Data-Driven Transition Path Analysis Yields a Statistical Understanding of Sudden Stratospheric Warming Events in an Idealized Model
Year: 2022
Type: article
Abstract: Atmospheric regime transitions are highly impactful as drivers of extreme weather events, but pose two formidable modeling challenges: predicting the next event (weather forecasting) and characterizing the statistics of events of a given severity (the risk climatology). Each event has a different duration and spatial structure, making it hard to define an objective “average event.” We argue here that transition path theory (TPT), a stochastic process framework, is an appropriate tool for the task. We demonstrate TPT’s capacities on a wave–mean flow model of sudden stratospheric warmings (SSWs) developed by Holton and Mass, which is idealized enough for transparent TPT analysis but complex enough to demonstrate computational scalability. Whereas a recent article (Finkel et al. 2021) studied near-term SSW predictability, the present article uses TPT to link predictability to long-term SSW frequency. This requires not only forecasting forward in time from an initial condition, but also backward in time to assess the probability of the initial conditions themselves. TPT enables one to condition the dynamics on the regime transition occurring, and thus visualize its physical drivers with a vector field called the reactive current. The reactive current shows that before an SSW, dissipation and stochastic forcing drive a slow decay of vortex strength at lower altitudes. The response of upper-level winds is late and sudden, occurring only after the transition is almost complete from a probabilistic point of view. This case study demonstrates that TPT quantities, visualized in a space of physically meaningful variables, can help one understand the dynamics of regime transitions.
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: green
Awards: G1251818329, ENSEMBLE SIMULATION TECHNIQUES AND FAST RANDOMIZED ALGORITHMS, G2526998692, DMS/NIGMS 1: Design and Analysis of Machine Learning Approaches for Long Timescale Prediction from Short Trajectory Data, RTG: Research Training Group in Mathematical Modeling and Simulation, Collaborative Research: Framework: Improving the Understanding and Representation of Atmospheric Gravity Waves using High-Resolution Observations and Machine Learning, DOE COMPUTATIONAL SCIENCE GRADUATE FELLOWSHIP (CSGF) GRANT 2019 COHORT, The Jet Streams in a Warming World: Incorporating Moisture into Our Understanding of Midlatitude Circulation Change, DOE COMPUTATIONAL SCIENCE GRADUATE FELLOWSHIP (CSGF) GRANT 2019 COHORT, TO ENABLE AND ADVANCE NASA S SEARCH FOR LIFE BEYOND THE SOLAR SYSTEM THE VIRTUAL PLANETARY LABORATORY TEAM WILL FOCUS ON A COMPELLING SCIENTIFIC QUESTION: HOW DO WE RECOGNIZE WHETHER AN EXOPLANET CAN OR DOES SUPPORT LIFE? IN ADVANCE OF CHALLENGING OBSERVATIONS THE VPL WILL COMBINE SCIENTIFIC MODELS OBSERVATIONS AND FIELD AND LABORATORY DATA FROM MANY DISCIPLINES TO EXPLORE HOW A PLANET BECOMES AND REMAINS HABITABLE AND IDENTIFY NEW BIOSIGNATURES IN THE CONTEXT OF THEIR ENVIRONMENTS. THIS RESEARCH WILL INFORM TARGET SELECTION FOR JWST FROM K2 TESS AND GROUND-BASED EXOPLANET DISCOVERIES; HELP OBSERVERS RECOGNIZE THE SIGNS OF PLANETARY HABITABILITY AND LIFE; AND INFORM UPCOMING NASA MISSIONS THAT WILL OBSERVE TERRESTRIAL EXOPLANETS SUCH AS JWST AND THE HABEX OST AND LUVOIR MISSION CONCEPTS. FIVE RESEARCH TASKS WILL ADDRESS OUR SCIENTIFIC QUESTION. TASKS A-D PROVIDE A SUITE OF SELF-CONSISTENT PLANETARY ENVIRONMENTS BASED ON DATA AND MODELS THAT ARE VALIDATED AGAINST SOLAR SYSTEM PLANETS. THESE PLANETARY ENVIRONMENTS ARE THEN INPUT TO TASK E TO SIMULATE PLANETARY OBSERVATIONS AND DETERMINE THE DETECTABILITY OF SIGNS OF HABITABILITY AND LIFE. TASK A: SOLAR SYSTEM ANALOGS FOR EXOPLANETS. WE WILL USE OBSERVATIONS AND ATMOSPHERIC MODELS OF EARTH AND VENUS AS WELL AS REALISTIC SIMULATIONS OF THE DISTANT SOLAR SYSTEM AND OTHER PLANETARY SYSTEMS TO EXPLORE PROCESSES AND REMOTE-SENSING DISCRIMINANTS RELEVANT TO HABITABLE ENVIRONMENTS BIOSIGNATURES AND FALSE POSITIVES. TASK B: THE EARTH THROUGH TIME. WE WILL UNDERTAKE AN INTERDISCIPLINARY SYNTHESIS OF FIELD LABORATORY AND MODELING EFFORTS TO CONSTRAIN ENVIRONMENTAL ECOLOGICAL AND REMOTELY OBSERVABLE PARAMETERS OF EARLY EARTH AND SIMILAR EXOPLANETS AROUND OTHER STARS AS A FUNCTION OF DOMINANT METABOLISM. TASK C: THE HABITABLE PLANET. WE WILL DEVELOP AND USE COUPLED STELLAR ORBITAL INTERIOR AND 1-D TO 3-D ATMOSPHERIC MODELS TO EXPLORE TERRESTRIAL EXOPLANET FORMATION DYNAMICS COMPOSITION ATMOSPHERIC EVOLUTION AND HABITABILITY. THESE ANALYSES WILL INFORM HABITABILITY ASSESSMENTS TO PRIORITIZE PROMISING EXOPLANETS FOR BIOSIGNATURE SEARCHES. TASK D: THE LIVING PLANET. WE WILL INTEGRATE FIELD AND LABORATORY RESEARCH WITH COUPLED CHEMICAL CLIMATE AND ECOSYSTEM MODELS TO EXPLORE THE NATURE AND DETECTABILITY OF BIOSIGNATURES FOR DIFFERENT GEOCHEMICAL AND STELLAR ENVIRONMENTS. WE WILL IDENTIFY NEW REMOTESENSING ATMOSPHERIC SURFACE AND TEMPORAL BIOSIGNATURES AND THEIR FALSE POSITIVES AND NEGATIVES AND QUANTIFY THE ENVIRONMENTAL LIMITS TO PHOTOSYNTHETIC PRODUCTIVITY. TASK E: THE OBSERVER. WE WILL COMBINE OBSERVATIONS ANALYSIS TECHNIQUES AND SIMULATIONS TO IMPROVE THE DETECTION AND CHARACTERIZATION OF POTENTIALLY HABITABLE EXOPLANETS. WE WILL IMPROVE RETRIEVAL OF PLANETARY PROPERTIES FROM EXOPLANET MAPPING AND FROM DIRECT IMAGING TRANSIT TRANSMISSION AND HIGH-RESOLUTION SPECTROSCOPY. OUR ANALYSES WILL DETERMINE OPTIMAL MEASUREMENTS AND OBSERVATION STRATEGIES FOR TERRESTRIAL EXOPLANET CHARACTERIZATION. THE PROPOSED EFFORT BENEFITS ASTROBIOLOGY AND THE NAI WITH A PROVEN PRODUCTIVE INTERDISCIPLINARY SCIENCE TEAM WHOSE RESEARCH ADDRESSES KEY ASTROBIOLOGY PROGRAM GOALS INCLUDING CONSTRUCTING HABITABLE WORLDS IDENTIFYING EXPLORING AND CHARACTERIZING ENVIRONMENTS FOR HABITABILITY AND BIOSIGNATURES AND THE COEVOLUTION OF LIFE AND THE PHYSICAL ENVIRONMENT. THE UW S ASTROBIOLOGY DUAL-TITLE PHD PROGRAM AND OUR ASTROBIOLOGY RESEARCH OPPORTUNITIES FOR MINORITY UNDERGRADUATES STRONGLY SUPPORT THE TRAINING OF THE NEXT GENERATION OF ASTROBIOLOGISTS. OUR TEAM MEMBERS WILL CONTINUE TO PROVIDE KEY SCIENTIFIC LEADERSHIP FOR RELEVANT NASA MISSIONS. THE VPL WILL LEVERAGE THE MASSIVELY INTERDISCIPLINARY COLLABORATION ENABLED BY THE NAI TO PROVIDE A UNIQUE SYNTHESIS OF NASA PLANETARY AND EARTH SCIENCE MISSION DATA MODELING AND RETRIEVAL TECHNIQUES THAT INFORM AND SUPPORT NASA ASTROBIOLOGY AND ASTROPHYSICS MISSION OBJECTIVES.
OpenAlex ID: W4307001813