Global crop yield losses from recent warming

Global yields of the world-s six most widely grown crops--wheat, rice, maize, soybeans, barley, sorghum--have increased since 1961. Year-to-year variations in growing season minimum temperature, maximum temperature, and precipitation explain 30% or more of the variations in yield. Since 1991, climate trends have significantly decreased yield trends in all crops but rice, leading to foregone production since 1981 of about 12 million tons per year of wheat or maize, representing an annual economic loss of $1.2 to$1.7 billion. At the global scale, negative impacts of climate trends on crop yields are already apparent. Annual global temperatures have increased by {approx}0.4 C since 1980, with even larger changes observed in several regions (1). While many studies have considered the impacts of future climate changes on food production (2-5), the effects of these past changes on agriculture remain unclear. It is likely that warming has improved yields in some areas, reduced them in others, and had negligible impacts in still others; the relative balance of these effects at the global scale is unknown. An understanding of this balance would help to anticipate impacts of future climate changes, as well as to more accurately assess recent (and thereby project future) technologically driven yield progress. Separating the contribution of climate from concurrent changes in other factors--such as crop cultivars, management practices, soil quality, and atmospheric carbon dioxide (CO{sub 2}) levels--requires models that describe the response of yields to climate. Studies of future global impacts of climate change have typically relied on a bottom-up approach, whereby field scale, process-based models are applied to hundreds of representative sites and then averaged (e.g., ref 2). Such approaches require input data on soil and management conditions, which are often difficult to obtain. Limitations on data quality or quantity can thus limit the utility of this approach, especially at the local scale (6-8). At the global scale, however, many of the processes and impacts captured by field scale models will tend to cancel out, and therefore simpler empirical/statistical models with fewer input requirements may be as accurate (8, 9). Empirical/statistical models also allow the effects of poorly modeled processes (e.g., pest dynamics) to be captured and uncertainties to be readily quantified (10). Here we develop new, empirical/statistical models of global yield responses to climate using datasets on broad-scale yields, crop locations, and climate variability. We focus on global average yields for the six most widely grown crops in the world: wheat, rice, maize, soybeans, barley, and sorghum. Production of these crops accounts for over 40% of global cropland area (11). 55% of non-meat calories, and over 70% of animal feed (12)

Comment on "Methodology and results of calculating Central California surface temperature trends: evidence of human-induced climate change?" by Christy et al. (2006)

Understanding the causes of observed regional temperature trends is essential to projecting the human influences on climate, and the societal impacts of these influences. In their recent study, Christy et al. (2006, hereinafter CRNG06) hypothesized that the presence of irrigated soils is responsible for rapid warming of summer nights occurring in California's Central Valley over the last century (1910-2003), an assumption that rules out any significant effect due to increased greenhouse gases, urbanization, or other factors in this region. We question this interpretation, which is based on an apparent contrast in summer nighttime temperature trends between the San Joaquin Valley ({approx} +0.3 {+-} 0.1 C/decade) and the adjacent western slopes of the Sierra Nevada (-0.25 {+-} 0.15 C/decade), as well as the amplitude, sign and uncertainty of the Sierra nighttime temperature trend itself. We, however, do not dispute the finding of other Sierra and Valley trends. Regarding the veracity of the apparent Sierra nighttime temperature trend, CRNG06 generated the Valley and Sierra time-series using a meticulous procedure that eliminates discontinuities and isolates homogeneous segments in temperature records from 41 weather stations. This procedure yields an apparent cooling of about -0.25 {+-} 0.15 C/decade in the Sierra region. However, because removal of one of the 137 Sierra segments, from the most elevated site (Huntington Lake, 2140m), causes an increase in nighttime temperature trend as large as the trend itself (of +0.25 C/decade, CH06), and leads to a zero trend, the apparent cooling of summer nights in the Sierra regions seems, in fact, largely uncertain

Climate change uncertainty for daily minimum and maximum temperatures: a model inter-comparison

Several impacts of climate change may depend more on changes in mean daily minimum (T{sub min}) or maximum (T{sub max}) temperatures than daily averages. To evaluate uncertainties in these variables, we compared projections of T{sub min} and T{sub max} changes by 2046-2065 for 12 climate models under an A2 emission scenario. Average modeled changes in T{sub max} were slightly lower in most locations than T{sub min}, consistent with historical trends exhibiting a reduction in diurnal temperature ranges. However, while average changes in T{sub min} and T{sub max} were similar, the inter-model variability of T{sub min} and T{sub max} projections exhibited substantial differences. For example, inter-model standard deviations of June-August T{sub max} changes were more than 50% greater than for T{sub min} throughout much of North America, Europe, and Asia. Model differences in cloud changes, which exert relatively greater influence on T{sub max} during summer and T{sub min} during winter, were identified as the main source of uncertainty disparities. These results highlight the importance of considering separately projections for T{sub max} and T{sub min} when assessing climate change impacts, even in cases where average projected changes are similar. In addition, impacts that are most sensitive to summertime T{sub min} or wintertime T{sub max} may be more predictable than suggested by analyses using only projections of daily average temperatures

Interpretation of Recent Temperature Trends in California

Regional-scale climate change and associated societal impacts result from large-scale (e.g. well-mixed greenhouse gases) and more local (e.g. land-use change) 'forcing' (perturbing) agents. It is essential to understand these forcings and climate responses to them, in order to predict future climate and societal impacts. California is a fine example of the complex effects of multiple climate forcings. The State's natural climate is diverse, highly variable, and strongly influenced by ENSO. Humans are perturbing this complex system through urbanization, irrigation, and emission of multiple types of aerosols and greenhouse gases. Despite better-than-average observational coverage, we are only beginning to understand the manifestations of these forcings in California's temperature record

Impacts of Future Climate Change on California Perennial Crop Yields: Model Projections with Climate and Crop Uncertainties

Most research on the agricultural impacts of climate change has focused on the major annual crops, yet perennial cropping systems are less adaptable and thus potentially more susceptible to damage. Improved assessments of yield responses to future climate are needed to prioritize adaptation strategies in the many regions where perennial crops are economically and culturally important. These impact assessments, in turn, must rely on climate and crop models that contain often poorly defined uncertainties. We evaluated the impact of climate change on six major perennial crops in California: wine grapes, almonds, table grapes, oranges, walnuts, and avocados. Outputs from multiple climate models were used to evaluate climate uncertainty, while multiple statistical crop models, derived by resampling historical databases, were used to address crop response uncertainties. We find that, despite these uncertainties, climate change in California is very likely to put downward pressure on yields of almonds, walnuts, avocados, and table grapes by 2050. Without CO{sub 2} fertilization or adaptation measures, projected losses range from 0 to >40% depending on the crop and the trajectory of climate change. Climate change uncertainty generally had a larger impact on projections than crop model uncertainty, although the latter was substantial for several crops. Opportunities for expansion into cooler regions are identified, but this adaptation would require substantial investments and may be limited by non-climatic constraints. Given the long time scales for growth and production of orchards and vineyards ({approx}30 years), climate change should be an important factor in selecting perennial varieties and deciding whether and where perennials should be planted

Potential bias of model projected greenhouse warming in irrigated regions

Atmospheric general circulation models (GCMs) used to project climate responses to increased CO{sub 2} generally omit irrigation of agricultural land. Using the NCAR CAM3 GCM coupled to a slab-ocean model, we find that inclusion of an extreme irrigation scenario has a small effect on the simulated temperature and precipitation response to doubled CO{sub 2} in most regions, but reduced warming by as much as 1 C in some agricultural regions, such as Europe and India. This interaction between CO{sub 2} and irrigation occurs in cases where agriculture is a major fraction of the land surface and where, in the absence of irrigation, soil moisture declines are projected to provide a positive feedback to temperature change. The reduction of warming is less than 25% of the temperature increase modeled for doubled CO{sub 2} in most regions; thus greenhouse warming will still be dominant. However, the results indicate that land use interactions may be an important component of climate change uncertainty in some agricultural regions. While irrigated lands comprise only {approx}2% of the land surface, they contribute over 40% of global food production. Climate changes in these regions are therefore particularly important to society despite their relatively small contribution to average global climate

Assessing the benefits of crop albedo bio-geoengineering

It has been proposed that growing crop varieties with higher canopy albedo would lower summer-time temperatures over North America and Eurasia and provide a partial mitigation of global warming ('bio-geoengineering') (Ridgwell et al 2009 Curr. Biol. 19 1–5). Here, we use a coupled ocean–atmosphere–vegetation model (HadCM3) with prescribed agricultural regions, to investigate to what extent the regional effectiveness of crop albedo bio-geoengineering might be influenced by a progressively warming climate as well as assessing the impacts on regional hydrological cycling and primary productivity. Consistent with previous analysis, we find that the averted warming due to increasing crop canopy albedo by 0.04 is regionally and seasonally specific, with the largest cooling of ~1 °C for Europe in summer whereas in the low latitude monsoonal SE Asian regions of high density cropland, the greatest cooling is experienced in winter. In this study we identify potentially important positive impacts of increasing crop canopy albedo on soil moisture and primary productivity in European cropland regions, due to seasonal increases in precipitation. We also find that the background climate state has an important influence on the predicted regional effectiveness of bio-geoengineering on societally-relevant timescales (ca 100 years). The degree of natural climate variability and its dependence on greenhouse forcing that are evident in our simulations highlights the difficulties faced in the detection and verification of climate mitigation in geoengineering schemes. However, despite the small global impact, regionally focused schemes such as crop albedo bio-geoengineering have detection advantages

Weather-based forecasts of California crop yields

Crop yield forecasts provide useful information to a range of users. Yields for several crops in California are currently forecast based on field surveys and farmer interviews, while for many crops official forecasts do not exist. As broad-scale crop yields are largely dependent on weather, measurements from existing meteorological stations have the potential to provide a reliable, timely, and cost-effective means to anticipate crop yields. We developed weather-based models of state-wide yields for 12 major California crops (wine grapes, lettuce, almonds, strawberries, table grapes, hay, oranges, cotton, tomatoes, walnuts, avocados, and pistachios), and tested their accuracy using cross-validation over the 1980-2003 period. Many crops were forecast with high accuracy, as judged by the percent of yield variation explained by the forecast, the number of yields with correctly predicted direction of yield change, or the number of yields with correctly predicted extreme yields. The most successfully modeled crop was almonds, with 81% of yield variance captured by the forecast. Predictions for most crops relied on weather measurements well before harvest time, allowing for lead times that were longer than existing procedures in many cases

Seasonal cycles enhance disparities between low- and high-income countries in exposure to monthly temperature emergence with future warming

A common proxy for the adaptive capacity of a community to the impacts of future climate change is the range of climate variability which they have experienced in the recent past. This study presents an interpretation of such a framework for monthly temperatures. Our results demonstrate that emergence into genuinely 'unfamiliar' climates will occur across nearly all months of the year for low-income nations by the second half of the 21st century under an RCP8.5 warming scenario. However, high income countries commonly experience a large seasonal cycle, owing to their position in the middle latitudes: as a consequence, temperature emergence for transitional months translates only to more-frequent occurrences of heat historically associated with the summertime. Projections beyond 2050 also show low-income countries will experience 2–10 months per year warmer than the hottest month experienced in recent memory, while high-income countries will witness between 1–4 months per year hotter than any month previously experienced. While both results represent significant departures that may bring substantive societal impacts if greenhouse gas emissions continue unabated, they also demonstrate that spatial patterns of emergence will compound existing differences between high and low income populations, in terms of their capacity to adapt to unprecedented future temperatures