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
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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. - PowerPoint PPT Presentation
Transcript of Past and Projected Changes in Continental-Scale Agro-Climate Indices
Past and Projected Changes in Continental-Scale Agro-Climate
Indices
Adam TerandoNC Cooperative Research UnitNorth Carolina State University
2009 NPN RCN Meeting
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?
National Climatic Data Center: 2006
Global Mean Temperature over Land & Ocean
Global Scale
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.
Meehl et al. 2000
A Temperature Example
Heat Stress
Frost/FreezeCrop Growth
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
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1900
1880
1920
1940
1960
1980
2000
-0.6 -0.4 -0.2 0.0 0.2 0.4 0.6
1956
2005
1956
19751976
2000
1976
2005
1956
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
SPATIAL PATTERNS
Frost Trends(1956 – 2005)
Slope(Days/Year)
< -0.5
> 0.5
-1
1
0
a)
c)d)
b)
Slope (Days/Year)
< -0.5
> 0.5
Slope (Days/Year)
< -0.5
> 0.5
Slope (Days/Year)
< -0.5
> 0.5
Slope (Days/Year)
< -0.5
> 0.519/57/241956 - 1975
7/52/401976 - 2000
1/86/131956 - 2005
5/55/401976 - 2005
Growing Degree Day Trends(1956 – 2005)
Slope(Days/Year)
> 5
< -5
7
-7
0
a)
c)d)
b)
Slope (Deg. Days/Year)
< -5
> 5
Slope (Deg. Days/Year)
< -5
> 5
Slope (Deg. Days/Year)
< -5
> 5
Slope (Deg. Days/Year)
< -5
> 53/93/4
1956 - 200512/67/21
1976 - 2005
1976 - 200018/62/21
1956 - 197529/62/9
Heat Stress Index Trends(1956 – 2005)
(Degree DaysPer Year)
Slope
> 2.5
< -2.5
10
-10
0
a)
c)d)
b)
Slope (Deg. Days/Year)
< -2.5
> 2.5
Slope (Deg. Days/Year)
< -2.5
> 2.5
Slope (Deg. Days/Year)
< -2.5
> 2.5
Slope (Deg. Days/Year)
< -2.5
> 2.5
38/54/81956 - 1975
27/56/171976 - 2000
23/62/151976 - 2005
2/93/41956 - 2005
Perc
ent S
tatio
ns w
ith S
igni
fican
t Tre
nds
0
20
40
60
NE WarmingNW WarmingSE WarmingSW WarmingNE CoolingNW CoolingSE CoolingSW Cooling
Frost Days
0
20
40
60
Thermal Time
0
20
40
60
1956-1985 1961-1990 1966-1995 1971-2000 1976-2005
Heat Stress Index
Percent Stations with Agro-Climate Statistically Significant TrendsIndex Time Period Cooling Trend Warming TrendFR (n = 893) 1956-1985 9.9 17.6
1961-1990 4.5 21.41966-1995 3.4 26.21971-2000 3.7 25.01976-2005 2.4 36.7
TT (n = 943) 1956-1985 16.6 7.51961-1990 4.5 19.91966-1995 5.2 19.91971-2000 7.3 18.81976-2005 9.1 24.0
HSI (n = 736) 1956-1985 10.7 8.61961-1990 3.3 17.81966-1995 9.1 11.81971-2000 12.2 13.61976-2005 18.5 13.9
• Trends fairly consistent through time
-1.0
-0.5
0.0
0.5
1.0
1956-1985 1961-1990 1966-1995 1971-2000 1976-2005
Trend Period
Tren
d - F
rost
Day
s (D
ays/
Year
)a)
-10
-5
0
5
10
1956-1985 1961-1990 1966-1995 1971-2000 1976-2005
Trend Period
Tren
d - T
T (D
eg. D
ays/
Year
)
b)
-10
-5
0
5
10
1956-1985 1961-1990 1966-1995 1971-2000 1976-2005
Trend Period
Tren
d - H
SI (D
eg. D
ays/
Year
) c)
GCM Results
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
• 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?
Evaluating GCM Skill
• Poor performance for GDD and HSI evident in trend lines
• Good agreement with frost days
r = 0.52
SLPobs = -0.22SLPgcm = -0.21
r = 0.17
SLPobs = 0.50SLPgcm = 3.42
r = 0.03
SLPobs = 0.04SLPgcm = 1.59
GCM Arithmetic Mean
ObservationsGCM Results
Frost Days
HSI
GDD
Taylor Diagram
Taylor 2001
Observation or ‘Perfect’ Model
Correlation Coefficient
RMS Error
Model Result
Standard Deviation
“perfect” model
GCMs
Schneider et al. 2007
Model Weighting
Frost Days
Correlation Coefficient
Standard Deviation
Centered RMS Difference Thermal Time
Correlation Coefficient
Standard Deviation
Centered RMS Difference
Heat Stress Index
Correlation Coefficient
Standard Deviation
Centered RMS Difference
Correlation Coefficient
Standard Deviation
Centered RMS Difference
16
Hea
t Str
ess A
nom
aly
Year
Heat Stress Index
Hea
t Str
ess A
nom
aly
Year
Heat Stress Days
a) b)
c) d)
Negative Standard Deviations Positive Standard Deviations
Minimum Temperature Maximum Temperature
Corr
elati
on
bccr-bcm2.0
echam5-MPI
miroc3.2
mri-cgcm2.3.2observations
Projections
A2 Scenario
IPCC Emission Scenarios
Frost Days Thermal Time
Heat Stress Index
GCM Arithmetic Mean
2046-2065 Weighted Mean
2081-2100 Weighted Mean
Observations
GCM Results
• Projected changes large relative to model errors for 20th century
• Largest uncertainties (model spread) around HSI projections
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.
