year in review / yir in rəˈvyo͞o / • Granular account of accomplishments

FY 2025

• Used the CHELSA meterology dataset to drive LPJ - 3600x increase in resolution, 10s of Tbs of data, 50 CPU years of compute, lots of engineering. Grist for the ML mill (?) (more soon!)

• Contributed to a paper on best practices in geoscientific model software development

• Recalibrated the LPJ-EOSIM model, read a bunch of literature, and began writing a manuscript detailing the improvements to LPJ

• Presented a poster on low latency biospheric carbon modeling at AGU 2024. The aforementioned poster. I was more interested in the whole biogeosciences part of the conference but submitted to a computational section. Next year, I’ll submit to biogeosciences.

  I learned quite a bit from AGU and as always came away with many interesting ideas.

• Global Carbon Budget 2024 was published (currently in preprint). Fossil fuel emissions continue to rise through 2023 and the land sink declined.

• Implemented global parameter perturbation runs (many many many cores)

• Fixed an uninitialized memory bug in LPJ-EOSIM that led to non-deterministic results on some systems

FY 2024

Every year, UMD Earth System Science Interdisciplinary Center (ESSIC) asks us to submit a formal review, which includes listing the achievements over the past (fiscal) year. I wrote a lot of code this year (11k+ lines, tested), published data on LPDAAC, and ended up traveling for work quite a bit. I became much more familiar with the LPJ-EOSIM ecosystem, got on a few grants, and am going to be an author on at least two papers. It felt like a somewhat auspicious year ¹. Despite it being a productive year, I still identified blockers that slow down my workflows.

That being said, let’s recap, starting at coolest.

• Submitted LPJ-EOSIM simulations to TRENDYv13, the most recent version of the Global Carbon Budget’s land surface ensemble (and will be a coauthor!)
• Presented a plenary (keynote) presentation at ICOS 2024, on our work to do near real-time carbon flux anomaly detection. Me, presenting “A Near Real Time Framework for the Detection and Attribution of Carbon Flux Anomalies” at ICOS 2024 (in Versailles. Cool experience).
• Was interviewed for the NPR podcast The Pulse about my experience during the 2019 solar eclipse in Chile.
• Significantly improved the LPJ model - meaning LPJ was sped up ~100x in some cases! This timing is inaccurate since I wasn’t able to finish a full non-sped-up LPJ run and had to do an estimate based on 1/5 of the runtime. I refactored the I/O access patterns to allow better integration with the GPFS filesystem on NCCS Discover. Meaning I/O overhead (which previously was ~90% of the runtime (depending on number of threads of course)) was significantly reduced.
• Wrote a python package to format and upload LPJ-EOSIM data products to LPDAAC. I also read John Ousterhout’s A Philosophy of Software Design and carefully implemented his suggestions into my code.
• Set up the AWS infrastructure to push data to LPDAAC. This required quite a bit of interdisciplinary collaboration.
• Ported LPJ to an AWS ParallelCluster which allowed two international PhD students to run LPJ in the cloud. I was also tech support for them.
• Entirely rewrote the infrastructure for distributing LPJ. There were no tests and many of the R source files used outdated libraries, so I modernized and modularized it with Python.