NSF Expeditions in Computing Understanding Climate Change: A Data Driven Approach

1. NSF Expeditions in ComputingUnderstanding Climate Change:  A Data Driven Approach Vipin Kumar University of Minnesota kumar@cs.umn.edu www.cs.umn.edu/~kumar

2. Climate Change: The defining issue of our era • The planet is warming • Multiple lines of evidence • Credible link to human GHG (green house gas) emissions • Consequences can be dire • Extreme weather events, regional climate and ecosystem shifts, abrupt climate change, stress on key resources and critical infrastructures • There is an urgency to act • Adaptation: “Manage the unavoidable” • Mitigation: “Avoid the unmanageable” • The societal cost of both action and inaction is large Russia Burns, Moscow Chokes NATIONAL GEOGRAPHIC, 2010 The Vanishing of the Arctic Ice cap ecology.com, 2008 Key outstanding science challenge: Actionable predictive insights to credibly inform policy

3. Understanding Climate Change - Physics based Approach General Circulation Models: Mathematical models with physical equations based on fluid dynamics Cell Parameterization and non-linearity of differential equations are sources for uncertainty! Clouds Land Anomalies from 1880-1919 (K) Ocean Projection of temperature increase under different Special Report on Emissions Scenarios (SRES) by 24 different GCM configurations from 16 research centers used in the Intergovernmental Panel on Climate Change (IPCC) 4th Assessment Report. Figure Courtesy: ORNL A1B: “integrated world” balance of fuels A2: “divided world” local fuels B1: “integrated world” environmentally conscious IPCC (2007)

4. Understanding Climate Change - Physics based Approach General Circulation Models: Mathematical models with physical equations based on fluid dynamics Disagreement between IPCC models Cell Parameterization and non-linearity of differential equations are sources for uncertainty! Clouds Land Anomalies from 1880-1919 (K) Ocean Physics-based models are essential but not adequate • Relatively reliable predictions at global scale for ancillary variables such as temperature • Least reliable predictions for variables that are crucial for impact assessment such as regional precipitation Regional hydrology exhibits large variations among major IPCC model projections Figure Courtesy: ORNL “The sad truth of climate science is that the most crucial information is the least reliable” (Nature, 2010)

5. Data-Driven Knowledge Discovery in Climate Science • From data-poor to data-rich transformation • Sensor Observations: Remote sensors like satellites and weather radars as well as in-situ sensors and sensor networks like weather station and radiation measurements • Model Simulations: IPCC climate or earth system models as well as regional models of climate and hydrology, along with observed data based model reconstructions "The world of science has changed ... data-intensive science [is] so different that it is worth distinguishing [it] … as a new, fourth paradigm for scientific exploration." - Jim Gray • Data guided processes can complement hypothesis guided data analysis to develop predictive insights for use by climate scientists, policy makers and community at large.

6. NSF Expedition: Understanding Climate Change - A Data-Driven Approach Project aim: A new and transformative data-driven approach that complements physics-based models and improves prediction of the potential impacts of climate change • “... data-intensive science [is] …a new, fourth paradigm for scientific exploration." - Jim Gray Transformative Computer Science Research • Science Contributions • Data-guided uncertainty reduction by blending physics models and data analytics • A new understanding of the complex nature of the Earth system and mechanisms contributing to adverse consequences of climate change • Success Metric • Inclusion of data-driven analysis as a standard part of climate projections and impact assessment (e.g., for IPCC) Predictive Modeling Enable predictive modeling of typical and extreme behavior from multivariate spatio-temporal data Relationship Mining Enable discovery of complex dependence structures: non-linear associations or long range spatial dependencies Complex Networks Enable studying of collective behavior of interacting eco-climate systems High Performance Computing Enable efficient large-scale spatio-temporal analytics on exascale HPC platforms with complex memory hierarchies

7. Transformative Computer Science Research Enabling large-scale data-driven science for complex, multivariate, spatio-temporal, non-linear, and dynamic systems: • Fusion plasma • Combustion • Astrophysics • …. Complex Networks Enable studying of collective behavior of interacting eco-climate systems The Expedition is an end-to-end demonstration of this major paradigm for future knowledge discovery process. Nonlinear, space-time lag, geographical, multi-scale Predictive Modeling Enable predictive modeling of typical and extreme behavior from multivariate spatio-temporal data Relationship Mining Enable discovery of complex dependence structures such as non linear associations or long range spatial dependencies relationships Community structure- function-dynamics kernels , features, dependencies Nonlinear, spatio-temporal, multivariate Nonlinear, spatio-temporal, multi-variate, persistence, long memory High Performance Computing Enable efficient large-scale spatio-temporal analytics on future generation exascale HPC platforms with complex memory hierarchies Large scale, spatio-temporal, unstructured, dynamic

8. Objective To develop methods for discovery of complex dependence structures (e.g., nonlinear associations, long-range spatial dependencies) Relationship Mining Dynamic behavior of the high and low pressure fields corresponding to NOA climate index (Portis et al, 2001) Computer Science Innovations • Define a notion of “transactions” for association analysis in spatio-temporal data • Adapt association analysis to continuous data • Reduce the number of redundant patterns and assess statistical significance of the identified patterns • Define a new approach to association analysis that trades off completeness for a smaller, simpler set of patterns and far smaller computational requirements Impact • Facilitate discovery of complex dependence structures in dynamic systems • Understand relationships between fire size and frequency to precipitation, land cover, ocean temperature, etc • Characterize impacts of sea surface temperature on hurricane frequency and intensity • Understand feedback effects, the key uncertainty in climate science

9. Complex Networks Objectives • To develop algorithms for characterization of network structure, function, and dynamics • To enable large-scale comparative analysis of climate networks to increase prediction confidence • Node degree in a correlation-based climate network Computer Science Innovations • Construct multivariate networks to capture nonlinear spatio-temporal interactions • Characterize network dynamics at multiple spatial scales over different time periods • Scale graph mining algorithms to the required size and number for comparative analysis of multiple real-world networks Impact • Apply network structure-function-dynamics methods to complex systems • Understand intricate interplay between topology and dynamics of the climate system over many spatial scales • Explain the 20th century great climate shifts from the collective behavior of interacting subsystems

10. To advance nonparametric multivariate spatio-temporal probabilistic regression models to incorporate nonlinear dependencies To develop latent variable models to characterize spatio-temporal climate states To advance multivariate extreme value theory through geometric and probabilistic generalizations of quantiles Alok Choudhary, NWU Nagiza Samatova, NCSU Vipin Kumar, UMN Predictive Modeling Objectives Correlated Gaussian Processes for multivariate spatiotemporal regression w/ nonlinear dependencies + = Multivariate quantiles for extreme value analysis Impact Computer Science Innovations GP1 GP2 • Uncertainty quantified predictions of climate variables, e.g., precipitation, over space-time • Abrupt climate change detection, typical climate characterization • Modeling regional climate change and extreme climate events, e.g., hurricanes • Applications beyond climate data, e.g., finance, healthcare, bioinformatics, networks • Take into account nonlinear spatial, temporal, and multivariate dependencies • Minimize assumptions on temporal evolution, e.g., no`Markov’ assumptions • Deal with curse of dimensionality in nonlinear extreme value analysis (recency, frequency, duration) and quantiles • Scale algorithms yet ensure uncertainty quantification

11. High Performance Computing Objectives • To investigate a “co-design” approach to scalable analysis of complex dependence structures • To develop parallel and scalable statistical and data mining functions and kernels • To develop sophisticated parallel I/O techniques that fully exploit complex memory hierarchy (disks, SSDs, DRAMs, accelerators) An architecture with accelerators such as GPUs. Some/all nodes have accelerators and many cores. Computer Science Innovations • The first“co-design” approach that will take into account exploration of nonlinear associations, long-range spatial dependencies, I/O requirements and optimizations, presence of accelerators and multiple suitable programming models for HPC systems • Novel optimizations to deal with data-and-compute intensive computations on exascale platforms with complex memory hierarchies Impact • Analytical kernels that will scale many data mining algorithms • Analytics algorithms designed for effective exploration of complex spatio-temporal dependence structures

12. CS and Climate Science Synergies Physics based models inform data mining More credible variables (e.g., SST)  more crucial variables (e.g., precipitations/hurricanes) Better modeled processes (e.g., atmospheric physics)  more crucial processes (e.g., land surface hydrology) Global and century scale  regional and decadal scale Data mining informs physics based models Better understanding of climate processes Improved insights for parametrization schemes

13. Measures of Success in CS Research Does the proposed research enable significantly better data analysis than before or enable new kinds of analysis? • Predictive Modeling • Improved capabilities for multivariate spatio-temporal regression that can simultaneously capture the dependencies between spatial-temporal objects (e.g., temperature, pressure) and help find novel climate patterns not found by standard approaches • New capabilities for detecting extreme events using quantiles and quantile regression for multivariate data • Complex Networks • Detection of known climate patterns and tracking of the evolution of climate patterns in time using complex networks that capture non-linear relationships in multivariate and multiscale data • Relationship Mining • Creation of entirely new capabilities in association mining from approaches that extend / modify traditional approaches to handle spatio-temporal data • Creation of a completely novel approach for association analysis that reduces the number of patterns and the time required to find them • Improvement in the performance of complex networks and predictive models by using nonlinear relationships that more faithfully represent reality • High Performance Computing • Scalable analytics code for spatio-temporal data • Enabling of large scale data driven science that serves as a demonstration of the value of the data driven paradigm

14. Climate-based Measures of Success • Ultimate success is to have data-driven analysis included as a standard part of climate projections and impact assessment (e.g., for IPCC). • Achieving this will require measuring and demonstrating success in two key areas • Filling critical gaps in climate science (Nature, 2010) • Reduction of uncertainty in climate predictions at regional and decadal scales • Improved predictive insights for key precipitation processes • Providing credible assessments of extreme hydro-meteorological events • New knowledge about climate processes (e.g., teleconnection patterns) • Improve climate change impact assessments and inform policy • Distinguishing between natural and anthropogenic causes • Improved assessments of climate change risks across multiple sectors • Specific use cases will provide the context for these evaluations

15. Science Impact View: Evaluation Methodology • Can we improve the projection (e.g., hurricane frequency, intensity)? • Can the data-driven model identify: • which regional climate variables are informative/causal (e.g., SST, wind speed)? • what is the relationship (+/- feedback) between these variables? • To what extent does the data-driven model inform climate scientists? Forecast & Compare w/ Observation Data 1950 2100 1870 2010 (now) Train Model Forecast & Multi-model Comparison Hindcast Prediction • Reanalysis data • Observed data • Model projection data • Observed data Illustrative Example for Hurricane Frequency Use Case

16. CS Technologies Co-design-based scalable analytics of complex dependence structures Yr5+ Yr5 Nonparametric multivariate spatio-temporal probabilistic regression models Identify drought and pluvial event triggers to improve prediction Understand and quantify the uncertainty of feedback effects in climate Yr4 Project climate extremes at regional and decadal scales Multivariate extreme value theory based on geom./prob. quantiles Yr3 Understand relationships between fire size and frequency to precipitation, land cover, ocean temperature, etc. Association analysis-based interplay between complex dependence structures Yr2 Yr1 Characterize impacts of sea surface temperature on hurricane frequency and intensity Networks for multivariate nonlinear space-time interactions Long-memory, long-range, multiscale Heterogeneous Multivariate Non-linear Non-stationary non-i.i.d Conceptual View: Driving Use-Cases Characterize feedbacks contributing to abrupt climate regime changes Data Challenges

17. Example Driving Use Cases Regime Shift in Sahel Impact of Global Warming on Hurricane Frequency Onset of major 30-year drought over the Sahel region in 1969 Sahel zone Regime shift can occur without any advanced warning and may be triggered by isolated events such as storms, drought Validate w/ hindcasts Build hurricane models Find non-linear relationships El Nino Events 1930s Dust Bowl Discovering Climate Teleconnections Affected almost two-thirds of the U.S. Centered over the agriculturally productive Great Plains Drought initiated by anomalous tropical SSTs (Teleconnections) Nino 1+2 Index