The Future of Food: Modeling Climate Impacts on Crop Growth
Centuries ago, humans primarily ate food that was local from the regions in which they lived. Today, global food supply chains are a vital part of modern life. Bananas, rice, and coffee are all crops that are enjoyed around the world but produced in very specific climates and regions.
Scientists have long warned that climate change could severely impact this supply chain. In fact, climate change is already having real world impacts. Not only by delivering more extreme weather events, but changes in global temperature and extreme wind and rainfall can have devastating effects on crops and agriculture. Since crop yields - like coffee - are often centralized to certain areas and regions of the world, global supply chains can be severely impacted when there are regional shifts in climate – like a 1 degree increase in average temperature - or when a region is hit with unexpected weather events.
Mitigating the risks of weather is now crucial, not simply so people can continue to enjoy coffee for breakfast, but more importantly because the climate impacts on crops could have devastating economic impacts to entire regions and industries.
How Global Crop Supply Chains Are Already Being Impacted by Climate
Drought, water scarcity, wind, heavy rainfall, and frost can all be detrimental to crops. Scientists estimate that the areas that grow wheat that will be impacted by drought will nearly double in the next 20 to 50 years. Globally, we could lose half of the land that is suitable to growing coffee due to climate change by 2050. Wine (grapes), fish, chickpeas, peaches, corn, and many more crops are being particularly hard-hit by climate change, and this may just be the beginning.
The concentration of crops to one region is an important risk factor. For instance, over 80% of global almond supply is produced in water scarce California. For a crop that requires an enormous amount of water to grow, some researchers are already looking at the possibility of shifting the industry to Oregon and Washington state as a viable alternative.
But it’s not just future impacts that are concerning. Take hazelnuts for example; Turkey was the largest supplier of hazelnuts in the world, producing over 70% of the global hazelnut supply. In 2014, frost damaged Turkey’s hazelnut crop resulting in a sharp increase in the cost of hazelnuts and a global shortage. In 2018, an August storm caused tens of thousands of tons of hazelnut crops to get washed into the Black Sea. These climate events affected over 4,000 farmers in the region and put a damper on a massive global hazelnut industry, which is estimated to reach $9.5 billion by 2026.
The concentration of global supplies to specific regions is concerning as even minor fluctuations in crop yield could have disastrous global impacts. This fluctuation can leave companies that rely on crops like almonds or hazelnuts vulnerable to both supply chain delays and increased costs.
How weather forecasting and climate modeling can help vulnerable crops
In order to reduce the risk and potential damages resulting from weather and climate extremes on crops, it is important to have reliable predictions of extreme weather in both a long- and short-term time scale. Climate modeling is one way to do this.
Predictions of the Earth’s future are produced using what’s called Global Circulation Models (GCMs). These models provide scientific insights into the evolution of global climate systems in months or centuries. But because they must cover the whole globe, the resulting data is coarse and is not always adequate to address the inter-disciplinary questions for specific projects, regions, or timeframes.
For this, climate scientists—like those at RWDI—are using what’s called dynamic downscaling. Dynamic downscaling is the use of high-resolution regional simulations to dynamically interpolate the effects of large-scale climate processes of GCMs to regional or local scales of interest. In other words, dynamic downscaling aims to take the coarse data from a GCM and process and refine this data in order to address a specific impact study, need, or project. It’s more granular and more specific to answering a question like, “can I grow almonds in Oregon 50 years from now?” or “does it make sense to build homes near this particular coast?” or “will a wind farm be sustainable in this particular field?”
It’s questions like this that companies who rely on crops, or who build homes or wind farms, would be asking. The answers will help them make the best decisions for the longevity of their operations, while understanding the potential impacts to their bottom dollar.
The Long-Term Benefits of Dynamic Downscaling
Unlike Global Climate Models (GCMs), dynamic downscaling offers site-specific data that is crucial to making business decisions. This modeling technique can:
- Produce data for any type of analysis: like temperature, wind speed, or precipitation
- Extract hourly data for any point in the future or for any historical period
- Be applied to similar domains anywhere in the world
- Offer 4 km resolution – a highly refined data set that can be applied to specific fields or regions as opposed to course scales of GCM
- Be conducted for any region in the world
- Be conducted for any application that needs future climate data
The applications for this kind of weather modeling are endless. Given the ongoing climate challenges we are facing globally, modeling provides opportunities to consider both existing and potential future locations. Not only is the potential for site-specific modeling going to be helpful, but the fact that modeling can help a multitude of industries from crops and agriculture to airports, buildings, bridges, wind farms, and more. This makes climate modeling one of the most versatile and necessary tools in planning and designing projects from civil to industrial.