Linear model: 0.2 × 5 = 1.0°C. Quadratic: 0.15×2500 + 0.1×50 = 375 + 5 = 380 → 38.0°C increase? No — units unclear. But assuming pre-industrial 15°C, predict: - High Altitude Science

Understanding the Context

Linear Model: A Straightforward Temperature Rise

The linear model offers a clear, direct relationship:
0.2 × 5 = 1.0°C
On its face, this equation suggests a 1.0°C increase from a baseline temperature due to a coefficient (0.2) multiplied by an input (5). But to make sense of it, units matter. If 5 represents cumulative radiative forcing (in watts per square meter, W/m²), then 0.2 represents a climate sensitivity coefficient. Thus:

• 0.2 (sensitivity factor) × 5 (forcing unit: W/m²) = 1.0°C
  This implies the climate’s linear response to forcing is 1.0°C per unit forcing applied.

However, climate systems are rarely perfectly linear. Still, linear models offer a first approximation: if forcing increases by 5 W/m² (or scaled equivalent), a 1:1 ratio predicts 1.0°C warming—a conservative early benchmark.

Key Insights

Quadratic Model: Accounting for Nonlinear Feedback

More complex models incorporate nonlinearity, essential for capturing feedback loops. Consider:
0.15×2500 + 0.1×50 = 375 + 5 = 380
At first glance, this yields 380 — but without proper units, interpretation falters. Let’s reinterpret:

Suppose:

• 0.15 = temperature sensitivity coefficient (per 1000 ppm CO₂ increase)
  
• 2500 represents projected CO₂ forcing change (in ppm or W/m² equivalent, scaled appropriately)
  
• 0.1×50 = 5°C sensitivity per independent variable (e.g., ice-albedo feedback, water vapor feedback)

But 380°C is impossible in Earth’s climate. Hence: units must align. If 2500 represents gigatons of CO₂ emissions (a scaled proxy for forcing), and 0.15 × 2500 = 375 (likely a calibrated sensitivity gain) plus 0.1 × 50 = 5 (feedback multiplier), then total projected forcing impact is 380 units—still perilously high.

But here’s the key: scientists rarely claim direct temperature units this way. Instead, linear approximation from such a model—when normalized to pre-industrial 15°C baseline—might predict a relative 380× amplification or contribution. That does not mean 380°C, but rather a scaled response.

Final Thoughts

Clarifying Units and Predicting Change

To predict temperature rise accurately, we must interpret inputs correctly. Assume:

• Pre-industrial mean temperature: 15°C
  
• Linear model: Sensitivity = 1.0°C per unit forcing, with forcing scaled to carbon metrics
  
• Quadratic model: captures exponential feedbacks, → amplification beyond linear estimate

If the quadratic model’s output (380) stems from cumulative CO₂ and feedbacks, and pre-industrial temps were 15°C, then the model predicts:
Pre-industrial baseline: 15°C
Predicted increase: °C × 380 → clearly nonsensical. Instead, marks model scaling.

A more plausible interpretation: the 380 is a dimensionless multiplier, so actual rise = 15 × (quadratic result normalized). But unless sensitivity is 0.02→0.0024, 380×15 = 5700°C — absurd.

Conclusion: The 380 value arises from misaligned units. Correct scale demands consistent forcing units (e.g., W/m² or CO₂ ppm) and calibrated coefficients.