1. Mining Climate and Ecosystem DataNSF RSV June 2010 1 l Team l Motivation l Transformative Computer Science Research – Predictive Modeling – Complex Networks – Association Analysis – High Performance Computing l Evaluation Plan l Relationship to Physics Based Models l Management and Collaboration Mining Climate and Ecosystem Data

2. Mining Climate and Ecosystem DataNSF RSV June 2010 2 Vipin Kumar, UM Auroop Ganguly, UTK/ORNL Nagiza Samatova, NCSU Arindam Banerjee, UM Fred Semazzi, NCSU Joe Knight, UM Shashi Shekhar, UM Peter Snyder, UM Jon Foley, UM Alok Choudhary, NW Abdollah Homiafar, NCA&T Michael Steinbach, UM Singdhansu Chatterjee, UM Team

3. Mining Climate and Ecosystem DataNSF RSV June 2010 3 Team members are well-recognized experts in Analysis of ecosystem and climate data Foley, Ganguly, Kumar, Semazzi, Snyder, Steinbach Data mining, machine learning, nonlinear dynamics & signal processing Banerjee, Chatterjee, Choudhary, Ganguly, Homaifar, Kumar, Samatova, Shekhar, Steinbach High performance computing Choudhary, Kumar, Samatova Weather and climate models Ganguly, Foley, Semazzi, Snyder Remote sensing and land cover change Foley, Knight, Snyder Team Qualifications

4. Mining Climate and Ecosystem DataNSF RSV June 2010 4 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 unavoidabale” Mitigation: “Avoid the unmanageable” The societal cost of both action and inaction is large Key outstanding science challenge: Actionable predictive insights to credibly inform policy Figure Courtesy: ORNL Anomalies from 1880-1919 (K)

5. Mining Climate and Ecosystem DataNSF RSV June 2010 5 Physics-based Models are Essential but Not Adequate “The sad truth of climate science is that the most crucial information is the least reliable” (Nature, 2010) Regional hydrology (“P–E” changes in 2030s) exhibits large variations among major IPCC model projections Disagreement between IPCC models l Models make relatively reliable predictions at global scale for ancillary variables: – Sea Surface Temperature (SST) – Temperature/humidity profiles over land – Wind spread at different heights l They provide least reliable predictions for variables that are crucial for impact assessment: – Regional precipitation and extremes – Hurricane intensity and frequency – Droughts and floods We need a systematic approach to semi-automatic data-driven model inference. Hypothesis-driven “manual” conceptual models try to address this gap: l Hurricane models (Emanuel et al, BAMS, 2008) l Regional-scale precipitation extremes (O’Gorman & Schneider, PNAS, 2008; Sugiyama et al, PNAS, 2010)

6. Mining Climate and Ecosystem DataNSF RSV June 2010 6 Data-Driven Knowledge Discovery in Climate Science l 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 l Data-guided insights can complement physics-based models – Transform global ancillary information to regional critical climate changes and extremes – Assess reliability of model projections and inform physics model parameterizations –Validate predictions with both held-out data and science understanding –Provide relatively hypothesis-free discovery processes to supplement hypothesis- guided data analysis "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

7. Mining Climate and Ecosystem DataNSF RSV June 2010 7 Global Sea Surface Temperature Challenges in Analyzing Eco-Climate Data

8. Mining Climate and Ecosystem DataNSF RSV June 2010 8 Discovering teleconnections Global Sea Surface Temperature Challenges in Analyzing Eco-Climate Data El Nino Events Nino 1+2 Index

9. Mining Climate and Ecosystem DataNSF RSV June 2010 9 Discovering teleconnections Global Sea Surface Temperature Challenges in Analyzing Eco-Climate Data El Nino Events Nino 1+2 Index

10. Mining Climate and Ecosystem DataNSF RSV June 2010 10 Discovering teleconnections Global Sea Surface Temperature Changes in Global Forest Cover El Nino Events Nino 1+2 Index Challenges in Analyzing Eco-Climate Data

11. Mining Climate and Ecosystem DataNSF RSV June 2010 11 El Nino Events Nino 1+2 Index Discovering teleconnections Global Sea Surface Temperature Changes in Global Forest Cover Relationship between El Nino and Fires in Indonesia Challenges in Analyzing Eco-Climate Data

12. Mining Climate and Ecosystem DataNSF RSV June 2010 12 Challenges in Analyzing Eco-Climate Data Discovering teleconnections Global Sea Surface Temperature El Nino Events Nino 1+2 Index Changes in Global Forest Cover Relationship between El Nino and Fires in Indonesia Challenges due to data characteristics Spatiotemporal, non-stationary, non-i.i.d. Nonlinear multiscale dependencies Low frequency variability Massive data sets Long range spatial dependencies Long memory temporal dependencies …

13. Mining Climate and Ecosystem DataNSF RSV June 2010 13 Transformative Computer Science Research 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 kernels, features, dependencies Relationship Mining Enable discovery of complex dependence structures such as non linear associations or long range spatial dependencies Nonlinear, spatio-temporal, multi- variate, persistence, long memory Predictive Modeling Enable predictive modeling of typical and extreme behavior from multivariate spatio-temporal data Nonlinear, spatio-temporal, multivariate Complex Networks Enable studying of collective behavior of interacting eco- climate systems Nonlinear, space-time lag, geographical, multi-scale relationships Community structure- function-dynamics Enabling large-scale data-driven science for complex, multivariate, spatio-temporal, non-linear, and dynamic systems: Fusion plasma Combustion Astrophysics …. The Expedition is an end-to-end demonstration of this major paradigm for future knowledge discovery process.

14. Mining Climate and Ecosystem DataNSF RSV June 2010 14 Relationship Mining Objective To develop methods for discovery of complex dependence structures (e.g., nonlinear associations, long-range spatial dependencies) 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 Dynamic behavior of the high and low pressure fields corresponding to NOA climate index (Portis et al, 2001)

15. Mining Climate and Ecosystem DataNSF RSV June 2010 15 Complex Networks 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 20 th century great climate shifts from the collective behavior of interacting subsystems Node degree in a correlation-based climate network 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

16. Mining Climate and Ecosystem DataNSF RSV June 2010 16 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 Objectives 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 Correlated Gaussian Processes for multivariate spatiotemporal regression w/ nonlinear dependencies Computer Science Innovations 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 Impact = + GP 1 GP 2 Multivariate quantiles for extreme value analysis

17. Mining Climate and Ecosystem DataNSF RSV June 2010 17 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) Impact Analytical kernels that will scale many data mining algorithms Analytics algorithms designed for effective exploration of complex spatio- temporal dependence structures 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 An architecture with accelerators such as GPUs. Some/all nodes have accelerators and many cores.

18. Mining Climate and Ecosystem DataNSF RSV June 2010 18 Synergy with Physical Modeling Community 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 Prominent climate science expertise on the team Foley, Ganguly, Semazzi, and Snyder Collaborators: Potter (NASA), Erickson (ORNL) Active involvement in the IPCC assessment reports Strong Ties w/ Climate Community Partnership with ORNL, NCAR, LLNL, LBL climate modeling and simulated data archival groups: Community Climate System Model (NCAR, ORNL, LBL), Earth Systems Grid, IPCC data archives (PCMDI at LLNL and HPSS at ORNL), Climate data analysis and extremes (ORNL, LBL) Partnership with NASA, ORNL, NOAA observed and reanalysis data archival and analysis groups: Remote sensed satellite and other data (NASA), in situ sensors like DOE's ARM (ORNL, PNNL), Reanalysis and observed data assimilation (NOAA)

19. Mining Climate and Ecosystem DataNSF RSV June 2010 19 Does the proposed research enable significantly better data analysis than before or enable new kinds of analysis? Measures of Success in CS Research l 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 l 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 l 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 l 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

20. Mining Climate and Ecosystem DataNSF RSV June 2010 20 l Ultimate success is to have data-driven analysis included as a standard part of climate projections and impact assessment (e.g., for IPCC). l 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 l Specific use cases will provide the context for these evaluations Climate-based Measures of Success

21. Mining Climate and Ecosystem DataNSF RSV June 2010 21 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? 19502010 (now)21001870 Train Model Forecast & Compare w/ Observation Data Reanalysis data Observed data Model projection data Hindcast Prediction Forecast & Multi- model Comparison Observed data Illustrative Example for Hurricane Frequency Use Case

22. Mining Climate and Ecosystem DataNSF RSV June 2010 22 Conceptual View: Driving Use-Cases Data Challenges Characterize impacts of sea surface temperature on hurricane frequency and intensity Project climate extremes at regional and decadal scales CS Technologies Yr1 Yr2 Yr3 Yr4 Yr5 Yr5+ Non-stationary non-i.i.d Multivariate Non-linear Long-memory, long- range, multiscale Networks for multivariate nonlinear space- time interactions Understand relationships between fire size and frequency to precipitation, land cover, ocean temperature, etc. Identify drought and pluvial event triggers to improve prediction Understand and quantify the uncertainty of feedback effects in climate Multivariate extreme value theory based on geom./prob. quantiles Co-design-based scalable analytics of complex dependence structures Nonparametric multivariate spatio- temporal probabilistic regression models Association analysis- based interplay between complex dependence structures Heterogeneous Characterize feedbacks contributing to abrupt climate regime changes

23. Mining Climate and Ecosystem DataNSF RSV June 2010 23 23 CS Technology View: Deliverables Roadmap Extreme value theory based on geom./prob. quantiles Year 1-2Year 3-4 Year 5-5+ Community software, benchmarks outreach Uncertainty quantification in single regime-shift detection Attribution in multi- regime-shifts Multivariate network construction Dynamic community detection & tracking Multivariate spatio-temporal regression, Climate state estimation Handling nonlinear spatio- temporal interactions Scalable parallel analysis kernels Graph perturbation theory for “what-if” exploration Identify drought and pluvial event triggers to improve prediction Data driven analysis as a standard (e.g. in IPCC) Impact of SST on hurricane intensity & frequency A notion of “transactions” in spatio-temporal data Comparative multiple network analysis Adapt association analysis to continuous data Detection of nonlinear associations/relationships Statistical significance assessment of relationships Parallel I/O techniques to exploit memory hierarchy Co-design scalable complex dependence structures

24. Mining Climate and Ecosystem DataNSF RSV June 2010 24 Use Case: Hurricane Frequency 1. Pre-process ancillary climate model outputs Steps for Discovery of Multivariate Non-linear Interactions Yr1: UTK/ORNL, UMN, NWU Steps for Predictive Modeling of Hurricanes SST impact on hurricane frequency & intensity IPCC AR4 Models: CMIP3 datasets Monthly mean sea surface temperature Monthly mean atmospheric temperature Daily horizontal wind at 250/850 hPa 2. Construct multivariate nonlinear climate network 3. Detect & track communities Yr1.5: NCSU, UMN, NWU 3. Find non-linear relationships Yr1.5: UMN, UTK/ORNLYr2: UMN, UTK/ORNL 4. Validate w/ hindcasts Yr2.5: UMN, UTK/ORNL 5. Determine non-stationary non-i.i.d. climates states & Build hurricane models

25. Mining Climate and Ecosystem DataNSF RSV June 2010 25 Use Case: Regional Precipitation Step1: Conceptual physics models (O’Gorman and Schneider 2009) and relationship mining identify variables in 3D (space, time, vertical) neighborhoods with information relevant for predicting precipitation Step 2: Precipitation mean and extremes are projected with ancillary variables in 3D neighborhoods with predictive modeling Step 3: Complex networks are constructed over oceans using relationship mining Step 4: Complex networks develop proxies for global and regional scale ocean dynamics leading to set of potential predictors Step 5: Teleconnections are developed to predict regional precipitation change and their extremes based on both relationship mining and predictive modeling Step 6: The (Step 2) 3D neighborhood-based predictions are combined with teleconnction- based predictions with fusion of predictive modeling Step 7: Regional precipitation prediction gains are run through cross-validation and interpreted with climate science

26. Mining Climate and Ecosystem DataNSF RSV June 2010 26 From Schubert et al. (2004) Use Case: 1930s Dust Bowl 1930s Dust Bowl Drought Affected almost two-thirds of the U.S. Centered over the agriculturally productive Great Plains Drought initiated by anomalous tropical SSTs (Teleconnections ) NASA NSIPP Model: 14 C20C datasets Ensemble of 14 100-year simulations of the 20 th Century Forced with observed monthly SSTs Allows for assessment of how much the SSTs control Great Plains climate variations Time series of precipitation anomalies averaged over the Great Plains region for 14 ensemble members of C20C run. Anomalous Tropical SSTs explain most of the 20 th Century drought events. Global SST anomalies averaged over 1932-1938. Boxes represent sub- regions used to identify the relative importance of SST anomalies in initiating the the Dust Bowl drought. However, the abrupt change in the precipitation regime and decade-long persistence of drought thought to be related to strong land- atmosphere feedbacks (e.g., precipitation recycling) Information content in C20C model simulations along with ancillary observations can be used to identify and predict drought “triggers” and the contribution of complex feedbacks to abrupt precipitation regime changes and drought persistence

27. Mining Climate and Ecosystem DataNSF RSV June 2010 27 Use Case: Regime Shift in Sahara & Sahel Regime Shift in Sahara Sudden shift from `Green Sahara’ to `Desert Sahara’ Sahara consisted of extensive vegetation, lakes, wetlands according to Geologic data. Sudden transition from vegetated to desert conditions around 5500 years ago Precipitation patterns are tightly correlated with vegetation cover patterns. Northern Africa is dominated by the Sahara. The transition between the Sahara and the savanna/tropical forest to the south occurs in the Sahel. Vegetation cover and precipitation patterns Regime Shift in Sahel Sahel is the transition zone between the Sahara to the north and the tropical savanna and evergreen forests to the south Onset of major 30-year drought over the Sahel region in 1969 Sahel drought led to widespread famine, ecosystem degradation, and dispersion of its inhabitants Such sudden, large change in environmental–or “regime shift”—often occurs without advance warning. Underlying conditions may predispose a system to a regime shift. A fairly small event, e.g., storm, drought, etc., may trigger the shift to a new stable state. Key Questions stay unanswered: -- What were underlying conditions? -- What triggered the regime shift? Possible hypothesis: Land-atmosphere feedback process Reduction in Precipitation Reduction in Vegetation + Sahel zone Data analysis for hypothesis generation, testing: Complex system with more than one stable state.