scientists around the world through data portals or other means.

Our task was more constrained in scope, but touches many of the issues in the climate house Budhu put the Disaster Prevention/Mitigation Topic in a workable framework Preparedness – Response – Recovery It’s also useful to try to put issues in a financial context

How do we enable your discipline to develop applications scalable to the exascale using co-design techniques? • An extreme version of what we call application readiness primarily by working with the entire software stack • Scalability project: goal to improve application scaling & performance • HW: cpu performance as well as efficient data movement; extremely important to explore tradeoffs, particularly from the methods point of view in applications • SW: reconsider assumptions driving specific methods and implementation (e.g., double precision arithmetic) Need for kernals, compact apps, to full applications for evaluation • There are opportunities to influence the software stack where one important goal could be support of interoperable codes • What standards need to be emphasized? How can we team to influence the development and adoption of standards

Given scalable exascale applications, what scientific outcomes would be expected in 2020+ timeframe? • Global simulation frameworks that will more realistically capture major modes of variability including robust statistics of climate extremes • Numerical Weather Prediction skill will improve allowing greater fidelity and longer time scales for preparedness/response/recovery to extreme events • ensembles will play a major role in all improvements in predictive skill • Complete biogechemical component capable of accurately describing the fate of carbon in the climate system (traditional greenhouse gases, ozone, …) • More realistic representation of the global water cycle down to regional scales Precipitation regimes (e.g., monsoons) Frequency and intensity of precipitation Seasonal variability in precipitation

Given scalable exascale applications, what scientific outcomes would be expected in 2020+ timeframe? • Hurricane/typhoon resolving capabilities to accurately represent the statistics of frequency, intensity, and correlation with landfall events • Important to risk management • Should be able to explore the use of a high resolution global model, coupled to a regional/local scale hazard model, coupled to a spatially explicit land surface model, with a nested agent based model to capture human behavior, using a Monte Carlo approach mode to quantify uncertainties at each step, you would have a sufficiently large computational challenge for exascale capabilities • changing climate <=> land cover/use <=> human migration • Improvements in risk management for human built infrastructure • improved access to environmental analytics across a wide range of economic impacts

What new emerging opportunities do you see in the exascale era? • Workflows that enable uncertainty quantification as part of the development process • Comprehensive multi-variate optimization, with formal parametric uncertainty estimates and characterization of error propagation

Model-Observation Integration • Identify the scientific problems that would benefit from daily (or regular) Large Eddy Simulation (LES), single column modeling (SCM) and perhaps cloud resolving modeling (CRM) • Explore ways to maximize the benefits of regular LES/SCM/CRM, confronted with observations • Drive measurement and modeling strategies and needs to advance specific science problems Courtesy P Siebesma, KNMI Why? • Subgrid variability for the thermodynamic variables needs to be taken into account in any GCM for parameterizations of convection, clouds and radiation in a consistent way. • Large Eddy Simulations (LES) in combination with observations is a useful tool to obtain this subgrid variability and to help develop GCM parameterizations for these cloud related processes.

What new emerging opportunities do you see in the exascale era? • Global supply chain interdependencies • Ability to conduct comprehensive optimized explorations of food, water, energy, health, transportation, etc. dependencies • Dynamic monitoring of Earth system resources • Energy, water, critical infrastructure, human conditions • Understand the stresses, abundances, etc.

Outline a plan for sustaining these teams and collaborations through 2020+. Incomplete

Breakout Report Global Change Prediction for Disaster Prevention/Mitigation