Research Scientist, Climate Modeling

The Allen Institute for Artificial Intelligence; Seattle (WA); On-site; USD 167,000 - 261,000; Full time

Job summary

Allen Institute for AI (Ai2) seeks one or more Research Scientists for a new three-year project to develop an AI-powered physics-informed hybrid climate model trained on historical data that can deliver multidecadal forecasts more accurate than current state-of-the-art. This on-site role is based in Seattle.

The project is grant-supported with funding through September 22, 2029, and continuation depends on renewal or new funding.

Qualifications

• Experience in AI and earth science with climate-modeling focus.
• Ability to develop AI-powered, physics-informed climate models.

Responsibilities

• Develop AI-powered physics-informed climate models for multidecadal forecasts.
• Collaborate with climate science centers and ML researchers.
• Advance an open-source emulator and validate against historical data.

Skills

AI research

Earth science

Climate modeling

ML

Tools

JAX

Job description

Persons in these roles are expected to work from our offices in Seattle. On-site requirements vary based on position and team. If you have questions about on-site work arrangements for this role, please ask your recruiter.

Our base salary range is $167,030 - $260,570, and in addition we have generous bonus plans to provide a competitive compensation package.

Who You Are:

We seek one or more Research Scientists for a new three-year project to develop an AI-powered physics-informed hybrid climate model trained on historical data that is capable of multidecadal forecasts that are more accurate than the current state of the art. We’re looking for someone with experience in AI and earth science who is excited to push the frontier in climate model development.

This position is supported by a grant with a current period of performance ending September 22, 2029. Continuation of the position beyond that date depends on renewal, extension, or identification of additional funding. Ai2 will make reasonable efforts to notify the employee if funding for the position is expected to end.

Who We Are:

Ai2’s Climate Modeling team is an international pioneer in using machine learning (ML) methodologies to improve on current physics-based climate models. Our fast, accurate autoregressive open-source emulator, ACE, stably reproduces the climate and weather extremes of an existing climate model or a reanalysis. We have coupled ACE to a ML ocean model, trained it to realistically account for changing CO2 concentrations, and downscaled (super-resolved) ACE outputs from their 100 km native scale to to km-scale detail. Our tight-knit team of scientists and software engineers works closely with leading physics-based climate modeling centers to obtain unique training and testing data and get expert feedback on our progress. We collaborate with other leading research groups doing related AI work.

Physically-based climate models discretize equations representing individual time-evolving processes like clouds, rain, wind, land and sea-ice, and ocean currents on a computational grid. Some processes (e.g. clouds) are less reliably encoded than others (e.g. winds). Hence such climate models have biases in representing present-day climate and produce an undesirably large range of projections of future climate change for a given human-caused change in CO2 or other climate forcings. Current AI-based climate models reduce present-day climate bias, but don’t generalize well to future climate change.

We are starting a 3-year project funded by google.org to develop an open source ‘AI-first’ climate model that can demonstrably project future climates more accurately than current physically-based climate models when trained on historical observations by judicious design of the AI to incorporate physical principles that reliably apply in any climate, seen or unseen. The model should be lightweight and easy for an ML-savvy climate scientist to train and deploy. Our starting point is the Google Research NeuralGCM model, a hybrid ML model that uses a conventional discretization of winds encoded in JAX and learns a column-local representation of all other atmospheric and land