Multi-annual prediction of drought and heat stress to support decision making in the wheat sector

Balakrishnan Solaraju-Murali, Nube Gonzalez-Reviriego, Louis-Philippe Caron, Andrej Ceglar, Andrea Toreti, Matteo Zampieri, Pierre-Antoine Bretonnière, Margarita Samsó Cabré and Francisco J. Doblas-Reyes

npj climate and atmospheric science
Work Package 4
Link: https://doi.org/10.1038/s41612-021-00189-4

Highlights

The agriculture industry is particularly exposed to the impacts of future climate change and could benefit from predictions made by climate models running several years into the future. This study assesses the ability of these models to predict two user-relevant drought and heat stress indices, which are particularly important for the wheat sector. The team found that climate models can skilfully predict these indicators better than a forecast based on past climate data over several wheat-growing regions globally. The methodology of this study will also be useful in further studies in other sectors. Climate services based on these findings could help set the near-term priorities for the agriculture sector in adapting to the impacts of climate change, as well as assist policymakers in supporting adaptation and mitigation efforts to limit the impact of future climate change.

Background
Changes in the climate, including the frequency and severity of extreme events, pose a particular threat to wheat production. This sector is strongly influenced by the prevailing climate and could benefit from insights gained from climate model predictions of future climate change. This is particularly true of decadal predictions which run for several years in the future, the timescale of much of the strategic planning undertaken in the wheat sector. Despite this, there is little scientific analysis of the impact such predictions could have on agricultural planning for climate impacts. This study aims to address this, using decadal climate models to predict the Standardised Precipitation Evapotranspiration Index (SPEI) and the Heat Magnitude Day Index (HMDI), two important user-relevant indices associated with wheat yield. Understanding how well climate models can predict these indices paves the way for their operational use by the industry, helping the sector mitigate and adapt to the impacts of climate change.

Results
The team found that decadal climate predictions are generally more skilful at predicting five-year averages of drought and heat stress indices than a forecast built solely on past climate data. This applied to several of the world’s key wheat-growing regions. Calibrating the model predictions to have the same annual variation as observed data was an important step to improve their skill. The predictions also benefitted from initialisation, that is running them over a period in the recent past in order to align the climate model’s natural variability with that of the observation-based data, then allowing them to run into the future. This study proves the utility of decadal climate predictions for the wheat sector, which could use these predictions to inform decisions on irrigation infrastructure, crop rotation strategies or breeding priorities, for instance. Policymakers can also use these findings when formulating new climate impact mitigation and adaptation policies, while this study’s proven methodology can also be applied to other climate-sensitive crops, such as maize and rice, or to other sectors where water management plays a fundamental role.

Methods
This paper used decadal predictions from the Community Earth System Model Decadal Prediction Large Ensemble (CESM-DPLE), developed by the National Center for Atmospheric Research (NCAR). This is a set of 10-year predictions beginning each year from 1960 to 2014. To test the accuracy and skill of these predictions, they are compared against observed climate data on temperature and rainfall from the JRA-55 and GPCC Version 2018 datasets, respectively. The SPEI and HMDI indices were chosen as the focus for this study owing to their particular importance to the wheat sector.

Policy relevance
This study has strong relevance to future decisions made by the agriculture industry in adapting to, and mitigating the impacts of, climate change. It can also form the basis of new climate services for the sector, supported by ongoing modelling, as well as lending its methodology to studies in other sectors. Studies of this kind can inform policymakers in helping industries adapt to near-term climate change and the impacts it will bring.