Rapid Indian ocean warming threatens Agricultural productivity in North America

doi:10.21203/rs.3.rs-2491188/v1

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Rapid Indian ocean warming threatens Agricultural productivity in North America

https://doi.org/10.21203/rs.3.rs-2491188/v1

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During the recent several decades, the Indian Ocean has recorded the largest warming trends in the globe and has led to pronounced regional and global impacts. However, its impact, particularly on agricultural productivity and relevant terrestrial carbon sink is unclear. Here we investigated how and to what extent the tropical Indian Ocean (TIO) influences the changes in US state-level crop yields and gross primary productivity (GPP) using multiple datasets including observation and process-driven from Earth System Model experiment. We found that crop yields in the US declined with increasing TIO temperature by 5–20 percent. Our results suggest that the reduction in agricultural productivity may be mainly attributed to the deficit of precipitation induced by atmospheric teleconnection from the tropical Indian Ocean to the United States (US) through the North Pacific. We also found that the reduction in gross primary productivity (GPP) is approximately 25 percent from multiple datasets and carbon sink loss of 0.25 P g C yr^− 1 over the US. A strong TIO warming in near future could enhance the negative impact on human life through adverse climate conditions and reduction in crop yields, and suppress further carbon-climate feedback.

Biological sciences/Ecology/Climate-change ecology

Earth and environmental sciences/Ecology/Forest ecology

Scientific community and society/Agriculture

The tropical Indian Ocean (TIO) has experienced significant warming trends over the past few decades^{1,2,3,4,5,6,7} and the trend is much more than that of global mean sea surface temperature (SST). TIO warming has a pronounced impact on East Asia, Central Africa, India, Australia, North America, and Atlantic climate^8,9,10,11, and it may have possible effects on vegetation activities. The agricultural activities and terrestrial gross primary productivity (GPP) in north America account for 15% of global GPP and have experienced strong changes during recent decades^{12,13,14, 15, 16}. The changes in GPP may be attributed to not only anthropogenic warming^13,15,16 but also remote impact by North Atlantic oscillation¹⁷, Arctic Oscillation¹⁸, and ENSO^19,20,21,22 through changing temperature and precipitation over North America through atmospheric teleconnection^23,24,25,26. However, the impact of the TIO on agricultural productivity and GPP in North America remains unclear.

Here, we demonstrate the negative impact of the TIO on terrestrial productivity and agricultural yields over North America on interannual timescales and reveal possible physical processes for the link between TIO and vegetation activities related to a carbon sink. We conducted a historical experiment using an Earth system model with enforced control of SST anomalies over the IO through observations. We found that the reduction in the crop yield could be explained by a deficit of precipitation through TIO-related atmospheric teleconnection. Furthermore, under strong anthropogenic forcing, their impact on agricultural productivity may be strengthened by a warmer TIO SST, resulting in threatening crop yield over North America.

Reduced terrestrial primary productivity by tropical Indian ocean warming

We conducted regression analysis using multiple data sets to quantify the impact of the tropical Indian Ocean (IO) on vegetation terrestrial gross primary productivity (GPP) over North America. We defined the tropical Indian Ocean SST index (TIO index) as the SST anomalies averaged over a selected region marked by a box (Supplementary Fig. S1a). We used a multi-dataset: the normalized difference vegetation index (NDVI, 1982–2015), GPP data from an improved Light Use Efficiency model (LUEM, 1982–2016), and GPP data derived from a global network of eddy-covariance towers (Fluxcom, 1982–2011). The regression analysis provided significant evidence that the terrestrial GPP over north America is linked to the tropical Indian ocean. The results showed strong negative vegetation activities in the central and US, Taiga Shield East, and Alaska, while moderate positive terrestrial GPP in northwestern Canada and subtropical steppe in Mexico. The NDVI shows strong negative GPP and vegetation activities over the eastern part of the North American region and Alaska (Fig. 1a). The peak of negative regression coefficients occurs in the central and north US, while weak positive GPP in the west of Canada. The LUEM captures those patterns related to the Indian Ocean in the US and Alaska with a relatively stronger regression coefficient of 40–50 g C m^− 2 month^− 1 but differently shows a positive regression coefficient in the west of Canada (Fig. 1b). In the Fluxcom, strong negative vegetation coefficients were seen in the US, north of Mexico and east of Canada, while positive coefficient in the west of Canada with similar magnitude of those of the area with negative value (Fig. 1c).

We examined whether the negative impact of TIO on North America GPP reproduced in the simulated GPP data from the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) (Fig. 1e). We used 36 individual models with various driving forcings such as CRU and NCEP reanalysis data. The results show that most models simulate negative GPP changes by TIO warming. Approximately 20% of models show strong negative GPP coefficients linked to TIO warming, which are comparable to the observed. However, poor models show relatively weak GPP coefficients or no significant impact on the GPP. The multi-model ensemble mean of the model is consistent with the satellite-based vegetation index (NDVI) and data-driven model data (LUEM and Fluxcom) although the MME data underestimate the negative impact when compared to the observed.

We also examined the seasonal variance of the impact of TIO on GPP over the United States (US) from LUEM. The maximum TIO warming occurs during late spring (Supplementary Fig. S2a), while the peak of climatological GPP was seen in June-July (Fig. 1d), suggesting that there is a few-month lag between TIO warming and the resultant reduction in terrestrial productivity over the US. Also, the TIO during June is relatively highly correlated to August GPP anomalies over the US with 0.62 of a correlation coefficient (P < 0.05) (Supplementary Fig. S2b). Noted that the correlation coefficients between GPP and TIO are not much changed with increasing lead months. The regressed coefficient of the US GPP on TIO with two months lags shows a peak in August (Fig. 1d). These results suggested that extensive negative terrestrial GPP patterns related to TIO warming commonly occur US region.

Impact of the tropical Indian Ocean on climate over North America

The TIO warming could change climate conditions over north America through atmospheric teleconnection. The drought stress (or water storage limit) induced by TIO may affect terrestrial GPP and agricultural productivity^{22,23,24,25,26}. The warming of the tropical Indian Ocean is linked to higher sea level pressure (SLP) anomalies in the US and higher geopotential height (GHT) in the upper troposphere like a barotropic mode through global atmospheric teleconnection, enhancing local weak heating (Fig. 2a). The heating in the IO also induces SLP meridional dipole pattern over North Atlantic region (Fig. 2c), accompanied by anomalous cyclonic flow in the subtropics, which weakened northward moisture transport of moisture from south of US and reduced strong resultant precipitation (Fig. 2b). Easterly anomalies in the west coast of Mexico may also play some role on reducing precipitation over Mexico regions. The less precipitation induced by TIO contributed to reducing GPP over the US.

The regressed horizontal pattern of near-surface temperature related to TIO show large-scale warming in north America except, Alaska, western Canada, a part of the southwest US, and far-eastern Canada. The peak of the warming was seen in the US. The rising motion by TIO warming generates divergent flow, inducing wave patterns in the upper troposphere - negative GHT in northeast Asia and positive GHT over eastern Siberia, and negative GHT in northwestern Canada and positive GHT over the east US (Supplementary Fig. S3). Noted that this wave propagation reaches to and Europe region through North Atlantic ocean. Strong higher GHT over the US with anti-cyclonic anomalies indicates significant warming by reducing cloud fraction. The horizontal pattern of upper tropospheric GHT anomalies is consistent with those in near-surface temperature anomalies related to TIO (Fig. 2a), suggesting that TIO could contribute to warming in North America.

We hypothesized that the negative GPP changes in the US may be mainly controlled by the changes in the precipitation linked to TIO; the hypothesis was examined using the individual models of MsTMIP. We calculated regressed coefficients of temperature and precipitation onto TIO and ratios of the coefficients to their climatological value. As shown in Fig. 2d, when the precipitation by TIO warming largely decreases, the GPP over the US was significantly reduced with a correlation coefficient of 0.76. The GPP is reduced by 20% for 12–13% of reduction in precipitation. On the other hand, the US GPP tends to be reduced with increasing temperature by TIO. However, only 3–4% of temperature increases for a 20% of reduction in GPP. The results suggested that the reduction in US GPP by TIO warming may be mainly attributed to less precipitation rather than higher temperature. It is also consistent with previous studies, which showed that vegetation activities over the US are mainly dependent on precipitation rather than temperature^{22,23,24,25,26,27,28}.

Indian ocean – US vegetation activities evidenced through historical simulations

To explore whether TIO warmings indeed affect terrestrial GPP, we have conducted two sets of numerical experiments using an Earth system model (see the Methods section). The first is a historical simulation with the observed tropical Indian ocean SST (HIS_OBS). The model was freely coupled in other areas, and external forcings were based on the CMIP6 historical simulation protocol. Similarly, we conducted another model simulation by nudging the climatological SST over the TIO region (HIS_CLM) to remove the impact of Indian ocean warming on the Earth system. The model in this study was integrated from 1900 to 2020 with historical external forcing and 35-year data (1982–2016) utilized for the analysis. The model’s capability in the simulation of the observed TIO-GPP over north America relationship was confirmed by showing the resemblance between the observed regression maps of global GPP onto the TIO for 1980–2020 and those simulated (Supplementary Fig. S4).

The model by the observed SST anomalies over the TIO region (HIS_OBS) captured the observed horizontal patterns of negative terrestrial GPP anomalies reasonably well for the US region but their magnitudes are overestimated (Supplementary Fig. S5a). Corresponding 2m temperature and precipitation patterns over the US (Supplementary Fig. S5b and S5c) are also similar to observation. The model shows strong warming and dryness over the US but their peak shifts westward slightly. The GHT anomalies propagate from the Indian ocean to North America through the Bering sea (Supplementary Fig. S6b). The large positive anomalies over the US could contribute to warming. Noted that the magnitude of positive anomalies is slightly weaker than observation. Cyclonic anomalies develop over the central US during TIO warming, reducing the northward transport of moisture from the south of the US to northeast America and the resultant reduced precipitation (Supplementary Fig. S6a). Note that strong warming and dryness were simulated in Canada, which is due to stronger atmospheric teleconnection by TIO from the model.

On the other hand, the model with climatological SST over the Indian ocean produce positive GPP anomalies over US and Canada (Supplementary Fig. S5d), while negative values were simulated in Alaska. This horizontal pattern is almost opposite to those from the model with observed IO SST (e.g. Figure 3). Cold temperature anomalies are simulated in the west and east of the US, while warm anomalies in north Canada and south of the US (Supplementary Fig. S5e). The precipitation anomalies are positive in most of North America except the central US (Supplementary Fig. S5f). The changes in precipitation seem to be consistent with positive GPP anomalies. The positive GPP may be attributed to strong wet anomalies with enhanced moisture transport. These model results suggest that TIO could induce negative GPP over the US region.

Role of Pacific on Tropical Indian ocean-US GPP relationship

To explore whether the Pacific affects TIO-US GPP interactions or not, we conducted a suite of historical runs with observed TIO SST and climatological Pacific SST. The regression analysis showed the coefficient is very small (Fig. 3, red bar), suggesting the change in GPP by TIO may be not significant under fixed Pacific SST conditions. In observation, the TIO generated cold anomalies over the northern Pacific with anticyclonic anomalies (Supplementary Fig. S7), which may be a favorable condition for atmospheric teleconnection by TIO, resulting in warm and dry climate conditions over North America (Supplementary Fig S8a). On the other hand, we additionally conducted a historical simulation with observed TIO SST and climatological SST over North Atlantic Ocean to estimate the role of the north Atlantic ocean on the TIO -US GPP relationship (Fig. 3, yellow bar). The result shows the negative impact of the TIO on terrestrial productivity with a slightly smaller magnitude than that from historical simulation with observed TIO SST and coupled freely over other ocean basins (Fig. 3, green bar; Supplementary Fig S8b), suggesting that the north Atlantic ocean may play a minor role in TIO-US GPP relationship.

Impact of Tropical Indian ocean on US crop yield

The tropical Indian ocean may affect crop yields because it largely changes air temperature, circulations, and precipitation over the US region during vegetation growing seasons. We examined whether TIO indeed is related to US state-level crop yields. We selected three representative crops, which could be affected by climate conditions, although they are also affected by human activities such as crop management methods. The results showed that all three crop yields are negatively correlated with the TIO, particularly over the US region including the Great Plain. For all three crops, annual yields in the US tend to reduce with warming TIO. The reduction of crop yields by warming of TIO is consistent with the decline in terrestrial GPP over the US. For wheat, negative correlation coefficients are seen in Tennessee, North Carolina, and Alabama but the coefficient is slightly smaller than other crops (< 0.4) (Fig. 4a). Although changes in crop yields related to anomalous Arctic warming show negative relations in most of the regions, a few states in the northwestern United States exhibit positive relations, especially for wheat yield. This pattern can be explained by precipitation increases. For soybean, a higher correlation coefficient was observed in the Kanas and Tennessee (<-0.6), and moderated correlation in Nebraska, Arkansas, North Carolina, Kentucky, and Alabama (<-0.5) (Fig. 4b). The Corn crop yields are closely correlated to TIO with a relatively higher coefficient (>-0.5) in the Nebraska, Kanas, Ohio, and Kentucky. Positive relation with TIO was seen in North Dakota (< 0.5) (Fig. 4c).

Also, we examined how much TIO affects US state-level crop yields. Figure 4d-f shows the composite difference of three crop yields between a warm and cold year in the TIO. Noted that composite analysis was conducted for the year when TIO anomalies are relatively large (> one standard deviation of TIO index). The changes in three crop yields by TIO are approximately 8–30% of climatological annual yields for US states. For soybean, the large impact of TIO on crop yield occurs in the Kanas, Alabama, and Kentucky with a range of 20–30% of annual yields (Fig. 4e). For Corn and Wheat, the impact of TIO is relatively smaller than that of soybean (10–20%). For Soybean and wheat, the composite difference tends to be large with an increasing correlation coefficient, suggesting that the relationship between crop yields and TIO may be significant (Fig. 4d and 4f). However, the composite difference seems to be less sensitive to the correlation coefficient, suggesting that the corn yields are not reduced linearly with increasing TIO index. These results indicate that the TIO index may contribute to estimating near-future crop yields and this issue will be discussed in further studies.

In this study, we demonstrated a negative relationship between tropical Indian ocean warming and vegetation activities including agricultural productivity. The observed and model results support that the TIO warming could reduce 15–20% of carbon sink in the US and 5–20% of US state-level crop yield. These results suggest that the tropical Indian ocean condition plays an important role in predicting terrestrial productivity and agricultural crop yields and increasing their predictability. Since the Indian Ocean experienced the largest warming trends exceeding global trends during the recent half century, it may induce a significant reduction in vegetation resources and agricultural productivity. It also induces a dry and warmer climate, which is an adverse condition for human society.

We further estimated the sensitivity of the north America GPP response to TIO warming. We conducted additional historical simulations with observed IO SST patterns and various trends (0.01–0.70°C per 71 years). Different trends were calculated by multiplying different scale factors with the observed SST (See Methods). The model was freely coupled in other areas, and external forcings were based on the CMIP6 historical simulation protocol. Historical runs that nudged various TIO SST warming (Supplementary Fig. S9) confirmed that increasing TIO SST could lead to a reduction of GPP over the US region. The TIO warming of 0.1C induces 15 g C m^− 2 month ^− 1, which is approximately 5% of the climatological value. Thus, IO warming may contribute to influencing US GPP remotely and further local crop yield changes.

The ecosystem in the US will be damaged by more dried climate conditions induced by TIO under strong greenhouse gas warming. Under strong greenhouse warming, the interannual variation of TIO will be increased and thus their impact on GPP anomalies could be increased through a more severely dry atmosphere and soil moisture conditions. The decadal-scale impact of TIO on GPP in North America is also dominant. The strong negative GPP anomalies related to TIO were seen from observation and model simulations, suggesting that TIO could be informative to forecast decal-scale GPP anomalies.

Diagnosis of the observed data

To obtain the monthly mean SST, we used the National Oceanic and Atmospheric Administration Extended Reconstructed SST version 5 (ERSST) ³⁰. Near-surface temperature, wind, sea-level pressure, and geopotential height (1982–2016) are obtained from the ERA (European Centre for Medium-Range Weather Forecasts Reanalysis)-Interim³¹ (http://apps.ecmwf.int/datasets/data/interim-full-moda). For near-surface temperature and precipitation, we used Climatic Research Unit (CRU) TS3.23³² (http://www.cru.uea.ac.uk). The tropical Indian Ocean (TIO) index is defined as averaged sea surface temperature over 60◦ E–100◦ W, 10S◦ − 10◦ N) from ERSST during June. This region shows the strongest warming trend in the recent 70 years (see Supplementary Fig. S1). The NDVI data are obtained from the Global Inventory Modeling and Mapping Studies (GIMMS) database for the period 1982–2016 (https://ecocast.arc.nasa.gov/; ftp://210.72.14.198). We used the LUEM data- GPP modeled at 8 km spatial resolution for each of the 35 years from 1982–2016. The GPP is based on the well-known Monteith light use efficiency (LUE) equation but was improved with optimized spatially and temporally explicit LUE values derived from selected FLUXNET tower site data³³. In addition, we also included eddy covariance flux-based GPP data, derived from a diagnostic model calibrated using global flux site-level data using an MTE machine learning technique (Fluxcom) as a largely independent observation-based terrestrial primary production data set. These data-driven GPP data are provided by the Max Planck Institute for biogeochemistry for the period 1982–2011³⁴. In addition, we used the simulated GPP from the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP)³⁵, involving 36 individual models (http://nacp.ornl.gov). To examine whether models capture the impact of the tropical Indian Ocean on GPP over north America and whether the less precipitation by TIO contributes to mainly US GPP reduction. We used GPP, soil temperature, and soil moisture. We used United States crop-yield national and state-level data (1982–2016) from ‘Quick Stats 2.0’ website, which is provided by the National Agricultural Statistics Service of the United States Department of Agriculture (http://www.nass.usda.gov/Quick_Stats).

Earth system model

The resolution of the atmospheric model was T63L47. The ocean model has a grid resolution of 1°, with a meridional resolution refined to 1/3° over the equatorial region. The model uses 46 vertical layers, with the upper 15 layers being in the top 100 m. NESM3.0 simulated reasonable climatology with the key characteristics of climate variabilities. The NESM includes the JSBACH3 model, which could simulate the interannual variability of the GPP and ecosystem^40,41.42.

Historical simulations

We conducted idealized simulations using pre-industrial forcings. Only the ocean model of NESM3.0 was integrated for 4000 years with atmospheric forcings to elucidate the stable upper- and deep-ocean initial conditions. Second, NESM3.0 was integrated for 500 years, with an initial condition that 4000 years of ocean simulation would be performed with preindustrial forcings based on the CMIP6 protocol, to obtain an initial condition with a stable equilibrium state between the atmosphere and ocean. For historical runs, we conducted a historical simulation with the observed TIO SST or climatological SST. The model was freely coupled in other areas, and external forcings were based on the CMIP6 historical simulation protocol. Four ensemble members were used in two groups of the historical simulations. Additionally, a suite of historical simulations was conducted by nudging the observed TIO-SST pattern with different trends. We obtained SST data with monotonically reduced trends (0.01–0.7 C 50 years^− 1) by multiplying various scale factors (starting with 0.01 and an increment of 0.05) on the observed TIO-SST time series.

Data availability

All observed data used in this study are publicly available (https://psl.noaa.gov/data/gridded/ data.20thC_ReanV3.html; https://psl.noaa.gov/data/gridded/data.noaa.ersst.v5.html). The data can be downloaded from https://figshare.com/articles/dataset/IO-NA_warming_chain/ 19771102.

Code availability

The codes used in this study can be downloaded here: https://figshare.com/articles/dataset/IO-USGPP_ codes/19771150.

Acknowledgments

Y.-M.Y. is supported by the National Natural Science Foundation of China (NSFC042088101) and the National Research Foundation of Korea (NRF-2022R1A2C1013296). S.-I.A. is supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (NRF-2018R1A5A1024958).

Author contributions statement

Y.-M.Y., S.-I.A., and B.W. conceived the idea. Y.-M.Y. performed the model experiments and analyses. S.-I.A., Y.-M.Y., S.-W.Y., B.W, Z.Z., J.L., J.-Y.L., F.L., and J.-H.P. wrote the manuscript. All authors provided critical feedback and helped shape the research, analysis, and manuscript.

Competing interests

The authors declare no competing interests.

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Rapid Indian ocean warming threatens Agricultural productivity in North America

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Abstract

Figures

Introduction

Results

Reduced terrestrial primary productivity by tropical Indian ocean warming

Impact of the tropical Indian Ocean on climate over North America

Indian ocean – US vegetation activities evidenced through historical simulations

Role of Pacific on Tropical Indian ocean-US GPP relationship

Impact of Tropical Indian ocean on US crop yield

Discussion

Methods

Diagnosis of the observed data

Earth system model

Historical simulations

Declarations

References

Additional Declarations

Supplementary Files

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