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Supplementary Information Cooling of US Midwest summer temperature extremes from cropland intensification Nathaniel D. Mueller, Ethan E. Butler, Karen A. McKinnon, Andrew Rhines, Martin Tingley, N. Michele Holbrook, and Peter Huybers

Cooling of US Midwest summer temperature extremes from cropland intensification

SUPPLEMENTARY INFORMATIONDOI: 10.1038/NCLIMATE2825

NATURE CLIMATE CHANGE | www.nature.com/natureclimatechange 1

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Discussion of “warming hole” literature Despite large-scale warming trends over the past century, several cooling, or “warming hole”, features have received considerable attention. Perhaps the most prominent and commonly-investigated feature is cooling in the Southeastern US, which appears prominently during May and June1. Several analyses have examined drivers of the Southeastern pattern; these include the influence of sea surface temperatures (SSTs), precipitation, and both anthropogenic and biogenic aerosols1-4. Another feature recently described is a cooling of July–September mean temperatures since 1950 in the southern Great Plains, which has been linked to SST modulation of the Great Plains low-level jet and resulting precipitation5. A similar mechanism is projected to strengthen in the future under warming6. Annual and seasonal average temperature trends over a portion of the upper Midwest region have been investigated and compared with climate model results, with few climate models replicating the observed trends in the region7. This same study identified teleconnections between annual temperatures in the region and SSTs in the equatorial Pacific and the North Atlantic. Discussion of cultivar changes Adoption of modern cultivars may impact evapotranspiration, and common garden studies using collections of historical crop varieties have shown lower canopy temperatures in more recent cultivars of wheat8, soybean9, and maize10, with the maize signal observed most strongly under water stress. For the species with a C3 photosynthetic pathway (wheat and soybean), lower canopy temperatures appear to be correlated with greater stomatal conductance8 and photosynthetic rates or capacity8,9. In maize, a C4 species in which greater gas exchange would have little growth benefit, lower temperatures may be the result of changes in root architecture and greater access to soil water10,11. Maize breeding programs have also produced cultivars with delayed leaf senescence10,11 that can increase late-season photosynthesis. Tillage trends As mentioned in the main text, there has been a considerable shift in the tillage regimes utilized by farmers. These trends are documented by Horowitz et al., who note that 35.5% of US cropland devoted to eight major crops in 2009 was in no-till management12. No-till practices are particularly common for soybean, and approach 50% of all acres.

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Literature Cited 1. Portmann, R. W., Solomon, S. & Hegerl, G. C. Spatial and seasonal patterns in climate change,

temperatures, and precipitation across the United States. Proceedings of the National Academy of Sciences USA 106, 7324–7329 (2009).

2. Robinson, W. A., Reudy, R. & Hansen, J. E. General circulation model simulations of recent cooling in the eastern United States. Journal of Geophysical Research: Atmospheres (1984–2012) 107, ACL 4–1–ACL 4–14 (2002).

3. Meehl, G. A., Arblaster, J. M. & Branstator, G. Mechanisms contributing to the warming hole and the consequent US east–west differential of heat extremes. Journal of Climate 25, 6394–6408 (2012).

4. Goldstein, A. H., Koven, C. D., Heald, C. L. & Fung, I. Y. Biogenic carbon and anthropogenic pollutants combine to form a cooling haze over the southeastern United States. Proceedings of the National Academy of Sciences USA 106, 8835–8840 (2009).

5. Weaver, S. J. Factors associated with decadal variability in Great Plains summertime surface temperatures. Journal of Climate 26, 343–350 (2013).

6. Pan, Z. et al. Altered hydrologic feedback in a warming climate introduces a ‘warming hole’. Geophysical Research Letters 31, 1–4 (2004).

7. Kunkel, K. E., Liang, X.-Z., Zhu, J. & Lin, Y. Can CGCMs simulate the twentieth-century ‘warming hole’ in the central United States? Journal of Climate 19, (2006).

8. Fischer, R. A. et al. Wheat yield progress associated with higher stomatal conductance and photosynthetic rate, and cooler canopies. Crop Science 38, 1467–1475 (1998).

9. Keep, N. R. Characterization of physiological parameters in soybean with genetic improvement in seed yield. (2013).

10. Barker, T. et al. Improving drought tolerance in maize. Plant Breeding Reviews 25, 173–253 (2005). 11. Hammer, G. L. et al. Can changes in canopy and/or root system architecture explain historical

maize yield trends in the US corn belt? Crop Science 49, 299–312 (2009). 12. Horowitz, J., Ebel, R. & Ueda, K. ‘No-till’ farming is a growing practice. 1–22 (United States

Department of Agriculture Economic Research Service, 2010). 13. Leibensperger, E. M. et al. Climatic effects of 1950–2050 changes in US anthropogenic aerosols–

Part 1: Aerosol trends and radiative forcing. Atmospheric Chemistry and Physics 12, 3333–3348 (2012). 14. Leibensperger, E. M. et al. Climatic effects of 1950–2050 changes in US anthropogenic aerosols–

Part 2: Climate response. Atmospheric Chemistry and Physics 12, 3349–3362 (2012). 15. Ramankutty, N. & Foley, J. A. Estimating historical changes in global land cover: Croplands from

1700 to 1992. Global Biogeochemical Cycles 13, 997–1027 (1999). 16. North Atlantic Oscillation (NAO). NOAA Climate Prediction Center at

<http://www.cpc.ncep.noaa.gov/data/teledoc/nao.shtml> 17. Guanter, L. et al. Global and time-resolved monitoring of crop photosynthesis with chlorophyll

fluorescence. Proceedings of the National Academy of Sciences USA 111, E1327–E1333 (2014).

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sample division sample n 5th pctile trend (ºC decade-1) p-value 50th pctile trend

(ºC decade-1) p-value 95th pctile trend (ºC decade-1) p-value

JJA precipitation trend < -9 mm decade-1 18 0.00 ns -0.05 ns -0.05 ns -9 =< JJA precipitation trend < -3 mm decade-1 123 0.06 ns -0.01 ns -0.04 ns -3 =< JJA precipitation trend < 3 mm decade-1 437 0.06 <0.05 0.01 ns -0.02 ns 3 =< JJA precipitation trend < 9 mm decade-1 183 0.05 ns -0.05 ns -0.12 <0.01 JJA precipitation trend >= 9 mm decade-1 38 0.05 ns -0.07 ns -0.15 <0.01 crop area trend < -3% grid cell decade-1 26 0.03 ns -0.04 ns -0.10 <0.05 -3 ≤ crop area trend < -1% grid cell decade-1 123 0.04 ns -0.01 ns -0.08 <0.05 -1 ≤ crop area trend < 1% grid cell decade-1 426 0.06 ns 0.01 ns -0.02 ns 1 ≤ crop area trend 3% grid cell decade-1 214 0.07 ns -0.04 ns -0.10 <0.05 crop area trend ≥ 3 % grid cell decade-1 13 0.07 ns 0.03 ns -0.07 ns irrigation trend < 1% county area decade-1 700 0.06 ns 0.00 ns -0.05 ns 1 ≤ irrigated area trend < 3% county area decade-1 54 0.05 ns -0.04 ns -0.07 <0.05 3 ≤ irrigated area trend < 5% county area decade-1 20 0.06 ns -0.06 ns -0.11 <0.01 5 ≤ irrigated area trend < 7% county area decade-1 5 0.03 ns -0.12 <0.05 -0.13 <0.01 irrigated area trend ≥ 7% county area decade-1 4 0.12 ns -0.18 <0.01 -0.30 <0.01 NPPan trend < 0.5 g C m-2 yr-2 440 0.06 ns 0.03 ns 0.00 ns 0.5 ≤ NPPan trend < 2.5 g C m-2 yr-2 152 0.05 ns -0.04 ns -0.10 <0.05 2.5 ≤ NPPan trend < 4.5 g C m-2 yr-2 82 0.06 ns -0.09 ns -0.17 <0.01 4.5 ≤ NPPan trend < 6.5 g C m-2 yr-2 50 0.06 ns -0.11 <0.05 -0.21 <0.01 NPPan trend ≥ 6.5 g C m-2 yr-2 4 0.12 ns -0.18 <0.01 -0.30 <0.01 NPPan trend < 0.5 g C m-2 yr-2, <10% irrigation 411 0.06 <0.05 0.03 ns 0.00 ns 0.5 ≤ NPPan trend < 2.5 g C m-2 yr-2, <10% irrigation 108 0.05 ns -0.05 ns -0.12 <0.05 2.5 ≤ NPPan trend < 4.5 g C m-2 yr-2, <10% irrigation 61 0.07 ns -0.08 ns -0.17 <0.01 NPPan trend ≥ 4.5 g C m-2 yr-2, <10% irrigation 46 0.05 ns -0.11 <0.05 -0.22 <0.01

ns = not significant Table S1. Quantile regression trends and statistical significance for weather stations categorized by local land use and land cover change. We report the number of weather stations (n), average trends across stations for June–August maximum temperature percentiles (5th, 50th, and 95th), and significance of the temperature trends calculated using bootstrapping. Trends are calculated as in Figure 1, excluding the Dust Bowl (1930s) and the period of maximum aerosol-induced cooling (1970s-1990s).

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exclude 1930s exclude 1930s and 1970s–1990s

explanatory variable Pearson's r Spearman's ρ Pearson's r Spearman's ρ

JJA precip trend –0.22 –0.26 –0.24 –0.28

crop area trend –0.03 –0.05 –0.03 –0.04

irrigated area trend –0.15 0.17 –0.13 0.18

NPPan trend –0.48 –0.47 –0.52 –0.50

Table S2. Correlations between trends in precipitation, land use, and the local trend in 95th percentile temperatures for weather stations across the continental United States. We report Pearson’s correlation coefficient (r) and Spearman’s rank correlation coefficient (ρ). Temperature and precipitation trends are calculated excluding only the Dust Bowl of the 1930s or excluding the 1930s and the 1970s–1990s (those years with the greatest aerosol influence on temperatures).

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Figure S1. Sensitivity of JJA 95th percentile quantile regression trends to different starting dates and date exclusions. Columns from left to right include trends fit using all years, trends fit excluding the Dust Bowl (1930s), trends fit excluding the years reported to include maximum aerosol-induced cooling (1970s-1990s), and trends fit excluding both the 1930s and the 1970s-1990s. We find that the cooling trend is relatively robust to both of these influences, and that a hotspot of cooling over the upper Midwest can be seen even when fitting the trend since 1980. The largest alteration in the spatial pattern is seen when the trend is fit starting in 1960 and 1970. It is possible that the removal of aerosol forcing and subsequent warming13,14 over the eastern US dominates during these periods.

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N

JJA Tx95 trend 1910−2014 (°C decade−1)−0.5 0 0.5

1910

–201

419

20–2

014

1930

–201

419

40–2

014

1950

–201

419

60–2

014

1970

–201

419

80–2

014

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N

July−August Tx95 trend 1990−2014 (°C yr−1)−0.05 0 0.05

JJA 95th percentile Tx trend (ºC decade-1)

0 0.5–0.5

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N

JJA Tx95 trend 1910−2014 (°C decade−1)−0.5 0 0.5

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N

JJA Tx95 trend 1920−2014 (°C decade−1)−0.5 0 0.5

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N

JJA Tx95 trend 1930−2014 (°C decade−1)−0.5 0 0.5

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N

JJA Tx95 trend 1940−2014 (°C decade−1)−0.5 0 0.5

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N

JJA Tx95 trend 1950−2014 (°C decade−1)−0.5 0 0.5

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N

JJA Tx95 trend 1960−2014 (°C decade−1)−0.5 0 0.5

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N

JJA Tx95 trend 1970−2014 (°C decade−1)−0.5 0 0.5

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N

JJA Tx95 trend 1980−2014 (°C decade−1)−0.5 0 0.5

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N

JJA Tx95 trend 1910−2014 (°C decade−1)−0.5 0 0.5

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N

JJA Tx95 trend 1920−2014 (°C decade−1)−0.5 0 0.5

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N

JJA Tx95 trend 1930−2014 (°C decade−1)−0.5 0 0.5

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N

JJA Tx95 trend 1940−2014 (°C decade−1)−0.5 0 0.5

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N

JJA Tx95 trend 1950−2014 (°C decade−1)−0.5 0 0.5

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N

JJA Tx95 trend 1960−2014 (°C decade−1)−0.5 0 0.5

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N

JJA Tx95 trend 1970−2014 (°C decade−1)−0.5 0 0.5

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N

JJA Tx95 trend 1980−2014 (°C decade−1)−0.5 0 0.5

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N

JJA Tx95 trend 1910−2014 (°C decade−1)−0.5 0 0.5

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N

JJA Tx95 trend 1920−2014 (°C decade−1)−0.5 0 0.5

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N

JJA Tx95 trend 1930−2014 (°C decade−1)−0.5 0 0.5

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N

JJA Tx95 trend 1940−2014 (°C decade−1)−0.5 0 0.5

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N

JJA Tx95 trend 1950−2014 (°C decade−1)−0.5 0 0.5

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N

JJA Tx95 trend 1960−2014 (°C decade−1)−0.5 0 0.5

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N

JJA Tx95 trend 1970−2014 (°C decade−1)−0.5 0 0.5

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N

JJA Tx95 trend 1980−2014 (°C decade−1)−0.5 0 0.5

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N

JJA Tx95 trend 1920−2014 (°C decade−1)−0.5 0 0.5

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N

JJA Tx95 trend 1930−2014 (°C decade−1)−0.5 0 0.5

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N

JJA Tx95 trend 1940−2014 (°C decade−1)−0.5 0 0.5

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N

JJA Tx95 trend 1950−2014 (°C decade−1)−0.5 0 0.5

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N

JJA Tx95 trend 1960−2014 (°C decade−1)−0.5 0 0.5

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N

JJA Tx95 trend 1970−2014 (°C decade−1)−0.5 0 0.5

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N

JJA Tx95 trend 1980−2014 (°C decade−1)−0.5 0 0.5

exclude 1930s, 1970s–1990sexclude 1930s exclude 1970s–1990sall years included

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1920 1940 1960 1980 20000

2

4

6

8

10

12x 106

year

hect

ares

Illinois

(a) maizesoybeanw wheats wheatd wheatoatsbarleycanolapeanutpima cottonricesorghumsunf (no oil)sunf (oil)upl cottonmaize sildry beanhay (alf)hay (ex alf)

1920 1940 1960 1980 20000

1

2

3

4

5

6x 106

year

hect

ares

Indiana

(b) maizesoybeanw wheats wheatd wheatoatsbarleycanolapeanutpima cottonricesorghumsunf (no oil)sunf (oil)upl cottonmaize sildry beanhay (alf)hay (ex alf)

1920 1940 1960 1980 20000

2

4

6

8

10

12x 106

year

hect

ares

Iowa

(c) maizesoybeanw wheats wheatd wheatoatsbarleycanolapeanutpima cottonricesorghumsunf (no oil)sunf (oil)upl cottonmaize sildry beanhay (alf)hay (ex alf)

1920 1940 1960 1980 20000

1

2

3

4

5

6

7

8

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10x 106

year

hect

ares

Minnesota

(d) maizesoybeanw wheats wheatd wheatoatsbarleycanolapeanutpima cottonricesorghumsunf (no oil)sunf (oil)upl cottonmaize sildry beanhay (alf)hay (ex alf)

1920 1940 1960 1980 20000

1

2

3

4

5

6

7

8

9

10x 106

year

hect

ares

Nebraska

(e) maizesoybeanw wheats wheatd wheatoatsbarleycanolapeanutpima cottonricesorghumsunf (no oil)sunf (oil)upl cottonmaize sildry beanhay (alf)hay (ex alf)

1920 1940 1960 1980 20000

2

4

6

8

10

12

14x 106

year

hect

ares

North Dakota

(f) maizesoybeanw wheats wheatd wheatoatsbarleycanolapeanutpima cottonricesorghumsunf (no oil)sunf (oil)upl cottonmaize sildry beanhay (alf)hay (ex alf)

1920 1940 1960 1980 20000

1

2

3

4

5

6

7x 106

year

hect

ares

Ohio

(g) maizesoybeanw wheats wheatd wheatoatsbarleycanolapeanutpima cottonricesorghumsunf (no oil)sunf (oil)upl cottonmaize sildry beanhay (alf)hay (ex alf)

1920 1940 1960 1980 20000

1

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9x 106

year

hect

ares

South Dakota

(h) maizesoybeanw wheats wheatd wheatoatsbarleycanolapeanutpima cottonricesorghumsunf (no oil)sunf (oil)upl cottonmaize sildry beanhay (alf)hay (ex alf)

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Figure S2. Historical changes in total cropland (dashed line) and harvested hectares of 19 major crop types for (a) Illinois, (b) Indiana, (c) Iowa, (d) Minnesota, (e) Nebraska, (f) North Dakota, (g) Ohio, and (h) South Dakota. These states cover most of the pronounced cooling in Tx95. Total cropland from the Ramankutty dataset15 is calculated as the sum of areas from all half-degree grid cells whose centroids are located within the state outline, and data is only available through 2007. Harvested areas are state totals from the USDA for each crop type, and missing data (NaNs) are not displayed, as is often observed in the earlier part of the record (e.g. no data for hay is provided for Iowa prior to 1919).

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Figure S3: Examples of county-level summer crop NPPan estimates derived from crop survey data for (a) Story County, Iowa; (b) Pembina County, North Dakota; (c) Hamilton County, Nebraska; and (d) Morrow County, Ohio.

1920 1940 1960 1980 20000

100

200

300

400

500

600

700

year

NPP

an g

C m

−2 y

r−1

Story County, Iowa

(a)

maizesoybeans wheatd wheatoatsbarleypeanutpima cottonricesorghumupl cottondry bean

1920 1940 1960 1980 20000

50

100

150

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250

300

year

NPP

an g

C m

−2 y

r−1

Pembina County, North Dakota

(b)

maizesoybeans wheatd wheatoatsbarleypeanutpima cottonricesorghumupl cottondry bean

1920 1940 1960 1980 20000

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600

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800

900

year

NPP

an g

C m

−2 y

r−1

Hamilton County, Nebraska

(c)

maizesoybeans wheatd wheatoatsbarleypeanutpima cottonricesorghumupl cottondry bean

1920 1940 1960 1980 20000

50

100

150

200

250

300

year

NPP

an g

C m

−2 y

r−1

Morrow County, Ohio

(d)

maizesoybeans wheatd wheatoatsbarleypeanutpima cottonricesorghumupl cottondry bean

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Figure S4. Time series of North Atlantic Oscillation (NAO) index from the NOAA Climate Prediction Center16. The blue line shows the annual average NAO index and the red line displays a 25-year Hamming window smoother.

1950 1960 1970 1980 1990 2000 2010−1.5

−1

−0.5

0

0.5

1

year

NAO

inde

x

yearly index25 year smoother

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Figure S5. Same as Figure 3 for (a) irrigated and (b) rainfed stations, but the 95th percentile quantile regression trends are fit including the 1930s. Quantile regression trends for (c) and (d) are fit to data starting in 1940. The 10th percentile PDSI drought threshold at each station is calculated uniquely for each selection of dates.

1920 1940 1960 1980 2000

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12

year

JJA

Tx a

nom

aly

(°C)

(b) yearly 95th percentiledrought QR trendnon−drought QR trend

1920 1940 1960 1980 2000

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year

JJA

Tx a

nom

aly

(°C)

(a) yearly 95th percentiledrought QR trendnon−drought QR trend

1920 1940 1960 1980 2000

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year

JJA

Tx a

nom

aly

(°C)

(d) yearly 95th percentiledrought QR trendnon−drought QR trend

1920 1940 1960 1980 2000

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year

JJA

Tx a

nom

aly

(°C)

(c) yearly 95th percentiledrought QR trendnon−drought QR trend

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Figure S6. Quantile regression trends for the 95th percentile of daily maximum temperatures calculated over (a) December–February (DJF), (b) March–May (MAM), (c) June–August (JJA), (d) and September–November (SON). For consistency with Figure 1, trends are calculated excluding the Dust Bowl (1930s) and the period of maximum aerosol-induced cooling over the eastern US (1970s-1990s).

(a)DJF

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N (b)

MAM

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N

(c)JJA

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N

95th percentile Tx trend (°C yr−1)−0.4 −0.2 0 0.2 0.4 0.6

(d)SON

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N

95th percentile Tx trend (°C yr−1)−0.4 −0.2 0 0.2 0.4 0.6

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Figure S7. Same as Figure S6, but trends are calculated only excluding the Dust Bowl (1930s).

(a)DJF

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N (b)

MAM

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N

(c)JJA

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N

95th percentile Tx trend (°C yr−1)−0.4 −0.2 0 0.2 0.4 0.6

(d)SON

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N

95th percentile Tx trend (°C yr−1)−0.4 −0.2 0 0.2 0.4 0.6

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Figure S8. Trends in seasonal precipitation for (a) December–February (DJF), (b) March–May (MAM), (c) June–August (JJA), (d) and September–November (SON). For consistency with Figure 1, trends are calculated excluding the Dust Bowl (1930s) and the period of maximum aerosol-induced cooling over the eastern US (1970s-1990s).

(a)DJF

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N (b)

MAM

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N

(c)JJA

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N

seasonal precipitation trend (mm decade−1)−20 −10 0 10 20

(d)SON

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N

seasonal precipitation trend (mm decade−1)−20 −10 0 10 20

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15

Figure S9. Same as Figure S8, but trends are calculated only excluding the Dust Bowl (1930s).

(a)DJF

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N (b)

MAM

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N

(c)JJA

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

40 ° N

50 ° N

seasonal precipitation trend (mm decade−1)−20 −10 0 10 20

(d)SON

120 ° W 110° W 100° W 90° W 80° W 70° W

30 ° N

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seasonal precipitation trend (mm decade−1)−20 −10 0 10 20

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Figure S10. Time series of JJA precipitation for stations in (a) areas with large growth in irrigation (≥5% county area decade-1) and (b) rainfed areas with large increases in NPPan (≥2.5 g C m-2 yr-2, excluding stations with >10% irrigated area). Annual and smoothed versions are shown, where smoothing is accomplished using a 25-year Hamming window. Daily averages are calculated across all selection stations with quality data for a given day, and then annual sums are calculated from this averaged quantity.

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© 2015 Macmillan Publishers Limited. All rights reserved

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Figure S11. Average monthly chlorophyll fluorescence from the GOME-2 satellite is calculated using data for 2007–2012, and the maximum monthly average fluorescence achieved in any month is plotted for every land grid cell north of 60°S. As noted in a previous analysis17, the US Midwest achieves the highest peak values observed on the planet.

© 2015 Macmillan Publishers Limited. All rights reserved

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Figure S12. A Voronoi tessellation is used to assign physical area to weather stations. All weather stations meeting quality screens for July–August are included in the triangulation. Each green polygon details the amount of area associated with a given weather station (red +).

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