# Climatology Expert

> Expert-level climatology covering climate system components, climate forcing and feedbacks, paleoclimatology, climate modeling, and climate change science.

- Skill: `luokai0/climatology-expert` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add luokai0/climatology-expert`
- Raw SKILL.md: https://api.skillmd.com/api/skills/luokai0/climatology-expert/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: luokai0 (https://skillmd.com/u/luokai0)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/luokai0/climatology-expert

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# Climatology Expert

## Before Starting
1. Climate variability or climate change focus?
2. Observational analysis or modeling?
3. Which timescale? (interannual, decadal, millennial)

## Core Expertise Areas

### Climate System
Components: atmosphere, ocean, cryosphere, land surface, biosphere.
Energy balance: incoming solar vs outgoing longwave radiation.
Greenhouse effect: CO2, H2O, CH4, N2O absorb and re-emit longwave radiation.
Albedo: fraction of solar radiation reflected — ice-albedo feedback amplifies warming.

### Climate Forcing and Feedbacks
Radiative forcing: change in net radiation caused by external factor (W/m2).
CO2 forcing: 3.7 W/m2 per doubling of CO2 concentration.
Positive feedbacks: ice-albedo, water vapor (strongest), cloud (uncertain sign).
Negative feedbacks: Planck (blackbody), lapse rate.
Climate sensitivity: equilibrium warming per doubling of CO2 — likely 2.5-4 C.

### Paleoclimatology
Ice cores: temperature (oxygen isotopes), CO2, dust, dating by annual layers.
Marine sediments: foram isotopes, SST proxies, long records to millions of years.
Tree rings: temperature and precipitation proxies, annual resolution.
Milankovitch cycles: orbital forcing — eccentricity (100 kyr), obliquity (41 kyr), precession (23 kyr).

### Climate Modeling
Energy balance models: simple, global or zonal average.
GCM: general circulation models, full 3D atmosphere and ocean.
CMIP: Coupled Model Intercomparison Project — ensemble of model projections.
Downscaling: statistical or dynamical methods to increase spatial resolution.

## Best Practices
- Distinguish internal variability from forced trends
- Use multiple lines of evidence for attribution
- Compare model output to multiple observational datasets
- Assess model spread as uncertainty in projections

## Common Pitfalls
| Pitfall | Fix |
|---|---|
| Confusing weather and climate | Climate is statistics of weather over 30+ years |
| Ignoring aerosol forcing | Aerosols offset some greenhouse warming |
| Single model projection as truth | Always use ensemble range for uncertainty |
| Misinterpreting paleo proxies | Proxies integrate multiple climate variables |

## Related Skills
- meteorology-expert
- oceanography-expert
- geology-expert