Climatology and Evolution of the Antarctic Peninsula Föhn Wind‐Induced Melt Regime From 1979–2018
Year: 2021
Type: article
Abstract: Warm and dry föhn winds on the Antarctic Peninsula (AP) cause surface melt that can destabilize vulnerable ice shelves. Topographic funneling of these downslope winds through mountain passes and canyons can produce localized wind‐induced melt that is difficult to quantify without direct measurements. Our Föhn Detection Algorithm (FöhnDA) identifies the surface föhn signature that causes melt from measurement by 12 Automatic Weather Stations on the AP, that train a machine learning model to detect föhn in 5 km Regional Atmospheric Climate Model 2 (RACMO2.3p2) simulations and in the ERA5 reanalysis model. We estimate the fraction of AP surface melt attributed to föhn and possibly katabatic winds and identify the drivers of melt, temporal variability, and long‐term trends and evolution from 1979–2018. We find that föhn wind‐induced melt accounts for 3.1% of the total melt on the AP and can be as high at 18% close to the mountains where the winds funnel through mountain canyons. Föhn‐induced surface melt does not significantly increase from 1979–2018, despite a warmer atmosphere and more positive Southern Annular Mode. However, a significant increase (+0.1 Gt y‐1) and subsequent decrease/stabilization occur in 1979–1998 and 1999–2018, consistent with the AP warming and cooling trends during the same time periods. Föhn occurrence, more than föhn strength, drives the annual variability in föhn‐induced melt. Long‐term föhn‐induced melt trends and evolution are attributable to seasonal changes in föhn occurrence, with increased occurrence in summer, and decreased occurrence in fall, winter, and early spring over the past 20 years.
DOI: 10.1029/2020jd033682
Institutions: University of California, Irvine, Utrecht University
Language: English
Subfield: Atmospheric Science
Field: Earth and Planetary Sciences
Domain: Physical Sciences
SDG: Climate action
Open Access status: bronze
Awards: AUTOMATED WEATHER STATION AND AWS-LIKE NETWORKS ARE THE PRIMARY SOURCE OF SURFACE-LEVEL METEOROLOGICAL DATA IN REMOTE POLAR REGIONS. THESE NETWORKS HAVE DEVELOPED ORGANICALLY AND INDEPENDENTLY AND DELIVER DATA TO RESEARCHERS IN IDIOSYNCRATIC ASCII FORMATS THAT HINDER AUTOMATED PROCESSING AND INTERCOMPARISON AMONG NETWORKS. MOREOVER STATION TILT CAUSES SIGNIFICANT BIASES IN POLAR AWS MEASUREMENTS OF RADIATION AND WIND DIRECTION. RESEARCHERS NETWORK OPERATORS AND DATA CENTERS WOULD BENEFIT FROM AWS- LIKE DATA IN A COMMON FORMAT AMENABLE TO AUTOMATED ANALYSIS AND ADJUSTED FOR KNOWN BIASES. THIS PROJECT ADDRESSES THESE NEEDS BY DEVELOPING A SCIENTIFIC SOFTWARE WORKFLOW CALLED "JUSTIFIED AWS" (JAWS) TO INGEST LEVEL 2 (L2) DATA IN THE MULTIPLE FORMATS NOW DISTRIBUTED HARMONIZE IT INTO A COMMON FORMAT AND DELIVER VALUE-ADDED LEVEL 3 (L3) OUTPUT SUITABLE FOR DISTRIBUTION BY THE NETWORK OPERATOR ANALYSIS BY THE RESEARCHER AND CURATION BY THE DATA CENTER. POLAR CLIMATE RESEARCHERS CURRENTLY FACE DAUNTING PROBLEMS INCLUDING HOW TO EASILY: 1. AUTOMATE ANALYSIS (SUBSETTING STATISTICS UNIT CONVERSION) OF AWS-LIKE L2 ASCII DATA. 2. COMBINE OR INTERCOMPARE DATA AND DATA QUALITY FROM AMONG UNHARMONIZED L2 DATASETS. 3. ADJUST L2 DATA FOR BIASES SUCH AS AWS TILT ANGLE AND DIRECTION. JAWS ADDRESSES THESE COMMON ISSUES BY HARMONIZING AWS L2 DATA INTO A COMMON FORMAT AND APPLYING ACCEPTED METHODS TO QUANTIFY QUALITY AND ESTIMATE BIASES. SPECIFICALLY JAWS ENABLES USERS AND NETWORK OPERATORS TO 1. CONVERT L2 DATA (USUALLY ASCII TABLES) INTO A NETCDF-BASED L3 FORMAT COMPLIANT WITH METADATA CONVENTIONS (CLIMATE-FORECAST AND ACDD) THAT PROMOTE AUTOMATED DISCOVERY AND ANALYSIS. 2. INCLUDE VALUE-ADDED L3 FEATURES LIKE THE RETROSPECTIVE ITERATIVE GEOMETRY-BASED (RIGB) TILT ANGLE AND DIRECTION CORRECTIONS SOLAR ANGLES AND STANDARDIZED QUALITY FLAGS. 3. PROVIDE A SCRIPTABLE API TO EXTEND THE INITIAL L2-TO-L3 CONVERSION TO NEWER AWS-LIKE NETWORKS AND INSTRUMENTS. POLAR AWS NETWORK EXPERTS AND NSIDC DAAC PERSONNEL EACH WITH DECADES OF EXPERIENCE WILL HELP GUIDE AND DELIBERATE THE L3 CONVENTIONS IMPLEMENTED IN STAGES 2-3. THE PROJECT WILL START ON JULY 1 2017 AT ENTRY TECHNOLOGY READINESS LEVEL 3 AND WILL EXIT ON JUNE 30 2019 AT TRL 6. JAWS IS NOW A HETEROGENEOUS COLLECTION OF SCRIPTS AND METHODS DEVELOPED AND VALIDATED AT UCI OVER THE PAST 15 YEARS. AT EXIT JAWS WILL COMPRISE THREE MODULAR STAGES WRITTEN IN OR WRAPPED BY PYTHON INSTALLABLE BY CONDA: STAGE 1 INGESTS AND TRANSLATES L2 DATA INTO NETCDF. STAGE 2 ANNOTATES THE NETCDF WITH CF AND ACDD METADATA. STAGE 3 DERIVES VALUE-ADDED SCIENTIFIC AND QUALITY INFORMATION. THE LABOR- INTENSIVE TASKS INCLUDE TURNING OUR HETEROGENEOUS WORKFLOW INTO A ROBUST STANDARDS-COMPLIANT EXTENSIBLE WORKFLOW WITH AN API BASED ON BEST PRACTICES OF MODERN SCIENTIFIC INFORMATION SYSTEMS AND SERVICES. IMPLEMENTATION OF STAGES 1-2 MAY BE STRAIGHTFORWARD THOUGH TEDIOUS DUE TO THE MENAGERIE OF L2 FORMATS INSTRUMENTS AND ASSUMPTIONS. THE RIGB COMPONENT OF STAGE 3 REQUIRES ONGOING ASSIMILATION OF ANCILLARY NASA DATA (CERES AIRS) AND USE OF AUTOMATED DATA TRANSFER PROTOCOLS (DAP THREDDS). THE IMMEDIATE TARGET RECIPIENT ELEMENTS ARE POLAR AWS NETWORK MANAGERS USERS AND DATA DISTRIBUTORS. L2 BOREHOLE DATA SUFFERS FROM SIMILAR INTEROPERABILITY ISSUES AS DOES NON-POLAR AWS DATA. HENCE OUR L3 FORMAT WILL BE EXTENSIBLE TO GLOBAL AWS AND PERMAFROST NETWORKS. JAWS WILL INCREASE IN SITU DATA ACCESSIBILITY AND UTILITY AND ENABLE NEW DERIVED PRODUCTS (BOTH ARE AIST GOALS). THE PI IS A LONG-STANDING RESEARCHER OPEN SOURCE SOFTWARE DEVELOPER AND EDUCATOR WHO UNDERSTANDS OBSTACLES TO HARMONIZING DISPARATE DATASETS WITH NASA INTEROPERABILITY RECOMMENDATIONS. OUR TEAM PARTICIPATES IN RELEVANT GEOSCIENCE COMMUNITIES INCLUDING ESDS WORKING GROUPS ESIP AGU AND EARTHCUBE., INCORPORATE MORE REALISTIC SURFACE-ATMOSPHERE RADIATIVE COUPLING IN E3SM, NRT-DESE: Team Science for Integrative Graduate Training in Data Science and Physical Science, Improved projections of future Antarctic surface melt rates
OpenAlex ID: W3093398400