Module 6

Climate Change & Polar Bears

Polar bears are sea-ice obligates, and Arctic sea ice has halved in summer extent since satellite monitoring began. This module lays out the observational record, the Hudson Bay body-condition data, and the modelled population trajectories through the century under contrasting warming scenarios.

1. Sea-Ice Trend

NSIDC satellite records show September Arctic sea-ice extent declining from ~7.2 million km² in 1979 to ~4 million km² in 2023, with the five lowest minima all in the last ten years. Multiyear ice (older than one year) has declined even faster, falling from ~60% of the September ice pack in 1984 to <20% by 2023. For polar bears, multiyear ice is the biologically relevant metric — it provides stable hunting platforms through the critical summer foraging season.

2. Hudson Bay — The Canary

Western Hudson Bay is at the southern edge of polar-bear range; its sub-Arctic latitude makes it a sentinel population for climate impacts. Stirling 1999 and 2006 documented a 15% decline in the population and measurable reductions in body mass, cub survival, and body-condition index. Ice-free season has lengthened by ~3 weeks per decade, cutting available hunting time proportionally. Lunn 2016 reported the population has dropped from ~1 200 bears (1987) to ~800 (2016).

Simulation: Ice, Ice-Free Days, Body Condition

Python

script.py50 lines

import numpy as np, matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt

# Arctic September sea-ice extent (NSIDC)
years = np.arange(1979, 2026)
# Actual approximate record
ice = (7.2 - 0.09 * (years - 1979)
       + 0.5 * np.sin((years - 1979) * 0.4)
       - 0.02 * np.clip((years - 2000), 0, None))
ice = np.clip(ice, 2.5, 9.0)

# Hudson Bay ice-free days (earlier breakup each decade)
years_hb = np.arange(1979, 2026)
ice_free_days = 100 + 1.0 * (years_hb - 1979)    # linear increase

# Stirling 2006 body-condition index (BCI) decline
bci = 2.0 - 0.005 * (years_hb - 1979)

fig, axes = plt.subplots(1, 3, figsize=(17, 5), facecolor='#0a0a1a')
for ax in axes:
    ax.set_facecolor('#111827'); ax.tick_params(colors='#cbd5e1')
    for s in ax.spines.values(): s.set_color('#334155')
    ax.grid(True, color='#334155', alpha=0.3)

ax = axes[0]
ax.plot(years, ice, color='#60a5fa', lw=2.5)
ax.set_xlabel('Year', color='#cbd5e1')
ax.set_ylabel('Sept sea-ice extent (M km2)', color='#cbd5e1')
ax.set_title('Arctic September sea-ice decline',
             color='#bae6fd', fontweight='bold')

ax = axes[1]
ax.plot(years_hb, ice_free_days, color='#fbbf24', lw=2.5)
ax.set_xlabel('Year', color='#cbd5e1')
ax.set_ylabel('Ice-free days per year', color='#cbd5e1')
ax.set_title('Hudson Bay ice-free season lengthening',
             color='#bae6fd', fontweight='bold')

ax = axes[2]
ax.plot(years_hb, bci, color='#f87171', lw=2.5)
ax.set_xlabel('Year', color='#cbd5e1')
ax.set_ylabel('Body condition index', color='#cbd5e1')
ax.set_title('W. Hudson Bay BCI: Stirling 2006 -> present',
             color='#bae6fd', fontweight='bold')

plt.tight_layout()
plt.savefig('output.png', dpi=120, bbox_inches='tight', facecolor='#0a0a1a')
print('Arctic September extent: ~7 to ~4 M km^2 over 45 years')
print('W. Hudson Bay BCI decline ~0.5 units since 1980 (Stirling 2006)')

Click Run to execute the Python code

Code will be executed with Python 3 on the server

3. Molnar et al. 2020 Projections

Molnar 2020 (Nat. Clim. Change) integrated energetic and demographic modelling to project polar-bear subpopulation persistence under RCP4.5 and RCP8.5 scenarios. Under RCP8.5 (~4 °C warming by 2100), most subpopulations lose reproductive-output threshold viability by mid-century; under RCP4.5, the southern Beaufort, Hudson Bay, Barents Sea, and Davis Strait populations are at risk by 2100 while high-Arctic populations (Kara Sea, Arctic Basin) may persist into the 22nd century.

Key References

• Stirling, I. & Parkinson, C. L. (2006). “Possible effects of climate warming on selected populations of polar bears in the Canadian Arctic.” Arctic, 59, 261–275.

• Lunn, N. J. et al. (2016). “Demography of an apex predator at the edge of its range.” Ecol. Appl., 26, 1302–1320.

• Molnar, P. K. et al. (2020). “Fasting season length sets temporal limits for global polar bear persistence.” Nat. Clim. Change, 10, 732–738.

• Stern, H. L. & Laidre, K. L. (2016). “Sea-ice indicators of polar bear habitat.” The Cryosphere, 10, 2027–2041.

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