Agricultural drought over water-scarce Central Asia aggravated by internal climate variability

A severe agricultural drought swept Central Asia in 2021, causing mass die-offs of crops and livestock. The anthropogenic contribution to declines in soil moisture in this region over recent decades has remained unclear. Here we show from analysis of large ensemble simulations that the aggravation of agricultural droughts over southern Central Asia since 1992 can be attributed to both anthropogenic forcing and internal variability associated with the Interdecadal Pacific Oscillation (IPO). Although the negative-to-positive phase transition of IPO before 1992 offset human-induced soil moisture decline, we find that the positive-to-negative phase transition thereafter has doubled the externally forced rate of drying in the early growing season. Human-induced soil moisture loss will probably be further aggravated in the following century due to warming, albeit with increasing precipitation, and our simulations project that this trend will not be counterbalanced by the IPO phase change. Instead, this internal variability could modulate drying rates in the near term with an amplitude of −2 (+2) standard deviation of the IPO trend projected to amplify (weaken) the externally forced decrease in surface soil moisture by nearly 75% (60%). The findings highlight the need for the interplay between anthropogenic forcing and the natural variability of the IPO to be considered by policymakers in this climate-sensitive region. The interplay between anthropogenic forcing and internal variability associated with the Interdecadal Pacific Oscillation has exacerbated agricultural droughts over southern Central Asia since 1992, according to large ensemble simulations.

precipitation are blamed for the parched land in 2021, the ongoing worsening droughts are accompanied by increased evapotranspiration due to high temperature and decreased precipitation 5,11,12 . Attribution studies have found that the rapid warming trend in the past half century over Central Asia is mainly forced by anthropogenic activities 13 . The observed changes in Central Asian precipitation contain both forced responses and internal variations [14][15][16] . However, soil moisture, as the crucial indicator of agricultural and ecological droughts, has received less attention in understanding the cause of its observed changes in Central Asia.
There is a growing concern regarding the detection and attribution of long-term changes in soil moisture at regional or global Article https://doi.org/10.1038/s41561-022-01111-0 surface temperatures (SST) in the Pacific Ocean that can last from 20 to 30 years (refs. [29][30][31], has exacerbated agricultural droughts over water-scarce southern Central Asia in its early growing season since 1992. The IPO evolution could also modulate the externally forced drying trend in the near-term projection.

Observed drying trend
We focus on the surface soil moisture content in April-May-June (AMJ), which is the early time of the growing season in Central Asia 32 . Plants in this region mainly depend on shallow soil water and shallow groundwater to survive 5 . Since the 1950s, a decline in surface soil moisture has been seen over southern Central Asia (Extended Data Fig. 1). The drying tendency has accelerated since the 1990s with decreasing rates at −4.53%, −1.29% and −2.12% per decade during 1992-2020 for ERA5-Land, GLEAM and ESACCI, respectively ( Fig. 1 and Extended Data Table 1). The soil moisture deficit in 2021 was one of the most severe droughts in the past 70 years, which was −11.79% lower than the climatological mean (1995-2014) based on ERA5-Land (Fig. 1a).
scales. While soil moisture deficits are attributed to anthropogenic warming in America 17,18 , Europe 9,19,20 , the Northern Hemisphere 21 and global land 22 , natural climate variability is also suggested to drive severe agricultural drought at regional scales 8,18,23 . Understanding the influence of external forcing and internal variability on soil moisture change over Central Asia needs to separate the forced and unforced responses. The US Climate Variability and Predictability Program (CLIVAR) Working Group on Large Ensembles has set up a data archive containing large ensembles of historical and scenario simulations for several models 24 . Unlike Coupled Model Intercomparison Project (CMIP)-like multi-model simulations with each model having few members, which are mainly used to reveal externally forced signals, large ensemble simulations from a single climate model are powerful tools to understand and quantify the relative contribution of internal variability and external forcing [24][25][26][27][28] . In this study, based on three sets of large ensemble simulations, we provide evidence that the combined impact of external forcing and the phase change of the Interdecadal Pacific Oscillation (IPO), a long-term oscillation of sea   Agricultural drought is related to meteorological factor changes, including temperature and precipitation 33 . The surface soil moisture over southern Central Asia was negatively correlated with contemporaneous surface air temperature (r = −0.62; p < 0.01) (Extended Data Fig. 1a,b). The rising temperature increases the rate of evapotranspiration and depletes surface soil moisture 34,35 . Central Asia has witnessed a significant warming trend in the past half century (Extended Data Fig. 2e, f). The observed warming trend was stronger after 1992, as indicated by the warming rate of 0.36 K per decade and 0.49 K per decade in 1950-1992 and 1992-2020, respectively. The enhanced warming rate after 1992 partly explained the amplified drying trend in soil moisture. Agricultural drought is also related to deficient rainfall. The surface soil moisture content was highly correlated with early precipitation in March-April-May (r = 0.73; p < 0.01) (Extended Data Fig. 1c,d). The deficiency of precipitation over an extended period of time can result in a water shortage for soil and environment, and a lag usually occurs between the meteorological drought and agricultural drought [36][37][38] . The decreased precipitation in spring and increased temperature-based evapotranspiration in AMJ result in imbalanced water supplies over southern Central Asia after 1992, which further leads to the significant drying trend (Fig. 1b and Extended Data Fig. 2).

Impact of external forcing
Is the accelerated drying trend since 1992 related to anthropogenic external forcing? The trends in surface soil moisture derived from the three sets of large ensemble simulations were examined (Fig. 1c). Under the same external forcing, 40 ensemble members of CESM1-CAM5 exhibited diverse soil moisture trends ranging from −1.36% to 1.40% per decade in 1950-1992 and −2.44% to 2.30% per decade in 1992-2020. There was a weak but significant drying trend of −0.07% per decade (p < 0.05) before 1992 and a larger drying trend of −0.08% per decade after 1992 for the ensemble mean ( Fig. 1c   Article https://doi.org/10.1038/s41561-022-01111-0 Table 1). The significant weak decreasing trends can also be found for CSIRO-Mk3-6-0 and MPI-ESM-LR in both periods, which were −0.23% and −0.60% per decade (p < 0.01) for CSIRO-Mk3-6-0 and −0.40% and −0.65% per decade (p < 0.01) for MPI-ESM-LR in 1950-1992 and 1992-2020, respectively (Fig. 1c). Hence the ensemble means of the large ensembles indicate that the external forcing has a weak but statistically significant contribution to the drying surface soil moisture over southern Central Asia since 1950, in particular after 1992. Anthropogenic activities, especially the emission of greenhouse gases, has resulted in the significant warming trend in the past half century and the accelerated warming rate after 1990s 13 . The human-induced warming further leads to the increased evapotranspiration and decreased soil moisture. In addition, the spatial pattern of the long-term warming trend due to external forcing has suppressed spring precipitation over southern Central Asia 15,39 , which was also favourable for the reduction in soil moisture content in AMJ.

Impact of the internal mode of IPO
While the ensemble mean simulation, as a measure of the response to external forcing, can reproduce the accelerated drying trend over southern Central Asia after 1992, the simulated drying trend was far weaker than the observation ( Fig. 1c and Extended Data Table 1). Among the large ensemble members, there were some realizations that can reproduce the observed magnitude change, indicating the contribution of internal variability (Fig. 1c). The SST trend difference between the Min10 (the mean of 10 members with the largest negative trends of soil moisture in 1992-2020) and the ensemble mean of all members were analysed and a negative phase of an IPO-like pattern was found, which featured a cooling trend over the tropical central eastern Pacific and a warming trend over the subtropical western Pacific (Fig. 2a-c). The IPO index decreased during 1992-2020 for the Min10 of all models (Fig. 2f). Following the accelerated drying trend over southern Central Asia since 1992, the positive-to-negative phase transition of the IPO was indeed seen in the observations (Fig. 2d,e).
Without the impact of external forcing, the positive-to-negative phase transition of IPO alone can lead to a decrease in soil moisture over southern Central Asia during 1992-2020. Following the transition of the IPO to a negative phase, anomalous SST trends will strengthen the Walker circulation and further enhance (suppress) convective activities over the Indo-western Pacific warm pool (central eastern    In addition, the IPO shifted from a negative to a positive phase during 1950-1992 in observations (Fig. 2e), which was favourable for more precipitation and wetter soil moisture over southern Central Asia before 1992 (Fig. 2f and Extended Data Fig. 3).

Attribution of observed drying trends to climate drivers
To quantify the relative contribution of external forcing and IPO, the IPO evolution was adjusted in each member based on the observed IPO index during the whole period (Methods). The original IPO-related soil moisture changes for each member were replaced by those induced by the observed IPO evolution. Therefore, the ensemble mean of the adjusted members represents the combination of the responses to external forcing and the observed IPO phase transition.
While the externally forced soil moisture trends during 1950-1992 ranged from −0.40% to −0.07% per decade for the three models, the negative-to-positive phase transition of IPO was favourable for wetter conditions over southern Central Asia at a rate of 0.22% to 0.55% per decade (Fig. 3a,c, Extended Data Fig. 4 and Extended Data Table 1). After adjustment, the IPO-induced increase in soil moisture offset the externally forced response, showing a weak trend in this period (−0.05% to 0.48% per decade). During 1992-2020, the adjustment with the observed positive-to-negative IPO transition decreased the soil moisture trend from −0.65% to −0.08% per decade to −1.34% to −1.08% per decade, which was closer to the observed trend (Fig. 3b,c, Extended Data Fig. 4 and Extended Data Table 1). The simulated IPO-induced trend was −1.00% to −0.61% per decade during 1992-2020, which was comparable to the externally forced drying trend. All the models converged towards a high probability of an accelerated drying trend over southern Central Asia after adjustment (Fig. 3b,c).
We further examined the contributions of IPO and anthropogenic external forcing by analysing the 20-member IPO pacemaker simulations of CESM1.1 and its corresponding historical simulations. In IPO pacemaker simulations, while the atmosphere and ocean are freely coupled in the historical simulations, the SST anomalies over the tropical Pacific vary synchronously with the observations (Methods). Whereas the historical simulations represent the response to external forcing, the pacemaker simulations represent the combined impact of observed IPO evolution and external forcing. The soil moisture decreased at rates of −0.79% per decade and −2.44% per decade for the ensemble mean of historical and pacemaker simulations, respectively. By considering both anthropogenic external forcing and IPO-related internal SST changes, the pacemaker simulations reasonably reproduced the observed drying trend after 1992 (Fig. 3c), further demonstrating the crucial contribution of internal variability of IPO.

Future changes
Under the representative concentration pathway version 8.5 (RCP8.5) emission scenario, the externally forced surface soil moisture content over southern Central Asia features a continued decreasing trend at rates of −0.87%, −0.09% and −1.24% per decade to the end of the century for the ensemble mean of CESM1-CAM5, CSIRO-Mk3-6-0 and MPI-ESM-LR, respectively (Fig. 4a). Multi-model projections of CMIP6 models also show a reduction in surface soil moisture over southern Central Asia under external forcing in both low-and high-emissions scenarios 10 . While the aggravated agricultural drought in AMJ will continue, the spring precipitation is projected to increase under the external forcing 46 . To reveal the discrepancy among the future changes in precipitation and soil moisture content, we binned spring precipitation and AMJ surface air temperature over southern Central Asia into 5 × 5 or 10 × 10 bins and assessed the anomalies of surface soil moisture in each bin in observation and large ensemble simulations (Extended Data Fig. 5). For precipitation with similar magnitudes, surface soil moisture decreases as surface air temperature rises with promoted evapotranspiration. Among springs with positive precipitation anomalies, only 6.33% to 19.39% and 11.35% to 21.89% springs experienced negative soil moisture anomalies during 1950-1992 and 1992-2020, and the ratio would come to 16.78% to 30.32% and 29.24% to 56.16% for the 2 °C and 4 °C warming levels (Fig. 4c,d). Hence, although the precipitation will increase during the twenty-first century associated with the moistening atmosphere under external forcing, agricultural drought still will become more severe due to human-induced continuous warming and related increase of evapotranspiration.
The near-term externally forced projection can be amplified or reduced by internal variability 40,47 . Given the evidence that the recent externally forced drying trend in southern Central Asia is accelerated due to the IPO evolution, will the IPO modulate the externally forced drying trend in the near term? We excluded all of the IPO's influence by removing the soil moisture variations that are linearly related to the IPO index in each member (Methods). After the removal, the standard deviations (SDs) of the internal component of the linear trends during 2021-2040 among all members are reduced by 15% to 32% (Fig. 5a), indicating that the spread among different members can be partly explained by the IPO phase transition.
To reveal the role of the IPO on the near-term projection, we calculated the SD (0.23 °C) of the observational IPO index during 1950-2020 and assumed that the IPO will shift from +1SD to −1SD or from −1SD to +1SD in 2021-2040. The IPO phase change of 2SDs over a 20-year period (± 0.23 °C per decade) is comparable to the observational trends in 1950-1992 (0.15 °C per decade) and 1992-2020 (−0.28 °C per decade). We then replaced the original IPO-related soil moisture changes for each member by those induced by the assumed fixed IPO evolution (Methods). If an amplitude of −2SD of the IPO trend was predicted to be superimposed onto the external forcing from 2021 to 2040, the multi-model average would show a significant decrease in soil moisture by −1.52 (−2.19 to −0.46 for the model spread) % per decade (Fig. 5b), which is 0.65 (0.42, 0.83) % per decade drier than the externally forced drying trend (−0.87 (−1.36, −0.04) % per decade), indicating nearly 75% enhancement of the drying trend. In contrast, if the IPO shifts from −1SD to +1SD, the negative-to-positive phase transition will weaken the externally forced drying trend by 0.52 (0.12, 0.86) % per decade, indicating nearly 60% weakening of the drying trend. The phase transition of the IPO could modulate the externally forced drying trend in the following decades and has a notable impact on the soil moisture trends over southern Central Asia in the near term.

Implications of a continued drying trend
The southern part of Central Asia has been facing an increase in aridity in its early growing season since the 1990s. The drying trend is not limited to surface soil but can also be found in deeper soil layers (Extended Data Fig. 6). Dominated by anthropogenic warming, agricultural droughts over southern Central Asia were expected to be exacerbated by the end of the century. The region with the highest population density at the foothill of the mountain was more likely to face a further enhanced agricultural drought (Extended Data Fig. 7). Superimposed onto the external forcing, the IPO evolution not only has intensified the decreasing tendency of soil moisture during 1992-2020 but would also modulate the externally forced drying trend in the near-term projection. Thus, improved IPO predictions are necessary for developing effective adaptation and mitigation strategies for decisionmakers of Central Asian countries. If the IPO turns into its warm phase in the next few decades, as some previous studies supposed 40 , an amplitude of +2SD of the IPO trend will weaken the externally forced decreasing rate of surface soil moisture content by nearly 60% during 2021-2040.
In Central Asia, crop and livestock production is the dominant contributor to GDP and employment share 33,48 . The increasingly serious agricultural droughts not only directly influence crops and livestock but are also a challenge for production, living, ecological environments, social stability and even regional security 49,50 . A catastrophic drought during 2000-2001 revealed that due to structural factors, including rural poverty and unsustainable management of natural resources, agricultural drought in Central Asia was extremely likely to further lead to socioeconomic drought 33 . Our results show that the decreasing trend in surface soil moisture since the 1990s will continue in the twenty-first century under the RCP8.5 scenario regardless of how the IPO changes unless we take ambitious climate action and reach global peaking of greenhouse gas emissions as soon as possible to achieve a climate-neutral world. In addition to agricultural drought, there are increasing risks of meteorological drought 51 and hydrological drought 52,53 in southern Central Asia. Overall, our findings have highlighted an urgent need of developing a thorough risk-management plan at sectoral, local, country and even multi-country levels to adapt and mitigate the enhanced agricultural droughts for food security and livestock production.

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Soil moisture
In this study, we focused on agricultural drought by analysing the surface soil moisture content. Multi-source soil moisture products were used, including: (1) ERA5-Land reanalysis soil moisture data derived from the Land Surface Scheme of the operational Integrated Forecasting System used at European Centre for Medium-Range Weather Forecasts with a spatial resolution of 0.1 × 0.1° during 1950-2021, soil moisture contents over 0-7 cm, 7-28 cm, 28-100 cm and 100-289 cm are available 54

Climate data
The monthly precipitation provided by the Met Office Hadley Center gridded land surface extremes indices (HadEX3) 58 was used to examine the relationship between surface soil moisture and regional precipitation. Monthly gridded observational temperature from the Berkeley Earth Surface Temperature dataset (BEST) 59 was used to reveal the relationship between surface soil moisture and regional temperature. Details of the datasets are provided in Supplementary Table 1.

Large simulations
To investigate the impact of internal variability and external forcing, three sets of large ensemble simulations from the US CLIVAR Working Group on Large Ensembles were used, including CESM1-CAM5, CSIRO-Mk3-6-0 and MPI-ESM-LR. These simulations are selected because at least 30 members are available and monthly surface soil moisture content is provided. A detailed introduction of the models is provided in Supplementary Table 2. The historical simulations covering the period of 1850-2005 were driven by observed historical changes in radiative forcing following phase 5 of the Coupled Model Intercomparison Project (CMIP5) 60 . To satisfy the analysis of long-term trends during 1950-2020, the simulations were extended to 2020 following the representative concentration pathway version 8.5 (RCP8.5) emissions scenario. The three models can reasonably reproduce the climatological mean of surface soil moisture (Supplementary Fig. 1), the long-term trends in surface temperature (Supplementary Fig. 2) and the relationship between precipitation over southern Central Asia and moisture transport from the Indian Ocean ( Supplementary Fig. 3).

Pacemaker simulations
To confirm the impact of IPO on Central Asian soil moisture, the results of 20-member coupled model ensembles of tropical Pacific 'pacemaker' simulations performed with the National Center for Atmospheric Research Community Earth System Model version 1 (NCAR CEMS1) were compared with the corresponding historical simulations. The Pacific pacemaker simulations were forced by historical radiative forcing through 2005 and the RCP8.5 radiative forcing thereafter like the historical simulations, but the SST anomaly was nudged to be observations in the eastern tropical Pacific. Further details on the model and simulations can be found in Deser et al. 61,62 .

Separating externally forced and internally unforced signals
Take CESM1-CAM5 as an example. A certain variable V(i) of member i in CESM1-CAM5 is influenced by both external forcing and internal variability. As all members are driven by the same external forcing, the ensemble means of V(i) for all members can be regarded as the response to external forcing V forced and are represented as: Thus, V(i) can be separated as: where V internal (i) is the internal component estimated as the residual of the original V(i) minus the model forced response V forced .

IPO definition
The IPO index is defined as the difference between the SST anomalies averaged over the central equatorial Pacific ( 65 with a correlation coefficient of 0.99 (p < 0.01). In particular, the IPO index for each ensemble member of the simulations was obtained by the internal part of SST, namely SST internal (i).

Adjustment of soil moisture based on observational IPO
An 'adjustment' method following Salzmann and Cherian 66 was used in this study. The core idea of this method is replacing the original simulated IPO-related soil moisture trend with that induced by observed IPO evolution, which is expressed as: where: A internal (i) is the adjusted term and can be expressed as: where r V,IPO (i) is the regression coefficient of the soil moisture regressed onto the IPO index during the study period, reflecting the response rate of soil moisture to IPO evolution for member i. r V,IPO (i)∂ t IPO (i) denotes the original IPO-related soil moisture trend in member i, and r V,IPO (i)∂ t IPO OBS denotes the soil moisture trend related to the observed IPO evolution with the same amplitude. While ∂ t V forced represents the response of soil moisture to external forcing, the ensemble mean of ∂ t V internal_adj (i) can represent the soil moisture trend contributed by the observed IPO evolution.

Removing IPO's influence
To remove the IPO's influence, we calculated the IPO index for each member and removed the soil moisture variations that are linearly related to the IPO index through a linear regression, which is expressed as: Article https://doi.org/10.1038/s41561-022-01111-0

Statistical analysis
The area-weighted method was used to calculate the regional average. A 9-year running mean was applied to observation and model outputs to isolate the interdecadal signal. The Theil-Sen trend estimation method was used in this study to estimate the linear trend, which is not sensitive to outliers 67 . The non-parametric MK test was used to detect the presence of the trends 68,69 .

Data availability
The large ensemble simulations are publicly available at the multi-model large ensemble archive of NCAR Climate Data Gateway (https://www.cesm.ucar.edu/projects/community-projects/MMLEA/

Code availability
The data in this study is analysed with NCAR Command Language. All relevant codes used in this work are available upon request from the corresponding author.