Student Internship: Open Science for Reflective's SAI Uncertainty Database at Reflective

San Francisco, California, United States -

Full Time

Start Date

Immediate

Expiry Date

08 Jun, 26

Salary

2000.0

Posted On

10 Mar, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Jupyter Notebook, Cloud Computing, Data Visualization, Atmospheric Dynamics, Climate Modeling, Earth System Modeling, GeoMIP Simulations, Xarray, Numpy, Pandas, Matplotlib, Reproducible Research, Quantitative Analysis

Industry

Climate Data and Analytics

Description

Sunlight reflection may be the only available option, alongside dramatic emissions reductions, adaptation, and rapid scaling of carbon removal, to rapidly limit many climate impacts over the coming decades. But we don’t know nearly enough about it to make a scientifically-informed decision about potential deployment – and we’re not on a trajectory for rapid, legitimate decision making. Reflective is a philanthropically-funded initiative to develop the necessary knowledge base and do the requisite technology research and development, urgently and responsibly. At Reflective we have recently launched a public database of priority uncertainties related to SAI. This is a structured, continually updated assessment of these uncertainties, evaluated by degree of uncertainty and consequence, to help identify and prioritize future research. This project will develop a prototype for a future version of this database in which every entry is quantified with a Jupyter Notebook based analysis which runs on our Cloud Hub. This will allow anyone to test, verify, and expand upon the analysis easily, without any of the friction that comes from, for example, needing to download large amounts of data before reproducing a workflow. The Reflective Cloud Hub is a JupyterHub platform that gives researchers free, browser-based access to climate model datasets and compute resources in the cloud. It hosts analysis-ready outputs from the most recent SAI simulations alongside shared code and tutorials, lowering the barrier to entry for open, reproducible SAI research. This project will make use of the Cloud Hub, and the most recent GeoMIP simulations (G6-1.5K-SAI and G6-1.5K-HiLLA) which are available on it, to explore one key climate uncertainty relevant to SAI, and to demonstrate a best practice use of open science approaches to produce a living assessment of that uncertainty using the latest earth system modeling. INTERNSHIP OVERVIEW We are seeking a highly motivated Graduate (Ph.D. or Master's level) or late stage undergraduate Intern for a 10-15 week research project. The intern can select one of several example tracks as shown below, depending on their level of prior experience, or propose a project of their own. The goal of this internship is as much about demonstrating open science workflows, and trialling public communication of uncertainty via quantitative analysis, as carrying out the specific analysis. This role can be fully remote or based in San Francisco in our office in Embarcadero. Candidates must be US citizens or residents and based in the continental US. Applications will be accepted until March 31, 2026. POTENTIAL RESEARCH TRACKS The intern will have the flexibility to choose their own or select one of the following focus areas based on their expertise and research interests: Track A - Mid-latitude extreme winter weather response to SAI Suitable for a graduate student candidate with experience in atmospheric dynamics Objective - quantify changes in extreme winter weather in Northern Hemisphere mid-latitudes under the most recent GeoMIP (G6-1.5K-SAI and G6-1.5K-HiLLA) simulations, and investigate links to changes in large-scale modes of variability (e.g. the North Atlantic Oscillation) and the position of the sub-polar jet. Relevant uncertainty - Extratropical Circulation Track B - The monsoon response to High-latitude low-altitude (HiLLA) SAI Suitable for a graduate student candidate with experience in atmospheric dynamics Objective - assess how the seasonality of forcing (i.e. summer-hemisphere only forcing) under HiLLA strategies impacts (1) the seasonality of interhemispheric temperature differences and (2) (as a result) monsoon responses. The project will compare key monsoon metrics across G6-1.5K-HiLLA and G6-1.5K-SAI simulations. Relevant Uncertainty - Tropical Circulation Track C - Comparing the Arctic response in G6-1.5K-SAI and G6-1.5K-HiLLA Suitable for a late-stage undergraduate candidate with strong Python experience Objective - compare the response of key metrics of Arctic change and tipping risk between G6-1.5K-SAI and -HiLLA, to explore how the more seasonal and polar forcing under HiLLA strategies results in a different Arctic response. Metrics will include the sea-ice area, its seasonal cycle and spatial distribution, drivers of permafrost thaw, AMOC decline and Greenland ice sheet mass balance. Relevant uncertainties - Ice sheets & Glaciers, AMOC Track D - The relationship across models between patterns of meridional AOD and residual climate change. Suitable for a late-stage undergraduate candidate with strong Python experience Objective - investigate how the meridional pattern of aerosol optical depth relates to the pattern of climate impacts (residual changes in temperature, precipitation etc.) under G6-1.5K-SAI/-HiLLA. The key output will be a quantitative analysis of the relationship (or lack of one) across models of spatial patterns of forcing vs response, in order to inform the judgement of degree of “decision-relevance” associated with the uncertainty in aerosol transport. Relevant uncertainty - Aerosol spatial distribution DESIRED SKILLS AND QUALIFICATIONS Essential: Currently enrolled in a graduate program (M.S. or Ph.D.) in Atmospheric Science, Climate Physics, Earth System Modeling, or a closely related field, or in the late-stage of an undergraduate program in a related field (e.g. Physics, Earth science) Essential: Programming skills in Python (xarray, numpy, pandas, matplotlib) for (geospatial) data and visualization. Desired: Knowledge of climate science, atmospheric dynamics or earth system modelling Bonus: Prior experience working with earth system model simulations. COMPENSATION We are committed to providing competitive compensation and comprehensive benefits to our team. We offer fixed salary levels based on experience and role to minimize biases in compensation and to ensure team members are paid the same for doing the same work. This position is a 10-15 week full-time internship paid $2,000/week. DIVERSITY At Reflective, recruiting, hiring, mentoring, and retaining a diverse workforce is critical to our success. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran. Reflective is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

Responsibilities

The intern will develop a prototype for a future version of the SAI Uncertainty Database, quantifying each entry with a Jupyter Notebook analysis running on the Cloud Hub to demonstrate open science workflows. This involves exploring a key climate uncertainty relevant to Solar Aerosol Injection (SAI) using recent GeoMIP simulations.