By

Anthony R. LupoDepartment of Soil, Environmental, and Atmospheric

Science302 E ABNR BuildingUniversity of MissouriColumbia, MO 65211

General Outline:

Causes of interannual and interdecadal global temperature variations.

Some real results from the midwest USA. (acknowledgement: K. Birk, 2006)

Interannual variability and climate change.

The Earth’s climate (as part of the Earth – Atmosphere System) is generally considered to be a boundary – value problem.

The earth-atmosphere system, courtesy of Dr. Richard Rood.(http://aoss.engin.umich.edu/class/aoss605/lectures/)

On the time-scale of a year to a decade, the dominant phenomenon in the climate system is the El Niño and Southern Oscillation (and the SST variations that accompany it) (e.g. Mokhov et al. 2004)

The El Niño cycle has been known for some time by the scientific community (e.g., Sir Gilbert Walker), and impacts the global weather beyond the tropical Pacific.

From Lupo et al. 2007 (monthly temperatures over a 50 year period)

Power spectra of that same temperature record (cycles per decade)

ENSO impacts the position of the jet stream and other phenomena which impact the temperatures on the time-scale of seasons (e.g. via blocking – Wiedenmann et al. 2002; or the MJO – e.g. Mo, 1999, 2000)Region Events Days Duration BI

N HemisphereAtlantic 12.9 / 12.5 / 13.6 113.1 / 105.3 / 111.2 8.8 / 8.4 / 8.2 3.30 / 3.36 / 3.50Pacific 6.5 / 6.5 / 7.4 45.3 / 49.4 / 59.2 7.0 / 7.6 / 8.0 2.80 / 3.35 / 3.01

Continental 4.7 / 5.8 / 5.2 40.1 / 46.8 / 39.8 8.4 / 8.1 / 7.7 2.70 / 2.63 / 2.52Total 24.1 / 24.8 / 26.2 198.5 / 201.5 / 210.2 8.2 / 8.1 / 8.0 3.05 / 3.19 / 3.17

S. HemisphereAtlantic 1.0 / 1.0 / 0.7 5.6 / 6.3 / 4.3 5.6 / 6.0 / 6.0 3.62 / 3.00 / 3.15Pacific 9.0 / 7.8 / 7.0 70.6 / 56.9 / 54.4 7.8 / 7.3 / 7.8 2.82 / 2.74 / 2.83Indian 1.1 / 0.9 / 0.7 7.8 / 6.2 / 3.8 6.8 / 7.0 / 5.3 2.78 / 2.88 / 2.57Total 11.1 / 9.7 / 8.4 83.9 / 69.4 / 62.4 7.5 / 7.2 / 7.4 2.89 / 2.78 / 2.83

ENSO will impact the mean position and strength of general circulation features such as the Indian Monsoon, the Tropical Atlantic Trough (which impacts hurricane frequency and strength in the Atlantic – e.g. Gray, 1984; Lupo et al. 2008)

All these will have an impact on the number and strength of extreme weather (warm and cold) within seasons, and numerous studies show this.

There are other factors that may influence ENSO (e.g. Landscheidt uses the polarity of the Earth’s magnetic field to predict ENSO.

Further, there are studies that indicate that interannual variability (as related to ENSO) can be modulated or tempered by interdecadal variability (e.g. PDO, solar forcing, etc).

Study Region (Birk - 2006) (data gathered from 1900 – 2005 – where possible)

Minneapolis, MN: ENSO variability changes from north to south across our region. ENSO variability stronger in PDO 1 phase (north).

Minneapolis, MNENSO Temperature variability PDO1/PDO2

Total El Niño Neutral La Niña ENSO

Season Temperature Anomaly Anomaly Anomaly Variability

Annual 45.1 +1.6/+0.2 -0.1/-0.2 -0.1/-0.2 1.8/0.4

Winter 16.8 +4.1/+0.7 -0.5/-0.3 -1.2/-0.9 5.3/1.7

Spring 44.9 +2.7/+0.1 +0.4/-0.8 -0.4/-0.4 3.1/1.0

Summer 70.6 +0.9/+0.2 +0.2/-0.1 +0.1/0.0 0.8/0.3Fall 48.0 -1.1/-0.4 -0.5/+0.6 +1.0/+0.4 2.1/1.0

Fayetteville, AR: ENSO variability changes from north to south across our region. ENSO variability not stronger in either phase. Fayetteville, AR

ENSO Temperature variability PDO1/PDO2

Total El Niño Neutral La Niña ENSO

Season Temperature Anomaly Anomaly Anomaly Variability

Annual 58.5 -0.3/-0.3 -0.3/+0.1 +0.9/+0.6 1.2/0.9

Winter 39.1 +0.3/-0.5 -1.0/+0.1 +0.7/+1.9 1.8/2.5

Spring 57.8 -0.7/-0.4 -0.1/0.0 +1.0/+0.4 1.7/0.8

Summer 77.1 +0.3/+0.2 +0.4/0.0 +0.5/-0.7 0.2/0.8

Fall 60.0 -1.2/-0.5 -0.3/0.0 +1.4/+0.8 2.6/1.3

El Niño winter (1997) La Niña winter (1971)

(2 major blocking) (4 major blocking )

Recently, Zorita et al. 2008 use statistical analysis and conclude that it is highly improbable that the last 10 – 20 years of the global data set should contain the warmest years.

They first assumed a stationary climate and then used two autoregressive models, one including long term variability, the other using short term variability.

They also conclude that this is indicative of the anthropogenic forcing since the probability is very low that nature could produce so many warm years at the end of a data set.

Here, a simple model of the global temperatures since the mid-to-late 1800’s is built using three simple functions which are in phase with, or mimic, what could be natural variations and demonstrate that the latest years in the data set could easily be the warmest.

This analysis does not invoke statistical analysis, nor do we claim here to provides a physical explanation for the global temperature record, or models the physics realistically, it is for demonstration purposes!

A more complete and similar model can be found in Klyshtorin and Lybushkin (2003, 2007)

From K. Birk (2006) (paper in preparation)

FIG. 2.1. Annual global surface temperature departuresfrom the 1961 to 1990 average. [Sources: NOAA/NCDC; CRU/UKMO (HadCRUT3); and NASA GISS.] (From BAMS, July 2008)

Natural Forcing 1 (solar? coming out of the little ice age? Something yet to be discovered?) Linear trend at the rate of 2 units per 125 time units.

gi

ti

0 20 40 60 80 100 120 140

4

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2

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Natural Forcing 2 (oceans? Pacific Decadal Oscillation? NAO? AMO? – here amplitude 1 unit and period of 62.5 years)

bi

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0 20 40 60 80 100 120 140

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Natural Forcing 3 (oceans? ENSO? – here amplitude 1 unit and period of ~6.0 years – similar to that found by Birk, 2006)

ai

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0 20 40 60 80 100 120 140

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2

0

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Natural Forcing 1+2+3 (where do you think the five warmest years will be?)

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Summary and Conclusions:

The interannual variation in global temperatures is likely dominated by the El Nino Cycle. These have an impact on regional climates worldwide.

The strength of the ENSO cycle is modulated by other natural cycles, on both longer and shorter time-scales.

Summary and Conclusions:

In the midwestern USA, our research demonstrates that the Pacific Decadal Oscillation modifies the strength of the ENSO cycle.

It is likely that interannual and interdecadal cycles as forced by natural processes will continue to occur regardless of how the climate changes

Summary and Conclusions:

It is easy to demonstrate that the fact that the warmest years in the global observed record have occurred recently can be explained without needing to invoke statistical arguments.

Questions?

Comments?

Criticisms?

LupoA@missouri.edu