© Vipin Kumar UCC –Aug 15, 2011 NSF Expeditions in Computing Understanding Climate Change: A Data Driven Approach Vipin Kumar University of Minnesota

© Vipin Kumar UCC - Aug 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 Key outstanding science challenge: Actionable predictive insights to credibly inform policy Russia Burns, Moscow Chokes NATIONAL GEOGRAPHIC, 2010 The Vanishing of the Arctic Ice cap ecology.com, 2008

© Vipin Kumar UCC - Aug Understanding Climate Change - Physics based Approach General Circulation Models: Mathematical models with physical equations based on fluid dynamics Figure Courtesy: ORNL Anomalies from (K) Parameterization and non-linearity of differential equations are sources for uncertainty ! Cell Clouds Land 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) 4 th Assessment Report. A1B: “integrated world” balance of fuels A2: “divided world” local fuels B1: “integrated world” environmentally conscious IPCC (2007)

© Vipin Kumar UCC - Aug Understanding Climate Change - Physics based Approach General Circulation Models: Mathematical models with physical equations based on fluid dynamics Figure Courtesy: ORNL Anomalies from (K) Parameterization and non-linearity of differential equations are sources for uncertainty ! Cell Clouds Land Ocean “The sad truth of climate science is that the most crucial information is the least reliable” (Nature, 2010) 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 Disagreement between IPCC models

© Vipin Kumar UCC - Aug 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 "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.

© Vipin Kumar UCC - Aug NSF Expedition: Understanding Climate Change - A Data-Driven Approach 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 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) “... data-intensive science [is] …a new, fourth paradigm for scientific exploration. " - Jim Gray Project aim: A new and transformative data-driven approach that complements physics- based models and improves prediction of the potential impacts of climate change

© Vipin Kumar UCC - Aug 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.

© Vipin Kumar UCC - Aug 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)

© Vipin Kumar UCC - Aug 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

© Vipin Kumar UCC - Aug 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

© Vipin Kumar UCC - Aug 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.

© Vipin Kumar UCC - Aug 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

© Vipin Kumar UCC - Aug 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

© Vipin Kumar UCC - Aug 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

© Vipin Kumar UCC - Aug 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? (now) 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

© Vipin Kumar UCC - Aug 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

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