{"id":260744,"date":"2023-06-05T13:17:19","date_gmt":"2023-06-05T11:17:19","guid":{"rendered":"https:\/\/climatescience.press\/?p=260744"},"modified":"2023-06-05T13:17:22","modified_gmt":"2023-06-05T11:17:22","slug":"if-we-imagine-really-bad-stuff-it-might-affect-crop-yields","status":"publish","type":"post","link":"https:\/\/climatescience.press\/?p=260744","title":{"rendered":"If We Imagine Really Bad Stuff, It Might Affect Crop Yields."},"content":{"rendered":"\n
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DCIM\\100MEDIA\\DJI_0151.JPG<\/figcaption><\/figure>\n\n\n\n

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From Watts Up With That?<\/a><\/p>\n\n\n\n

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Gotta love this study:<\/p>\n\n\n\n

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Emphasis mine. Oh, and it\u2019s all RCP 8.5 plugged into their imagineering engine.<\/p>\n\n\n\n

Abstract<\/h2>\n\n\n\n

Previous analyses of the possibility of global breadbasket failures have extrapolated risks based on historical relationships between climate and yields. However, climate change is causing unprecedented events globally<\/em><\/strong>, which could exceed critical threshold<\/em><\/strong>s<\/strong><\/em> and reduce yields, even if there is no historical precedent. This means that we are likely underestimating climate risks<\/strong><\/em> to our food system. In the case of wheat, parts of the USA and China show little historical relationship between yields and temperature, but extreme temperatures are now possible that exceed critical physiological thresholds in wheat plants. UNprecedented Simulated Extreme ENsemble (UNSEEN) approaches use large ensembles to generate plausible unprecedented events, which can inform our assessment of the risk to crops. <\/em><\/strong>We use the UNSEEN approach with a large ensemble of archived seasonal forecasts to generate thousands of plausible events over the last 40 years and compare the results with historically observed extreme temperature and precipitation. In the US midwest, extreme temperatures that would have happened approximately 1-in-100-years in 1981 now have a return period of 1-in-6 years, while in China, the current return period is on the order of 1-in-16 years. This means that in the US midwest, extreme temperatures that used to have a 1% chance to occur in 1981 now have a 17% chance to occur in any given year, while in China, the chance increased from 1% to 6%. Record-breaking years exceeding critical thresholds for enzymes in the wheat plant are now more likely than in the past, and these record-breaking hot years are associated with extremely dry conditions in both locations. Using geopotential height and wind anomalies from the UNSEEN ensemble, we demonstrate that strong winds over land pull dry air towards the regions these during extremely hot and dry unseen events. We characterize plausible extremes from the UNSEEN ensemble that can be used to help imagine otherwise unforeseen events, including a compound event in which high impacts co-occur in both regions, informing adaptation planning in these regions. Recent temperature extremes, especially in the US midwest, are unlikely to be a good proxy for what to expect in the next few years of today\u2019s climate, and local stakeholders might perceive their risk to be lower than it really is. We find that there is a high potential for surprise in these regions if people base risk analyses solely on historical datasets.<\/p>\n\n\n\n

Introduction<\/h2>\n\n\n\n

Given the global interconnectedness of the world\u2019s food system, simultaneous shocks to major food grain production areas (breadbaskets) can dramatically influence the price and availability of staple foods. Several studies have attempted to quantify the risk of multiple breadbasket failures due to climate shocks alone1<\/a>,2<\/a>,3<\/a><\/sup>. These studies have primarily extrapolated from historical patterns, quantifying the risk that climate shocks from the past could happen simultaneously in the future. However, climate change brings new and unprecedented events that can have consequences different from those experienced in the past, and history-based analyses might therefore under-estimate our current risk. In this study we depart from a focus on historical events, instead demonstrating how to visualize the risk of historically unprecedented events that might cross critical thresholds in major wheat-producing regions of the USA and China.<\/em><\/strong><\/p>\n\n\n\n

Most studies quantifying the risk of crop failure use historical relationships between climate and crop yields as the basis for assessing how future or unprecedented climate states might affect yields. For example2<\/a><\/sup> use historical yields to define a threshold for severe water stress in maize-growing regions of the US and China, and then they examine the change in risk of this threshold using large ensembles to simulate unprecedented extremes. Estimates of the risk of multiple breadbasket failures for different crops also take this approach, first estimating climate-yield relationships from historical data, and then extrapolating yield results based on changes to temperature and precipitation variables that were historically related to yield4<\/a><\/sup> In some regions, more than 50% of historical yield variability can be attributed to weather5<\/a><\/sup>.<\/p>\n\n\n\n

However, in a changing climate, climate-yield relationships will change. Never-before-experienced climate states and unprecedented events can have greater effects on crops than might be expected from a simple extrapolation of historical association.<\/strong><\/em> In particular for temperature, we might expect that never-before-experienced high temperatures could cause crop loss, even if there is no historical relationship between yield and temperature. Non-linearities in the response of crops to heat stress can mean the future looks distinctly different from the past. In addition, climate stressors can combine with other pressures to threaten agricultural productivity; these include conflict, pests, disease, soil health, seed quality, and irrigation, for example.<\/p>\n\n\n\n

Wheat (Triticum aestivum<\/em> L.) yields in parts of the United States and China do not show a strong relationship with temperature in observed or simulated datasets for the past6<\/a><\/sup>, and therefore extreme temperatures in these regions are not often included in models of potential breadbasket failure4<\/a><\/sup>. However, physiological models demonstrate that wheat plants are sensitive to temperature in several critical growth phases7<\/a><\/sup>. Generally, prolonged periods of extreme heat result in accelerated leaf senescence and a reduction in leaf expansion and radiation use efficiency. Short duration heat events are particularly harmful during sensitive development phases such as stem elongation. Heat extremes during grain filling can cause a reduction in the growth rate and the grain number8<\/a>,9<\/a><\/sup>, while heat stress during anthesis and may result in partial or complete sterility of the florets10<\/a>,11<\/a><\/sup>.<\/p>\n\n\n\n

Simulations for the end of the century show that unprecedented temperatures are likely to affect yields as higher thresholds are crossed12<\/a><\/sup> <\/em><\/strong>In fact, process-based and statistical models tend to agree that warming should negatively impact wheat yields13<\/a>,8<\/a><\/sup>, and a review of different model types found agreement that global wheat yield is likely to be negatively impacted by increasing temperatures with climate change14<\/a>,15<\/a><\/sup>. One solution to assess the impact of this nonlinearity is to use crop model simulations that can incorporate critical thresholds16<\/a>,17<\/a><\/sup> However, many of these crop models are developed based on historical yields, and many of them focus on annual extremes and \u201clikely\u201d ranges, rather than low-likelihood high-impact events.<\/p>\n\n\n\n

New methods to simulate unprecedented extremes can expand our understanding of what is possible, beyond historical events. Large ensembles of physics-based climate models can provide a larger sample of \u201calternative realities\u201d to calculate extreme value statistics18<\/a>,19<\/a>,20<\/a><\/sup> One example is the UNprecedented Simulated Extremes using ENsembles (UNSEEN) approach, using large ensembles of archived forecasts to better understand extremes21<\/a><\/sup>.<\/p>\n\n\n\n

To date, most studies of UNSEEN events or climate storylines have departed from a historical extreme event that has already happened, assessing plausible changes in frequency and magnitude<\/strong><\/em> (e.g. storm Desmond22<\/a><\/sup>). The approach has also been used to derive future impact analogs of historical events, such as a soybean (Glycine max<\/em> (L.) Merr.) drought in the future17<\/a><\/sup>.<\/p>\n\n\n\n

The UNSEEN approach can also be used to explore synthetic events\u2014events with no historical analog\u2014if the models have been properly assessed for their ability to produce realistic events23<\/a><\/sup>. Climate storylines that illustrate how record-breaking extremes might occur can expand our imagination to capture events that are plausible, yet never before experienced.<\/strong><\/em> Given that adaptation to climate change tends to be prompted by people\u2019s lived experience of extreme events24<\/a>,25<\/a>,26<\/a>,27<\/a><\/sup> visualizing such events before they happen can support preparedness and climate change adaptation.<\/p>\n\n\n\n

In this study, we use the UNSEEN approach to examine storylines of unprecedented heat in two wheat-producing regions of the world\u2019s breadbaskets, the USA and China. First, we assemble a large ensemble of archived forecasts for each region for temperature and precipitation, estimating the frequency of temperatures above critical growing thresholds. We estimate changes to the return periods of extreme temperatures with climate change, and consider the probability of a compound extreme of high temperatures and low rainfall in each region. While many other studies have focused on climate change in the far future, we explore the current-day climate, and how risks have already changed from the recent past, complementing work1<\/a><\/sup>.<\/p>\n\n\n\n

Read the rest of this \u201cpaper\u201d here.<\/a><\/p>\n\n\n\n