Jet stream controls on European climate and agriculture since 1300 ce

Xu, Guobao; Broadman, Ellie; Dorado-Liñán, Isabel; Klippel, Lara; Meko, Matthew; Büntgen, Ulf; De Mil, Tom; Esper, Jan; Gunnarson, Björn; Hartl, Claudia; Krusic, Paul J.; Linderholm, Hans W.; Ljungqvist, Fredrik C.; Ludlow, Francis; Panayotov, Momchil; Seim, Andrea; Wilson, Rob; Zamora-Reyes, Diana; Trouet, Valerie

doi:10.1038/s41586-024-07985-x

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Published: 25 September 2024

Jet stream controls on European climate and agriculture since 1300 ce

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Abstract

The jet stream is an important dynamic driver of climate variability in the Northern Hemisphere mid-latitudes^1,2,3. Modern variability in the position of summer jet stream latitude in the North Atlantic–European sector (EU JSL) promotes dipole patterns in air pressure, temperature, precipitation and drought between northwestern and southeastern Europe. EU JSL variability and its impacts on regional climatic extremes and societal events are poorly understood, particularly before anthropogenic warming. Based on three temperature-sensitive European tree-ring records, we develop a reconstruction of interannual summer EU JSL variability over the period 1300–2004 ce (R² = 38.5%) and compare it to independent historical documented climatic and societal records, such as grape harvest, grain prices, plagues and human mortality. Here we show contrasting summer climate extremes associated with EU JSL variability back to 1300 ce as well as biophysical, economic and human demographic impacts, including wildfires and epidemics. In light of projections for altered jet stream behaviour and intensified climate extremes, our findings underscore the importance of considering EU JSL variability when evaluating amplified future climate risk.

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Main

Under anthropogenic climate change, the Northern Hemisphere subpolar jet stream is projected by most models to weakly shift poleward and to show enhanced sinuosity^1,2. A wavier jet stream can result in more persistent and extreme jet stream anomalies that strongly affect mid-latitude weather patterns³. An amplified meridional configuration of the jet stream and the resulting jet stream latitudinal extremes can cause more intense and frequent extreme weather events, including increased persistence of summer heatwaves, droughts, floods and wildfires^1,4, that can exacerbate and compound anthropogenically driven climate extremes^5,6.

Intensified upper-level wind speed and vertical wind shear under climate change may also contribute to more severe climate extremes⁷ and quasi-resonance in the jet stream system can result in hemispheric-scale synchronization of climate extremes^6,8. With this multitude of drivers, many types of climate extreme are projected to increase in frequency, duration and intensity under anthropogenic warming and their interactions are projected to lead to compounding hazards and risks^6,9,10.

During recent decades, the number of climate extremes affecting the Northern Hemisphere mid-latitudes has increased^5,9,10 and the associated societal impacts have intensified in high-risk regions^11,12. Crop production, for instance, can be disrupted by extreme weather events such as droughts, floods and extreme heat^12,13,14. The growth and yield of maize, for example, the main crop cultivated in numerous regions worldwide, are strongly affected by water and heat stress¹⁵. Year-to-year yield variability can destabilize crop production and pose a serious challenge to food security¹³, even when it is superimposed on an increasing trend in the yield of the main cereal crops that is driven by improvements in crop production technology and management¹⁴, as well as by efforts to breed better climate-adapted plant species and varieties¹⁶. Moreover, the synchronization of climate extremes in key crop production regions across the globe can threaten food security on a global scale¹⁷.

The role of jet stream variability and configuration in triggering concurrent climate extremes that can synchronize harvest failures and food security risks has been established at the hemispheric scale¹⁷. Yet, jet stream variability and waviness are also reflected in regional-scale climate patterns and extremes^18,19. Whereas regional-scale jet stream variability has been shown to affect ecosystem functioning and disturbances^4,20,21, its societal impacts are largely undocumented.

In Europe, jet stream latitude (JSL) varies on daily²², seasonal¹⁹ and annual to-multi-decadal time scales²³ and is a primary dynamic driver of summer weather and climate extremes. European summer weather extremes are dynamically associated with the strength and persistence of cyclonic or anticyclonic (blocking) weather regimes^24,25, which in turn are related to the configuration of the mid-latitude jet stream over the North Atlantic–European (EU) domain^18,20. However, given short observational records and model biases, further insight into summer jet stream dynamics to understand how climate warming might affect the JSL, including its latitudinal displacements, is needed.

Here we present a tree-ring-based reconstruction of variability in summer JSL in the North Atlantic–Europe region (EU JSL) over the period 1300–2004 ce and use independent documentary data to investigate societal impacts of past EU JSL extremes. Annually resolved, tree-ring-based reconstructions of storm tracks²⁶ and jet stream variability^4,23 have illustrated the power of this proxy to investigate past climate dynamics. The 700+-year timespan of our study provides an opportunity to quantify changes in the frequency and intensity of climate extremes in Europe and their dynamic drivers. With its robust historical records of past conditions^27,28,29, the European continent provides an optimal setting to investigate the regionally explicit influence of pre-industrial JSL variability on climate extremes and their associated societal impacts.

Modern summer EU JSL impacts

Summer weather patterns in Europe are dynamically driven by JSL variability expressed by two main modes that collectively explain more than half of the JSL variance²⁰. The second mode, represented by the second principal component (PC) of the JSL (Methods and Extended Data Fig. 1), captures late summer (July–August) JSL variability over the European continent and is closely related to climate extremes experienced here (Fig. 1).

**Fig. 1: Influence of summer (July–August) EU JSL on European climate (1948–2018 ce).**

Anomalously northern EU JSL positions coincide with a trough over the northeastern Atlantic and anticyclonic conditions over southern Europe, propelling the JSL northward (Fig. 1a, Extended Data Fig. 2a,b and Extended Data Table 1). These excursions lead to cool and wet conditions over the British Isles (BRIT), whereas the northeastern Mediterranean (NEMED) experiences warm and dry summers, with temperature anomalies of up to 1 °C (Fig. 1c,e,g and Extended Data Fig. 2c–h). Reversed conditions occur when the summer EU JSL is positioned southwards (Fig. 1b), with an increased frequency of northerly blocking and anticyclonic conditions leading to warm and dry weather over BRIT, concomitant with increased cyclonic tendencies over southern Europe that promote relatively cool and wet conditions over NEMED (Fig. 1d,f,h).

Northern and southern summer EU JSL excursions thus induce a dipole between BRIT and NEMED summer weather that is reflected not only in a variety of climate variables (Fig. 1) but also in vegetation productivity. From 1950 to 2005 ce, extreme northern and southern EU JSL positions induced anomalies of up to 30% in modelled gross primary productivity and 50% in radial tree growth of European drought-sensitive beech²⁰.

Summer EU JSL variability also affects agricultural yields of major cereal crops, such as maize and wheat, particularly in NEMED (Fig. 2 and Extended Data Fig. 3). NEMED maize and wheat yields (1981–2016 ce) are anomalously low during dry and hot northern EU JSL summers (Fig. 2a,c and Extended Data Fig. 3), whereas southern EU JSL anomalies result in cool and wet NEMED summers and above-average maize and wheat yields (Fig. 2b,d and Extended Data Fig. 3). The NEMED results also highlight that wheat is more tolerant to climate extremes than maize³⁰. NEMED wheat yield generally decreases during northern EU JSL summers and increases during southern EU JSL summers, but the anomalies are weaker and less homogeneous than for maize.

**Fig. 2: Influence of EU JSL extremes on crop yield (1981–2016 ce).**

The impact of summer EU JSL variability on crop yield is weaker in BRIT compared with NEMED (Fig. 2 and Extended Data Fig. 3) but is generally of the same sign and crop yield thus does not reflect the EU-JSL-driven climate dipole between both regions. Similar to NEMED yield, BRIT wheat yield is generally lower than average during northern and higher than average during southern EU JSL excursions (Fig. 2c,d and Extended Data Fig. 3).

EU JSL reconstruction (1300–2004 ce)

We combined three well-replicated, temperature-sensitive tree-ring records to reconstruct summer EU JSL variability over the period 1300–2004 ce (Fig. 3a,b and Methods). We developed a new tree-ring maximum latewood density (MXD) record for NEMED and combined it with the existing records from BRIT³¹ and the Alps (ALP)³² (Methods and Extended Data Table 2). Each of the tree-ring chronologies extends back to at least 1300 ce, encodes a robust temperature signal strength and is significantly positively correlated with regional instrumental July–August temperature (r = 0.49 to 0.61; P ≤ 0.01; Extended Data Table 2). These correlations remain significantly positive after high- and low-pass filtering (Methods), showing consistent sensitivity to temperature across frequency domains (Extended Data Table 2). All chronologies are significantly correlated with the instrumental summer EU JSL target, with a negative correlation for the BRIT chronology (r = –0.27, P ≤ 0.05) and positive correlations for the ALP and NEMED chronologies (r = 0.36 and 0.47 respectively, P ≤ 0.05). The sign of these correlations reflects the EU-JSL-driven summer climate dipole over Europe: during northern (positive) EU JSL summers, climate conditions in BRIT are cooler and wetter than average (Fig. 1 and Extended Data Fig. 2), resulting in low density and negative tree-ring anomalies. In NEMED and ALP, the conditions are warmer and drier than average during northern EU JSL summers, resulting in high-density values and positive tree-ring anomalies. The reverse pattern occurs during southern (negative) EU JSL summers. Our reconstruction explains 38.5% (${R}_{{\rm{adj}}}^{2}=38.5 \% $, r =0.65; P ≤ 0.001; Extended Data Table 3a) of summer EU JSL variability over the observational period (1948–2004 ce; Fig. 3a) without trends in the regression model residuals (Extended Data Fig. 4a–d), indicating preservation of high- and low-frequency variability (Extended Data Fig. 4e,f). The calibration and verification results show that summer EU JSL can be skillfully reconstructed back to 1300 ce, with 34–41% of the variance explained and positive reduction of error (RE) and coefficient of efficiency (CE) values (Extended Data Table 3b).

**Fig. 3: Reconstructed summer EU JSL from 1300 to 2004 ce with composited extreme summer EU JSL years for climate variables in Europe.**

To optimize explained variance and sample replication, we combined MXD chronologies from opposite ends of the summer EU JSL dipole, with the trade-off that this approach constrained our ability to retain low-frequency variability (Methods). As such, our EU JSL reconstruction is dominated by inter-annual to multi-decadal-scale variability (Fig. 3b) and well-studied centennial-scale periods in European climate, such as the colder phases of the Little Ice Age³³, do not emerge. Nevertheless, a multi-decadal southern EU JSL anomaly from 1810 to 1839 ce overlaps with the cold early-nineteenth-century end phase of the Little Ice Age. The years 1813–1816 ce in particular, the southernmost EU JSL years on record, coincide with the eruptions of the tropical Mayon (1814 ce) and Tambora (1815 ce) volcanoes³⁴, suggesting a potential link between EU JSL position and volcanic activity that could have exacerbated the cold summer temperatures in NEMED.

Recent summer EU JSL values fall within the range of preceding centuries, except for the summer of 2010, which exhibits the northernmost EU JSL position in the instrumental record but is not captured by our reconstruction (1300–2004 ce). Other recent northern EU JSL anomalies, such as 2018, are similar in magnitude to historical northern anomalies (for example, 1387 ce and 1782 ce). Recent southern EU JSL anomalies (for example, 1955 ce and 2002 ce) are exceeded by southern positions in the early fourteenth and nineteenth centuries (Fig. 3b). Overall, the number of EU JSL extremes has increased in the nineteenth and twentieth centuries, with 40% more northern and southern EU JSL extremes compared with previous centuries (Extended Data Table 4).

Correlation maps between our summer EU JSL reconstruction and independent, gridded field reconstructions of summer 500 hPa geopotential height (Z500; 1659–2000 ce) (ref. ³⁵), temperature (1766–2000 ce) (ref. ³⁶), precipitation (1766–2000 ce) (ref. ³⁶) and self-calibrating Palmer Drought Severity Index (scPDSI, 1300–2004 ce) (ref. ³⁷) consistently show contrasting conditions over northwestern versus southeastern Europe that are consistent with twentieth-century patterns (Extended Data Fig. 5). These patterns are, therefore, robust through time, except for the nineteenth century, when volcanic activity dominated European temperature patterns and weakened the EU-JSL-driven dipole.

Historical summer EU JSL and impacts

The climate dipole between BRIT and NEMED also emerges in independently reconstructed historical climate extremes when compositing past EU JSL extreme summers (Fig. 3c–j). The pattern is further reflected in composites of extreme climate years in BRIT and NEMED derived from independent historical documentary datasets (Fig. 4, Methods and Extended Data Table 5). During past centuries, hot and dry extremes in NEMED have occurred predominantly during northern EU JSL anomalies and wet and cold extremes in NEMED have occurred predominantly during southern EU JSL anomalies (Fig. 4). The opposite pattern arises in BRIT, where hot and dry BRIT summers are characterized by southern EU JSL anomalies and wet and cold BRIT summers are characterized by northern EU JSL anomalies (Fig. 4). These EU JSL anomalies are least pronounced for cold BRIT summers (Fig. 4b), corroborating the comparatively weak BRIT temperature anomalies of the EU JSL composite analyses (Figs. 1c,d and 3e,f).

**Fig. 4: Reconstructed summer EU JSL anomalies during historical climate extreme events.**

Apart from historical temperature and precipitation extremes, significant EU JSL anomalies also occurred during years experiencing pronounced natural hazards (Figs. 4d and 5c). Historically documented summer floods in NEMED have generally occurred when southern EU JSL anomalies drive wet and cool conditions (Fig. 4d), whereas historical wildfire years (1450–1940 ce; Methods) occurred mostly during northern EU JSL anomalies that drive hot and dry NEMED conditions (Fig. 5c).

**Fig. 5: Reconstructed summer EU JSL anomalies during historical agricultural and biophysical extreme events.**

Years of pronounced agricultural and biophysical extremes were also frequently characterized by significant EU JSL anomalies (Fig. 5). Regional impacts of past EU JSL variability were more pronounced for historical NEMED grape harvests, particularly during poor grape harvest years, compared with grain prices (Fig. 5a,b). This may be because, compared with yield, grain price is a more indirect proxy of agricultural productivity that is influenced by many more factors other than climate^38,39. Nevertheless, grain price results are in line with grain yield results for the modern period (Fig. 2): BRIT and NEMED grain prices show similar EU JSL response patterns, despite contrasting climatic conditions between the two regions (Fig. 5a). NEMED wheat prices were low, indicating above-average yields, during cool southern EU JSL summers (Fig. 5a). Also BRIT grain prices were relatively low in years when the EU JSL was in a southern position, but BRIT summers were relatively dry. By contrast, BRIT grain prices were typically slightly higher following the cold and wet conditions of northern EU JSL summers.

Historical grape yield datasets are not available from BRIT, but in NEMED, cool southern EU JSL summers had a distinct negative effect on grape harvest, including harvest delays, low yields and poor wine quality (Fig. 5b and Extended Data Fig. 6a). This historical influence of EU JSL position on NEMED grape harvests is corroborated by modern grape yields, which show anomalously low yields following southern EU JSL summers (Extended Data Fig. 6b). By contrast, historical NEMED grape harvest typically occurred early in the hot and dry summers of northern EU JSL anomalies, resulting in good grape harvests, early grape ripening and harvest dates and wine quality (Extended Data Fig. 6a).

The EU-JSL-induced dipole between NEMED and BRIT is also imprinted in historical records of biophysical and demographic extremes, including wildfires, epidemics and human mortality (Fig. 5c). In NEMED, plague years were predominantly associated with the cold and wet summers of southern EU JSL anomalies. These patterns are reversed for BRIT, where increases in epidemics and human mortality followed cold and wet northern EU JSL summers.

Discussion

As the impacts of anthropogenic climate change continue to intensify, there is a growing interest in understanding how dynamical variability drives extreme climate events and societal impacts^6,11,17. For example, certain summertime jet stream configurations are linked to atmospheric blocking^20,24 that contributes to the amplified heatwave trend in Europe⁶, whereas other configurations can trigger compound heat waves in key breadbasket regions in the Northern Hemisphere, potentially threatening global food security¹⁷. Our results indicate that this relationship between jet stream position, climate and society has existed over centuries and emphasizes the association between EU JSL and extreme events.

Using the wealth of historical documents available on the European continent, we find a long-term relationship between EU-JSL-driven climate extremes and agricultural productivity (Fig. 5). These insights offer the prospect of improved understandings of the variable and sometimes contrasting agroeconomic fortunes observed across Europe over time. However, modern climate–harvest relationships cannot be unequivocally transferred to the past because of evolving farming and breeding practices, as well as differences in seed types and their climatic sensitivity^16,38,40. Furthermore, the limitations of documentary data must be considered when interpreting our analysis of historical agriculture and climate^38,39. For example, in smaller regions such as the British Isles, grain prices are not always fully representative of local harvest conditions because of storage between harvests, inter-regional grain trade^38,39 and differing regional sensitivities to drought or water excess¹². For some important contemporary crops, such as drought-sensitive maize, no long historical records are available. For others, the climate–harvest relationships are identified in our analysis, but they are complex.

In the Mediterranean climate of NEMED, the effect of water stress and drought on wheat yield can be substantial¹³, resulting in low yields during northern EU JSL summers (Figs. 2 and 5a). In temperate BRIT, wheat yields are negatively affected by excess water¹³, also resulting in low yield during wet, northern EU JSL summers (Figs. 2 and 5a). These contrasting yield sensitivities in differing climates may explain why neither modern wheat yield data nor historical wheat and barley price data reflect the summer EU-JSL-induced dipole. Even in temperate regions such as BRIT, however, high temperatures can reduce grain yield through direct effects on crop development, as well as indirect, non-linear effects that increase atmospheric water demand³⁰. These heat stress effects will increase under future anthropogenic warming¹⁰.

Despite these qualifications, when compiling independent documentary datasets for NEMED and BRIT, we find significant biophysical (for example, grape harvest), economic (for example, wine quality and grain price) and demographic (for example, mortality and epidemics) impacts of summer EU JSL variability (Fig. 5) that represent first- to third-order impacts in the climate–society interaction model^27,41. The seasonality of our reconstruction probably contributes to the strength of these agricultural and societal impacts: summer is the primary growing season for grapes and maize, as well as for grain north of the Mediterranean region, and is the season when temperature variability exerts its strongest influence on historical grain prices⁴⁰. For Mediterranean wheat and barley, cooler and wetter summers can delay plant senescence arising from (terminal) thermal and drought stress, thereby extending the grain filling period and promoting higher yields^42,43. Summer is also, generally, the peak season for plague-related mortality because of increased flea activity and human mobility⁴⁴.

The impacts of cool and wet summers on historical crop failures^29,38,41, grain prices⁴⁰ and the emergence, intensification and diffusion of the plague and epidemics^27,28,45 have been previously documented. Our study demonstrates that EU JSL variability creates a dipole in the cool and wet summers between BRIT and NEMED that is reflected in historical epidemics and mortality patterns (Fig. 5c).

Given the complexity of climate–society interactions and the many potential interactions between societal components^29,46, the consistency of the observed association between extremes in the EU JSL spatial pattern and past biophysical, economic and demographic stressors is striking. Yet it is, nonetheless, explicable given the many known climate–societal impact pathways and the prospect of cascades and amplifying feedback. For instance, failed harvests can result in food scarcity, malnutrition and compromised immune functions, creating cascading effects through epidemics that may initiate feedback in which reduced agricultural labour prolongs or exacerbates harvest reductions^27,44. Today, biophysical (for example, wildfires), agricultural (for example, crop production), health (for example, heat stress) and economic impacts represent major and interrelated climate risks that have been identified as priorities for European policy on climate adaptation¹⁰. The Russian heatwave of 2010, for instance, was primarily caused by a long-lived blocking event, linked to a northerly jet stream position and resulted in extreme wildfires, 55,000 heat-related deaths and a 25% reduction in wheat production in Russia^11,47. By highlighting the historical link between EU JSL variability and extremes in climate, ecosystems and human systems, our results may contribute to risk assessments of cascade effects associated with future compound climate extremes^9,10,11.

Beyond highlighting the intricacies of these climate–societal relationships, our reconstruction shows that EU JSL variability over the past 705 years has consistently created a summertime climate dipole (Fig. 3 and Extended Data Fig. 5). Our reconstruction demonstrates the role of large-scale circulation patterns in driving extreme events over centuries in the past, as has already been demonstrated for the instrumental period¹⁷. This long-term record also provides a perspective that can contextualize modern interactions between EU JSL and weather extremes. Most extreme values in the instrumental period are associated with anomalously northern EU JSL positions, possibly corroborating evidence for a northward shift in the jet stream under anthropogenic warming². Our results suggest that NEMED wildfire risk will increase if the EU JSL shifts northward (Fig. 5c), which could also amplify the effects of anthropogenic warming on wildfire, in line with increased fire activity in North America under northern JSL conditions^4,21. We further find that northern EU JSL extremes can lead to major crop yield reductions at both ends of the climate dipole (Fig. 2), potentially challenging food security in Europe. Historically, northern EU JSL extremes have led to high grain prices, the spread of epidemics and above-average human mortality in BRIT (Fig. 5).

By extending the EU JSL record back to the pre-industrial era, we can also improve our understanding of its role as a driver of climate extremes under dynamic (rather than thermodynamic) forcing and help inform model development and attribution studies^4,25,48. Most projections of future Northern Hemisphere jet stream behaviour under continued warming suggest a weak northward shift and enhanced waviness^1,2. These projections of future jet stream behaviour are informative but uncertain because of our incomplete understanding of jet stream dynamics⁴⁹. Records of historical EU JSL offer a long-term perspective on the frequency and intensity of atmospheric configurations that lead to extreme weather events⁵⁰. The EU-JSL-driven summer climate dipole is an important configuration to consider for a future in which warmer temperatures and intensified summer climate extremes may exacerbate conditions on both sides of the dipole.

Methods

Jet stream latitude calculations

Our target for reconstruction is the second mode of interannual summer JSL variability over the EU domain (30° W–40° E). This mode of EU JSL explains about 26% of its variability and represents JSL variability over Central and Eastern Europe (5° W–40° E; Fig. 1a,b and Extended Data Fig. 1), whereas the first mode represents JSL variability primarily over Western Europe²⁰ and is not a focus of this study. To calculate the target time series for summer EU JSL variability, we defined monthly JSL as the latitude of maximum average monthly 300 hPa zonal wind speed for each 2.5° longitudinal window from 30° W–40° E for latitudes from 20° N–90° N (refs. ^63,64,65,66). This analysis is based on the monthly scalar zonal wind data from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) globally gridded reanalysis dataset⁶⁷ (NCEP/NCAR Reanalysis 1, 2.5° × 2.5° resolution; 1948–2018 ce). We then averaged monthly JSL data for each longitude for July and August, the period in which our tree-ring chronologies are generally most sensitive to temperature variability (Extended Data Table 2). Next, we performed a principal components analysis (PCA) for the July–August EU JSL time series (Extended Data Fig. 1) and used the resulting PC2 time series (EU JSL) as our target for reconstruction. Positive EU JSL values indicate that the JSL is further north with respect to the mean climatology over Central and Eastern Europe, whereas negative values indicate a southern position (Fig. 1a,b).

To explore the relationship between interannual variability in observed summer EU JSL and regional climate, we conducted field Pearson correlation analyses between the July–August EU JSL time series and July–August geopotential height at 500 hPa (Z500) from the NCEP/NCAR reanalysis⁶⁷, July–August temperature, precipitation and the scPDSI from the Climatic Research Unit (CRU) ts4.05 datasets⁶⁸ (Extended Data Fig. 2). We completed these correlation analyses for the common period covered by both the CRU climate data and the NCEP/NCAR reanalysis (1948–2018 ce) using the KNMI Climate Explorer⁶⁹ (https://climexp.knmi.nl/correlate.cgi).

Apart from the field correlation analyses, we conducted a composite analysis of the reconstructed gridded climate data during northern and southern summer EU JSL extremes (Fig. 1). We used the peak-over-threshold method⁷⁰ to select extremes in the summer EU JSL time series. We defined extreme positive EU JSL years as higher than the 80th percentile (D80) and negative EU JSL years as lower than the 20th percentile (D20) over the instrumental period (1948–2018 ce; Extended Data Table 1). In these field correlation and composite analyses, we use the Benjamini–Hochberg false discovery rate (FDR) correction for P-values to mitigate the increase in false positives from multiple testing⁷¹. We set α_FDR = 0.1 to maintain a global α level of 0.05 (ref. ⁷²). The P_FDR was estimated as follows:

$${P}_{{\rm{FDR}}}={\max }_{j=1,...,k}\left[{P}_{j}:{P}_{j}\le (j/N){\alpha }_{{\rm{FDR}}}\right]$$

(1)

where α_FDR is the control level for FDR, P_j is the P-value of the jth local test after sorting P-values in an ascending order and N is the total number of local tests. Grid cells with P-values of local test less than P_FDR are considered significant.

Agricultural productivity

To quantify the influence of summer EU JSL variability on crop yield, we created composite maps for our two regions of interest (BRIT: 50° N–60° N and 10° W–5° E and NEMED: 35° N–50° N and 10° E–30° E; Fig. 2) from the Global Dataset of Historical Yield (GDHY), a gridded (0.5° × 0.5°) annual dataset for 1981–2016 ce that includes maize (NEMED only) and wheat yields⁷³. Composites were created for the northern and southern extreme (D80 and D20) summer EU JSL years (Fig. 2).

We also obtained annual crop yield data from the Food and Agriculture Organization of the United Nations (https://www.fao.org/faostat/en/#data/QCL), which covers the period from 1961 ce to 2021 ce. Historical documentary yield and phenological records are available for grapes and historical grain price series are available for wheat and barley (Extended Data Table 5), making it possible, with caution, to compare modern and historical results. We selected the crop yield data of the Food and Agriculture Organization for the UK, Ireland and the Netherlands (BRIT) and for Bulgaria, Albania, Greece and Italy (NEMED). For BRIT maize, data spanning the entire period of the dataset were only available for the Netherlands. For each country, we applied a 20-year smoothing spline detrending method to the annual crop yield data, using the ratio between the raw crop yield values and fitted values to remove trends unrelated to climate, such as those pertaining to agricultural management and fertilization^14,16. We then pooled these country-scale crop yield data per region to examine the influence of EU JSL extremes on regional crop yield variability (Extended Data Fig. 3). We used D80 and D20 (northern and southern) EU JSL positions from 1961 to 2018 ce as extreme event years in the comparison (Fig. 2).

To emphasize the influence of EU JSL extremes on crop yield during the instrumental period, we compared the difference in crop yield during northern and southern (D80 and D20) EU JSL extremes using a Wilcoxon signed-rank test (Extended Data Figs. 3 and 6). To equalize the size of the groups, we randomly resampled the larger groups several times to get a representative downsized group matching the size of the smallest one.

Regional tree-ring proxy records

To reconstruct interannual variability in July–August EU JSL, we combined annually resolved tree-ring records that are sensitive to summer temperature from three European regions in which summer climate is influenced by summer EU JSL variability (Fig. 1 and Extended Data Table 2). Two of the chronologies (BRIT and ALP) have been previously published^31,32, and we complemented these with a newly developed chronology from NEMED. To keep a highly replicated series (more than 20 samples) with robust expressed population signal (EPS; ≥0.85) (refs. ^74,75), we limited our analysis of the BRIT chronology to start at 1300 ce, because replicated series and EPS are lower from 1200 to 1299 ce (fig. 1 of ref. ³¹). The ALP and NEMED chronologies are based on maximum latewood density (MXD), whereas the BRIT chronology is based on a low-frequency (low-pass-filtered) tree-ring width (RW) chronology combined with a high-frequency (high-pass-filtered) blue intensity (BI) chronology to minimize the signal of forest disturbance in the final chronology³¹. Specifically, the BRIT samples were collected from living and subfossil pine wood in central Scotland³¹, which contains disturbance-related growth release due to centuries of woodland exploitation. A disturbance-corrected RW dataset was created to address this issue and to reconstruct historical temperature variability^31,76. This involved detrending the RW data from living material using the signal-free procedure⁷⁷ with either a negative exponential or linear function, whereas subfossil RW data were detrended using a regional curve standardization approach (RCS)⁷⁸ to preserve low-frequency variability. The detrended RW series were then scaled to the 1720–1897 ce reference period and combined into a single chronology³¹. Similar to RW, both the living and subfossil BI series were separately detrended using linear regression functions and rescaled for the period 1720–1897 ce before averaging into a single BI series. A temperature reconstruction may suffer from potential low-frequency biases based only on BI (ref. ³¹). The final BRIT chronology integrates high-frequency variability from the BI series with low-frequency variability from the RW series³¹. BRIT chronology showed a strong summer (July–August) temperature signal consistent across a vast spatial scale, including the British Isles, parts of western Europe, Scandinavia and the northern Iberian Peninsula³¹.

We used the published ALP (ref. ³²) and BRIT (ref. ³¹) reconstructions in our analysis, including the original detrending methods (RCS) used in those studies. For the NEMED chronology, we combined newly developed Pinus heldreichii MXD measurements from Lura National Park in Albania (41.48° N, 20.14° E, 1,900 m above sea level (a.s.l.); 75 series) and the Pirin Mountains in Bulgaria (41.42° N, 23.30° E, 2,150 m a.s.l.; 93 series)⁷⁹ with existing P. heldreichii MXD time series from Mount Smolikas (192 series)^80,81 and Mount Olympus (65 series)⁸² in Greece. We combined all 425 individual tree-ring series into a single file and performed a COFECHA-based cross-dating quality test^83,84 to develop a NEMED master chronology that extends back to 1200 ce. We removed age-related trends from individual series using RCS (refs. ^78,85,86). To address differences in absolute wood density values between different tree sizes and measurement setups⁷⁴, we transformed each contributing MXD series into z-scores before combining individual series into the NEMED chronology by averaging the detrended series using a biweight robust mean⁸⁷ in the R package dplR (ref. ⁸⁸). Finally, we truncated the NEMED chronology based on an EPS threshold of 0.85 (refs. ^75,86) (Extended Data Fig. 7a).

To assess the summer temperature sensitivity of the chronologies, we correlated each chronology with an instrumental time series of July–August temperature, derived from the CRU ts4.05 gridded dataset⁶⁸. We averaged gridded temperature time series for the 2° × 2° grid cells surrounding the location of each chronology and calculated Pearson correlation coefficients starting in 1901 ce through the most recent year of each chronology (Extended Data Table 2). To investigate chronology–temperature relationships at different frequency domains, we calculated correlation coefficients for both the unfiltered and high-pass-filtered time series, in which the high-pass filter was calculated as the difference between the current year and the previous year.

Before reconstructing summer EU JSL variability, the primary dynamic driver of summer temperature variability, we removed the thermodynamic effects of rising post-industrial greenhouse gas (GHG) emissions⁸⁹ from our chronologies. To achieve this, we assumed that recent, centennial-length large-scale (for example, across the extra-tropical Northern Hemisphere) temperature trends are primarily caused by GHG forcing, whereas regional residuals are primarily related to the dynamical component. We averaged annual temperature variability for the 1901–2018 ce period over the extra-tropical Northern Hemisphere using the CRU ts4.05 dataset (Extended Data Fig. 7b). We then decomposed both this large-scale temperature time series and each of the tree-ring chronologies into low- and high-frequency time series using a 100-year loess smoothing method and established an empirical relationship between the low-frequency temperature and tree-ring time series (Extended Data Fig. 7c). We then adjusted the low-frequency time series of the tree-ring chronologies using the residuals of the empirical model (that is, the original low-frequency time series minus the empirically modelled values)⁹⁰. This procedure removes the low-frequency trend over the post-industrial period (1901–present), presumed to be caused by rising GHG, not by EU JSL variability from the chronologies (Extended Data Fig. 7d,e). Finally, we combined the adjusted low-frequency and original high-frequency time series of the chronologies to create the final, adjusted chronologies (Extended Data Fig. 7f).

EU JSL reconstruction

To reconstruct summer EU JSL variability, we used a stepwise multiple linear regression (MLR) model (Extended Data Table 3) with summer EU JSL as the reconstruction target (dependent variable) and the three adjusted regional tree-ring chronologies as the predictor variables (independent variables). We used the adjusted R-square (${R}_{{\rm{adj}}}^{2}$), an F-test and Akaike information criterion (AIC) parameters to evaluate the MLR models as each predictor entered the model. The residuals from the final MLR model expressed a normal distribution with no trend (Extended Data Fig. 4a,b). The residuals were homoscedastic and no outliers were detected using Cook’s distance index (Extended Data Fig. 4c,d). We evaluated the reconstruction skill based on reduction of error (RE), coefficient of efficiency (CE) (ref. ⁸⁶) and ${R}_{{\rm{adj}}}^{2}$ over two periods of equal length covering the common period between our reanalysis-based target and the chronologies (1948–1975 ce and 1976–2004 ce) (Extended Data Table 3b). Our calibration and verification trials show that summer EU JSL can be skillfully reconstructed back to 1300 ce (1300–2004 ce), with 36–41% of the variance explained in the verification period and positive RE and CE statistics. Finally, we scaled the reconstruction to fit the mean and variance of the summer EU JSL target over the period of overlap (1948–2004 ce) and estimated uncertainty in the reconstruction based on the calibration uncertainty using the 95% prediction interval derived from the MLR of the reconstruction against the target (Fig. 3a,b). We further calculated the correlation coefficients between the reconstructed and instrumental summer EU JSL at high and low frequencies using a 20-year smoothing spline to high- and low-pass filter series, respectively (Extended Data Fig. 4c). We also conducted a cross-wavelet coherency analysis between the instrumental and reconstructed summer EU JSL to evaluate coherence in their variability^88,91 (Extended Data Fig. 4d).

Reconstructed EU JSL field correlations

To place the relationship between summer EU JSL variability and regional climate in a centennial-scale context, we conducted spatial Pearson correlation and composite analyses between our summer EU JSL reconstruction and independent gridded climate reconstruction fields (Fig. 3 and Extended Data Fig. 5). These climate field reconstructions include July–August Z500 (1659–1999 ce) (ref. ³⁵), July–August temperature (1766–2000 ce) (ref. ³⁶), July–August precipitation (1766–2000 ce) (ref. ³⁶) and June–July–August scPDSI data from the Old World Drought Atlas (OWDA) (800–2012 ce) (ref. ³⁷). We tested the significance of the correlations for each grid point for their maximum period of overlap by controlling the false discovery rate at α_FDR ≤ 0.1. We conducted these spatial correlation analyses for the entire period of overlap, as well as for three sub-periods: 1701–1800, 1801–1900 and 1901–2000 ce (Extended Data Fig. 5). For scPDSI, the correlation analysis for the most recent period extends to 2004 ce. For Z500, the analysis for the oldest period extends back to 1659 ce. For temperature and precipitation, the analysis for the oldest period is truncated and begins at 1766 ce.

Reconstructed summer EU JSL impacts

To analyse the climatic conditions over Europe during reconstructed summer EU JSL extremes, we used the peak-over-threshold method⁷⁰ to select extremes in the summer EU JSL reconstruction. We defined extreme positive years as those with D90 EU JSL and extreme negative years as those with D10 EU JSL over the entire reconstruction (1300–2004 ce; 71 positive and 71 negative extremes; Extended Data Table 4). We then conducted a composite analysis with the aforementioned reconstructed gridded climate data during the positive and negative summer EU JSL extremes (Fig. 3c–j).

To investigate EU JSL anomalies during past European climate extremes, we compared historical summer EU JSL positions during event years characterized by extreme climate, natural hazards and societal extremes with those of non-extreme years (Figs. 4 and 5 and Extended Data Table 5) using the Wilcoxon signed-rank test for each included dataset. In all cases, we equalized the sizes of the groups (D90, D10 and non-extreme) by resampling the larger groups several times to get a representative downsized group matching the size of the smallest one. We extracted climate extreme event years for two sub-regions (BRIT: 11° W–3° E, 49° N–59° N and NEMED: 15° E–30° E, 40° N–45° N) from the reconstructed temperature and precipitation gridded fields³⁶ (Fig. 4a,b). For each time series, we defined extreme years as higher than D90 and lower than D10 over the entire reconstruction period. We complemented these gridded-data-based climate extreme event series with four continuous summer temperature records and two continuous precipitation records that are based on instrumental and/or documentary datasets from BRIT and NEMED (Extended Data Table 5). Event years in the continuous series were defined based on the D90 and D10 values. For the summer temperature data in ref. ⁵¹, we used the Category III cluster (warm summer; n = 75) as hot events and the Category I cluster (cool summer, n = 74) (from table 5 of ref. ⁵¹) as cold events, using only the most recent year of these multi-year extreme events in our analysis (Extended Data Table 5). We further compiled one independent, discrete temperature and two independent, discrete precipitation reconstructions (Extended Data Table 5). Hot extremes in ref. ⁵² (NEMED) records were defined as years with the hottest summers in Northern–Central Italy, Southern Italy and/or Greece (from table 5 of ref. ⁵²). Dry extremes in the discrete BRIT record were defined as years with the highest calculated aridity index in England and Wales (from fig. 2 of ref. ⁵³).

We further extracted natural hazards (wildfires and floods) and harvest event datasets from a variety of sources (Extended Data Table 5). We compiled a NEMED wildfire event dataset by combining all fire years recorded in tree-ring-based fire-scar records from Greece⁹², Bulgaria⁹³ and western Turkey⁹⁴ into a single list of event years. We excluded fires that occurred more recently than 1940 ce, because in these more recent years, events such as the Second World War (1940–1945) battles, the establishment of forest services and land-use changes have left a strong human (non-climate-related) fingerprint on recent wildfires in the region⁹². All flood datasets were based on discrete instrumental or documentary BRIT or NEMED datasets (Extended Data Table 5). For NEMED floods, we combined all years when floods occurred in several locations in Northern Italy (tables 1–3 of ref. ⁵⁴) in June–October into a single list of event years.

To analyse good and bad harvest years, we compiled discrete (all years of catastrophic grape quantity)⁶¹, as well as D10 and D90 values of continuous NEMED grape harvest datasets, including wine quality, vine harvest dates, church tithe (tax) wine harvest data and grape ripening dates⁵⁵. For the wine quality data⁵⁵, we selected the categories ‘very good’ as a good harvest and ‘very bad’ as a bad harvest (Extended Data Table 5). We complemented the grape harvest data with grain price data for both NEMED and BRIT. For BRIT, we examined price data from London (wheat and barley)⁶², and for NEMED, we used data from Italy and the peripheral cluster region for wheat in ref. ⁴⁰. To account for large step shifts in the continuous grain price data time series, assumed to result from factors not related to climate, we calculated D90 and D10 values for each 50-year period (n = 5 for each period) of the time series. We then compiled a dataset of high grain price (reflecting bad harvest) extreme events as the list of all D90 years and a dataset of low grain price (reflecting good harvest) extreme events as the list of all D10 years (Extended Data Table 5). We further compared the difference in grape harvest during northern and southern (D90 and D10 for the historical period and D80 and D20 for the instrumental period) EU JSL extremes using a Wilcoxon signed-rank test⁹⁵ (Extended Data Fig. 6).

To analyse the potential relationships between summer EU JSL variability and past societal disruptions, we used datasets on epidemics and mortality (Fig. 5c) over the maximum period of overlap between the two datasets (Extended Data Table 5). For BRIT epidemics and human mortality, we used discrete documentary datasets from Ireland compiled in ref. ²⁸. We derived a NEMED plague dataset by extracting NEMED (38° N–46° N; 13° E–23° E; Fig. 1a) plague events from ref. ⁴⁵. We defined NEMED plague events as years when three or more plague outbreaks occurred, which corresponds to the D90 value.

Data availability

All reconstructed and instrumental EU JSL datasets can be found at Zenodo (https://doi.org/10.5281/zenodo.13120683)⁹⁶ and are freely available at the NOAA National Centers for Environmental Information (NCEI). The reconstructed summer EU JSL can be found in the NOAA Paleoclimatology Database. The NCEP/NCAR data can be found at https://psl.noaa.gov/. The Climatic Research Unit CRUts temperature data can be found at https://catalogue.ceda.ac.uk/uuid/10d3e3640f004c578403419aac167d82. The ALP and BRIT temperature reconstructions can be found at the NCEI/NOAA International Tree-Ring Data Bank at https://www.ncei.noaa.gov/products/paleoclimatology/tree-ring.

Code availability

The code used in this study can be found at Zenodo (https://doi.org/10.5281/zenodo.13120683)⁹⁶.

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Acknowledgements

This study was supported by the Career Foundation from the Shaanxi Province and Northwest University, the programme of Light of West China Program of the Chinese Academy of Sciences to G.X., US National Science Foundation (NSF) CAREER grant AGS-1349942 to V.T., by funding from the Spanish Programa de Ayudas Beatriz Galindo, Secretaría de Estado de Universidades, Investigación, Desarrollo e Innovación (BG20/00065), Proyectos de Generación de Conocimiento 2021, Agencia Estatal de Investigación (PID2021-128759OA-I00) and Ayudas para Incentivar la Consolidación Investigadora (CNS2022-135228) to I.D.-L. and by the Swedish Research Council (grant nos. 2018-01272 and 2023-00605) to A.S. and F.C.L.; F.L. was supported by a European Research Council (ERC) Synergy Grant (4-OCEANS; grant agreement no. 951649). We are grateful to everyone who was involved in the development of the individual tree-ring datasets and to the numerous researchers who made their data available through the International Tree-Ring Data Bank (ITRDB). We also thank NOAA/OAR/ESRL PSL, Boulder, Colorado, for providing the NCEP/NCAR Reanalysis data.

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Authors and Affiliations

Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi’an, China
Guobao Xu
Laboratory of Tree-Ring Research, University of Arizona, Tucson, AZ, USA
Guobao Xu, Ellie Broadman, Matthew Meko, Tom De Mil, Diana Zamora-Reyes & Valerie Trouet
State Key Laboratory of Cryospheric Sciences, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, China
Guobao Xu
Departamento de Sistemas y Recursos Naturales, Universidad Politécnica de Madrid, Madrid, Spain
Isabel Dorado-Liñán
Geschäftsbereich Klima und Umwelt, Deutscher Wetterdienst, Offenbach, Germany
Lara Klippel
Department of Geography, University of Cambridge, Cambridge, UK
Ulf Büntgen & Paul J. Krusic
Global Change Research Institute, Czech Academy of Sciences (CzechGlobe), Brno, Czech Republic
Ulf Büntgen & Jan Esper
Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland
Ulf Büntgen
Department of Geography, Faculty of Science, Masaryk University, Brno, Czech Republic
Ulf Büntgen
Forest Is Life, TERRA Teaching and Research Centre, Gembloux Agro Bio-Tech, University of Liège, Gembloux, Belgium
Tom De Mil
Department of Geography, Johannes Gutenberg University, Mainz, Germany
Jan Esper
Stockholm Tree Ring Laboratory, Department of Physical Geography, Stockholm University, Stockholm, Sweden
Björn Gunnarson
Nature Rings – Environmental Research and Education, Mainz, Germany
Claudia Hartl
Panel on Planetary Thinking, Justus-Liebig-University, Gießen, Germany
Claudia Hartl
Department of History, Stockholm University, Stockholm, Sweden
Paul J. Krusic & Fredrik C. Ljungqvist
Regional Climate Group, Department of Earth Sciences, University of Gothenburg, Gothenburg, Sweden
Hans W. Linderholm
Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden
Fredrik C. Ljungqvist
Swedish Collegium for Advanced Study, Linneanum, Uppsala, Sweden
Fredrik C. Ljungqvist
Trinity Centre for Environmental Humanities and Department of History, Trinity College Dublin, Dublin, Ireland
Francis Ludlow
Dendrology Department, University of Forestry, Sofia, Bulgaria
Momchil Panayotov
Chair of Forest Growth and Dendroecology, Institute of Forest Sciences, University of Freiburg, Freiburg, Germany
Andrea Seim
Department of Botany, University of Innsbruck, Innsbruck, Austria
Andrea Seim
School of Earth and Environmental Sciences, University of St Andrews, Fife, UK
Rob Wilson
Belgian Climate Centre, Uccle, Belgium
Valerie Trouet

Authors

Guobao Xu
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Ellie Broadman
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Isabel Dorado-Liñán
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Lara Klippel
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Matthew Meko
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Ulf Büntgen
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Tom De Mil
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Jan Esper
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Björn Gunnarson
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Claudia Hartl
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Paul J. Krusic
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Hans W. Linderholm
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Fredrik C. Ljungqvist
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Francis Ludlow
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Momchil Panayotov
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Andrea Seim
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Rob Wilson
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Diana Zamora-Reyes
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Valerie Trouet
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Contributions

G.X. and V.T. designed the study with input from E.B., I.D.-L., L.K., M.M., A.S., F.C.L., J.E. and R.W.; L.K., M.M., M.P., C.H., J.E., B.G., U.B., R.W. and V.T. developed the tree-ring data; G.X., E.B., I.D.-L., L.K., M.M., F.L. and V.T. analysed the data; and G.X., E.B., V.T., I.D.-L. and M.M. wrote the paper with input from all other authors. All authors contributed to the discussion and interpretation.

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Correspondence to Valerie Trouet.

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Nature thanks Martín Hadad, Fernando Martinez-Moreno and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Principal Component Analysis (PCA) results of summer jet stream latitude (JSL) for the instrumental period (1948–2018 ce).

Longitudes that were selected to represent the second mode of July-August EU JSL variability (5° W–40° E; ref. ²⁰) are highlighted in bold. The total explained variance of each PC is shown on the axis title.

Extended Data Fig. 2 Spatial Pearson correlations between instrumental summer EU JSL and climate variables in Europe for the period 1948–2018 ce.

Panels (a) and (b) show July-August 500 hPa geopotential height from the NCEP/NCAR dataset⁶⁷; (c) and (d) show July-August temperature from the CRU ts4.05 dataset⁶⁸; and (e) and (f) show July-August precipitation from the CRU ts4.05 dataset⁶⁸, (g) and (h) show July-August self-calibrating Palmer Drought Severity Index⁶⁸. Panels (a), (c), (e), and (g) show the correlations performed on raw data (both EU JSL and climate variables), whereas (b), (d), (f) and (h) show the correlations performed on linearly detrended instrumental July-August EU JSL and climate datasets. Colored composite maps are shown only where p ≤ 0.1 and areas with statistically significant values after controlling for the false discovery rate (αFDR ≤ 0.1) are cross-hatched.

Extended Data Fig. 3 Influence of summer EU JSL extremes on crop yield during the instrumental period (1961–2018 ce).

Boxplots show the distribution of maize, wheat and barley yield during northern (red) versus southern (blue) summer EU JSL extremes in NEMED and BRIT. We determined EU JSL extremes by calculating the D80 (north, n = 12 years) and D20 (south, n = 12 years) over the period 1961–2018 ce. The lower and upper extremes are indicated by whiskers and dots on each boxplot represent outliers. Significant differences, calculated using a Wilcoxon signed-rank test, are represented by p < 0.1 and two (p ≤ 0.01) asterisks. The “ns” indicates the result is not significant. Crop yield data are derived from the Food and Agricultural Organization (FAO) and were detrended using a 20-year smoothing spline (see Methods).

Extended Data Fig. 4 Variability and distribution of the residuals derived from the multiple linear regression reconstruction model and comparison between reconstructed and instrumental summer EU JSL for the period 1948–2004 ce.

(a) Scatter plot between residuals and fitted values in the linear regression, (b) Q-Q plot of the standardized residuals, (c) scatter plot between standardized residuals and fitted values, and (d) scatter plot between standardized residuals and leverage values. The vertical dashed line indicates the Cook’s distance in panel (d). The comparison is shown for both high- and low-frequency variability, after applying a 20-year smoothing spline. In panel (e), r represents the Pearson correlation coefficient between reconstructed and instrumental EU JSL. For the low-frequency series, the level of significance is not provided since the effective degrees of freedom were not adjusted. Panel (f) shows a complementary cross-wavelet coherency analysis between instrumental and reconstructed summer EU JSL. Instrumental data is derived from the NCEP/NCAR reanalysis product.

Extended Data Fig. 5 Spatial correlations between the summer EU JSL reconstruction and gridded July-August climate reconstructions for Europe.

(a) July-August 500 hPa geopotential height (Z500)³⁵ for 1659–1999 ce; (b) July-August temperature³⁶ for 1766–2000 ce; (c) July-August precipitation³⁶ for 1766–2000 ce; and (d) June-July-August Old World Drought Atlas (OWDA) self-calibrating Palmer Drought Severity Index (scPDSI)³⁷ for 1300–2004 ce. Panels in the far-left column show the entire period of overlap between the climate data and the EU JSL reconstruction, followed by the data over three roughly 100-year subperiods: the 18^th, 19^th, and 20^th centuries (from left to right). Colored correlation maps are shown only where p ≤ 0.1 and areas with statistically significant values after controlling for the false discovery rate (αFDR ≤ 0.1) are cross-hatched.

Extended Data Fig. 6 Influence of summer EU JSL extremes on historical grape phenology and instrumental grape yield.

(a) Boxplots showing the distribution of NEMED grape phenology (grape ripening date, vine date, and wine quality) data during historical northern (red) versus southern (blue) summer EU JSL extremes in NEMED region (data for BRIT not available). We determined EU JSL extremes by calculating D90 and D10 values over the period of overlap between grape phenology records and the EU JSL reconstruction. Wine quality deviation values were inversed for visualization purposes. Details of the grape phenology series, such as locations, references, period, and proxies can be found in Extended Data Table 5. (b) Boxplots showing the distribution of instrumental grape yield (1961–2018 ce) during northern (red) versus southern (blue) summer EU JSL extremes in BRIT and NEMED. We determined EU JSL extremes by calculating D80 (north, n = 12 years) and D20 (south, n = 12 years) values over the instrumental period 1961–2018 ce. Grape yield data are derived from the Food and Agricultural Organization (FAO) and were detrended using a 20-year smoothing spline (see Methods). In panels (a) and (b), we calculated statistical significance in grape phenology and yield distribution using a Wilcoxon signed-rank test. Significant results are represented by one (p ≤ 0.05) and two (p ≤ 0.01) asterisks in panel (a) and by p-values in panel (b). The lower and upper extremes are indicated by whiskers, and dots on each boxplot represent outliers.

Extended Data Fig. 7 NEMED chronology, chronology adjustment, and comparison of raw and adjusted chronologies.

(a) The NEMED tree-ring maximum latewood density (MXD) chronology and sample depth (horizonal gray lines). The tree-ring chronology was truncated to the year with an expressed population signal (EPS) value above 0.85. The regional curve standardized (RCS) chronology is shown in black, and the low-pass filter (50-year smoothing spline) version of the chronology is shown in blue. Panels (b)-(e) show the scheme of chronology adjustment performed to remove the thermodynamic trend from the tree-ring chronology. The example shown here is for the NEMED chronology. Panel (b) shows interannual (black) and low-frequency (blue) variability of extra-tropical annual Northern Hemisphere (NH) temperature anomalies averaged over 0-360°E and 0-90°N (CRU ts4.05 dataset⁶⁸) and then calculated using a 100-year loess smooth filter. Panel (c) shows the regression lines between the NH low-frequency temperature anomalies and the NEMED tree-ring chronology. Dots represent the raw data; the blue and red curves represent linear and non-linear regressions, respectively. Panel (d) shows the difference in low frequency variability of the original NEMED tree-ring chronology before (black) and after (blue) removing the thermodynamic effect caused by greenhouse gas forcing. In panel (e), low-frequency variability was highlighted by using a 50-year low-pass filter smoothing spline. Panel (f) shows the comparison between raw BRIT, NEMED, and ALP tree-ring chronologies (gray) and the same tree-ring chronologies adjusted after removing the thermodynamic trend (blue). Low-frequency variability of the adjusted chronology is shown in black and was calculated by low-pass filtering the original series using a 50-year smoothing spline. Further information on each chronology can be found in Extended Data Table 2.

Extended Data Table 1 Years with extreme summer EU JSL position for the instrumental period (1948–2018 ce)

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Extended Data Table 2 Summarized information for the tree-ring chronologies used for the summer EU JSL reconstruction

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Extended Data Table 3 The stepwise multiple linear regression model selection and calibration and verification statistics for the July-August EU JSL reconstruction

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Extended Data Table 4 Years with extreme negative and positive values for each century of the reconstructed summer EU JSL (1300–2004 ce)

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Extended Data Table 5 Information on the temperature, precipitation, flood, crop price, grape phenology, wildfire, epidemics, and human mortality data used for the group comparison analyses for BRIT (blue) and NEMED (red) (Figs. 4, 5, and Extended Data Fig. 6)

Full size table

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Xu, G., Broadman, E., Dorado-Liñán, I. et al. Jet stream controls on European climate and agriculture since 1300 ce. Nature (2024). https://doi.org/10.1038/s41586-024-07985-x

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Received: 22 March 2024
Accepted: 21 August 2024
Published: 25 September 2024
DOI: https://doi.org/10.1038/s41586-024-07985-x

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Modern summer EU JSL impacts

EU JSL reconstruction (1300–2004 ce)

Historical summer EU JSL and impacts

Discussion

Methods

Jet stream latitude calculations

Agricultural productivity

Regional tree-ring proxy records

EU JSL reconstruction

Reconstructed EU JSL field correlations

Reconstructed summer EU JSL impacts

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Acknowledgements

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