Research Interests
Climate Model Development and Assessment
Climate Data
  • Extreme weather
  • Detection and characterization algorithms
  • Large-scale drivers of regional climate
  • Climate data mining
Multi-Resolution Regional Climate Modeling

The next century will see unprecedented changes to the climate system which will have significant repercussions on global human activity and international policy. The IPCC special report on extreme weather reports with confidence that the next century will see substantial warming, with a corresponding increase in regional temperature extremes and drought conditions, increases in the frequency of heavy precipitation events in wet areas, and increases in tropical cyclone wind speeds. These trends are of a broad global nature and do not necessarily reflect the influence of the changing global climate on regional scales, which are absolutely key for planning on the local, state and federal level. For this reason, an understanding of changing regional climate and its associated uncertainty is an unmet challenge that must be addressed in the coming decade.

Multi-resolution climate modeling is a cutting-edge technology, with efforts only recently directed towards support for multiple mesh scales within a single framework. It is also a timely endeavor, since demands for fine-scale resolution of atmospheric features have taxed the limits of our most powerful computing systems. However, even as multi-resolution software systems have moved forward, there has been a discernible lag in our understanding of exactly how these systems improve the representation of regional-scale behavior. Intuitive regional metrics which include both expectation and variability of seasonal precipitation and surface temperature, as well as the count and variability of extreme weather events, can be readily estimated by multi-resolution Earth modeling systems. These metrics are of considerable importance in informing regional-scale policy and implementation for agricultural planning, forest fire prevention, urban development, and many other relevant fields. A better understanding of the uncertainty present in the estimates of these metrics is significant for both improving our scientific understanding of the Earth system and lending credibility to the conclusions drawn from these models.

Mixed Finite-Element Methods for Modeling the Atmosphere

Atmospheric models consist of two components: a set of physical parameterizations, which are responsible for sub-grid-scale physics, and a dynamical core, which solves the grid-scale fluid equations. Atmospheric dynamical cores use a variety of methods from computational fluid dynamics to satisfy this role, including finite differences, finite volumes, spectral transform and, more recently, finite element methods (FEM). The greatest advantage of FEM is their use of a compact stencil, which enables them to be achieve optimal parallel performance on modern supercomputers with hundreds of thousands of processors. In addition, FEM can also have many desirable mathematical properties, including mass and energy conservation, high-order accuracy, as well as preservation of discrete analogues of the gradient, divergence and curl operators. Recently a class of numerical methods built on mixed finite-element methods (MFEM) have been proposed, which use staggered pressure and velocity nodes to store data on a grid. These methods retain the properties of traditional FEM, but further are among the most accurate approaches for simulating wave-like phenomena. Since geophysical fluids are largely governed by wave-like motion, MFEM appear particularly well suited for geophysical modeling.

This work aims to understand how arbitrary-order MFEM can be applied in the context of modeling global geophysical flows, in particular atmospheric motions. It approaches this task in three stages: First, by understanding how FEM can be used in conjunction with semi-Lagrangian methods for the problem of passive tracer transport; second, by analyzing the treatment of linear wave-like motion by MFEM in the context of geophysical problems; and third, by implementing a global 3D dynamical core based on MFEM.

Detection and Characterization: Atmospheric Blocking

Climate change is anticipated to cause both prolonged and more intense heat waves. Therefore, it is in the public interest to know what sorts of changes in frequency and duration of these events can be anticipated in the upcoming decades. Atmospheric blocks, stationary high-pressure systems that cause deviations in the jet stream, are a major component in such events. A detection algorithm for tracking atmospheric blocks is being developed and tested on global reanalysis data; after the results have been verified, the algorithm will be applied to data that has been forced based upon a number of potential climate scenarios. Other questions of interest include the dynamics that precede and succeed the formation of these blocks.

Figure: Atmospheric block density (block duration at least 120 hours) from ERA-Interim data.

Detection and Characterization: Extratropical Cyclones

Extratropical cyclones (ETCs) are extreme weather events associated with transient low-pressure systems occurring in the mid-latitudes, known for producing damaging levels of wind, precipitation, low temperatures, and flooding. Under future projections of climate change, ETCs are hypothesized to decrease in number but increase in intensity. ETC statistics from the Community Earth System Model (CESM) are assessed using an automated detection and characterization-based approach. This method is applied to multi-year global simulations with static climatological forcing using experiments developed by the U.S. Climate Variability and Predictability (CLIVAR) project and simulated using the global Community Atmosphere Model (CAM) version 5.1 at two resolutions (approximately 100 km and 25 km at the mid-latitudes). This study investigates the robustness and sensitivity of our detection algorithm using climate variability experiments, which allow for isolation of changes to ETC features with different forcing mechanisms such as elevated SSTs and higher atmospheric carbon dioxide concentrations. Since ETCs have a large socioeconomic impact and are expected to cause $2.4 billion damage globally in the next century, better understanding of how these storms have altered with climate change is crucial to plan and minimize disaster impacts.

Figure: Difference in ETC counts between experiments and climo, using the new results.

Sierra Nevada Snowpack

Snowpack is crucial for the western USA, providing around 75% of the total fresh water supply and buffering against seasonal aridity impacts on agricultural, ecosystem, and urban water demands. The resilience of the California water system is largely dependent on natural stores provided by snowpack. This resilience has shown vulnerabilities due to anthropogenic global climate change with future trends of western USA SWE declining by 40-70%, snowfall decreasing by 25-40%, and more winter storms tending towards rain rather than snow. The volatility of Sierran snowpack is largely driven by orographically forced weather fronts and large-scale teleconnections, such as atmospheric river events, necessitating the use of high-resolution global scale modeling tools. Variable-resolution global climate modeling (VRGCM) is a promising next-generation tool to analyze California’s past and future hydroclimatic trends. VRGCMs serve as a bridge between regional and global models by providing added resolution in areas of interest, eliminating lateral boundary forcings (and resultant model biases), and allowing for more dynamically inclusive large-scale climate teleconnection signals, all while providing serious computational efficiency upgrades. My current research focuses on validating these next-generation models with the goal of using them to project future climate change scenario impacts on several of California’s key hydroclimate metrics (e.g., snowpack, snowfall, and precipitation) to inform water managers and policy makers and help them prepare for climate change impacts facing the state.

Figure: Winter season (DJF) average snow water equivalent over the Sierra Nevada mountain range, as simulated by a variety of models.

Variable-Resolution California Climate

Regional climate modeling using multi-scale and regionally-refined climate models is an emerging field. Work is ongoing on the assessment of the variable-resolution option within the Community Earth System Model (CESM) for long-term (26 years) regional climate simulation over California, in contrast with one of the traditional regional climate models, the Weather Research and Forecasting (WRF). The results show that variable-resolution CESM is competitive in representing regional climatology on both annual and seasonal time scales. This assessment will add value to the use of VRGCMs for projecting climate change over the coming century and improve our understanding of both past and future regional climate related to fine-scale processes.

Figure: Taylor diagram of temperature and precipitation related variables associated with climate over California.

Atmospheric Dynamical Core Development

Work is ongoing on the development of high-order vertical discretizations for climate and weather simulation. More accurate and robust methods are needed for the non-hydrostatic equations applied to high-resolution models. In particular, we focus on the dynamic influence of steep topography on atmospheric flows. To meet these needs, we employ finite element methods known for their flexibility and suitability for massively parallel computing. A variety of physical and mathematical considerations arise when simulating the atmosphere in the non-hydrostatic regime. The goal of this research is to find and implement methods that reproduce vertical motion in the atmosphere at high resolution and will ultimately lead to improved precipitation forecasting at high resolutions with an emphasis on mountainous regions.

Figure: Vertical velocity in the presence of a Schar-type mountain range.

Large-Scale Drivers of Regional Climate in California

The large-scale drivers of many important regional-scale climatological features in California are still not well-understood. This work focuses on meso-scale atmospheric dynamics, and implications of regional climate change. Current work focuses on sea breeze and coastal fog detection/attribution, investigating possible interactions and future changes under the climate change. Methods currently used including analyzing observation and reanalysis datasets, classifying indicators and synoptic-scale predictors, as well as running model simulations with WRF and CESM.

Figure: Geopotential height anomalies on days where CFSR reanalysis indicates the occurrence of a central valley sea breeze event.

Numerical Analysis

Computing power has increased exponentially over the past decade, stimulating a significant burst of new research focused on developing software that can take advantage of the rapidly advancing hardware. In particular, it has become increasingly important to design atmospheric models which are capable of scaling on systems with tens to hundreds of thousands of processors. Hence, significant effort has been directed to the study and application of modern numerical techniques to simulating the atmosphere.

The most popular modern schemes include discontinuous Galerkin, spectral element and finite-volume methods. These methods all have well known advantages and disadvantages, however the comparative performance and accuracy of these methods for smooth, well-resolved problems is largely missing from the literature. Although the modeling community has pressed forward with the usage of these methods in dynamical models, there remains a significant number of unanswered mathematical problems that remain to be answered. Without a rigorous mathematical foundation, spurious errors are bound to arise in geophysical models which may pollute the solution in unexpected ways.

Our work on numerical analysis aims to close these gaps in the understanding of these methods, and establish a solid mathematical foundation for the development of atmospheric dynamical cores.

Figure: Shortest wave mode which is resolved to at most 0.5 percent error in the advective dispersion relation of the numerical method. Wave modes below this threshold are considered poorly resolved by the numerical method.

Adaptive Mesh Refinement and Variable Resolution Grids for Atmospheric Simulations

Adaptive Mesh Refinement (AMR) techniques provide an attractive framework for atmospheric flows since they allow an improved resolution in limited regions without requiring a fine grid resolution throughout the entire model domain. The model regions at high resolution are kept at a minimum and can be individually tailored towards the research problem associated with atmospheric model simulations.

The climate system is characterized by complex nonlinear interactions over a broad range of temporal and spatial scales. Our research objective is to determine how these multi-scales interact and how to use enabling computational tools to mathematically represent scale interactions in climate models. The research focuses in particular on scale interactions in the so-called dynamical core of Atmospheric General Circulation Models (GCM). The dynamical core refers to the fluid dynamics component of a GCM and encompasses the numerical methods used to solve the equations of motion on the resolved scales. The research explores how Adaptive Mesh Refinement (AMR) and other variable resolution grid techniques allow high resolution meshes in regions of interest like the eye of a tropical cyclone or over mountainous terrain. It thereby suggests pathways how to bridge the scale discrepancies between local, regional and global phenomena, a key frontier in climate modeling.

The variable-resolution approach is focused on cubed-sphere computational meshes that have the potential to become a standard in future GCMs. Cubed-sphere grids offer an almost uniform grid point coverage on the sphere. They deliver high performance and almost perfect scaling characteristics on massively parallel computer architectures. The grid is ideally suited for local grid refinements that are based on DoE's AMR software framework Chombo from the Lawrence Berkeley National Laboratory (LBNL). Chombo is under development by DoE's Applied Partial Differential Equations Center (APDEC) under the leadership of our collaborator Dr. Phillip Colella. Both hydrostatic and nonhydrostatic dynamical core designs are developed, implemented and assessed in our team using high-order conservative and oscillation-free finite-volume numerical schemes. In addition, we investigate the numerical schemes in a 2d shallow-water framework that serves as an ideal testbed for 3d model developments. At a later stage our simulations will also involve an in-depth investigation of the validity of physical parameterizations at different spatial scales. The latter will be done in close collaboration with the National Center for Atmospheric Research (NCAR).

Figure: (Left) Adaptive mesh refinement on the cubed-sphere using the Chombo framework, used for advection of a cosine bell around the equator. (Center, Right) A block-structured finite-volume (FV) shallow-water model on a latitude-longitude grid. These figures show how the refined regions track the relative vorticity fields of a barotropic instability and an idealized tropical cyclone in the Southern Hemisphere.

Tools for Climate Data Assessment

The Geometrically Exact Conservative Remapping (GECoRe) package has been developed as a new toolset for high-order conservative remapping of scalar fields between the latitude-longitude and cubed-sphere grid. This work has been in collaboration with Dr. Peter Lauritzen.

Figure: The cubed-sphere grid is a leading candidate for next-generation global models due to its quasi-uniformity and rectangular grid structure. These properties lead to effective parallelization of models built on the cubed-sphere and eases the implementation of high-order numerical methods.

Latest update: March 10th, 2017