Abnormal climate and its impact on market economy: solar radiation and rice price during the 1830s famine in Japan

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Specifically, we reconstructed the solar radiation from 1821 to 1850 using descriptions from 18 historical diaries. This represents a novel approach to analyzing climatic impacts on agriculture and the economy during this period. The results show that lower solar radiation, indicative of poor weather conditions, was associated with higher rice prices, particularly during the summer of 1836. Applying principal component analysis to the reconstructed solar radiation data revealed spatiotemporal patterns that elucidated the link between climatic anomalies and their impacts on agricultural production and market prices during the Tenpō Famine. This demonstrates the sensitivity of market prices and economic stability to climatic fluctuations. By utilizing high-resolution data, this study reveals more detailed connections between climate, agriculture, and economic fluctuations than previously reported. Our findings provide valuable historical perspectives and significantly impact contemporary climate adaptation strategies and policymaking. Additionally, this study suggests further research directions and encourages continued exploration of the relationship among climate change, agriculture, and economic fluctuations, inspiring future research in this field. Earth and environmental sciences/Climate sciences/Climate change/Climate change impacts Earth and environmental sciences/Environmental social sciences/Environmental economics climatic impact economic fluctuation Tenpō Famine solar radiation Japan rice Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction How do societies respond to the varying climates? This question not only arouses academic curiosity but also leads us to understand how our society can adapt to ongoing climate change. Accordingly, we approached this issue by focusing on early modern Japan (1603–1867), which experienced severe famine due to poor harvests caused by an abnormal climate. Indeed, early modern Japanese society was vulnerable to abnormal climates; hence, investigating such occurrences can provide insights into vulnerable areas in the modern era. We will focus on the 1830s, when the Tenpō Famine, one of the greatest famines in Japanese history, occurred. The Tenpō Famine is an ideal case study because it occurred nationwide, with regional differences, and the analytical data (for example, old diaries and price records) is easily accessible. During the Tenpō Famine, many people were starved to death, particularly in the northeastern region [ 1 ]. However, the degree of damage varies even in the northeast region [ 2 ]. Therefore, it is necessary to closely examine the weather in each region. Historical climatology has developed significantly through proxy data such as tree rings, lake sediment cores, and historical records, providing insights into past climatic conditions, particularly for the pre-19th century and early modern periods. Brázdil et al. [ 3 ] summarized European climate reconstructions using these proxies, while Neukom et al. [ 4 ] analyzed regional climate patterns and the factors influencing them, including external influences. Brönnimann et al. [ 5 ] highlighted the 1835 Cosigüina volcanic eruption, which released a large amount of sulfur dioxide into the stratosphere, as a potential contributor to the abnormal climate after the eruption. These studies demonstrate the value of historical data for understanding global and regional climate variations. Hirano and Mikami [ 6 ] reconstructed winter climate data for Japan, whereas Mikami [ 7 ] focused on summer climate reconstruction. These studies have utilized historical weather records to clarify the characteristics of climate change. However, traditional proxy data, such as tree rings and lake sediments, often lack the seasonal and regional resolutions necessary for a detailed analysis of historical climate variations. Furthermore, previous reconstructions based on historical diaries were limited in their temporal scope. They lacked continuous year-round data or coverage across multiple seasons and regions, leaving gaps in our understanding of climate variation at finer temporal and spatial resolutions. Ichino et al. [ 8 ] reconstructed monthly solar radiation data for 1821–1850 to address these limitations based on historical weather records. Their study produced year-round data from 18 locations across Japan, enabling the identification of regional solar radiation patterns. This dataset provides a critical foundation for examining the relationships between climate variation, agricultural production, and economic indicators such as rice prices. Nishimori and Yokozawa [ 9 ] demonstrated that rice yields in western Japan are highly sensitive to solar radiation, whereas temperature plays a more significant role in the Tohoku region. These findings underscore the importance of Ichino et al.’s data in understanding the impacts of climate variation during events such as the Tenpō Famine. Recently, the temporal resolution of economic data has also improved. Although rice prices, representing the early modern Japanese economy, have historically been observed only once annually in the 12th month of the Japanese lunisolar calendar year, continuous data can now be obtained daily and monthly, allowing for more detailed observations. In light of this update, we can transcend previous research on the relationship between the climate and society in the 1830s. Most research on the Tenpō Famine only conducts qualitative analyses, and the only study that uses paleoclimatic and quantitative economic data is Hamano [ 10 ]. At a one-year resolution, he examined the relationship between climate, food availability, and demography in Japan in the 1830s. He showed that cool summers in 1833, 1836, and 1838 led to food shortages in subsequent winters and diminished birth rates in 1834 and 1837. He inferred a causal relationship among these three variables, which should be updated in at least two respects. To determine the climatic conditions, Hamano [ 10 ] used weather information compiled from diaries in the Historical Weather Database [ 11 ] and considered the number of rainy days in July to indicate summer temperatures, as Mikami [ 12 ] demonstrated for Tokyo. Hamano [ 10 ] reported that there were only a few rainy days in July 1836 in the Tohoku region, in the northern part of the Japanese mainland. However, circumstantial evidence suggests that the temperature was lower during this period; this indicates that the number of rainy days cannot be converted to temperatures in the Tohoku region of Japan. Second, to reflect food supply and demand, Hamano applied rice prices in Osaka for the 12th month of the Japanese lunisolar calendar year, as compiled by Iwahashi [ 13 ]. However, as explained later, there is a problem with using the rice price in the 12th month of the year to represent the rice price for that year. From spring, when rice planting begins, to autumn, when the harvest is gathered, people trade rice considering the weather. The market’s reaction to weather and the forecast for the rice harvest cannot be reconstructed from the prices of the 12th month alone. A pioneering study by Hamano [ 10 ] identified various unresolved issues; however, follow-up studies have not been performed. Understanding climate change and its devastating impacts on societies in the past requires social and economic information regarding such periods; this involves reconstructing spatial patterns of climate variation at a higher temporal resolution than that provided by annual data. More detailed climatic and economic data can guide discussions on climate variation and its effects on historical societies. Therefore, this study sought to reconstruct monthly mean solar radiation data from 1821 to 1850 based on weather descriptions recorded in 18 historical diaries. It also discusses the abnormal seasonal climate and its economic effects, focusing on the Tenpō Famine in Japan in the 1830s. The remainder of this paper is organized as follows. Section 2 provides an overview of the data. Section 3 presents the distribution map of the reconstructed solar radiation and analyzes its relationship with rice price data. Section 4 discusses the results and concludes. Methods Reconstruction of solar radiation patterns Data Daily weather descriptions from 18 historical diaries from 1821 to 1850, over 30 years, were collected from the Historical Weather Database (HWDB) developed by Yoshimura [ 14 ]. In the HWDB, dates are translated from the Japanese lunisolar calendar to the Gregorian calendar. Table 1 and Fig. 1 show the locations at which the data were recorded. Table 1 Locations of historical diaries and Japan Meteorological Agency (JMA) observatories utilized in this study Historical Diary No. Historical Diary Location Latitude (°N) Longitude (°E) Recording Period JMA Observatory Location JMA Observation End a 1 Hirosaki 40.61 140.47 1661–1867 Aomori 2 Hachinohe 40.50 141.49 1792–1867 Hachinohe September 2007 3 Morioka 39.70 141.16 1661–1840 Morioka 4 Kawanishi 38.00 140.05 1830–1889 Yamagata 5 Nikko 36.75 139.75 1685–1871 Utsunomiya 6 Yokohama 35.44 139.64 1806–1889 Tokyo 7 Hachioji 35.66 139.32 1720–1885 Tokyo 8 Kofu 35.65 138.57 1747–1872 Kofu 9 Ise 34.48 136.70 1683–1889 Nagoya 10 Kyoto 35.01 135.77 1796–1866 Nara 11 Ikeda 34.82 135.48 1714–1892 Osaka 12 Tanabe 33.72 135.38 1814–1869 Osaka 13 Tsuyama 35.01 135.77 1702–1868 Nara 14 Hagi 34.41 131.40 1739–1867 Hamada September 2007 15 Kitakyushu 35.07 134.01 1811–1857 Fukuoka 16 Usuki 33.88 130.88 1674–1868 Oita 17 Isahaya 32.85 130.05 1700–1868 Nagasaki 18 Koyama 31.34 130.95 1825–1871 Kagoshima a JMA Observations available up to the month and year noted. The historical diaries provide weather descriptions; the JMA observatories serve as sources of solar radiation and Tenki-gaikyō (daily weather conditions) data during the daytime. Solar radiation and daytime weather conditions recorded at these locations by the Japan Meteorological Agency (JMA) from 1981 to 2010 were used to calculate the conversion parameters. The daily observed weather conditions from the JMA contained “ Tenki-gaikyō ” (a weather summary) twice daily, once during the day and once at night. These data were generated by local observatories following the method described in the Guidelines for Surface Weather Observation Statistics [ 15 ]. Daytime Tenki-gaikyō was applied in the current study and was assumed to be the most similar to the historical daily weather records. These data were generated by summarizing “ Tenki ,” a combination of cloudiness- and precipitation-related parameters recorded every three hours from 06:00 to 18:00. Daily total solar radiation ( S ) data from the the JMA's daily surface weather observations were used. The solar radiation reported in the Annual Report of the JMA from 1981 to 2010 was obtained from https://www.data.jma.go.jp/risk/obsdl/ and used to determine the conversion parameters. Table 1 and Fig. 1 show the JMA observatories from which these data were available for estimating historical solar radiation. Reconstruction of solar radiation patterns Although historical weather records have been used to reconstruct data related to historical climatic variations in various parts of Japan during specific seasons (e.g., summer or winter), few studies have estimated monthly or daily climatic parameters, regardless of season or location, using proxy data such as historical weather records, tree rings, and lake sediment cores. Our group presented a schematic for estimating solar radiation based on weather descriptions. These weather descriptions include reports of clouds or rainfall between the sun and an observer standing on the ground; they may indicate the degree of reduction in incident solar radiation caused by clouds and other atmospheric aerosols. Thus, solar radiation is strongly related to weather conditions, as noted in the weather records. Hence, weather descriptions reflect the amount of solar radiation on the Earth’s surface. Accordingly, solar radiation, particularly the total downward solar radiation at the surface, was selected as the target variable for estimation. This study estimated the historical solar radiation using written and verbal expressions of weather conditions from historical documents, namely diaries and observational logbooks. Weather descriptions were categorized into three levels and converted into solar radiation using the conversion parameters determined from modern observations (Table 2 ). As previously explained, historical diaries and the JMA’s modern weather descriptions, including solar radiation recorded using instruments, are necessary for reconstructing solar radiation patterns. Table 2 Specifications of weather levels and the classification of weather descriptions Type Category of weather description HD a TG b Weather level k Fine 1 1 Fine, partly cloudy 2 Half fine, half cloudy Cloudy, partly fine Cloudy / Half fine, half rainy 2 Cloudy, partly rainy 3 Half cloudy, half rainy Rainy, partly cloudy Rainy / snowy 3 a Type HD: For weather descriptions in historical documents b Type TG: Weather descriptions from the Japan Meteorological Agency, Tenki-gaikyō . The method used in this study was derived from the equation Ichino et al. [ 16 ] described, which elucidated the relationship between solar radiation and modern weather descriptions and developed a method to estimate solar radiation from modern weather descriptions. This equation is based on the relationship between modern solar radiation and Tenki-gaikyō . Daily total solar radiation ( S ) was obtained from the daily JMA surface weather observations. Normalized S is referred to as q and is defined by Eq. (1): q = S/S TOA (1) where S TOA is the daily insolation received at a horizontal surface at the top of the atmosphere and is computed using the equations described by Kondo and Xu [ 17 ]. Although it varied throughout the historical period, we consistently applied the recent value of 1.365 × 10 3 W/m 2 as the total solar irradiance (so-called solar constant) to calculate S TOA throughout this study. The average ratio of q for each month to weather level k is referred to as q mean (k) , which was calculated at each location where Tenki-gaikyō and solar radiation were used (Table 1 and Fig. 1 ). We speculate that the q mean (k) values for the 19th century and 1981–2010 are identical. Tenki-gaikyō was classified into three Tenki-gaikyō (TG) levels. The categorization method for weather records from historical documents, the nine categories of Tenki-gaikyō [ 16 ], and the five types of classification are listed in Table 2 . Categories that describe weather conditions were established according to the method described by Ichino et al. [ 16 ] and named according to the common descriptions found in Tenki-gaikyō , ranging from the most favorable (fine) to the least favorable (rainy or snowy). These categories were designed to organize a wide range of weather descriptions systematically. For instance, “rainy or snowy” encompasses weather conditions such as rainy, snowy, graupel, hail, and sleet. Solar radiation and weather conditions are strongly correlated. Therefore, we initially explored the quantitative relationship between q and Tenki-gaikyō . Figure 2 shows the distribution of q for each weather level in Tokyo from 1981 to August 2010, suggesting that q is distinguishable based on the weather level. The solar radiation values for all months were highly correlated with daily weather descriptions. Consequently, the weather levels deduced from weather descriptions were converted into solar radiation using Eq. (2): S ej = q mean (k j )* S TOA j (2) where S ej is the estimated solar radiation, j is the day, and k j is the weather condition indicated by “weather level.” S TOA j is the daily solar radiation that reaches the top of the atmosphere on day j , and q mean (k j ) is a conversion parameter determined by weather level k j and calculated using weather observations recorded at the JMA from 1981 to 2010. The errors in S ej compared with the observed S were not sufficiently small. However, the variation was similar to the observed S . The method uses the q mean (k j ) corresponding to the three weather conditions for each day. Therefore, we discuss the monthly mean S ej described by Ichino et al. [ 16 ] to minimize ambiguity. Historical weather descriptions were categorized into appropriate weather levels, k to estimate historical solar radiation and Tenki-gaikyō . Based on our recent investigation, we adopted the three-level HD classification shown in Table 2 , developed by comparing weather descriptions from historical diaries with Tenki-gaikyō and examining their characteristics. As shown in Table 2 , fine was designated as weather level k = 1, rainy as k = 3, and snowy, graupel, hail, and sleet were considered equivalent to rainy ( k = 3). As mentioned in the typical weather expressions for the categories, k = 2 includes other weather descriptions, unlike Tenki-gaikyō . For example, temporary clouds in historical diaries were categorized as k = 2, whereas the weather in Tenki-gaikyō was classified as k = 1. In historical diaries, a rainy day that temporarily stops the rain is also categorized as k = 2; however, in the case of Tenki-gaikyō , it is categorized as k = 3. Parameter determination with modern observations To calculate q mean (k) , the observed weather conditions and solar radiation were required. Instrumental observations were rarely provided at the same location where the weather was recorded in the historical diary. Observational data at the JMA locations listed in Table 1 and Fig. 2 were used to estimate and calculate the parameters. This study is based on the climatic division of solar radiation provided by the Japan Weather Association (JWA). The similarities between the seasonal patterns of solar radiation determine these climatic divisions. Ichino and Mikami [ 18 ] examined the range of spatial applicability of q mean (k) at a location to estimate the monthly mean solar radiation using the approach of Ichino et al. [ 16 ], with q mean (k) applied to other locations. The results indicated errors in the estimations using q mean (k) at the same locations. The JMA locations used for the estimation in Table 1 were determined based on the climatic division of solar radiation by the JWA concerning these results. Analysis of the spatiotemporal structure of solar radiation To determine the spatial and temporal scales of the variation in solar radiation, we performed Principal Component Analysis (PCA). The procedure ‘prcomp’ of R 4.3.0 was used with its default options. Its input consisted of monthly values of the normalized solar radiation q for one month (June, July, August, or September) from 1821 to 1850 at 18 locations which are shown in Table 1 . The output consists of 18 components, each of which is a pair of an eigenvector and a sequence of its scores representing the variation's spatial and temporal structures. Only such sets of observations that have valid values at all locations can contribute to constructing the eigenvectors. However, the q value was missing in some locations in some months because the number of days with valid records was less than 2/3 of the total days in the month. There are too many months of missing observations at certain locations to be ignored. We tentatively replaced missing values with zero anomalies; that is, we assumed that q was the same as the average of the 30 years. This assumption is likely to underestimate the actual variation. However, it is unlikely that false signals will be generated. Rice price The method used in this study to estimate solar radiation during past periods can be applied throughout Japan regardless of the specific season or region. It can also be used to estimate the climate during periods of less than one year and seasonal changes. Such an approach will supplement the discussion on human society and climatic factors, as it provides data better suited to the temporal resolution of social change than other proxy data that are limited to specific seasons (e.g., summer or winter). Furthermore, our method enables the estimation of solar radiation from daily weather descriptions at various temporal resolutions, allowing for averaging over specific periods, such as months, the growing season of a crop, or an integrated value rather than a simple average value over a set timeframe, as well as the estimation of annual totals. The flexibility of our method facilitates comparison with diverse indicators, such as production, yield, price, and other economic factors. To confirm this, we compared the reconstructed solar radiation with rice production. Data on agricultural yields are required to rigorously understand the social impacts of climate change. However, although historical records of accurate yields have been preserved in minimal areas, no historical statistics are available to determine the yields for the entire country. Therefore, previous studies have used prices as a proxy for output, especially rice prices, for which the most continuous data are available; this study follows suit. Rice was not only the staple food of the time but also a symbolic good of early modern Japan. Rulers collect rice as a tax and sell it to the market to generate financial revenue. Therefore, continuous time-series data on rice, among other grains, have been preserved in historical documents. However, caution must be exercised when using rice prices as a proxy indicator. First, we must ask whether trade with other countries causes fluctuations in rice prices. However, this question did not apply to the present study. From 1639 until its collapse in 1867, the Tokugawa Shogunate imposed a strictly controlled trade regime prohibiting rice import and export. After 1639, rice was produced throughout Japan and consumed domestically without being exported or imported [ 19 ]. Additionally, the national population did not change considerably during the observation period [ 20 ], suggesting that consumer demand remained unchanged. Thus, it appears that the primary supply factors (yield) affected the price of rice, and the climate affected its supply. Second, were any other events, such as riots, typhoons, or government price controls, significantly impacted rice prices? Regarding riots in 1837 in Osaka, which had the most extensive rice market at the time, one of the officials caused a large-scale riot to denounce the inadequacy of the shogunate’s rice price reduction policy and the injustice of the officials. This riot upsets rice prices and should be carefully considered when observing later fluctuations in rice prices; however, if we preemptively conclude, its impact is limited. Rice prices rose immediately after the riots, but the effect was short-lived, partly because the riots were suppressed within one day. Instead, we believe the upward pressure on rice prices was exerted by the lack of solar radiation after the riots. In the 1830s, the shogunate instituted various measures to reduce rice prices. For example, stockpiled rice was released, and orders were given to local lords to bring more rice to Osaka and Edo (Tokyo). However, as these measures were ineffective and caused the riots described above in some respects, it is safe to conclude that they did not significantly change the price of rice. Therefore, using rice prices as a proxy indicator of rice output is acceptable. Prior studies, such as Hamano [ 10 ], use rice prices as a proxy indicator to understand rice production. He applied the rice price in Osaka in the last month of the Japanese lunisolar calendar year rather than the average yearly price to reflect food supply and demand, as compiled by Iwahashi [ 13 ]. In other words, a problem exists with this process. Rice is harvested in the 9th or 10th month (around October or November in the solar calendar) of each year; hence, the rice price in the 12th month is representative of the rice price for that year. However, this was not the case in poor harvest years. As shown in Fig. 3 , a bad crop in year t can lead to higher prices in the summer of year t and higher prices in the spring and summer of the following year ( t + 1 ). Prices rise in the summer, before the rice harvest (in autumn), because rice merchants have a nationwide information network about rice crops. During the summer, they trade rice based on the expected annual harvests of the year [ 19 , 21 ]. Suppose rice production recovers in year t + 1 and prices in the 12th month in year t + 1 would be lower. Therefore, year t + 1 would be understood to have been a year of low rice prices, even though the first half of year t + 1 suffered from high rice prices. Thus, in a bad harvest year, the price of rice in the 12th month should not be considered representative of that year’s price. To update the pioneering work, Hamano [ 10 ], we refer to monthly rice price data from the “Sho Sōba no Hikae” (a record of market activities) stored in the archives of the Mitsui Group in Tokyo [ 22 ]. The database contains monthly reports on the prices of essential products. Among these records, we evaluated documents from 1833 to 1839 that recorded the monthly price of rice in Osaka. In principle, monthly price refers to the price at the beginning of each month. We converted these from a lunisolar calendar to a solar calendar. Here, we refer to the price of rice produced in Kumamoto, Ishikawa, Fukuoka, Yamaguchi, and Hiroshima (Fig. 3 ). Approximately 30 brands of rice, differentiated by production area, are traded in the Osaka rice market, of which five are leading brands [ 19 ]. The brands’ prices referred to here are not those in a specific region, such as Kumamoto, but the prices formed in the Osaka rice market. As rice produced in Kumamoto is sent to Osaka and traded in the central market, the Osaka rice market, it was also considered at the time to reflect the trends in supply and demand nationwide [ 21 ], just as Toyota’s stock price is deemed to be a proxy index that indicates the economic situation of the whole of Japan rather than the financial situation of Toyota City, where Toyota’s headquarters is located. There is a reason why the rice brands we refer to are biased towards Western Japan. Rice produced in Eastern Japan was transported to Edo by sea and traded there; however, unlike in Osaka, wholesale traders traded rice on a one-to-one basis, and prices were not widely publicized. Therefore, obtaining time-series data on rice prices in Edo is almost impossible. However, this bias did not hinder the analysis. It has been pointed out that the price of rice in Edo was highly dependent on the price of rice in Osaka [ 21 ]. It is also known that the government (The shogunate) used the Osaka rice price as a benchmark when implementing policies to adjust rice prices [ 19 ]. Therefore, the prices of the five brands referred to here represent rice supply and demand. Results Reconstructed solar radiation patterns We reconstructed the monthly mean solar radiation from 1821 to 1850 based on weather descriptions from 18 historical diaries. Here, we assume that the q mean (k) , the average of q for each weather level, is the same for the diaries during the historical period as for the current weather conditions of the JMA. Our analysis focused on 1836, marked by the most severe famine of the Tenpō era. Although frequent cool summers and crop failures have been suggested as causes of famines, the mechanisms that trigger these events remain unclear. Figure 4 illustrates the reconstructed solar radiation anomalies from 1835 to 1837 as monthly deviations from the 30-year mean (1821–1850). The maps indicate a significant reduction in solar radiation in the summer of 1836, especially in central Japan, which aligned with the severe economic conditions of the Tenpō Famine. Although daily estimates were initially generated, monthly averages were used to minimize estimation errors. Only the months with at least 20 recorded diary entries were included to ensure data reliability. This methodology reduces uncertainties arising from biases in diary weather descriptions or disparities between the diary records and Tenki-gaikyō . During the summer of 1836, solar radiation in the east-west zone of Japan, including Kanto, Kinki, and northern Kyushu (locations 5–15), decreased significantly, approximately 10% below the historical average for July and August. In contrast, the solar radiation levels in Tohoku (locations 1–4) to the north and southern Kyushu (location 18) to the south remained relatively stable, highlighting distinct regional variations in solar radiation patterns. The reconstructed solar radiation levels from May to September 1836 were consistently lower across central Japan. In contrast, the solar radiation levels in spring (February to April) and autumn (September to November) were within the average range. Spatio-temporal structure of solar radiation as revealed by Principal Component Analysis The PCA of q was performed as described in Section 2.2. The proportions of variance in the first principal component (PC1) were 39, 42, 46, and 31%, respectively, for June, July, August, and September. The eigenvectors of PC1 for each month are shown in Fig. 5 A (a-d). In all four months, the signs were almost the same everywhere. Opposite signs occur in the Tohoku region (north of 38 °N), but the magnitude is not large there. For convenience of comparison, the sign of the eigenvectors is so taken that the values are positive in most of the locations. When the score of PC1 is negative, solar radiation is weaker than normal in the zone around 32–37 °N, such as from Kyushu to Kanto. The time series of the PC1 scores are shown in Fig. 5 B. Although the eigenvectors for the four months were not exactly the same, we combined the scores of the four months to browse the sequence. Large negative values indicated a lack of solar radiation in the zone around 32–37 °N. Remarkably, the large negative values lasted from July to September 1836 and July to August 1838, when Japan experienced years of cool summers and poor rice harvesting. The score for 1833, another well-known cool summer in the Tohoku region, was not much different from zero. It can be said that this summer had a solar radiation anomaly pattern different from that of PC1. We do not discuss other principal components here because the spatial patterns of the eigenvectors were not coherent from one month to another, and their proportion of the variance was, at most, 17%. Climate variation and the market economy Next, we examine the relationship between the price of rice in Osaka and the amount of solar radiation, as shown in Fig. 6 . Figure 6 plots the rice price trend and shows that from August 1836 to September 1837, rice prices were higher than in a typical year (50–70 monme) [ 19 ]. Contrasting this movement in rice prices with the PCA1 Scores reveals that the rice price trend correlates with the PCA1 scores from June to September of 1836. This indicates that market participants were pessimistic about the lack of solar radiation between June and September 1836 (represented by the blue bar extending downward in Fig. 6 ), and they placed buy orders for rice before the harvest in October, anticipating a rise in rice prices. The rice crops were poor, and the price of rice continued to rise until the summer of 1837. One event that must be mentioned during this period was the Ōshio Heihachirō’s Rebellion. On March 25, 1837 (the 19th day of the 2nd month in the solar-lunar calendar), Ōshio Heihachirō, a bureaucrat in the Osaka Magistrate’s Office, instigated an armed uprising in Osaka City, decrying the government’s inaction toward the soaring rice prices. However, the rebellion was suppressed within a day, and it remains uncertain whether the subsequent surge in rice prices after April 4, 1837, was directly linked to it. It is more likely that the sharp rise in the rice price during this period was caused by the market's pessimism about a poor rice harvest due to a lack of solar radiation after April. The recovery of solar radiation from June to August 1837 stopped this trend, and rice prices began to fall in October 1837. Such a recovery of solar radiation during the rice-growing season likely buoyed the perception of a prospective good rice crop. As this trend in rice prices applies to all five rice brands; fluctuations in solar radiation should have substantially impacted rice prices at that time. The same can be said of 1834–1835. The lack of sunlight in August 1834 caused the price of rice to rise, which continued to rise until solar radiation recovered in the summer of 1835. Previous studies that focused solely on rice prices in the 12th month (around January of the following year on the modern solar calendar) overlooked the significance of people’s perceptions of the harvest for the corresponding year. Discussion Different behavior of solar radiation and temperature In the summer season in Japan, low values of solar radiation tend to coincide with low values of temperature, and both factors can lead to low rice yields. However, these factors do not always behave similarly. The spatial structure of PC1 of solar radiation, as mentioned in Section 3.2, somewhat resembles the eigenvector of PC1 of surface air temperature (July and August 1901–1974) by Mikami [ 23 ], which has the same sign everywhere, with a maximum in the zone around 35–37 °N. It is different, however, that the eigenvector of PC1 of temperature has such values in the Tohoku region (38–42 °N) that are larger than half of its maximum, while that of solar radiation has much smaller values. According to alternate sources, despite moderate levels of reconstructed solar radiation in the Tohoku region during the summer of 1836, climatic conditions were anomalously cool. For example, Sekisetsu Chihō Nōson Keizai Chōsa-Sho [ 24 ] compiled a chronological table of agricultural disasters in six prefectures (administrative divisions in modern Japan) in the Tohoku region, and it recorded cool anomalies in 1836 in four of these prefectures. The effects of these two factors on crop yield were also different. Nishimori and Yokozawa [ 9 ] studied the climatic factors affecting rice yields in Japan using data from the modern period (1979–1994). They used multiple regression analysis to explain rice yields based on air temperature and solar radiation. Their results showed that rice yield is sensitive to temperature in the northeastern part of Japan and solar radiation in the western part. Thus, the climate is likely to be cooler all over Japan in years when the PC1 score of solar radiation has large positive values, even though the solar radiation anomaly in the northernmost part is small. In such years, the rice yield is likely to be lower because of low solar radiation in the western part of Japan and low temperatures in the northeastern part. Volcanic eruptions Volcanic eruptions that inject a large amount of sulfur dioxide into the stratosphere will likely impact the global climate. Sigl et al. [ 25 ] list such eruptions in the time frame of our study in the database. In the time frame of our study, the Cosigüina volcano in Nicaragua erupted in January 1835 [ 26 ], and the volcanic explosivity index (VEI) as defined by Newhall and Self [ 27 ] was 5. According to Sigl et al. [ 25 ], the sulfur dioxide which was injected into the stratosphere is estimated to be 9 teragrams. In addition, Hutchison et al. [ 28 ] demonstrate that the Zavaritskii caldera in Simushir Island of the Kurils erupted in 1831, injecting 12 teragrams of sulfur dioxide. Robock [ 29 ] and Marshall et al. [ 30 ] discussed how the climate system responds to volcanic forcing. Some of the responses were straightforward. Sulfate aerosols reduce the amount of solar radiation that arrives at the surface and are likely to cause lower surface temperatures in many parts of the world. However, this response is not just that. For example, the temperature anomalies in the lower troposphere in the northern middle latitudes in winter following the eruption of Pinatubo in 1991 were positive somewhere and negative elsewhere (see Plate 8 of Robock [ 29 ]). The propagation of planetary waves in the atmosphere is likely involved. Therefore, the spatial patterns of the anomalies may be quite different according to the situation of the zonal winds, even with similar forcing. As we reconstructed from historical diaries, variations in solar radiation at the surface may contain some effect of volcanic forcing; however, it is probably not a direct effect of aerosols but an indirect effect via clouds, which planetary waves may also modulate. We consider that discussing its cause-and-effect relationship would be precarious and that it will become fruitful when we can reconstruct global-scale patterns of anomalies. Conclusions Despite recent advancements in climate reconstruction from tree rings and other sources that provide climate data with a resolution of approximately one month, these reconstructions are limited to early to mid-summer, posing challenges in obtaining climate or weather information for September and October, which are crucial months that impact rice yields. Moreover, while annual rice prices traditionally serve as indicators of socioeconomic conditions, disaggregating them into monthly rice prices reveals the significant impact of solar radiation from July to September on prices in the central market, exemplified by observations from 1836 to 1838, as depicted in Fig. 4 . Figure 4 shows that while solar radiation levels in July and August remained similar between 1836 and 1838, there was a notable disparity in September. In 1836, rice prices rose after September due to a lack of solar radiation. In contrast, in 1838, rice prices remained high owing to the recovery of solar radiation during the same month (Fig. 6 ). These distinctions are discernible only through the enhanced resolution of the climate and price data. This correlation between the reconstructed solar radiation and rice price trends underscores our successful estimation of the climatic conditions that impact rice production. Our study shows that the interannual climate variations in Japan during the 1830s affected societal wellness. For example, an abnormally cool summer in 1836 was followed by a surge in rice prices, which persisted until the summer of 1837. Even during the Tenpō Famine period, marked by severe food shortages, significant year-to-year and within-a-year variations in rice prices occurred. Given the nuanced relationship between climate and the economy, which is intricately linked to seasonal activities, it is important to examine climate and economic data monthly. Moreover, establishing a nationwide rice market system makes it essential to assess climate patterns on a synoptic scale spanning thousands of kilometers; this necessitates examining weather description records across multiple locations to capture a broader climatic context. Declarations Conflict of interest statement The authors declare no conflicts of interest. Permission to reproduce material from other sources Not applicable. Funding statement This study was supported by JSPS KAKENHI (grant numbers 17540410, 18H03794, 20K01152, 21H03776, 21H05180, 22H04938, and 23K00974); Joint Support-Center for Data Science Research (grant numbers 027RP2021, 041RP2022, and 044RP2023); and Ishi Memorial Securities Research and Promotion Foundation (grant number 2023-4). Author Contribution Conceptualization: Mika Ichino, Yasuo Takatsuki, Kooiti Masuda, Takehiko Mikami; Data curation: Mika Ichino, Yasuo Takatsuki, Kooiti Masuda, Takehiko Mikami; Formal analysis: Mika Ichino, Yasuo Takatsuki, Kooiti Masuda; Funding acquisition: Mika Ichino, Yasuo Takatsuki, Kooiti Masuda; Investigation: Mika Ichino, Yasuo Takatsuki, Kooiti Masuda, Takehiko Mikami: Methodology: Mika Ichino, Kooiti Masuda; Project administration: Mika Ichino, Yasuo Takatsuki; Supervision: Kooiti Masuda, Takehiko Mikami; Validation: Mika Ichino, Yasuo Takatsuki, Kooiti Masuda, Takehiko Mikami; Visualization: Mika Ichino, Yasuo Takatsuki, Kooiti Masuda, Takehiko Mikami; Writing – original draft: Mika Ichino, Yasuo Takatsuki, Kooiti Masuda; Writing – review & editing: Mika Ichino, Yasuo Takatsuki, Kooiti Masuda, Takehiko Mikami. All authors have read and approved the final manuscript. Acknowledgement An earlier version of this paper was presented at the Association for Asian Studies 2023 Annual Conference on March 19, 2023, the annual conference of the Japan Society of Hydrology and Water Resources on September 4, 2023, and the European Geoscience Union General Assembly 2024 on April 17, 2024. The authors are grateful to the attendees of these meetings for their helpful comments and recommendations Data Availability Historical Weather Database data are available at http://tk2-202-10627.vs.sakura.ne.jp; observational data from the JMA (Section 2.3) from 1981 to 2010 are available from the ‘Kako-no kishō data download’ (past weather data download) on the JMA web page (https://www.data.jma.go.jp/risk/obsdl/); Economic data are available from the “Sho Sōba no Hikae” (Record of Market Activities), which is stored in the archives of the Mitsui group in Tokyo. References Bolitho, H. 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The method for estimating global solar radiation based on weather records: Toward the climatic reconstruction in the historical period. Tenki 48 , 823–830 (2001). [in Japanese]. Kondo, J. & Xu, J. Seasonal variations in the heat and water balances for nonvegetated surfaces. J. Appl. Meteorol. 36 , 1676–1695 (1997). Ichino, M. & Mikami, T. Spatial and temporal differences of global solar radiation: Applicability of mean daily clearness index. Geogr. Rep. Tokyo Metrop Univ. 38 , 15–21 (2003). Takatsuki, Y. The Dojima Rice Exchange: from Rice Trading to Index Futures Trading in Edo Period Japan (Japan Publishing Industry Foundation for Culture, 2022). Bassino, J. P., Broadberry, S., Fukao, K., Gupta, B. & Takashima, M. Japan and the great divergence, 730–1874. Explor. Econ. Hist. 72 , 1–22 (2019). Takatsuki, Y. & Hisamatsu, T. The role of information in the rice exchange: Yamagata Bantō’s great knowledge (1806). Eur. J. Hist. Econ. Thought . 30 , 395–409 (2023). Mitsui, B. Kinsei Kōki ni Okeru Shuyō Bukka no Dōtai (Key Commodity Price Dynamics in the Late Early Modern Period of Japan) (University of Tokyo, 1989). [in Japanese]. Mikami, T. Representation of the anomaly patterns of summer temperature over Japan using principal component analysis and its dynamic climatological considerations. Geor Rev. Jpn . 48 , 784–797 (1975). [in Japanese with English abstract]. Sekisetsu Chihō Nōson Keizai Chōsa-Sho. (Snowy region rural economy survey). Tōhoku chihō kyōsaku ni kansuru shiteki chōsa (Historical survey on harvest failures in the Tohoku region). Sekisetsu Chihō Nōson Keizai Chōsa-Sho, Yamagata. [in Japanese]. (1935). Sigl, M., McConnell, J. R. & Toohey, M. Volcanic stratospheric sulfur injection between 1733 and 1895 CE based on the eVolv2k-plus-D4i ice-core eruption catalogue [dataset]. PANGAEA (2023). https://doi.org/10.1594/PANGAEA.960975 Longpré, M. A., Stix, J., Burkert, C., Hansteen, T. & Kutterolf, S. Sulfur budget and global climate impact of the A.D. 1835 eruption of Cosigüina volcano, Nicaragua. Geophys. Res. Lett. 41 , 6667–6675. https://doi.org/10.1002/2014GL061205 (2014). Newhall, C. G. & Self, S. The volcanic explosivity index (VEI) an estimate of explosive magnitude for historical volcanism. J. Geophys. Res. 87 , 1231–1238. https://doi.org/10.1029/JC087iC02p01231 (1982). Hutchison, W. et al. The 1831 CE mystery eruption identified as Zavaritskii Caldera, Simushir Island (Kurils). Proc. Natl Acad. Sci. U. S. A. 122, e2416699122 (2025). Robock, A. Volcanic eruptions and climate. Rev. Geophys. 38 , 191–219. https://doi.org/10.1029/1998RG000054 (2000). Marshall, L. R. et al. Volcanic effects on climate: recent advances and future avenues. Bull. Volcanol . 84 , 54. https://doi.org/10.1007/s00445-022-01559-3 (2022). Additional Declarations No competing interests reported. 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13:45:04","extension":"html","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":124863,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7136394/v1/31ceda3533d919a488b15652.html"},{"id":91869906,"identity":"6d5dc130-34a6-4d83-93ff-34f5be38bd0f","added_by":"auto","created_at":"2025-09-22 13:45:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1441070,"visible":true,"origin":"","legend":"\u003cp\u003eLocation points of the historical diaries and Japan Meteorological Agency (JMA) observatories utilized in this study. The red circles present the historical weather recording locations, as listed in Table 1. 1: Hirosaki, 2: Hachinohe, 3: Morioka, 4: Kawanishi, 5: Nikko, 6: Yokohama, 7: Hachioji, 8: Kofu, 9: Ise, 10: Kyoto, 11: Ikeda, 12: Tanabe, 13: Tsuyama, 14: Hagi, 15: Kitakyushu, 16: Usuki, 17: Isahaya, 18: Koyama. The blue circles indicate the Japan Meteorological Agency observatories used for estimations. 1: Aomori, 2: Hachinohe, 3: Morioka, 4: Yamagata, 5: Utsunomiya, 6: Tokyo, 7: Tokyo, 8: Kofu, 9: Nagoya, 10: Nara, 11: Osaka, 12: Osaka, 13: Nara, 14: Hamada, 15: Fukuoka, 16: Oita, 17: Nagasaki, and 18: Kagoshima\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7136394/v1/eb5539df38aa40b845190320.png"},{"id":91869908,"identity":"48388b45-19a8-4495-b71b-371890885149","added_by":"auto","created_at":"2025-09-22 13:45:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":42430,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of \u003cem\u003eq\u003c/em\u003efor each weather level, from fine (\u003cem\u003e\u003cstrong\u003ek \u003c/strong\u003e\u003c/em\u003e= 1) to rainy (\u003cem\u003e\u003cstrong\u003ek \u003c/strong\u003e\u003c/em\u003e= 3), in Tokyo in August from 1981 to 2010. The maximum, 95% point, 75% point, average, median, 25% point, 5% point, and minimum values are presented. The x-axis presents weather levels, \u003cem\u003e\u003cstrong\u003ek\u003c/strong\u003e\u003c/em\u003e. Typical weather descriptions for each \u003cem\u003eweather level\u003c/em\u003e \u003cem\u003e\u003cstrong\u003ek\u003c/strong\u003e\u003c/em\u003eare listed in \u003cem\u003ethe Type\u003c/em\u003e TG in Table 2\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7136394/v1/347a7caf656ebd4d210d9727.png"},{"id":91871833,"identity":"c08f0b6b-66cb-4751-a160-0a6e2c2d9861","added_by":"auto","created_at":"2025-09-22 14:01:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":149717,"visible":true,"origin":"","legend":"\u003cp\u003eHistorical map of early modern Japan. Rice produced in the areas shown here was transported to Osaka by sea and traded under names such as Kumamoto rice and Ishikawa rice. The rice prices we are referring to here are not the prices for local areas such as Kumamoto and Ishikawa, but the prices are formed in the central rice market in Osaka.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7136394/v1/9245e46c3c191ddac9489dad.png"},{"id":91869914,"identity":"71795d4a-8540-4f89-9c38-9413459e839c","added_by":"auto","created_at":"2025-09-22 13:45:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":982885,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution maps of reconstructed monthly mean solar radiation at 18 locations across Japan. These values represent the ratio of estimates from January to December during 1835, 1836, and 1837 to the average of 30 years from 1821 to 1850.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7136394/v1/119a2a7f7ac20fc9b4ae9b9a.png"},{"id":91870357,"identity":"6978f7a4-da40-4992-9a7b-4c637e649258","added_by":"auto","created_at":"2025-09-22 13:53:03","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":390936,"visible":true,"origin":"","legend":"\u003cp\u003ePC1 of monthly solar radiation in 1821–1850\u003c/p\u003e\n\u003cp\u003e(A): Eigenvector of the PC1 of monthly solar radiation in 1821–1850, represented by the shape and color of markers shown at the locations of diaries in the map. (a) June. (b) July. (c) August. (d) September. Note that the sign of the eigenvector for September was reversed.\u003c/p\u003e\n\u003cp\u003e(B):Time series of the annual scores of the PC1 of monthly solar radiation in 1821–1850, represented by direction and length of vertical line segments. The scores for June (blue), July (green), August (red), and September (grey) for each year are put in that order. Note that the signs for the scores for September were reversed.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7136394/v1/836844edc7cb2749eb52dbbe.jpg"},{"id":91869907,"identity":"635f76ab-bd4b-4629-b994-32fae6f0e162","added_by":"auto","created_at":"2025-09-22 13:45:03","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":118882,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly rice price from 1833 to 1839 in Osaka. (Source) “Sho Sōba no Hikae” (a record of market activities) stored in the archives of the Mitsui Group in Tokyo [22]. Each marker on the graph represents a monthly price converted from a lunisolar calendar to a solar calendar. Since 1835 and 1838 include leap months, the conversion to the solar calendar results in 13 months. Monme was the standard unit of value used in Osaka at the time. The PCA1 score displays the values for each year, spanning the four months from June to September. Lower values of PC scores indicate less solar radiation.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7136394/v1/d2b07e968e2c26ae15c4d9db.jpg"},{"id":103251035,"identity":"b48e0ab4-f19c-4ad9-a03c-6e9bb59d3b7b","added_by":"auto","created_at":"2026-02-23 16:02:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4137014,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7136394/v1/69cd0e76-8a01-4fa8-91d8-bfdbda65a3f2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Abnormal climate and its impact on market economy: solar radiation and rice price during the 1830s famine in Japan","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHow do societies respond to the varying climates? This question not only arouses academic curiosity but also leads us to understand how our society can adapt to ongoing climate change. Accordingly, we approached this issue by focusing on early modern Japan (1603\u0026ndash;1867), which experienced severe famine due to poor harvests caused by an abnormal climate. Indeed, early modern Japanese society was vulnerable to abnormal climates; hence, investigating such occurrences can provide insights into vulnerable areas in the modern era.\u003c/p\u003e\u003cp\u003eWe will focus on the 1830s, when the Tenpō Famine, one of the greatest famines in Japanese history, occurred. The Tenpō Famine is an ideal case study because it occurred nationwide, with regional differences, and the analytical data (for example, old diaries and price records) is easily accessible. During the Tenpō Famine, many people were starved to death, particularly in the northeastern region [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, the degree of damage varies even in the northeast region [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Therefore, it is necessary to closely examine the weather in each region.\u003c/p\u003e\u003cp\u003eHistorical climatology has developed significantly through proxy data such as tree rings, lake sediment cores, and historical records, providing insights into past climatic conditions, particularly for the pre-19th century and early modern periods. Br\u0026aacute;zdil et al. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] summarized European climate reconstructions using these proxies, while Neukom et al. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] analyzed regional climate patterns and the factors influencing them, including external influences. Br\u0026ouml;nnimann et al. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] highlighted the 1835 Cosig\u0026uuml;ina volcanic eruption, which released a large amount of sulfur dioxide into the stratosphere, as a potential contributor to the abnormal climate after the eruption. These studies demonstrate the value of historical data for understanding global and regional climate variations. Hirano and Mikami [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] reconstructed winter climate data for Japan, whereas Mikami [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] focused on summer climate reconstruction. These studies have utilized historical weather records to clarify the characteristics of climate change.\u003c/p\u003e\u003cp\u003eHowever, traditional proxy data, such as tree rings and lake sediments, often lack the seasonal and regional resolutions necessary for a detailed analysis of historical climate variations. Furthermore, previous reconstructions based on historical diaries were limited in their temporal scope. They lacked continuous year-round data or coverage across multiple seasons and regions, leaving gaps in our understanding of climate variation at finer temporal and spatial resolutions.\u003c/p\u003e\u003cp\u003eIchino et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] reconstructed monthly solar radiation data for 1821\u0026ndash;1850 to address these limitations based on historical weather records. Their study produced year-round data from 18 locations across Japan, enabling the identification of regional solar radiation patterns. This dataset provides a critical foundation for examining the relationships between climate variation, agricultural production, and economic indicators such as rice prices. Nishimori and Yokozawa [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] demonstrated that rice yields in western Japan are highly sensitive to solar radiation, whereas temperature plays a more significant role in the Tohoku region. These findings underscore the importance of Ichino et al.\u0026rsquo;s data in understanding the impacts of climate variation during events such as the Tenpō Famine.\u003c/p\u003e\u003cp\u003eRecently, the temporal resolution of economic data has also improved. Although rice prices, representing the early modern Japanese economy, have historically been observed only once annually in the 12th month of the Japanese lunisolar calendar year, continuous data can now be obtained daily and monthly, allowing for more detailed observations. In light of this update, we can transcend previous research on the relationship between the climate and society in the 1830s. Most research on the Tenpō Famine only conducts qualitative analyses, and the only study that uses paleoclimatic and quantitative economic data is Hamano [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. At a one-year resolution, he examined the relationship between climate, food availability, and demography in Japan in the 1830s. He showed that cool summers in 1833, 1836, and 1838 led to food shortages in subsequent winters and diminished birth rates in 1834 and 1837. He inferred a causal relationship among these three variables, which should be updated in at least two respects.\u003c/p\u003e\u003cp\u003eTo determine the climatic conditions, Hamano [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] used weather information compiled from diaries in the Historical Weather Database [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and considered the number of rainy days in July to indicate summer temperatures, as Mikami [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] demonstrated for Tokyo. Hamano [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] reported that there were only a few rainy days in July 1836 in the Tohoku region, in the northern part of the Japanese mainland. However, circumstantial evidence suggests that the temperature was lower during this period; this indicates that the number of rainy days cannot be converted to temperatures in the Tohoku region of Japan.\u003c/p\u003e\u003cp\u003eSecond, to reflect food supply and demand, Hamano applied rice prices in Osaka for the 12th month of the Japanese lunisolar calendar year, as compiled by Iwahashi [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, as explained later, there is a problem with using the rice price in the 12th month of the year to represent the rice price for that year. From spring, when rice planting begins, to autumn, when the harvest is gathered, people trade rice considering the weather. The market\u0026rsquo;s reaction to weather and the forecast for the rice harvest cannot be reconstructed from the prices of the 12th month alone. A pioneering study by Hamano [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] identified various unresolved issues; however, follow-up studies have not been performed.\u003c/p\u003e\u003cp\u003eUnderstanding climate change and its devastating impacts on societies in the past requires social and economic information regarding such periods; this involves reconstructing spatial patterns of climate variation at a higher temporal resolution than that provided by annual data. More detailed climatic and economic data can guide discussions on climate variation and its effects on historical societies. Therefore, this study sought to reconstruct monthly mean solar radiation data from 1821 to 1850 based on weather descriptions recorded in 18 historical diaries. It also discusses the abnormal seasonal climate and its economic effects, focusing on the Tenpō Famine in Japan in the 1830s.\u003c/p\u003e\u003cp\u003eThe remainder of this paper is organized as follows. Section 2 provides an overview of the data. Section 3 presents the distribution map of the reconstructed solar radiation and analyzes its relationship with rice price data. Section 4 discusses the results and concludes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eReconstruction of solar radiation patterns\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eData\u003c/em\u003e\u003c/p\u003e\u003cp\u003eDaily weather descriptions from 18 historical diaries from 1821 to 1850, over 30 years, were collected from the Historical Weather Database (HWDB) developed by Yoshimura [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In the HWDB, dates are translated from the Japanese lunisolar calendar to the Gregorian calendar. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e show the locations at which the data were recorded.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLocations of historical diaries and Japan Meteorological Agency (JMA) observatories utilized in this study\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHistorical Diary No.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHistorical Diary Location\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLatitude (\u0026deg;N)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLongitude (\u0026deg;E)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRecording Period\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eJMA Observatory Location\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eJMA Observation End\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHirosaki\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e140.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1661\u0026ndash;1867\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAomori\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHachinohe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e141.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1792\u0026ndash;1867\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHachinohe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSeptember 2007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMorioka\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e141.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1661\u0026ndash;1840\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMorioka\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKawanishi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e38.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e140.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1830\u0026ndash;1889\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYamagata\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNikko\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e139.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1685\u0026ndash;1871\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUtsunomiya\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYokohama\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e139.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1806\u0026ndash;1889\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTokyo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHachioji\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e139.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1720\u0026ndash;1885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTokyo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKofu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e138.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1747\u0026ndash;1872\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eKofu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e34.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e136.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1683\u0026ndash;1889\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNagoya\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKyoto\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e135.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1796\u0026ndash;1866\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNara\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIkeda\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e34.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e135.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1714\u0026ndash;1892\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOsaka\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTanabe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e33.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e135.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1814\u0026ndash;1869\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOsaka\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTsuyama\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e135.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1702\u0026ndash;1868\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNara\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHagi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e34.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e131.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1739\u0026ndash;1867\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHamada\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSeptember 2007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKitakyushu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e134.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1811\u0026ndash;1857\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFukuoka\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUsuki\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e33.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e130.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1674\u0026ndash;1868\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOita\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIsahaya\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e130.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1700\u0026ndash;1868\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNagasaki\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKoyama\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e130.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1825\u0026ndash;1871\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eKagoshima\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ea\u003c/sup\u003e JMA Observations available up to the month and year noted.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eThe historical diaries provide weather descriptions; the JMA observatories serve as sources of solar radiation and \u003cem\u003eTenki-gaikyō\u003c/em\u003e (daily weather conditions) data during the daytime.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSolar radiation and daytime weather conditions recorded at these locations by the Japan Meteorological Agency (JMA) from 1981 to 2010 were used to calculate the conversion parameters. The daily observed weather conditions from the JMA contained \u0026ldquo;\u003cem\u003eTenki-gaikyō\u003c/em\u003e\u0026rdquo; (a weather summary) twice daily, once during the day and once at night. These data were generated by local observatories following the method described in the Guidelines for Surface Weather Observation Statistics [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Daytime \u003cem\u003eTenki-gaikyō\u003c/em\u003e was applied in the current study and was assumed to be the most similar to the historical daily weather records. These data were generated by summarizing \u0026ldquo;\u003cem\u003eTenki\u003c/em\u003e,\u0026rdquo; a combination of cloudiness- and precipitation-related parameters recorded every three hours from 06:00 to 18:00.\u003c/p\u003e\u003cp\u003eDaily total solar radiation (\u003cb\u003eS\u003c/b\u003e) data from the the JMA's daily surface weather observations were used. The solar radiation reported in the Annual Report of the JMA from 1981 to 2010 was obtained from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.data.jma.go.jp/risk/obsdl/\u003c/span\u003e\u003cspan address=\"https://www.data.jma.go.jp/risk/obsdl/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e and used to determine the conversion parameters. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e show the JMA observatories from which these data were available for estimating historical solar radiation.\u003c/p\u003e\u003cp\u003e\u003cem\u003eReconstruction of solar radiation patterns\u003c/em\u003e\u003c/p\u003e\u003cp\u003eAlthough historical weather records have been used to reconstruct data related to historical climatic variations in various parts of Japan during specific seasons (e.g., summer or winter), few studies have estimated monthly or daily climatic parameters, regardless of season or location, using proxy data such as historical weather records, tree rings, and lake sediment cores. Our group presented a schematic for estimating solar radiation based on weather descriptions. These weather descriptions include reports of clouds or rainfall between the sun and an observer standing on the ground; they may indicate the degree of reduction in incident solar radiation caused by clouds and other atmospheric aerosols. Thus, solar radiation is strongly related to weather conditions, as noted in the weather records. Hence, weather descriptions reflect the amount of solar radiation on the Earth\u0026rsquo;s surface. Accordingly, solar radiation, particularly the total downward solar radiation at the surface, was selected as the target variable for estimation.\u003c/p\u003e\u003cp\u003eThis study estimated the historical solar radiation using written and verbal expressions of weather conditions from historical documents, namely diaries and observational logbooks. Weather descriptions were categorized into three levels and converted into solar radiation using the conversion parameters determined from modern observations (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). As previously explained, historical diaries and the JMA\u0026rsquo;s modern weather descriptions, including solar radiation recorded using instruments, are necessary for reconstructing solar radiation patterns.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSpecifications of weather levels and the classification of weather descriptions\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eType\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory of weather description\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHD\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTG\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e\u003cp\u003e\u003cb\u003eWeather level k\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFine, partly cloudy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHalf fine, half cloudy\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCloudy, partly fine\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCloudy / Half fine, half rainy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCloudy, partly rainy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHalf cloudy, half rainy\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRainy, partly cloudy\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRainy / snowy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003ea\u003c/sup\u003eType HD: For weather descriptions in historical documents\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003eb\u003c/sup\u003e Type TG: Weather descriptions from the Japan Meteorological Agency, \u003cem\u003eTenki-gaikyō\u003c/em\u003e.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe method used in this study was derived from the equation Ichino et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] described, which elucidated the relationship between solar radiation and modern weather descriptions and developed a method to estimate solar radiation from modern weather descriptions. This equation is based on the relationship between modern solar radiation and \u003cem\u003eTenki-gaikyō\u003c/em\u003e. Daily total solar radiation (\u003cb\u003eS\u003c/b\u003e) was obtained from the daily JMA surface weather observations. Normalized \u003cb\u003eS\u003c/b\u003e is referred to as \u003cb\u003eq\u003c/b\u003e and is defined by Eq.\u0026nbsp;(1):\u003c/p\u003e\u003cp\u003e\u003cb\u003eq\u0026thinsp;=\u0026thinsp;S/S\u003c/b\u003e\u003csub\u003e\u003cb\u003eTOA\u003c/b\u003e\u003c/sub\u003e (1)\u003c/p\u003e\u003cp\u003ewhere \u003cb\u003eS\u003c/b\u003e\u003csub\u003e\u003cb\u003eTOA\u003c/b\u003e\u003c/sub\u003e is the daily insolation received at a horizontal surface at the top of the atmosphere and is computed using the equations described by Kondo and Xu [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Although it varied throughout the historical period, we consistently applied the recent value of 1.365 \u0026times; 10\u003csup\u003e3\u003c/sup\u003e W/m\u003csup\u003e2\u003c/sup\u003e as the total solar irradiance (so-called solar constant) to calculate \u003cb\u003eS\u003c/b\u003e\u003csub\u003e\u003cb\u003eTOA\u003c/b\u003e\u003c/sub\u003e throughout this study.\u003c/p\u003e\u003cp\u003eThe average ratio of \u003cb\u003eq\u003c/b\u003e for each month to weather level \u003cb\u003ek\u003c/b\u003e is referred to as \u003cb\u003eq\u003c/b\u003e\u003csub\u003e\u003cb\u003emean\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e(k)\u003c/b\u003e, which was calculated at each location where \u003cem\u003eTenki-gaikyō\u003c/em\u003e and solar radiation were used (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We speculate that the \u003cb\u003eq\u003c/b\u003e\u003csub\u003e\u003cb\u003emean\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e(k)\u003c/b\u003e values for the 19th century and 1981\u0026ndash;2010 are identical.\u003c/p\u003e\u003cp\u003e\u003cem\u003eTenki-gaikyō\u003c/em\u003e was classified into three \u003cem\u003eTenki-gaikyō\u003c/em\u003e (TG) levels. The categorization method for weather records from historical documents, the nine categories of \u003cem\u003eTenki-gaikyō\u003c/em\u003e [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and the five types of classification are listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eCategories that describe weather conditions were established according to the method described by Ichino et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and named according to the common descriptions found in \u003cem\u003eTenki-gaikyō\u003c/em\u003e, ranging from the most favorable (fine) to the least favorable (rainy or snowy). These categories were designed to organize a wide range of weather descriptions systematically. For instance, \u0026ldquo;rainy or snowy\u0026rdquo; encompasses weather conditions such as rainy, snowy, graupel, hail, and sleet.\u003c/p\u003e\u003cp\u003eSolar radiation and weather conditions are strongly correlated. Therefore, we initially explored the quantitative relationship between \u003cb\u003eq\u003c/b\u003e and \u003cem\u003eTenki-gaikyō\u003c/em\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the distribution of \u003cb\u003eq\u003c/b\u003e for each weather level in Tokyo from 1981 to August 2010, suggesting that \u003cb\u003eq\u003c/b\u003e is distinguishable based on the weather level. The solar radiation values for all months were highly correlated with daily weather descriptions. Consequently, the weather levels deduced from weather descriptions were converted into solar radiation using Eq.\u0026nbsp;(2):\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eS\u003c/b\u003e\u003csub\u003e\u003cb\u003eej\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e= q\u003c/b\u003e\u003csub\u003e\u003cb\u003emean\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e(k\u003c/b\u003e\u003csub\u003e\u003cb\u003ej\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e)* S\u003c/b\u003e\u003csub\u003e\u003cb\u003eTOA\u003c/b\u003e \u003cb\u003ej\u003c/b\u003e\u003c/sub\u003e (2)\u003c/p\u003e\u003cp\u003ewhere \u003cb\u003eS\u003c/b\u003e\u003csub\u003e\u003cb\u003eej\u003c/b\u003e\u003c/sub\u003e is the estimated solar radiation, \u003cb\u003ej\u003c/b\u003e is the day, and \u003cb\u003ek\u003c/b\u003e\u003csub\u003e\u003cb\u003ej\u003c/b\u003e\u003c/sub\u003e is the weather condition indicated by \u0026ldquo;weather level.\u0026rdquo; \u003cb\u003eS\u003c/b\u003e\u003csub\u003e\u003cb\u003eTOA\u003c/b\u003e \u003cb\u003ej\u003c/b\u003e\u003c/sub\u003e is the daily solar radiation that reaches the top of the atmosphere on day \u003cb\u003ej\u003c/b\u003e, and \u003cb\u003eq\u003c/b\u003e\u003csub\u003e\u003cb\u003emean\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e(k\u003c/b\u003e\u003csub\u003e\u003cb\u003ej\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e)\u003c/b\u003e is a conversion parameter determined by weather level \u003cb\u003ek\u003c/b\u003e\u003csub\u003e\u003cb\u003ej\u003c/b\u003e\u003c/sub\u003e and calculated using weather observations recorded at the JMA from 1981 to 2010.\u003c/p\u003e\u003cp\u003eThe errors in \u003cb\u003eS\u003c/b\u003e\u003csub\u003e\u003cb\u003eej\u003c/b\u003e\u003c/sub\u003e compared with the observed \u003cb\u003eS\u003c/b\u003e were not sufficiently small. However, the variation was similar to the observed \u003cb\u003eS\u003c/b\u003e. The method uses the \u003cb\u003eq\u003c/b\u003e\u003csub\u003e\u003cb\u003emean\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e(k\u003c/b\u003e\u003csub\u003e\u003cb\u003ej\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e)\u003c/b\u003e corresponding to the three weather conditions for each day. Therefore, we discuss the monthly mean \u003cb\u003eS\u003c/b\u003e\u003csub\u003e\u003cb\u003eej\u003c/b\u003e\u003c/sub\u003e described by Ichino et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] to minimize ambiguity.\u003c/p\u003e\u003cp\u003eHistorical weather descriptions were categorized into appropriate weather levels, \u003cb\u003ek\u003c/b\u003e to estimate historical solar radiation and \u003cem\u003eTenki-gaikyō\u003c/em\u003e. Based on our recent investigation, we adopted the three-level HD classification shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, developed by comparing weather descriptions from historical diaries with \u003cem\u003eTenki-gaikyō\u003c/em\u003e and examining their characteristics.\u003c/p\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, fine was designated as weather level \u003cb\u003ek\u003c/b\u003e\u0026thinsp;=\u0026thinsp;1, rainy as \u003cb\u003ek\u003c/b\u003e\u0026thinsp;=\u0026thinsp;3, and snowy, graupel, hail, and sleet were considered equivalent to rainy (\u003cb\u003ek\u003c/b\u003e\u0026thinsp;=\u0026thinsp;3). As mentioned in the typical weather expressions for the categories, \u003cb\u003ek\u003c/b\u003e\u0026thinsp;=\u0026thinsp;2 includes other weather descriptions, unlike \u003cem\u003eTenki-gaikyō\u003c/em\u003e. For example, temporary clouds in historical diaries were categorized as \u003cb\u003ek\u003c/b\u003e\u0026thinsp;=\u0026thinsp;2, whereas the weather in \u003cem\u003eTenki-gaikyō\u003c/em\u003e was classified as \u003cb\u003ek\u003c/b\u003e\u0026thinsp;=\u0026thinsp;1. In historical diaries, a rainy day that temporarily stops the rain is also categorized as \u003cb\u003ek\u003c/b\u003e\u0026thinsp;=\u0026thinsp;2; however, in the case of \u003cem\u003eTenki-gaikyō\u003c/em\u003e, it is categorized as \u003cb\u003ek\u003c/b\u003e\u0026thinsp;=\u0026thinsp;3.\u003c/p\u003e\u003cp\u003e\u003cem\u003eParameter determination with modern observations\u003c/em\u003e\u003c/p\u003e\u003cp\u003eTo calculate \u003cb\u003eq\u003c/b\u003e\u003csub\u003e\u003cb\u003emean\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e(k)\u003c/b\u003e, the observed weather conditions and solar radiation were required. Instrumental observations were rarely provided at the same location where the weather was recorded in the historical diary. Observational data at the JMA locations listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e were used to estimate and calculate the parameters. This study is based on the climatic division of solar radiation provided by the Japan Weather Association (JWA). The similarities between the seasonal patterns of solar radiation determine these climatic divisions.\u003c/p\u003e\u003cp\u003eIchino and Mikami [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] examined the range of spatial applicability of \u003cb\u003eq\u003c/b\u003e\u003csub\u003e\u003cb\u003emean\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e(k)\u003c/b\u003e at a location to estimate the monthly mean solar radiation using the approach of Ichino et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], with \u003cb\u003eq\u003c/b\u003e \u003csub\u003e\u003cb\u003emean\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e(k)\u003c/b\u003e applied to other locations. The results indicated errors in the estimations using \u003cb\u003eq\u003c/b\u003e\u003csub\u003e\u003cb\u003emean\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e(k)\u003c/b\u003e at the same locations. The JMA locations used for the estimation in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e were determined based on the climatic division of solar radiation by the JWA concerning these results.\u003c/p\u003e\u003cp\u003e\u003cem\u003eAnalysis of the spatiotemporal structure of solar radiation\u003c/em\u003e\u003c/p\u003e\u003cp\u003eTo determine the spatial and temporal scales of the variation in solar radiation, we performed Principal Component Analysis (PCA). The procedure \u0026lsquo;prcomp\u0026rsquo; of R 4.3.0 was used with its default options. Its input consisted of monthly values of the normalized solar radiation \u003cb\u003eq\u003c/b\u003e for one month (June, July, August, or September) from 1821 to 1850 at 18 locations which are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The output consists of 18 components, each of which is a pair of an eigenvector and a sequence of its scores representing the variation's spatial and temporal structures.\u003c/p\u003e\u003cp\u003eOnly such sets of observations that have valid values at all locations can contribute to constructing the eigenvectors. However, the \u003cb\u003eq\u003c/b\u003e value was missing in some locations in some months because the number of days with valid records was less than 2/3 of the total days in the month. There are too many months of missing observations at certain locations to be ignored. We tentatively replaced missing values with zero anomalies; that is, we assumed that \u003cb\u003eq\u003c/b\u003e was the same as the average of the 30 years. This assumption is likely to underestimate the actual variation. However, it is unlikely that false signals will be generated.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRice price\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe method used in this study to estimate solar radiation during past periods can be applied throughout Japan regardless of the specific season or region. It can also be used to estimate the climate during periods of less than one year and seasonal changes. Such an approach will supplement the discussion on human society and climatic factors, as it provides data better suited to the temporal resolution of social change than other proxy data that are limited to specific seasons (e.g., summer or winter).\u003c/p\u003e\u003cp\u003eFurthermore, our method enables the estimation of solar radiation from daily weather descriptions at various temporal resolutions, allowing for averaging over specific periods, such as months, the growing season of a crop, or an integrated value rather than a simple average value over a set timeframe, as well as the estimation of annual totals. The flexibility of our method facilitates comparison with diverse indicators, such as production, yield, price, and other economic factors.\u003c/p\u003e\u003cp\u003eTo confirm this, we compared the reconstructed solar radiation with rice production. Data on agricultural yields are required to rigorously understand the social impacts of climate change. However, although historical records of accurate yields have been preserved in minimal areas, no historical statistics are available to determine the yields for the entire country. Therefore, previous studies have used prices as a proxy for output, especially rice prices, for which the most continuous data are available; this study follows suit.\u003c/p\u003e\u003cp\u003eRice was not only the staple food of the time but also a symbolic good of early modern Japan. Rulers collect rice as a tax and sell it to the market to generate financial revenue. Therefore, continuous time-series data on rice, among other grains, have been preserved in historical documents.\u003c/p\u003e\u003cp\u003eHowever, caution must be exercised when using rice prices as a proxy indicator. First, we must ask whether trade with other countries causes fluctuations in rice prices. However, this question did not apply to the present study. From 1639 until its collapse in 1867, the Tokugawa Shogunate imposed a strictly controlled trade regime prohibiting rice import and export. After 1639, rice was produced throughout Japan and consumed domestically without being exported or imported [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Additionally, the national population did not change considerably during the observation period [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], suggesting that consumer demand remained unchanged. Thus, it appears that the primary supply factors (yield) affected the price of rice, and the climate affected its supply.\u003c/p\u003e\u003cp\u003eSecond, were any other events, such as riots, typhoons, or government price controls, significantly impacted rice prices? Regarding riots in 1837 in Osaka, which had the most extensive rice market at the time, one of the officials caused a large-scale riot to denounce the inadequacy of the shogunate\u0026rsquo;s rice price reduction policy and the injustice of the officials. This riot upsets rice prices and should be carefully considered when observing later fluctuations in rice prices; however, if we preemptively conclude, its impact is limited. Rice prices rose immediately after the riots, but the effect was short-lived, partly because the riots were suppressed within one day. Instead, we believe the upward pressure on rice prices was exerted by the lack of solar radiation after the riots.\u003c/p\u003e\u003cp\u003eIn the 1830s, the shogunate instituted various measures to reduce rice prices. For example, stockpiled rice was released, and orders were given to local lords to bring more rice to Osaka and Edo (Tokyo). However, as these measures were ineffective and caused the riots described above in some respects, it is safe to conclude that they did not significantly change the price of rice.\u003c/p\u003e\u003cp\u003eTherefore, using rice prices as a proxy indicator of rice output is acceptable. Prior studies, such as Hamano [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], use rice prices as a proxy indicator to understand rice production. He applied the rice price in Osaka in the last month of the Japanese lunisolar calendar year rather than the average yearly price to reflect food supply and demand, as compiled by Iwahashi [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In other words, a problem exists with this process.\u003c/p\u003e\u003cp\u003eRice is harvested in the 9th or 10th month (around October or November in the solar calendar) of each year; hence, the rice price in the 12th month is representative of the rice price for that year. However, this was not the case in poor harvest years. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, a bad crop in year \u003cem\u003et\u003c/em\u003e can lead to higher prices in the summer of year \u003cem\u003et\u003c/em\u003e and higher prices in the spring and summer of the following year (\u003cem\u003et\u0026thinsp;+\u0026thinsp;1\u003c/em\u003e). Prices rise in the summer, before the rice harvest (in autumn), because rice merchants have a nationwide information network about rice crops. During the summer, they trade rice based on the expected annual harvests of the year [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Suppose rice production recovers in year \u003cem\u003et\u0026thinsp;+\u0026thinsp;1\u003c/em\u003e and prices in the 12th month in year \u003cem\u003et\u0026thinsp;+\u0026thinsp;1\u003c/em\u003e would be lower. Therefore, year \u003cem\u003et\u0026thinsp;+\u0026thinsp;1\u003c/em\u003e would be understood to have been a year of low rice prices, even though the first half of year \u003cem\u003et\u0026thinsp;+\u0026thinsp;1\u003c/em\u003e suffered from high rice prices. Thus, in a bad harvest year, the price of rice in the 12th month should not be considered representative of that year\u0026rsquo;s price.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo update the pioneering work, Hamano [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], we refer to monthly rice price data from the \u0026ldquo;Sho Sōba no Hikae\u0026rdquo; (a record of market activities) stored in the archives of the Mitsui Group in Tokyo [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The database contains monthly reports on the prices of essential products. Among these records, we evaluated documents from 1833 to 1839 that recorded the monthly price of rice in Osaka. In principle, monthly price refers to the price at the beginning of each month. We converted these from a lunisolar calendar to a solar calendar. Here, we refer to the price of rice produced in Kumamoto, Ishikawa, Fukuoka, Yamaguchi, and Hiroshima (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Approximately 30 brands of rice, differentiated by production area, are traded in the Osaka rice market, of which five are leading brands [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe brands\u0026rsquo; prices referred to here are not those in a specific region, such as Kumamoto, but the prices formed in the Osaka rice market. As rice produced in Kumamoto is sent to Osaka and traded in the central market, the Osaka rice market, it was also considered at the time to reflect the trends in supply and demand nationwide [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], just as Toyota\u0026rsquo;s stock price is deemed to be a proxy index that indicates the economic situation of the whole of Japan rather than the financial situation of Toyota City, where Toyota\u0026rsquo;s headquarters is located.\u003c/p\u003e\u003cp\u003eThere is a reason why the rice brands we refer to are biased towards Western Japan. Rice produced in Eastern Japan was transported to Edo by sea and traded there; however, unlike in Osaka, wholesale traders traded rice on a one-to-one basis, and prices were not widely publicized. Therefore, obtaining time-series data on rice prices in Edo is almost impossible. However, this bias did not hinder the analysis. It has been pointed out that the price of rice in Edo was highly dependent on the price of rice in Osaka [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. It is also known that the government (The shogunate) used the Osaka rice price as a benchmark when implementing policies to adjust rice prices [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Therefore, the prices of the five brands referred to here represent rice supply and demand.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eReconstructed solar radiation patterns\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe reconstructed the monthly mean solar radiation from 1821 to 1850 based on weather descriptions from 18 historical diaries. Here, we assume that the \u003cb\u003eq\u003c/b\u003e \u003csub\u003e\u003cb\u003emean\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e(k)\u003c/b\u003e, the average of \u003cb\u003eq\u003c/b\u003e for each weather level, is the same for the diaries during the historical period as for the current weather conditions of the JMA. Our analysis focused on 1836, marked by the most severe famine of the Tenpō era. Although frequent cool summers and crop failures have been suggested as causes of famines, the mechanisms that trigger these events remain unclear.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the reconstructed solar radiation anomalies from 1835 to 1837 as monthly deviations from the 30-year mean (1821\u0026ndash;1850). The maps indicate a significant reduction in solar radiation in the summer of 1836, especially in central Japan, which aligned with the severe economic conditions of the Tenpō Famine. Although daily estimates were initially generated, monthly averages were used to minimize estimation errors. Only the months with at least 20 recorded diary entries were included to ensure data reliability. This methodology reduces uncertainties arising from biases in diary weather descriptions or disparities between the diary records and \u003cem\u003eTenki-gaikyō\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eDuring the summer of 1836, solar radiation in the east-west zone of Japan, including Kanto, Kinki, and northern Kyushu (locations 5\u0026ndash;15), decreased significantly, approximately 10% below the historical average for July and August. In contrast, the solar radiation levels in Tohoku (locations 1\u0026ndash;4) to the north and southern Kyushu (location 18) to the south remained relatively stable, highlighting distinct regional variations in solar radiation patterns. The reconstructed solar radiation levels from May to September 1836 were consistently lower across central Japan. In contrast, the solar radiation levels in spring (February to April) and autumn (September to November) were within the average range.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSpatio-temporal structure of solar radiation as revealed by Principal Component Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe PCA of \u003cb\u003eq\u003c/b\u003e was performed as described in Section 2.2. The proportions of variance in the first principal component (PC1) were 39, 42, 46, and 31%, respectively, for June, July, August, and September. The eigenvectors of PC1 for each month are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA (a-d). In all four months, the signs were almost the same everywhere. Opposite signs occur in the Tohoku region (north of 38 \u0026deg;N), but the magnitude is not large there. For convenience of comparison, the sign of the eigenvectors is so taken that the values are positive in most of the locations. When the score of PC1 is negative, solar radiation is weaker than normal in the zone around 32\u0026ndash;37 \u0026deg;N, such as from Kyushu to Kanto.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe time series of the PC1 scores are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB. Although the eigenvectors for the four months were not exactly the same, we combined the scores of the four months to browse the sequence. Large negative values indicated a lack of solar radiation in the zone around 32\u0026ndash;37 \u0026deg;N. Remarkably, the large negative values lasted from July to September 1836 and July to August 1838, when Japan experienced years of cool summers and poor rice harvesting. The score for 1833, another well-known cool summer in the Tohoku region, was not much different from zero. It can be said that this summer had a solar radiation anomaly pattern different from that of PC1.\u003c/p\u003e\u003cp\u003eWe do not discuss other principal components here because the spatial patterns of the eigenvectors were not coherent from one month to another, and their proportion of the variance was, at most, 17%.\u003c/p\u003e\u003cp\u003e\u003cb\u003eClimate variation and the market economy\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNext, we examine the relationship between the price of rice in Osaka and the amount of solar radiation, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e plots the rice price trend and shows that from August 1836 to September 1837, rice prices were higher than in a typical year (50\u0026ndash;70 monme) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Contrasting this movement in rice prices with the PCA1 Scores reveals that the rice price trend correlates with the PCA1 scores from June to September of 1836. This indicates that market participants were pessimistic about the lack of solar radiation between June and September 1836 (represented by the blue bar extending downward in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), and they placed buy orders for rice before the harvest in October, anticipating a rise in rice prices.\u003c/p\u003e\u003cp\u003eThe rice crops were poor, and the price of rice continued to rise until the summer of 1837. One event that must be mentioned during this period was the Ōshio Heihachirō\u0026rsquo;s Rebellion. On March 25, 1837 (the 19th day of the 2nd month in the solar-lunar calendar), Ōshio Heihachirō, a bureaucrat in the Osaka Magistrate\u0026rsquo;s Office, instigated an armed uprising in Osaka City, decrying the government\u0026rsquo;s inaction toward the soaring rice prices. However, the rebellion was suppressed within a day, and it remains uncertain whether the subsequent surge in rice prices after April 4, 1837, was directly linked to it. It is more likely that the sharp rise in the rice price during this period was caused by the market's pessimism about a poor rice harvest due to a lack of solar radiation after April. The recovery of solar radiation from June to August 1837 stopped this trend, and rice prices began to fall in October 1837. Such a recovery of solar radiation during the rice-growing season likely buoyed the perception of a prospective good rice crop. As this trend in rice prices applies to all five rice brands; fluctuations in solar radiation should have substantially impacted rice prices at that time.\u003c/p\u003e\u003cp\u003eThe same can be said of 1834\u0026ndash;1835. The lack of sunlight in August 1834 caused the price of rice to rise, which continued to rise until solar radiation recovered in the summer of 1835.\u003c/p\u003e\u003cp\u003ePrevious studies that focused solely on rice prices in the 12th month (around January of the following year on the modern solar calendar) overlooked the significance of people\u0026rsquo;s perceptions of the harvest for the corresponding year.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cb\u003eDifferent behavior of solar radiation and temperature\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn the summer season in Japan, low values of solar radiation tend to coincide with low values of temperature, and both factors can lead to low rice yields. However, these factors do not always behave similarly.\u003c/p\u003e\u003cp\u003eThe spatial structure of PC1 of solar radiation, as mentioned in Section 3.2, somewhat resembles the eigenvector of PC1 of surface air temperature (July and August 1901\u0026ndash;1974) by Mikami [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], which has the same sign everywhere, with a maximum in the zone around 35\u0026ndash;37 \u0026deg;N. It is different, however, that the eigenvector of PC1 of temperature has such values in the Tohoku region (38\u0026ndash;42 \u0026deg;N) that are larger than half of its maximum, while that of solar radiation has much smaller values.\u003c/p\u003e\u003cp\u003eAccording to alternate sources, despite moderate levels of reconstructed solar radiation in the Tohoku region during the summer of 1836, climatic conditions were anomalously cool. For example, Sekisetsu Chihō Nōson Keizai Chōsa-Sho [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] compiled a chronological table of agricultural disasters in six prefectures (administrative divisions in modern Japan) in the Tohoku region, and it recorded cool anomalies in 1836 in four of these prefectures.\u003c/p\u003e\u003cp\u003eThe effects of these two factors on crop yield were also different. Nishimori and Yokozawa [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] studied the climatic factors affecting rice yields in Japan using data from the modern period (1979\u0026ndash;1994). They used multiple regression analysis to explain rice yields based on air temperature and solar radiation. Their results showed that rice yield is sensitive to temperature in the northeastern part of Japan and solar radiation in the western part.\u003c/p\u003e\u003cp\u003eThus, the climate is likely to be cooler all over Japan in years when the PC1 score of solar radiation has large positive values, even though the solar radiation anomaly in the northernmost part is small. In such years, the rice yield is likely to be lower because of low solar radiation in the western part of Japan and low temperatures in the northeastern part.\u003c/p\u003e\u003cp\u003e\u003cb\u003eVolcanic eruptions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eVolcanic eruptions that inject a large amount of sulfur dioxide into the stratosphere will likely impact the global climate. Sigl et al. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] list such eruptions in the time frame of our study in the database.\u003c/p\u003e\u003cp\u003eIn the time frame of our study, the Cosig\u0026uuml;ina volcano in Nicaragua erupted in January 1835 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], and the volcanic explosivity index (VEI) as defined by Newhall and Self [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] was 5. According to Sigl et al. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], the sulfur dioxide which was injected into the stratosphere is estimated to be 9 teragrams. In addition, Hutchison et al. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] demonstrate that the Zavaritskii caldera in Simushir Island of the Kurils erupted in 1831, injecting 12 teragrams of sulfur dioxide.\u003c/p\u003e\u003cp\u003eRobock [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] and Marshall et al. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] discussed how the climate system responds to volcanic forcing. Some of the responses were straightforward. Sulfate aerosols reduce the amount of solar radiation that arrives at the surface and are likely to cause lower surface temperatures in many parts of the world. However, this response is not just that. For example, the temperature anomalies in the lower troposphere in the northern middle latitudes in winter following the eruption of Pinatubo in 1991 were positive somewhere and negative elsewhere (see Plate 8 of Robock [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]). The propagation of planetary waves in the atmosphere is likely involved. Therefore, the spatial patterns of the anomalies may be quite different according to the situation of the zonal winds, even with similar forcing.\u003c/p\u003e\u003cp\u003eAs we reconstructed from historical diaries, variations in solar radiation at the surface may contain some effect of volcanic forcing; however, it is probably not a direct effect of aerosols but an indirect effect via clouds, which planetary waves may also modulate. We consider that discussing its cause-and-effect relationship would be precarious and that it will become fruitful when we can reconstruct global-scale patterns of anomalies.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eDespite recent advancements in climate reconstruction from tree rings and other sources that provide climate data with a resolution of approximately one month, these reconstructions are limited to early to mid-summer, posing challenges in obtaining climate or weather information for September and October, which are crucial months that impact rice yields. Moreover, while annual rice prices traditionally serve as indicators of socioeconomic conditions, disaggregating them into monthly rice prices reveals the significant impact of solar radiation from July to September on prices in the central market, exemplified by observations from 1836 to 1838, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows that while solar radiation levels in July and August remained similar between 1836 and 1838, there was a notable disparity in September. In 1836, rice prices rose after September due to a lack of solar radiation. In contrast, in 1838, rice prices remained high owing to the recovery of solar radiation during the same month (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). These distinctions are discernible only through the enhanced resolution of the climate and price data. This correlation between the reconstructed solar radiation and rice price trends underscores our successful estimation of the climatic conditions that impact rice production.\u003c/p\u003e\u003cp\u003eOur study shows that the interannual climate variations in Japan during the 1830s affected societal wellness. For example, an abnormally cool summer in 1836 was followed by a surge in rice prices, which persisted until the summer of 1837. Even during the Tenpō Famine period, marked by severe food shortages, significant year-to-year and within-a-year variations in rice prices occurred. Given the nuanced relationship between climate and the economy, which is intricately linked to seasonal activities, it is important to examine climate and economic data monthly. Moreover, establishing a nationwide rice market system makes it essential to assess climate patterns on a synoptic scale spanning thousands of kilometers; this necessitates examining weather description records across multiple locations to capture a broader climatic context.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflict of interest statement\u003c/h2\u003e\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003ePermission to reproduce material from other sources\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding statement\u003c/h2\u003e\u003cp\u003eThis study was supported by JSPS KAKENHI (grant numbers 17540410, 18H03794, 20K01152, 21H03776, 21H05180, 22H04938, and 23K00974); Joint Support-Center for Data Science Research (grant numbers 027RP2021, 041RP2022, and 044RP2023); and Ishi Memorial Securities Research and Promotion Foundation (grant number 2023-4).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: Mika Ichino, Yasuo Takatsuki, Kooiti Masuda, Takehiko Mikami; Data curation: Mika Ichino, Yasuo Takatsuki, Kooiti Masuda, Takehiko Mikami; Formal analysis: Mika Ichino, Yasuo Takatsuki, Kooiti Masuda; Funding acquisition: Mika Ichino, Yasuo Takatsuki, Kooiti Masuda; Investigation: Mika Ichino, Yasuo Takatsuki, Kooiti Masuda, Takehiko Mikami: Methodology: Mika Ichino, Kooiti Masuda; Project administration: Mika Ichino, Yasuo Takatsuki; Supervision: Kooiti Masuda, Takehiko Mikami; Validation: Mika Ichino, Yasuo Takatsuki, Kooiti Masuda, Takehiko Mikami; Visualization: Mika Ichino, Yasuo Takatsuki, Kooiti Masuda, Takehiko Mikami; Writing \u0026ndash; original draft: Mika Ichino, Yasuo Takatsuki, Kooiti Masuda; Writing \u0026ndash; review \u0026amp; editing: Mika Ichino, Yasuo Takatsuki, Kooiti Masuda, Takehiko Mikami. All authors have read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eAn earlier version of this paper was presented at the Association for Asian Studies 2023 Annual Conference on March 19, 2023, the annual conference of the Japan Society of Hydrology and Water Resources on September 4, 2023, and the European Geoscience Union General Assembly 2024 on April 17, 2024. The authors are grateful to the attendees of these meetings for their helpful comments and recommendations\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eHistorical Weather Database data are available at http://tk2-202-10627.vs.sakura.ne.jp; observational data from the JMA (Section 2.3) from 1981 to 2010 are available from the \u0026lsquo;Kako-no kishō data download\u0026rsquo; (past weather data download) on the JMA web page (https://www.data.jma.go.jp/risk/obsdl/); Economic data are available from the \u0026ldquo;Sho Sōba no Hikae\u0026rdquo; (Record of Market Activities), which is stored in the archives of the Mitsui group in Tokyo.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBolitho, H. The Tempō Crisis, in The Cambridge History of Japan, Vol. 5 The Nineteenth Century (Cambridge University Press, (1989).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKikuchi, I. \u003cem\u003eUe to Shoku no Nihon-Shi\u003c/em\u003e (\u003cem\u003eJapanese History of Hunger and Food\u003c/em\u003e) (Yoshikawa Kōbunkan, Tokyo, [in Japanese]. (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBr\u0026aacute;zdil, R., Pfister, C., Wanner, H., von Storch, H. V. \u0026amp; Luterbacher, J. Historical climatology in Europe \u0026ndash; The state of the art. \u003cem\u003eClim. Change\u003c/em\u003e. \u003cb\u003e70\u003c/b\u003e, 363\u0026ndash;430 (2005).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNeukom, R., Steiger, N., G\u0026oacute;mez-Navarro, J. J., Wang, J. \u0026amp; Werner, J. P. No evidence for globally coherent warm and cold periods over the preindustrial Common Era. \u003cem\u003eNature\u003c/em\u003e \u003cb\u003e571\u003c/b\u003e, 550\u0026ndash;554 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBr\u0026ouml;nnimann, S. et al. Last phase of the Little Ice Age forced by volcanic eruptions. \u003cem\u003eNat. Geosci.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 650\u0026ndash;656 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHirano, J. \u0026amp; Mikami, T. Reconstruction of winter climate variations during the 19th century in Japan. \u003cem\u003eInt. J. Climatol\u003c/em\u003e. \u003cb\u003e28\u003c/b\u003e, 1423\u0026ndash;1434 (2008).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMikami, T. Climatic reconstruction in historical times based on weather records. \u003cem\u003eGeogr. Rev. Jpn Ser. B\u003c/em\u003e. \u003cb\u003e61\u003c/b\u003e, 14\u0026ndash;22 (1988).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIchino, M., Mikami, T. \u0026amp; Masuda, K. Fluctuations of global solar radiation in Japan during the first half of the 19th century as estimated from historical weather records. \u003cem\u003eJ. Geogr. (Tokyo)\u003c/em\u003e. \u003cb\u003e127\u003c/b\u003e, 543\u0026ndash;552 (2018). [in Japanese with English abstract].\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNishimori, M. \u0026amp; Yokozawa, M. Kikō Hendō Ijō Kishō ni yoru Nihon no Suitō Tanshū Hendō no Chiikiteki Henka (Regional change of yield per unit of paddy rice in Japan by climatic variability and abnormal weather conditions). \u003cem\u003eChikyū Kankyō (Glob Environ.\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e, 149\u0026ndash;158 (2001). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://airies.wikiplus.net/attach.php/6a6f75726e616c5f30362d326a706e/save/0/0/06_2-04.pdf\u003c/span\u003e\u003cspan address=\"https://airies.wikiplus.net/attach.php/6a6f75726e616c5f30362d326a706e/save/0/0/06_2-04.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e [in Japanese].\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHamano, K. 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[in Japanese].\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIchino, M., Sakamoto, N., Masuda, K. \u0026amp; Mikami, T. The method for estimating global solar radiation based on weather records: Toward the climatic reconstruction in the historical period. \u003cem\u003eTenki\u003c/em\u003e \u003cb\u003e48\u003c/b\u003e, 823\u0026ndash;830 (2001). [in Japanese].\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKondo, J. \u0026amp; Xu, J. Seasonal variations in the heat and water balances for nonvegetated surfaces. \u003cem\u003eJ. Appl. Meteorol.\u003c/em\u003e \u003cb\u003e36\u003c/b\u003e, 1676\u0026ndash;1695 (1997).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIchino, M. \u0026amp; Mikami, T. Spatial and temporal differences of global solar radiation: Applicability of mean daily clearness index. \u003cem\u003eGeogr. Rep. Tokyo Metrop Univ.\u003c/em\u003e \u003cb\u003e38\u003c/b\u003e, 15\u0026ndash;21 (2003).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTakatsuki, Y. \u003cem\u003eThe Dojima Rice Exchange: from Rice Trading to Index Futures Trading in Edo Period Japan\u003c/em\u003e (Japan Publishing Industry Foundation for Culture, 2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBassino, J. P., Broadberry, S., Fukao, K., Gupta, B. \u0026amp; Takashima, M. Japan and the great divergence, 730\u0026ndash;1874. \u003cem\u003eExplor. Econ. Hist.\u003c/em\u003e \u003cb\u003e72\u003c/b\u003e, 1\u0026ndash;22 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTakatsuki, Y. \u0026amp; Hisamatsu, T. The role of information in the rice exchange: Yamagata Bantō\u0026rsquo;s great knowledge (1806). \u003cem\u003eEur. J. Hist. Econ. 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Sekisetsu Chihō Nōson Keizai Chōsa-Sho, Yamagata. [in Japanese]. (1935).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSigl, M., McConnell, J. R. \u0026amp; Toohey, M. Volcanic stratospheric sulfur injection between 1733 and 1895 CE based on the eVolv2k-plus-D4i ice-core eruption catalogue [dataset]. \u003cem\u003ePANGAEA\u003c/em\u003e (2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1594/PANGAEA.960975\u003c/span\u003e\u003cspan address=\"10.1594/PANGAEA.960975\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLongpr\u0026eacute;, M. A., Stix, J., Burkert, C., Hansteen, T. \u0026amp; Kutterolf, S. Sulfur budget and global climate impact of the A.D. 1835 eruption of Cosig\u0026uuml;ina volcano, Nicaragua. \u003cem\u003eGeophys. Res. 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Volcanol\u003c/em\u003e. \u003cb\u003e84\u003c/b\u003e, 54. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00445-022-01559-3\u003c/span\u003e\u003cspan address=\"10.1007/s00445-022-01559-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"climatic impact, economic fluctuation, Tenpō Famine, solar radiation, Japan, rice","lastPublishedDoi":"10.21203/rs.3.rs-7136394/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7136394/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe investigated the interplay between climatic anomalies and economic fluctuations in early modern Japan (1603\u0026thinsp;\u0026minus;\u0026thinsp;1867), focusing on the Tenpō Famine of the 1830s. Specifically, we reconstructed the solar radiation from 1821 to 1850 using descriptions from 18 historical diaries. This represents a novel approach to analyzing climatic impacts on agriculture and the economy during this period. The results show that lower solar radiation, indicative of poor weather conditions, was associated with higher rice prices, particularly during the summer of 1836. Applying principal component analysis to the reconstructed solar radiation data revealed spatiotemporal patterns that elucidated the link between climatic anomalies and their impacts on agricultural production and market prices during the Tenpō Famine. This demonstrates the sensitivity of market prices and economic stability to climatic fluctuations. By utilizing high-resolution data, this study reveals more detailed connections between climate, agriculture, and economic fluctuations than previously reported. Our findings provide valuable historical perspectives and significantly impact contemporary climate adaptation strategies and policymaking. Additionally, this study suggests further research directions and encourages continued exploration of the relationship among climate change, agriculture, and economic fluctuations, inspiring future research in this field.\u003c/p\u003e","manuscriptTitle":"Abnormal climate and its impact on market economy: solar radiation and rice price during the 1830s famine in Japan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-22 13:44:59","doi":"10.21203/rs.3.rs-7136394/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-29T16:56:28+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-25T02:05:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"203623755017973016776456464686206657324","date":"2025-09-19T02:48:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-06T14:29:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"282228764267198954181102012287279289266","date":"2025-09-01T07:51:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"189080054239909846412576302298114819961","date":"2025-08-29T13:01:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"61391740995586732473839132873734275471","date":"2025-08-27T07:55:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-27T07:40:53+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-22T10:27:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-17T05:53:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-16T11:54:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-07-16T06:16:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"70e41af1-fac7-4413-a436-98d283d19b31","owner":[],"postedDate":"September 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":55054232,"name":"Earth and environmental sciences/Climate sciences/Climate change/Climate change impacts"},{"id":55054233,"name":"Earth and environmental sciences/Environmental social sciences/Environmental economics"}],"tags":[],"updatedAt":"2026-02-23T16:00:25+00:00","versionOfRecord":{"articleIdentity":"rs-7136394","link":"https://doi.org/10.1038/s41598-026-40316-w","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-02-18 15:57:05","publishedOnDateReadable":"February 18th, 2026"},"versionCreatedAt":"2025-09-22 13:44:59","video":"","vorDoi":"10.1038/s41598-026-40316-w","vorDoiUrl":"https://doi.org/10.1038/s41598-026-40316-w","workflowStages":[]},"version":"v1","identity":"rs-7136394","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7136394","identity":"rs-7136394","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}