Past and projected changes in continental scale agro climate indices
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Past and Projected Changes in Continental-Scale Agro-Climate Indices. Adam Terando NC Cooperative Research Unit North Carolina State University 2009 NPN RCN Meeting. Motivating Questions.

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Past and projected changes in continental scale agro climate indices

Past and Projected Changes in Continental-Scale Agro-Climate Indices

Adam Terando

NC Cooperative Research Unit

North Carolina State University

2009 NPN RCN Meeting


Motivating questions
Motivating Questions

  • Is the late 20th century warming found in the surface temperature record also observable in alternative climate measures that are critical to agricultural production and phenological observations in North America?

  • Do Global Climate Models (GCMs) have skill in hindcasting the observed trends?

  • What changes do GCMs predict for the future?


Global Mean Temperature over Land & Ocean

Global Scale

National Climatic Data Center: 2006


BUT…..

An increase in mean global surface temperature will not necessarily be reflected in the same manner for other manifestations of the climate system over the same time period and at different spatial scales.



A Temperature Example

Heat Stress

Frost/Freeze

Crop Growth


Agro climate indices
Agro-Climate Indices

  • Annual Frost Days (tmin < 0 oC)

  • Growing Degree Days (thermal time) for Corn (10 < tavg < 30 oC)

    • Strong correlation with crop growth

  • Heat-Stress Index (tmax > 30 oC)


US and Canadian Long-term

Historical Climate Networks


Trend time periods

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

1880

1900

1920

1940

1956

1956

1956

1960

1976

1976

1975

1980

2000

2000

2005

2005

2005

Trend Time Periods

  • 1956 – 2005: Good data coverage

  • Switch in 1970s

  • Warming signal detected then on global scale.

  • Also coincides with phase shift in North American tele-connections (i.e. PDO, NAO)

  • Most recent data



Frost Trends

(1956 – 2005)

Slope

(Days/Year)

< -0.5

> 0.5

-1

0

1


Growing Degree Day Trends

(1956 – 2005)

Slope

(Days/Year)

> 5

< -5

7

0

-7


Heat Stress Index Trends

(1956 – 2005)

Slope

(Degree Days

Per Year)

10

> 2.5

< -2.5

0

-10



a)

b)

c)

  • Trends fairly consistent through time



Gcm data
GCM Data

  • 17 GCMs available from Lawrence Livermore National Laboratory

  • Models used in IPCC AR4

  • Fewer years and model runs available for daily data than for monthly data (requires more storage!)

  • Typically 40 years available for 20th century (1961 – 2000), and two 20 years periods for 21st Century (2045 – 2065 and 2081 – 2100)


Questions
Questions

  • Do GCMs have skill in simulating past changes in agro-climate indices?

  • What future changes do GCMs predict?

  • Is the (projected) signal strong with respect to the model noise?



r = 0.52

r = 0.17

GCM Arithmetic

Mean

Observations

SLPobs = -0.22

SLPgcm = -0.21

SLPobs = 0.50

SLPgcm = 3.42

GCM Results

r = 0.03

SLPobs = 0.04

SLPgcm = 1.59

Frost Days

GDD

  • Poor performance for GDD and HSI evident in trend lines

  • Good agreement with frost days

HSI


Correlation Coefficient

RMS Error

Model Result

Observation or ‘Perfect’ Model

Standard Deviation

Taylor Diagram

Taylor 2001


Model Weighting

GCMs

“perfect” model

Schneider et al. 2007


Correlation Coefficient

Correlation Coefficient

Standard Deviation

Thermal Time

Frost Days

Centered RMS Difference

Standard Deviation

Centered RMS Difference

Heat Stress Index


16

Correlation Coefficient

Heat Stress Days

Heat Stress Index

Correlation Coefficient

a)

b)

Standard Deviation

Standard Deviation

Centered RMS Difference

Centered RMS Difference

Year

Year

c)

d)


Minimum Temperature

Maximum Temperature

Correlation

Negative Standard Deviations

Positive Standard Deviations


bccr-bcm2.0

echam5-MPI

miroc3.2

mri-cgcm2.3.2

observations



A2 Scenario

IPCC Emission Scenarios


GCM Arithmetic Mean

Observations

2046-2065 Weighted Mean

2081-2100 Weighted Mean

GCM Results

Thermal Time

Frost Days

Heat Stress Index


  • Projected changes large relative to model errors for 20th century

  • Largest uncertainties (model spread) around HSI projections


Conclusions
Conclusions

  • General signal agreement between Tavg and agro-climate indices.

  • Strong increase in Thermal Time and decrease in Frost Days that is not seen in HSI.

  • Still difficult for GCMs to model variables requiring high temporal resolution.

  • Ensemble mean has greater skill than indiviudal GCMs

  • Large changes in agro-climate indices predicted by GCMs for A2 scenario.


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