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Atmospheric and space scientists

Also called Meteorologist, Atmospheric scientist, Climate scientist, Climatologist, Weather forecaster

In brief

In May 2025, BLS counted 10,000 US atmospheric and space scientists, with median pay of $99,070 a year. Of the BLS counts since 2003, the highest was 10,850, in May 2014. The earliest figure here is an estimate of 400 from 1940, not measured the same way as today's count. The last full technology era for atmospheric and space scientists: High-resolution ensemble NWP on supercomputers, 2000 to 2023.

What the sources say

Official outlook

BLS projects employment to grow 2.6% from 2025 to 2035.

BLS Employment Projections 2025-35

AI exposure studies

Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. BLS publishes these counts as yearly survey snapshots and cautions against reading them as a precise trend. In May 2021 BLS changed how it estimates these counts (a model-based method using three years of survey data) and moved fully to the 2018 occupation codes, so part of a change across 2021 can come from the method rather than the work.

Last updated .

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APA

Future History. (2026, October 11). Atmospheric and space scientists: jobs and pay since 1940 [Data set; data as of BLS OEWS May 2025; BLS Employment Projections 2025-35]. https://futurehistory.earth/timeline/19-2021

Chicago

Future History. "Atmospheric and space scientists: jobs and pay since 1940." Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. Last modified October 11, 2026. https://futurehistory.earth/timeline/19-2021.

BibTeX

@misc{futurehistory_19_2021, author = {{Future History}}, title = {Atmospheric and space scientists: jobs and pay since 1940}, year = {2026}, month = oct, howpublished = {\url{https://futurehistory.earth/timeline/19-2021}}, note = {Last updated 2026-10-11. Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35}, }

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187519001925195019752000now
Country
2026
Known then as Signal Corps Observer / Weather Bureau Observer (civilian and military service)
Known then as Meteorologist (Weather Bureau station director title adopted 1915)
Known then as Meteorologist / Forecaster (Weather Bureau, military branches, and growing private sector)
Known then as Atmospheric Scientist / Meteorologist (NOAA era, specialization proliferates)
Known then as Atmospheric and Space Scientists (BLS SOC 19-2021)
Known today as Atmospheric and Space Scientists (BLS SOC 19-2021)
Projected to still be called Atmospheric and Space Scientists (BLS SOC 19-2021)
Data from the May 2025 survey
US Employment
10K
Est.

Stated in source: BLS OEWS

Official statistic
Report an error
Median Annual Wage
$99,070
Est.

≈ $96,530 in 2024 prices (derived by us, CPI-adjusted)

Official statistic
Report an error
Beat · 1870

February 9, 1870: President Ulysses S. Grant signs the congressional resolution creating the federal weather service under the US Army Signal Service. The very first meteorological reports are transmitted simultaneously from 24 stations on November 1, 1870. The "Division of Telegrams and Reports for the Benefit of Commerce" is the first professional home of what will become atmospheric science.

Beat · 1915

October 9, 1915: The head of the US Weather Bureau officially decrees that the title "Section Director" be changed to "Meteorologist" for station heads. This is the formal moment the job acquired its modern professional identity. Prior to this date, the occupation had no standardized title that distinguished it as scientific rather than clerical.

Beat · 1919

December 29, 1919: Charles F. Brooks of the Blue Hill Observatory in Milton, Massachusetts gathers approximately 600 weather professionals at a meeting of the American Association for the Advancement of Science in St. Louis and founds the American Meteorological Society. The initial membership comes primarily from the US Signal Corps and Weather Bureau. AMS is incorporated January 21, 1920; the Bulletin of the American Meteorological Society begins publication the same year. The founding signals that meteorology has enough practitioners, enough shared knowledge, and enough professional identity to sustain its own learned society.

Beat · 1941

After the attack on Pearl Harbor, US weather forecasting is placed under wartime censorship to prevent storm track and weather data from aiding invading forces. Simultaneously, the military need for weather forecasting explodes: operations in North Africa, the Pacific, and the D-Day planning all depend on detailed meteorological support. US training programs produce more than 6,000 new weather forecasters during the war, up from roughly 400 professionals in 1940. By 1945, more than 900 women are working for the Weather Bureau as observers and forecasters.

Beat · 1950

April 1950: Jule Charney, Ragnar Fjortoft, and a team including John von Neumann run the first numerical weather prediction on the ENIAC digital computer at Princeton. The calculation takes more than 24 hours and requires the machine to run continuously, but it produces a recognizable 24-hour pressure forecast. The Joint Numerical Weather Prediction Unit is established July 1, 1954 as a three-service collaboration. NWP transitions from research curiosity to operational infrastructure by 1958, fundamentally changing what it means to be a meteorologist.

Beat · 1970

October 3, 1970: The National Oceanic and Atmospheric Administration (NOAA) is established within the Department of Commerce, absorbing the Weather Bureau, the Coast and Geodetic Survey, and several other agencies. The Weather Bureau is renamed the National Weather Service. NOAA's creation signals federal recognition that atmospheric, oceanic, and space environment science belong in a single integrated agency, broadening the professional scope of what "atmospheric scientist" means beyond just surface weather forecasting.

Beat · 2024

ECMWF launches the AIFS (Artificial Intelligence Integrated Forecasting System) into operational use alongside the physics-based IFS, providing near-equal forecast skill at less than 1% of the computational cost. Google DeepMind's GraphCast, published in Science in December 2023, had already demonstrated that a graph neural network trained on 40 years of historical data could outperform ECMWF IFS on 90% of 1,380 standard forecast variables. For the first time in 70 years, the dominant framework for producing global weather guidance is not physics-based numerical simulation but machine learning from historical observational data. AMS 2025 sessions and WMO working groups begin the profession-wide reckoning with what this means for training, accreditation, and the forecaster's role.

How to read this number

OEWS is a point-in-time survey snapshot, not a continuous time series; BLS advises against using it for year-over-year trend comparison.

Each dot is a cited figure over time; the dotted line only links them (values between aren't measured). Hollow dots are estimates.

Official statistic: Copied by a script from a statistics agency table or a published study's table, not retyped by hand. BLS figures and the forecast and exposure tables are also re-checked against that table on every data run. Researched context: Researched from the cited source rather than copied from it by a script, so it is not machine-checked yet. Spot a mistake? Each figure has a “Report an error” link.

Cited forecasts · present → 2035

Will AI replace atmospheric and space scientists? What the research says

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, we show the disagreement rather than an average.

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, the spread is the honest signal.

What published forecasts say
Forecast change in the number of people doing this work, as published by each named source. Bars share one scale on every role, centred on zero and reaching the end at ±30%. Grey marks a small change, 3% or less either way.
BLS Employment Projections 2025-35
→ 2035
+2.6%
BLS National Employment Matrix, 2025-35 cycle, line for SOC 19-2021 (Atmospheric and space scientists): 10.7 thousand jobs in 2025, projected 10.9 thousand in 2035, a change of +0.3 thousand (+2.6%). Matrix employment counts all jobs in the occupation, including self-employed workers (1.2% of this occupation in 2025). The average for all occupations in this cycle is +3.5%.
Official statisticReport an error
How exposed is this work to AI (research measures)
Scores as published by each study. Each row says what it measures. These measure what AI could technically do, not forecasts of jobs lost.
Eloundou et al. (2023), "GPTs are GPTs"
2023 study · Share of tasks exposed to LLMs (β, human-rated)
60.9%
of tasks
What this measures

Published occupation-level exposure from the paper's occ_level.csv for O*NET-SOC 19-2021.00 Atmospheric and Space Scientists. Shown: human-rated β = 0.609, the share of the occupation's tasks an LLM could do in at least half the time at equal quality, counting tasks that need LLM-powered software at half weight (β = E1 + 0.5 × E2). Other published measures for this occupation: human-rated α (LLM alone) 0.348 and γ (all LLM-assisted) 0.87; GPT-4-rated β 0.565. This measures how much of the work an LLM could touch, not how many jobs disappear.

Official statisticReport an error
Today, in this role

What's changing day to day for atmospheric and space scientists

Three parts of your work where AI is already doing real lifting, and what stays yours.

AI is sitting alongside you hereEvaluate and integrate AI-based NWP guidance (GraphCast, ECMWF AIFS, FourCastNet, GenCast) alongside traditional physics-based model output (GFS, NAM, ECMWF IFS, HRRR) to produce daily zone forecasts, examining where AI and physics-based models agree or diverge at critical lead times

Evaluate and integrate AI-based NWP guidance (GraphCast, ECMWF AIFS, FourCastNet, GenCast) alongside traditional physics-based model output (GFS, NAM, ECMWF IFS, HRRR) to produce daily zone forecasts, examining where AI and physics-based models agree or diverge at critical lead times; diagnosing AI model failure signatures on extreme or out-of-distribution synoptic patterns; applying model output statistics (MOS) and AI postprocessing calibration to reduce AI-specific biases for your region; and issuing National Weather Service zone forecast products and Area Forecast Discussions that explicitly document model reasoning for the public record.[3],[5],[1]

Where your edge is

GraphCast and ECMWF AIFS have automated the majority of rote medium-range deterministic forecast production: global guidance that once required 1,000 CPU-hours now runs on a single GPU in under a minute. Your irreplaceable contribution is interpreting where AI guidance is wrong: current ML NWP systems fail systematically on extreme events (record-breaking ridge amplitudes, novel recurving cyclone tracks, deep-layer moisture advection events outside the training climatology), exactly the events where forecast error has the highest consequence. Build a personal verification library of cases where GraphCast diverged from GFS/IFS and the observed truth, categorized by synoptic regime. This case knowledge lets you weight AI guidance appropriately when the current pattern resembles a historical failure mode for ML models.

AI is sitting alongside you hereDevelop and validate AI weather model postprocessing and bias correction systems, training statistical and ML-based Model Output Statistics (MOS) systems that adjust raw AI NWP guidance for local biases specific to a station or region

Develop and validate AI weather model postprocessing and bias correction systems, training statistical and ML-based Model Output Statistics (MOS) systems that adjust raw AI NWP guidance for local biases specific to a station or region; evaluating AI model performance against archived observations using standard NWS verification metrics (Brier score, CRPS, track error MAE); designing hybrid forecast systems that blend physics-based and ML-based model output optimally for a specific weather element and geographic domain; and contributing to ECMWF/WMO interoperability standards for AI forecast product exchange.[10],[1]

Where your edge is

The operationalization of AI NWP guidance requires systematic bias characterization: GraphCast and AIFS have known biases for specific weather regimes (deep winter cyclones, tropical convection diurnal cycle, orographic precipitation) that MOS and postprocessing calibration can partially correct. This is a high-value technical skill in 2025-2026: national meteorological services and private weather companies both need meteorologists who can build and evaluate AI postprocessing systems rather than simply consuming AI output. Invest in Python-based statistical learning (scikit-learn, PyTorch) and NWP verification toolkits (METplus): the meteorologist who can quantify AI model uncertainty and build calibration corrections for their region is increasingly the most technically valuable forecaster at any NWS office or private weather firm.

AI is sitting alongside you hereAnalyze satellite remote sensing data for operational weather monitoring and research, processing GOES-R series (GOES-16/18/U) Level 1b radiance data through AI-based retrieval algorithms for cloud-top height, fire detection (FDC product), derived motion winds, and total precipitable water

Analyze satellite remote sensing data for operational weather monitoring and research, processing GOES-R series (GOES-16/18/U) Level 1b radiance data through AI-based retrieval algorithms for cloud-top height, fire detection (FDC product), derived motion winds, and total precipitable water; interpreting polar-orbiting (JPSS VIIRS, SNPP) microwave and infrared sounder retrievals for atmospheric profiling; applying convolutional neural network classifiers for tropical cyclone intensity estimation (Dvorak technique AI enhancement) and severe convective mode classification from satellite imagery.[14],[1],[12]

Where your edge is

GOES-R AI retrieval algorithms have automated the production of dozens of real-time meteorological products that previously required manual analyst interpretation: fire detection, cloud-top temperature, derived winds, and lightning density all arrive as pre-processed AI products rather than raw radiance data. Your scientific judgment is essential for two tasks: (1) diagnosing retrieval failures in challenging scenes (high solar zenith angle, multi-layer cloud, smoke aerosol contamination of fire detection algorithms) that produce systematic errors in AI products you are using downstream; and (2) integrating satellite data with ground truth observations and NWP output to assess whether a satellite-detected signal represents a real meteorological or environmental event. The GOES-U generation (launched 2024) adds a new solar imagery channel, learning its AI product suite is a near-term skill investment.

Where atmospheric and space scientists go next

Natural next steps for someone with your foundation: not exits, evolutions.

What each move takes

A direction you could grow

Natural Sciences Managers

Senior atmospheric scientists at NOAA/NWS program offices, national labs (NCAR, PNNL, ORNL), and private weather companies naturally transition into Natural Sciences Manager roles, directing research programs, managing forecasting service operations, and overseeing the transition from research to operational AI weather systems. The WMO Early Warnings for All initiative, the NOAA Weather-Ready Nation program, and the expanding private climate risk market all require managers who combine meteorological domain authority with program management and AI strategy capability. The transition to management offers a meaningful CRI uplift because managerial roles are structurally more insulated from automation than individual-contributor forecasting: the AI NWP disruption affects the production forecaster more directly than the program director who manages the forecasting operation's AI adoption roadmap. Transition difficulty is Medium because program management requires acquiring budget, contracting, personnel evaluation, and stakeholder communication skills not typically developed in operational forecasting or research careers.

What you'd add
  • · Federal program management: NOAA and NSF grant and cooperative agreement management; CPIC (Capital Planning and Investment Control) for IT and AI system procurements; federal acquisition basics for weather AI vendor contracts; GPRA program performance metrics for reporting to Congress
  • · AI strategy and technology transition: structuring research-to-operations (R2O) pathways for AI weather models from NSSL/ESRL research to NWS operational implementation; AI system evaluation frameworks (forecast verification standards, operational acceptance testing); vendor assessment for private AI weather model procurements
  • · Personnel and team development: NWS WFO workforce planning; mentoring forecasters on AI tool adoption; performance evaluation for hybrid AI+human forecast operations; managing interdisciplinary teams across meteorologists, software engineers, and ML researchers
  • · Stakeholder and policy communication: congressional testimony and legislative affairs for NOAA programs; media spokesperson training for high-impact weather events; inter-agency coordination (FEMA, FAA, DOT) for multi-hazard weather response
  • · Budget management: PPBE (Planning, Programming, Budgeting, and Execution) cycle at a federal agency or R&D lab; cost-benefit analysis for AI NWP infrastructure investments; competitive grant writing for NOAA Climate Program Office and NSF Atmospheric Sciences
What it takesSome new skills to pick up
Tools of the era

How has the work of atmospheric and space scientists changed since 1870?

The tools that defined the work, one line per era. Select an era to see how it reshaped the work.

  • Telegraph network and synoptic chart (Weather Bureau founding era)

    The foundational tool of professional meteorology was the electric telegraph combined with hand-drawn synoptic weather maps. The Signal Service established a nationwide network of observation stations connected by telegraph; readings were transmitted at fixed hours to the central office in Washington, where staff plotted surface observations onto charts and identified storm systems. This was the entire technical apparatus of the profession for its first fifty years: systematic observation, transmission, and human pattern recognition on paper maps. The Smithsonian Institution had pioneered the observation network in the 1850s, but it was the telegraph that turned weather observation from a correspondence exercise into near-real-time operational forecasting.

    Effect on the work

    The telegraph network created the occupation itself: without a communications infrastructure fast enough to outrun storms, professional weather forecasting was impossible. The first few dozen stations in 1870 grew to hundreds of stations by 1900, each requiring trained observers and creating a growing federal scientific workforce.

  • Radiosonde and upper-air sounding network (1937 US launch, operational by 1940s)

    The radiosonde, a small instrument package carried aloft by weather balloon to measure temperature, pressure, humidity, and wind at altitude, gave meteorologists their first systematic view of the three-dimensional atmosphere. The United States launched its first radiosonde in 1937; by the early 1940s a nationwide upper-air sounding network was operational, with ascents twice daily at dozens of stations. For forecasters, the radiosonde transformed the craft: upper-level charts showing jet stream position, temperature advection, and moisture flux became the foundation of synoptic analysis. The ability to see the atmosphere in three dimensions was the prerequisite for every subsequent development in numerical weather prediction.

    Effect on the work

    Upper-air analysis was cognitively demanding and created a new specialty within the profession. Radiosonde data processing, tephigram construction, and upper-air chart analysis required specialized training, contributing to the formalization of meteorological education and the establishment of university programs in atmospheric science during the 1940s and 1950s.

  • Numerical weather prediction on mainframe computers (ENIAC 1950, Joint NWP Unit 1954)

    The first numerical weather prediction was computed on the ENIAC digital computer at the Institute for Advanced Study in Princeton in April 1950, by a team led by Jule Charney and including John von Neumann. The calculation required round-the-clock operation of the machine and took more than a day to produce a single one-day forecast, but it worked: the equations of atmospheric dynamics could be solved on a computer. The Joint Numerical Weather Prediction Unit was established in July 1954 as a collaboration of the Weather Bureau, Air Weather Service, and Navy, and began issuing experimental numerical forecasts operationally by 1958. NWP on mainframes transformed the forecasting workflow: instead of pattern-matching from memory and charts, forecasters began interpreting model output, checking it against observations, and applying local knowledge to adjust the guidance. The meteorologist's role shifted from primary analyst to skilled interpreter.

    Effect on the work

    NWP did not reduce forecaster employment but changed the skill mix required. Proficiency in atmospheric dynamics and thermodynamics became more important; hand-drawing skills became less so. The transition drove the expansion of university atmospheric science programs: by 1970 every major research university offered graduate meteorology, and the profession had a credentialed academic pipeline for the first time.

  • Geostationary weather satellite (TIROS 1960, GOES series from 1975)

    TIROS-1, launched April 1, 1960, returned the first satellite images of Earth's cloud cover and immediately transformed tropical forecasting: for the first time, atmospheric scientists could see the structure of hurricanes and large-scale cloud systems over data-sparse ocean regions. The GOES (Geostationary Operational Environmental Satellite) series, beginning with GOES-1 in 1975, provided continuous coverage of the Western Hemisphere from geostationary orbit, enabling forecasters to track storm development, monitor cloud-top temperatures as a proxy for convective intensity, and analyze mesoscale weather features in near real-time. Satellite imagery became the primary situational awareness tool for severe weather forecasting and a defining feature of the television meteorologist's presentation.

    Effect on the work

    Satellite data processing created a new specialty (satellite meteorologist) and a large civilian government workforce at NOAA's satellite operations centers. The visual clarity of satellite imagery also democratized weather awareness for the public, fueling demand for broadcast meteorologists who could translate satellite imagery into accessible forecasts.

  • WSR-88D Doppler radar network (NEXRAD) and workstation-based forecast tools

    The WSR-88D (Weather Surveillance Radar, 1988 Doppler) network, deployed to more than a hundred sites across the United States beginning in the late 1980s and early 1990s, gave every NWS office the ability to see wind speed and direction inside precipitation systems. Doppler radar transformed severe weather operations: tornado lead times improved from near-zero (or negative, where the warning came after the tornado) to several minutes by the 2010s. The simultaneous rollout of AWIPS (Advanced Weather Interactive Processing System) workstations gave forecasters integrated access to model output, satellite, radar, and surface observations on a single screen. This represented the modern forecaster's workstation paradigm that persisted until AI NWP tools began to supplant parts of it after 2023.

    Effect on the work

    NEXRAD deployment required additional trained forecasters at newly opened round-the-clock Weather Forecast Offices, increasing NWS employment modestly in the early 1990s. The improved warning skill built public trust in NWS forecasts and reinforced the political case for maintaining a well-staffed federal forecasting workforce.

  • High-resolution ensemble NWP (GFS, ECMWF IFS, HRRR) on supercomputers

    The period from roughly 2000 to 2023 was the golden age of physics-based numerical weather prediction at scale. NOAA's Global Forecast System (GFS), ECMWF's Integrated Forecasting System (IFS), and the High-Resolution Rapid Refresh (HRRR) for convective-scale forecasting ran on supercomputers with ever-increasing horizontal resolution and ensemble size, delivering progressively more accurate forecasts. By the mid-2010s, day-5 forecast accuracy equaled what day-3 accuracy had been in the 1990s, and the seven-day forecast had become operationally reliable for planning purposes. The operational meteorologist's job became managing a rich ensemble of model guidance products: knowing which models performed best for specific weather patterns in their region, applying local MOS (Model Output Statistics) corrections, and communicating probabilistic uncertainty to the public.

    Effect on the work

    The supercomputer NWP era raised the technical floor for operational meteorology: a working knowledge of GFS vs. ECMWF model characteristics, ensemble probability interpretation, and MOS calibration became standard professional expectations. Private-sector weather companies (IBM Weather Company, DTN, Maxar) built businesses on value-added NWP post-processing, creating new employment outside the federal sector.

  • AI foundation model NWP: GraphCast, ECMWF AIFS, GenCast, Aurora, FourCastNet

    The publication of Google DeepMind's GraphCast in Science (December 2023) marked a qualitative break in the history of weather prediction. GraphCast, trained on decades of ERA5 reanalysis data, produced global forecasts in under one minute on a single TPU, outperforming ECMWF IFS on 90% of 1,380 test variables. DeepMind's GenCast (Nature, December 2024) generated probabilistic ensemble forecasts in several minutes, outperforming ECMWF ENS on 97.4% of 1,320 targets. ECMWF launched its AIFS (Artificial Intelligence Integrated Forecasting System) into operational use in 2024, providing near-IFS skill at less than 1% of IFS computational cost. Microsoft's Aurora foundation model (arXiv 2405.13063, May 2024) demonstrated skill across weather, air quality, space weather, and ocean wave prediction from a single 1.3-billion-parameter model. NVIDIA's FourCastNet and Earth-2 platform enabled km-scale regional downscaling at GPU speed. The paradigm shift is not incremental: the computational bottleneck that defined operational meteorology for decades has been removed. The forecaster who understands how AI NWP fails, and can translate its probabilistic output into accountable public guidance for high-impact events, is now the central value proposition of the profession.

    Effect on the work

    The rote production layer of medium-range global NWP guidance has been automated. NWS forecasters face pressure to adapt their role from global-model interpretation toward high-impact event judgment, communication, and AI forecast quality control.

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Atmospheric and Space Scientists employment and pay by year

On record since1870
Latest US employment10,000 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Latest US median pay$99,070 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Outlook+2.6% by 2035Source: BLS Employment Projections 2025-35Official statisticReport an error

In other countries

The latest figure each country's statistics agency publishes for this job. Each uses its own occupation definition, survey basis and currency, so the numbers are not directly comparable with each other or with the US figures.

  • Canada (NOC 21103)

    Employment, 2020
    915, Statistics Canada Census 2021 Official statistic Report an errorStatistics Canada Census of Population 2021: number of persons with employment income in NOC 21103 Meteorologists and climatologists, Canada, reference year 2020. A recipients basis (anyone with employment income during the year), slightly broader than a point-in-time employed count. Statistics Canada Open Licence.
    Median pay, 2020
    C$106,000, Statistics Canada Census 2021 Official statistic Report an errorStatistics Canada Census of Population 2021 (Table 98-10-0586): median employment income for NOC 21103 Meteorologists and climatologists, Canada, reference year 2020, full-year full-time workers (the basis comparable to the US OEWS full-time median). Statistics Canada Open Licence.
View all 27 years of cited US data, each linked to its source
YearUS employmentAnnual pay (median unless marked)SourceReport
1940400$2,200Est., NWS Heritage Virtual Lab / Commerce.gov (employment) Researched contextEst., NWS Heritage Virtual Lab (pay) Researched contextReport
19456,400n/aEst., NWS Heritage Virtual Lab / Department… Researched contextReport
1980n/a$28,000Est., American Meteorological Society Researched contextReport
20008,000n/aEst., NOAA National Weather Service Careers Researched contextReport
20036,490$65,670BLS OEWS May 2003 Official statisticReport
20047,070$70,100BLS OEWS May 2004 Official statisticReport
20057,050$73,940BLS OEWS May 2005 Official statisticReport
20068,250$77,150BLS OEWS May 2006 Official statisticReport
20078,750$78,390BLS OEWS May 2007 Official statisticReport
20088,860$81,290BLS OEWS May 2008 Official statisticReport
20098,320$84,710BLS OEWS May 2009 Official statisticReport
20108,640$87,780BLS OEWS May 2010 Official statisticReport
20119,640$89,790BLS OEWS May 2011 Official statisticReport
201210,190$89,260BLS OEWS May 2012 Official statisticReport
201310,800$87,030BLS OEWS May 2013 Official statisticReport
201410,850$87,980BLS OEWS May 2014 Official statisticReport
201510,370$89,820BLS OEWS May 2015 Official statisticReport
20169,800$92,460BLS OEWS May 2016 Official statisticReport
20178,940$92,070BLS OEWS May 2017 Official statisticReport
20189,310$94,110BLS OEWS May 2018 Official statisticReport
20199,290$95,380BLS OEWS May 2019 Official statisticReport
202010,210$99,740BLS OEWS May 2020 Official statisticReport
20218,520$94,570BLS OEWS May 2021 Official statisticReport
20229,900$83,780BLS OEWS May 2022 Official statisticReport
20239,310$92,860BLS OEWS May 2023 Official statisticReport
20248,780$97,450BLS OEWS May 2024 (employment) Official statisticBLS OEWS May 2024 (pay) Official statisticReport
202510,000$99,070BLS OEWS May 2025 Official statisticReport
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