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AI Automation Index
Will AI Replace Atmospheric and Space Scientists?
Task-level automation exposure for Atmospheric and Space Scientists: 27 O*NET tasks scored against their work activities, plus the simulator.
E-E-A-T Verified
Developed & audited by Gini Loh Automation Exposure
52%
weighted task index
27 of 27 tasks scored
Median Salary
$99,070 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
Low
10,000 people hold this job — size, not growth
Projected 2025–35
+2.6%
employment change
800 openings a year · BLS line item
Pay by state
BLS OEWS publishes 48 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.
$42,820 – $128,800
Highest medians
Maryland $128,800
California $124,920
Hawaii $121,010
New Jersey $120,180
Idaho $119,430
Lowest medians
New Hampshire $42,820
Nebraska $60,540
Puerto Rico $63,910
Arkansas $71,940
Pennsylvania $75,890
State figures come from the 19-2021 row in the May 2025 state panel. States where the estimate was suppressed or too small to publish are absent.
What the score is made of
Each task is mapped to its work activities, scored for AI automation potential, and weighted by how important those activities are to the job. These are the components.
Automation potential 52%
Importance-weighted mean of the scored tasks. This is the headline index.
Physical work 8%
Share of weighted tasks in hands-on or machine-operating work activities.
Judgment & influence 44%
Share in analysis, decisions, teaching, directing, advising, or caring for others.
Routine administration 12%
Share in documenting, recording, and administrative activities.
Rewritten by O*NET 0%
Share of weighted tasks O*NET rewrote in a recent update. Reported, not folded into the score.
Consequence of error 10/100
Low
O*NET's own rating of how serious an uncorrectable mistake would be here — respondents, not us. Deliberately not in the index: the score measures what AI can do with the task content, and multiplying it by stakes would be our judgment dressed as data.
Constituent Task Mix
27 tasks for this occupation, weighted by work-activity importance. Shares below are of total task weight, not task count.
High exposure (≥ 60% of the task is automatable) 20%
5 tasks
Medium exposure (≥ 35%) 69%
19 tasks
Low exposure (below 35%) 11%
3 tasks
How would you survive?
Move your week away from the high-exposure tasks and toward the ones that need judgment. The simulator recalculates the index as you shift the mix.
Simulate Upskilling →Constituent Tasks
#20208 · Core · 40% automatable
Develop or use mathematical or computer models for weather forecasting.
decision support — Physical theory needs expert scientific judgment
Develop theories or models of physical phenomena.
#20209 · Core · 70% automatable
Interpret data, reports, maps, photographs, or charts to predict long- or short-range weather conditions, using computer models and knowledge of climate theory, physics, and mathematics.
data analysis — Interprets data; domain expert validates meaning
Interpret research or operational data.
#20210 · Core · 58% automatable
Conduct meteorological research into the processes or determinants of atmospheric phenomena, weather, or climate.
data analysis — Climate data analysis aided; instrument networks human
Conduct climatological research.
#19759 · Core · 56% automatable
Formulate predictions by interpreting environmental data, such as meteorological, atmospheric, oceanic, paleoclimate, climate, or related information.
data analysis — Statistical analysis of environmental datasets is automatable
Analyze environmental data. Develop theories or models of physical phenomena.
#9068 · Core · 55% automatable
Broadcast weather conditions, forecasts, or severe weather warnings to the public via television, radio, or the Internet or provide this information to the news media.
information retrieval — Technical public assistance partly automatable; trust needs human
Provide technical information or assistance to public.
#9070 · Core · 72% automatable
Prepare forecasts or briefings to meet the needs of industry, business, government, or other groups.
text generation — Reports drafted; presentations need human delivery
Prepare scientific or technical reports or presentations.
#9069 · Core · 8% automatable
Gather data from sources such as surface or upper air stations, satellites, weather bureaus, or radar for use in meteorological reports or forecasts.
situated mobility — Environmental sampling needs field presence and hands
Collect environmental data or samples.
#21104 · Core · 68% automatable
Develop computer programs to collect meteorological data or to present meteorological information.
text generation — Code generation strong; human reviews and integrates
Test computer system operations to ensure proper functioning. Analyze design requirements for computer or electronics systems. Write computer programming code.
#20206 · Core · 55% automatable
Prepare weather reports or maps for analysis, distribution, or use in weather broadcasts, using computer graphics.
information retrieval — Technical public assistance partly automatable; trust needs human
Provide technical information or assistance to public.
#21105 · Core · 58% automatable
Develop and deliver training on weather topics.
text generation — Draft training materials; human tailors content
Develop training materials. Present information to the public. Teach classes in area of specialization.
#20211 · Core · 72% automatable
Prepare scientific atmospheric or climate reports, articles, or texts.
text generation — Environmental reports drafted; human verifies data
Prepare research or technical reports on environmental issues.
#19755 · Core · 58% automatable
Analyze climate data sets, using techniques such as geophysical fluid dynamics, data assimilation, or numerical modeling.
data analysis — Climate data analysis aided; instrument networks human
Conduct climatological research.
#20205 · Core · 58% automatable
Analyze historical climate information, such as precipitation or temperature records, to help predict future weather or climate trends.
data analysis — Climate data analysis aided; instrument networks human
Conduct climatological research.
#20203 · Core · 45% automatable
Consult with other offices, agencies, professionals, or researchers regarding the use and interpretation of climatological information for weather predictions and warnings.
interpersonal — Research collaboration is iterative human teamwork
Collaborate on research activities with scientists or technical specialists.
#21103 · Core · 55% automatable
Speak to the public to discuss weather topics or answer questions.
interpersonal — Public environmental communication needs trust and accountability
Communicate with the public on environmental issues. Provide educational information to the public.
#20207 · Core · 50% automatable
Apply meteorological knowledge to issues such as global warming, pollution control, or ozone depletion.
decision support — Synthesizes research; field application and validation human
Apply knowledge or research findings to address environmental problems.
#20202 · Core · 37% automatable
Perform managerial duties, such as creating work schedules, creating or implementing staff training, matching staff expertise to situations, or analyzing performance of offices.
interpersonal — Coordinating training is partly automatable but relational
Coordinate training activities. Manage human resources activities.
#9074 · Core · 40% automatable
Measure wind, temperature, and humidity in the upper atmosphere, using weather balloons.
physical manipulation — Measuring environmental characteristics needs instruments
Measure environmental characteristics.
#9076 · Supplemental · 32% automatable
Direct forecasting services at weather stations or at radio or television broadcasting facilities.
interpersonal — Directing technical operations needs human accountability
Direct technical activities or operations.
#9078 · Supplemental · 8% automatable
Collect air samples from planes or ships over land or sea to study atmospheric composition.
situated mobility — Environmental sampling needs field presence and hands
Collect environmental data or samples.
#20204 · Supplemental · 55% automatable
Teach college-level courses on topics such as atmospheric and space science, meteorology, or global climate change.
text generation — Science instruction content AI-assisted; delivery human
Instruct college students in physical or life sciences.
#9083 · Supplemental · 48% automatable
Design or develop new equipment or methods for meteorological data collection, remote sensing, or related applications.
decision support — Method development needs empirical validation
Develop environmental research methods.
#20212 · Supplemental · 58% automatable
Research the impact of industrial projects or pollution on climate, air quality, or weather phenomena.
data analysis — Analysis aided; primary environmental data collection human
Research environmental impact of industrial or development activities.
#19756 · Supplemental · 58% automatable
Conduct wind assessment, integration, or validation studies.
data analysis — Climate data analysis aided; instrument networks human
Conduct climatological research.
#9079 · Supplemental · 50% automatable
Conduct numerical simulations of climate conditions to understand and predict global or regional weather patterns.
data analysis — Models drafted; empirical calibration human
Develop mathematical models of environmental conditions.
#19758 · Supplemental · 58% automatable
Estimate or predict the effects of global warming over time for specific geographic regions.
data analysis — Analysis aided; primary environmental data collection human
Research environmental impact of industrial or development activities.
#19757 · Supplemental · 72% automatable
Create visualizations to illustrate historical or future changes in the Earth's climate, using paleoclimate or climate geographic information systems (GIS) databases.
text generation — Generative image tools produce visuals; human curates
Create images or other visual displays.
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How this was scored
- 1. Tasks. O*NET task statements for this occupation, from a release whose newest record is dated 08/2026.
- 2. Work activities. Each task maps to 1–5 Detailed Work Activities; each activity belongs to one of 41 Generalized Work Activities that O*NET rates for importance (1–5) in this occupation.
- 3. Automation potential. Every work activity carries one score for how far current AI can perform it end-to-end. Tasks inherit the mean of their activities.
- 4. Weighting. Task weight = 0.35 + 0.65 × (importance − 1)/4, scaled by how often the task is performed and by 0.7 for Supplemental tasks. Importance is the task's own O*NET rating where it exists (18,369 of 18,838 tasks); the rest inherit their work activities' importance. The index is the weighted mean.
Check: across the 910 occupations where O*NET also rates how far machine speed sets the pace, this index moves opposite to it (Spearman ρ = -0.49). The more a job is paced by equipment, the less of its task content AI can address — the relationship you would expect if the scoring is working.
Sources · O*NET 31.0 (task records to 08/2026) · BLS OEWS May 2025 · BLS projections 2025-2035 · rubric v2.1 · tasks for O*NET, not a forecast
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