The Diurnal Temperature Range and its Recent Evolution

1
The Diurnal Temperature Range and its Recent
Evolution

• Brian Olsen
• April 20, 2006

2
Outline

• Introduction to DTR
• Local Examples
• Global Trends
• What affects DTR?
• Conclusions

3
The Global Warming Signal
Multi-decadal pause in warming
IPCC, 2001
4
Diurnal Temperature Range (DTR)

• DTR High - Low
• High Temperature 81F
• Low Temperature 58 F
• DTR 81 F 58 F 23 F

Lowest in winter
Highest in summer
SLC WFO
5
KSLC Trends
Warming?
Definite Warming!
E. Crossman

• But why?
• What about a nearby rural location?

6
KENV Monthly Mean of Daily Tmax, Tmin, and DTR
(1913-2004)
General loss of warm colors in summer
Missing data
General gain of cool colors in winter
B. Olsen
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KENV Trends

• Tmax actually decreases
• Tmin increases
• DTR decreases

But where did the trend go?

• Obviously, this is a complicated problem

Pause in warming
B. Olsen
8
Global Trends(1950 - 2004)
StrongWarming
Steady
StrongWarming
Warming
Decreasing
Steady
Vose et al. 2005
Vose et al. 2005
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1950 2004 1979 - 2004
GreatestWarming
GreatestWarming
Vose et al. 2005
10
Global Trends(1979 2004)
Trendless
Vose et al. 2005
Vose et al. 2005
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Why? What Affects DTR?

• Urban Heat Island (increase minimums) local
  effect
• Irrigation Desertification not globally
  significant local effect
• Climate variables
• Cloud coverage and altitudeexplain 40 of
  variance
• All vars 55 of variance
• Other considerations
• Jet Contrails (Travis et al. 2004)
• Tropospheric aerosol
• GHG emissions

Karl et al. 1993
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Conclusions

• The planet is warming
• The planet is getting less cool
• Historically, minimum temps increased more than
  maximum temps gt decrease in DTR
• Winter / Spring warmed the most
• Rate of warming has increased since 1980
• Recently, minimum temps and maximum temps rise in
  step gt no trend in DTR
• Winter continues to become more mild
• Signal most apparent in Northern Hemisphere
• Cloud cover is significant

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(In)Conclusions

• DTR trend is difficult to measure
• difference between two large quantities
• Noisy / lots of temporal variability
• Wide regional variation
• Global generalizations may be a poor measure
• Local effects may overpower global trend
• Model / Observation discrepancy

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Thanks!