Even a small‐scale regional nuclear war, using much less than 1% of the global nuclear arsenal, could produce climate change unprecedented in recorded human history [ Robock et al ., 2007a ], reducing food production in the Midwest United States [ Özdoğan et al ., 2013 ] and China [ Xia and Robock , 2013 ]. Those results were based on only one climate model simulation [ Robock et al ., 2007a ] of 5 Tg of soot injected into the upper troposphere over India and Pakistan [ Toon et al ., 2007 ], and applying the resulting changes in surface air temperature, precipitation, and insolation to crop models simulating soybean and maize production in the United States and rice production in China. Now two more climate model simulations of the same scenario are available [ Mills et al ., 2014 ; Stenke et al ., 2013 ]. The results from the new models bracket the original results, making the climate response in this scenario much more robust, and also provide a measure of the range of possible responses. For China, the results are more variable than for global averages, as expected. Because China is the world's largest producer of grain, we have applied the climate change scenarios from all three models to rice, maize, and wheat production in China, and found much larger reductions in food production, especially for wheat. Because China is the world's largest producer of rice and wheat, and second (after the United States) in maize, and the food reduction lasts for a decade, these results suggest a food crisis not just for those living marginal existences, but for the entire world.

The potential for nuclear war to cause global famine has been known for three decades, since the nuclear winter research of the 1980s [ Turco et al ., 1983 ; Harwell and Cropper , 1989 ]. Smoke from fires ignited by nuclear weapons dropped on cities and industrial areas would block out the Sun, making it cold, dark, and dry at Earth's surface. This danger from a full‐scale nuclear war between the United States and Russia remains with us to this day [ Toon et al ., 2008 ].

2 Agricultural Simulations for China

We used the Decision Support System for Agrotechnology Transfer (DSSAT) crop model version 4.5 [Jones et al., 2003] to simulate crop responses to climate changes of a regional nuclear war at 51 locations in China for 10 years. The model was previously evaluated for rice and maize in China [Xia and Robock, 2013; Xia et al., 2013]. The evaluation for wheat is shown in Figures 1 and 2. While the model does not do as well for winter wheat as it does for spring wheat, rice, and maize, its performance is quite good. We used a 30 year control run with weather observations of 1978–2007 to get control yields of rice, maize, and wheat. To create nuclear war weather input for DSSAT, monthly simulated climate anomalies from the National Aeronautics and Space Administration Goddard Institute for Space Studies (GISS) ModelE [Robock et al., 2007a], the Solar Climate Ozone Links (SOCOL) [Stenke et al., 2013], and the Community Earth System Model‐Whole Atmosphere Community Climate Model [CESM1 (WACCM)] [Mills et al., 2014] were downscaled to daily anomalies to perturb 30 years of daily observations [Xia et al., 2013]. We used the average of climate anomalies of three ensemble members for each climate model, as the method showed no significant difference in crop production compared with averaging crop production forced by individual ensemble members of nuclear war simulations (Supporting Information Figures S1 and S2), with the exception of maize, for which the productivity reduction is slightly larger when using individual ensemble forcing. To exclude other influences, all default simulations used fixed fertilizer (150 kg/ha), fixed planting dates for each cultivar, constant CO 2 concentration (380 ppm), and no irrigation. The dependence of the results on different agriculture management practices was also investigated.

Figure 1 Open in figure viewer PowerPoint (a) Comparison of DSSAT‐simulated winter wheat yield (kg/ha) and observations for the eight provinces. R2 is the coefficient of determination. Also shown are time series of simulated winter wheat yield and observations for the top three winter wheat production provinces: (b) Henan, (c) Hebei, and (d) Shandong (1979–2007).

Figure 2 Open in figure viewer PowerPoint (a) Comparison of DSSAT‐simulated spring wheat yield (kg/ha) and observations for the four provinces. R2 is the coefficient of determination. Also shown are time series of simulated spring wheat yield and observations for the top three spring wheat production provinces: (b) Heilongjiang, (c) Neimenggu, and (d) Gansu (1979–2007).

Figure 3 shows monthly climate anomalies from the three climate models averaged over 51 locations (Table 1) in China compared with climate model control run conditions. The different atmospheric dynamics in the three climate models produce different lifetimes of black carbon in the atmosphere and hence cause slightly different climate responses after the injection of 5 Tg black carbon. However, a regional nuclear war between India and Pakistan results in cooler, drier, and darker conditions in China in all the three climate models, but of different magnitudes than the global averages [Robock et al., 2007a; Mills et al., 2014; Stenke et al., 2013]. Compared with the control, temperature drops immediately after the injection of black carbon on 1 May of year 0 in the GISS and SOCOL simulations and on 1 January of year 0 in WACCM (Figure 3a). The first winter after the nuclear conflict, GISS ModelE, WACCM, and SOCOL, showed temperature drops of 2.6, 4.0, and 3.4 K, respectively, and this cooling effect continues in GISS ModelE and WACCM through the end of year 9, while in SOCOL, the temperature is back to the control run values by year 6. Temperature reduction is much stronger in winter than summer (Figure 3a) because of a stronger Arctic Oscillation (AO) due to the larger stratospheric temperature gradient between the tropics and polar regions [Deser, 2000; Robock, 2000], which would enhance the Siberian High and the winter monsoon in East Asia [Gong et al., 2012]. Surface downwelling solar radiation under all sky conditions decreases immediately after the injection. In GISS ModelE and WACCM, 10 years are not long enough for solar radiation to recover back to the control level, but at year 5, SOCOL shows positive solar radiation anomalies already because of a shorter black carbon lifetime and local cloud responses (Figure 3c). A cooler continental surface reduces the temperature gradient between land and ocean and therefore reduces summer monsoon precipitation in Asia [Robock et al., 2007a]. The three models produce different precipitation changes in China, although overall they agree on precipitation reduction during the first several years after a regional nuclear war (Figure 3b). GISS ModelE shows summer precipitation reductions of 0.9 and 0.6 mm/day in years 0 and 1, respectively. Also, spring and fall precipitation simulated by GISS ModelE with a regional nuclear war have negative changes through all 10 years. However, summer precipitation after year 1 simulated by GISS ModelE changes in the opposite direction with gradually positive anomalies from 0.01 mm/day (year 2) to 0.4 mm/day (year 8). In particular, national weather anomalies weighted by maize production in 2008 show strong summer precipitation increases of 0.4–0.8 mm/day after year 1 (Supporting Information Figure S4). In the WACCM simulation, precipitation shows a consistent reduction during 10 years with the largest anomaly of −0.8 mm/day in spring and summer of year 0 and anomalies gradually approaching zero at the end of 10 years (Figure 3b). Precipitation changes simulated by SOCOL vary more strongly than the other two models with positive changes in years 2, 3, 4, 8, and 9.

Figure 3 Open in figure viewer PowerPoint Monthly climate anomalies for (a) temperature, (b) precipitation, and (c) surface downwelling solar radiation, calculated as the simulated climate after a regional nuclear war minus the control run. All lines are the average of all 51 locations in China (Table 1 ). The regional nuclear war occurred in year 0, 1 May in GISS ModelE and SOCOL, and 1 January in WACCM.

Table 1. Province Locations and Agricultural Data Used in DSSAT Simulations No. Province Crop Latitude (°N) Longitude (°E) Altitude (m) Area (kha) Production (kt) 1 Anhui Rice 31.9 117.2 28 1700 11,024 Maize 31.9 117.2 28 705 2,866 WW 30.5 117.1 20 2347 11,679 2 Beijing Rice 39.8 116.5 31 0.4 3 Maize 39.8 116.5 31 146 880 3 Fujian Rice 26.7 118.2 126 2670 437 Maize 24.5 118.1 139 136 37 4 Gansu Rice 40.3 97.0 1526 6 38 Maize 40.3 97.0 1526 557 2,654 SW 40.0 94.7 1139 290 1,136 5 Guangdong Rice 24.7 113.6 61 933 4,750 Maize 22.8 115.4 17 144 635 6 Guangxi Rice 22.0 108.6 15 151 877 Maize 25.3 110.3 164 490 2,072 7 Guizhou Rice 26.6 106.7 1224 686 4,576 Maize 27.3 105.3 1511 735 3,912 8 Hainan Rice 20.0 110.3 64 129 650 Maize 19.1 108.6 8 17 70 9 Hebei Rice 40.4 115.5 54 82 556 Maize 39.4 118.9 11 2841 14,422 WW 38.0 114.4 81 2413 12,205 10 Heilongjiang Rice 44.6 129.6 241 2391 15,180 Maize 48.1 125.9 235 3594 18,220 SW 47.4 127.0 239 239 895 11 Henan Rice 36.1 114.4 76 605 4,431 Maize 36.1 114.4 76 2820 16,150 WW 34.7 113.7 110 5260 30,510 12 Hubei Rice 30.3 109.5 457 1228 10,892 Maize 30.3 109.5 457 470 2,264 WW 30.3 109.5 457 1001 3,292 13 Hunan Rice 26.2 111.6 173 1255 8,831 Maize 27.5 110.0 272 241 1,280 14 Jiangsu Rice 34.3 117.2 41 2228 17,688 Maize 34.9 119.1 3 399 2,030 WW 34.3 117.2 41 2073 9,982 15 Jiangxi Rice 27.1 114.9 71 401 2,680 Maize 28.6 115.9 47 16 66 16 Jilin Rice 45.1 124.9 136 659 5,790 Maize 43.9 125.2 236 2923 20,830 SW 43.9 125.2 236 6 18 17 Liaoning Rice 42.4 122.5 79 659 5,056 Maize 41.5 120.5 170 1885 11,890 SW 42.4 122.5 79 10 49 18 Neimenggu Rice 43.6 118.1 799 98 705 Maize 40.2 104.8 1324 2340 14,107 SW 50.5 121.7 733 452 1,540 19 Ningxia Rice 38.5 106.2 1111 80 664 Maize 38.5 106.2 1111 209 1,499 SW 37.8 107.4 1348 131 510 20 Shandong Rice 37.5 117.5 12 131 1,104 Maize 37.5 117.5 12 2874 18,874 WW 36.6 109.5 96 3525 20,341 21 Shaanxi Rice 33.1 107.0 510 125 831 Maize 37.4 122.7 48 1157 4,836 WW 33.1 107.0 510 1140 3,915 22 Sichuan Rice 32.1 108.0 674 2662 20,254 Maize 28.8 104.6 341 1729 8,830 WW 32.1 108.0 674 1507 4,830 23 Tianjin Rice 39.1 117.1 13 15 105 Maize 39.1 117.1 13 160 843 24 Yunnan Rice 25.1 101.3 1301 947 5,775 Maize 25.1 101.3 1301 1326 5,296 25 Zhejiang Rice 29.0 118.9 82 691 5,099 Maize 30.2 120.2 42 26 111

Climate changes due to a regional nuclear war between India and Pakistan (or any other conflict that put 5 Tg soot into the subtropical upper troposphere) would affect agricultural activity in China. The changes of year 1 weather elements for the different provinces in China, averaged for all the three models, are shown in Figures 4a–4c, and the agricultural responses (after the climate changes from each of the models are applied to the agricultural model separately for each crop, and the yield changes are averaged) are shown in Figures 4d–4f, and summarized in Table 2 and Figure 5. The three major grains, rice, maize, and wheat, show lower yields at most locations in China. Different regional climates lead to different responses of crop yield perturbed by the same injection event.

Figure 4 Open in figure viewer PowerPoint (a–c) Maps of climate anomalies between simulated climate after a regional nuclear war and the climate control runs (year 1) (a) temperature (b) precipitation, and (c) surface downwelling solar radiation under all sky conditions. Blue indicates negative change, and pink indicates positive change. (d–f) Maps of crop yield changes (%) for year 1 after a regional nuclear war—(d) rice, (e) maize, and (f) wheat. The average of the response of the DSSAT model to anomalies from all the three climate models is shown. Brown indicates negative change, and green indicates positive change. See Table 1 for the list of provinces corresponding to the numbers. In (e), red numbers indicate summer maize and black numbers are spring maize. In (f), provinces with red numbers are planted with spring wheat, and provinces with black numbers are planted with winter wheat.

Table 2. Change of Grain Production During the Decade After a Regional Nuclear War First 5 Years (%) Second 5 Years (%) Defaulta (%) AIb (%) F200c (%) P10d (%) Defaulta (%) AIb (%) F200c (%) P10d (%) China maize −15 −9 −15 −20 −12 −4 −12 −15 China middle season rice −26 −16 −20 −26 −21 −10 −16 −21 China spring wheat −26 −36 −25 −26 −20 −28 −17 −18 China winter wheat −38 −32 −38 −44 −23 −14 −22 −24

Figure 5 Open in figure viewer PowerPoint Chinese production (Mt) and percentage changes of the major grains: (a) rice, (b) maize, (c) winter wheat, and (d) spring wheat. The error bars are one standard deviation of grain production simulated from climate forcing of three climate models including 30 climate conditions for each year. The gray area shows one standard deviation from the 30 year control run, illustrating the effect of interannual weather variations. The scale for production changes (right side of each panel) is different for wheat (c and d) than for rice (a) and maize (b).

In general, rice yield in northern China is damaged significantly while in southern China the rice yield reduction is mild (Figure 4d). Temperature reduction in southern China is not as strong as that in northern China (Figure 4a), which causes less yield reduction in southern China and even yield increase in certain locations. However, as the natural variability of annual average rice production in China is 12%, all four provinces that show positive changes are within this natural variability. Without changing the planting date (25 March) and without irrigation, rice grown in most regions of China (20 provinces) would suffer in a colder and drier environment with a yield decline of 5%–98%, and 15 out of 23 provinces show a reduction larger than 12%.

There are two types of maize in this study: summer maize, which is planted on 9 June in northern China and spring maize, which is planted on 19 April in central and southern China. Maize yield declines in most of the provinces in southern and northern China, while in central China, several provinces show a slight increase in yield after a regional nuclear war (Figure 4e). This response is partially due to the combination of temperature reduction and summer precipitation increase in certain provinces forced by climate change in GISS ModelE. Another reason for this positive change in Ningxia and Gansu (provinces 19 and 4 in Figure 4e) is that the control level of maize yield is low due to a relatively warm and dry environment, with no irrigation. When temperature goes down after the regional nuclear war, maize yield in Ningxia and Gansu increases compared with the control run. However, only three provinces (4, 19, and 21) have an increase greater than the natural variability (12%), while other provinces (provinces 1, 2, 6, 11, and 17) show decreases greater than 12%.

Wheat yield decreases in all the 12 provinces studied. Four northern provinces are planted with spring wheat on 25 March and the other eight provinces are planted with winter wheat on 16 October (Figure 4f). Although winter wheat needs a few weeks of cold before being able to flower, persistent snow cover would be disadvantageous. In addition, if the fall temperature is too low, winter wheat cannot sprout before freezing occurs. Therefore, even winter wheat—a cold crop—shows a large negative impact from a regional nuclear war.

Grain production was calculated by multiplying grain yield in each province by the grain planting area in 2008 (Table 1). The control level of grain production is lower than the actual national grain production, as no irrigation is applied during the simulation and not all provinces in China are simulated. We ran 30 simulations for each nuclear war year, and compared the average rice production summed for the 25 provinces to the average and standard deviation of our control runs in Figure 5a. In year 1, rice production is reduced by 30 megaton (Mt) (29%), falling well outside the control one standard deviation variability. Average rice production does not return to natural variability at the end of year 9. Similar to rice production, the strongest maize reduction is in year 1 with a value of 36 Mt (20%) of the average of three climate models. However, as climate forcing such as summer precipitation from the three climate models is different at major maize production locations (Supporting Information Figure S4), simulated maize production using different climate anomalies varies quite a bit. Maize simulations driven by climate anomalies of WACCM showed gradual recovery, but at the end of year 9, their maize production reduction is still 17% (Figure 5b). Chinese maize production forced by GISS ModelE shows only 11% reduction in the first 2 years after the regional nuclear war, and then is back to the level of the control run. This positive response of maize production forced by climate changes of GISS ModelE is mainly driven by its precipitation anomalies. We have switched climate forcing between GISS ModelE and WACCM—one variable one time, and precipitation itself can explain 64% of the difference between maize production forced by GISS ModelE and WACCM (Supporting Information Figure S7). Precipitation is also the controlling factor in maize simulation driven by the SOCOL climate anomalies. The strong maize production reduction in years 5–7 (Figure 5b) is due to strong summer precipitation reductions in major maize production regions during those years (Supporting Information Figure S4). During this period, simulations of spring wheat and winter wheat production driven by SOCOL climate anomalies show strong reductions as well (Figures 5c and 5d).

Different temperature anomalies predicted by three climate models induce different winter wheat production responses (Figure 5c). In SOCOL, the black carbon dispersion rate is faster than for GISS ModelE and WACCM, and hence surface temperature reductions last for a shorter period of time. Higher temperature (compared with GISS ModelE and WACCM) in fall insures that winter wheat can sprout before freezing, and the relative cold environment compared with the control condition benefits winter wheat before its flowering. Therefore, winter wheat production using SOCOL climate forcing shows no significant decrease due to a regional nuclear war. However, temperature reduction in the other two climate models continues through each of the first 9 years after the regional nuclear war, which causes winter wheat production to decline by 22.3 Mt (52%) and 29.4 Mt (69%) in year 1 for GISS ModelE and WACCM, respectively, and by 17.3 Mt (40%) and 20.8 Mt (49%) at the end of year 4. Spring wheat has different production changes among climate models as well (Figure 5d). Similar to maize, spring‐wheat‐dominated provinces show much weaker summer precipitation reduction in GISS ModelE compared with WACCM (Supporting Information Figure S6), which causes nearly no change in national spring wheat production when forced by GISS ModelE climate changes. The sudden drop in spring wheat production forced by SOCOL in years 4–6 is due to the combination of stronger summer precipitation reduction and less temperature reduction, which will enhance evaporation and therefore reduce the water available in the soil.

Crop yield sensitivity to climate change is different under different agriculture practices. Therefore, we tested crop yield changes in response to a regional nuclear war under four different agricultural managements including the one we used above, which we call the default run (Table 2). Also control runs under the four agriculture managements were examined. In general, if auto irrigation is applied, the crop production shows less reduction compared with the default run without irrigation (Figure 6) except for spring wheat, which indicates that for rice, maize, and winter wheat, with auto irrigation a regional nuclear war has less impact on yields, but for spring wheat, auto irrigation makes the negative climate impact stronger. As we are comparing crop yield under auto irrigation with the auto irrigated control run, the larger spring wheat reduction is because auto irrigation promotes spring wheat under the control run climate more than under the regional nuclear war climate. Although with auto irrigation, most crops show less reduction, the natural variability is largely reduced as well (Supporting Information Figure S8). Therefore, a regional nuclear war would cause significant crop production reduction in China during the first 5 years even with auto irrigation turned on (Table 2). Increasing fertilizer but without irrigation also reduces the impact of regional nuclear war on crops, especially for rice (Figure 6 and Supporting Information Figure S9). Additional 50 kg/ha nitrogen fertilizer would increase rice production by 17 Mt, which is 17% of the default control run. Planting crops 10 days later than the default run makes no difference (Figure 6 and Supporting Information Figure S10). Therefore, auto irrigation and more nitrogen fertilizer could help reduce the negative impacts on agriculture from a regional nuclear war, but even under these two agriculture practice scenarios, the crop production reductions are still significant during the first 5 years (Table 2).

Figure 6 Open in figure viewer PowerPoint Percentage changes of the major Chinese grains: (a) rice, (b) maize, (c) winter wheat, and (d) spring wheat under different agriculture management practices. Each line is the average of three crop simulations forced by three climate models. The error bars are one standard deviation of grain production changes driven by climate forcing of three climate models including 30 climate conditions for each year. The gray area shows the average of one standard deviation from the four control runs with different agriculture management, illustrating the effect of interannual weather variations. The scale for production changes is different for wheat (c and d) than for rice (a) and maize (b).

However, those results contain uncertainties. Although there are three climate models that performed the same regional nuclear war experiment, more climate models are needed to better understand regional precipitation changes as the three climate models in this paper differ on regional precipitation changes, especially for summer. Also, the downscaling method could make a difference in an agriculture impact study. Although our method is likely a good way to downscale temperature anomalies [Hawkins et al., 2013], creating precipitation input is more complicated and could produce differences, especially considering that precipitation is an important factor controlling the crop response in this study. In addition, we only used one crop model in this study, and crop models with different climate sensitivities would produce different crop yield responses even under the same climate forcing and the same agriculture management [Palosuo et al., 2011; Rötter et al., 2011; Asseng et al., 2013]. Therefore, to make a robust conclusion, it would be valuable to have more climate models and crop models repeat this study.