The response of air temperatures to widespread irrigation may represent an important component of past and/or future regional climate changes. The quantitative impact of irrigation on daily minimum and maximum temperatures (T_min and T_max) in California was estimated using historical time series of county irrigated areas from agricultural censuses and daily climate observations from the U.S. Historical Climatology Network. Regression analysis of temperature and irrigation changes for stations within irrigated areas revealed a highly significant (p < 0.01) effect of irrigation on June–August average T_max, with no significant effects on T_min (p > 0.3). The mean estimate for T_max was a substantial 5.0°C cooling for 100% irrigation cover, with a 95% confidence interval of 2.0°–7.9°C. As a result of small changes in T_min compared to T_max, the diurnal temperature range (DTR) decreased significantly in both spring and summer months. Effects on percentiles of T_max within summer months were not statistically distinguishable, suggesting that irrigation’s impact is similar on warm and cool days in California. Finally, average trends for stations within irrigated areas were compared to those from nonirrigated stations to evaluate the robustness of conclusions from previous studies based on pairwise comparisons of irrigated and nonirrigated sites. Stronger negative T_max trends in irrigated sites were consistent with the inferred effects of irrigation on T_max. However, T_min trends were significantly more positive for nonirrigated sites despite the apparent lack of effects of irrigation on T_min from the analysis within irrigated sites.

Together with evidence of increases in urban areas near nonirrigated sites, this finding indicates an important effect of urbanization on T_min in California that had previously been attributed to irrigation. The results therefore demonstrate that simple pairwise comparisons between stations in a complex region such as California can lead to misinterpretation of historical climate trends and the effects of land use changes.

Converting forest lands into bioenergy agriculture could accelerate climate change by emitting carbon stored in forests, while converting food agriculture lands into bioenergy agriculture could threaten food security. Both problems are potentially avoided by using abandoned agriculture lands for bioenergy agriculture. Here we show the global potential for bioenergy on abandoned agriculture lands to be less than 8% of current primary energy demand, based on historical land use data, satellite-derived land cover data, and global ecosystem modeling. The estimated global area of abandoned agriculture is 385-472 million hectares, or 66-110% of the areas reported in previous preliminary assessments. The area-weighted mean production of above-ground biomass is 4.3 tons/ha^-1 /y^-1, in contrast to estimates of up to 10 tons/ha/yr in previous assessments. The energy content of potential biomass grown on 100% of abandoned agriculture lands is less than 10% of primary energy demand for most nations in North America, Europe, and Asia, but it represents many times the energy demand in some African nations where grasslands are relatively productive and current energy demand is low.

» Article in the Stanford Report on Campbell et al. 
» Video by the Stanford News Service.

There is a widely recognized need in the scientific and policy communities for probabilistic estimates of climate change impacts, beyond simple scenario analysis. Here we propose a methodology to evaluate one major climate change impact - changes in global average yields of wheat, maize, and barley by 2030 - by a probabilistic approach that integrates uncertainties in climate change and crop yield responses to temperature, precipitation, and carbon dioxide. The resulting probability distributions, which are conditional on assuming the SRES A1B emission scenario and no agricultural adaptation, indicate expected changes of +1.6%, -14.1%, -1.8% for wheat, maize, and barley, with 95% probability intervals of (-4.1, +6.7), (-28.0, -4.3), (-11.0, 6.2) in percent of current yields, respectively. This fully probabilistic analysis aims at quantifying the range of plausible outcomes and allows us to gauge the relative importance of different sources of uncertainty.

A particularly pressing need from a risk analysis standpoint is to provide probabilistic assessments of impacts of climate change. General circulation models (GCMs) are powerful tools for the analysis of future changes in climate variables, and statistical analysis of their output can provide not only point estimates, but also a rigorous evaluation of the uncertainty inherent in future projections [Tebaldi et al., 2004, 2005; Tebaldi and Sanso´ , 2008; R. L. Smith, Bayesian modeling of uncertainty in ensembles of climate models, submitted to Journal of the American Statistical Association, 2007]. Recent work [Lobell and Field, 2007] has quantified through statistical regression analysis the relation between observed changes in temperature and precipitation and recorded changes in agricultural yields of several major crops at the global level. In this work we seek to draw a connection between these two areas of study, by assessing the potential impacts on global yields of three important crops of changes in temperature and precipitation as they are projected in the GCM experiments archived in the Coupled Model Intercomparison Project phase 3 (CMIP3) multi-model dataset. We choose to assess the sensitivity of crop yields to climate change through regression models rather than process-based crop models because of our focus on the quantification of uncertainties, since we are not aware of any systematic means to quantify the dependence of the process-based model results to the choice of a specific model and specific parameter values within each model. Our results are probabilistic projections of percent crop yield changes by 2030, compared to current yields, in the absence of adaptation practices.

The effect of elevated carbon dioxide (CO2) on crop yields is one of the most uncertain and influential parameters in models used to assess climate change impacts and adaptations. A primary reason for this uncertainty is the limited availability of experi- mental data on CO2 responses for crops grown under typical field conditions. However, because of historical variations in CO2, each year farmers throughout the world perform uncontrolled yield experiments under different levels of CO2. In this study, measure- ments of atmospheric CO2 growth rates and crop yields for individual countries since 1961 were compared with empirically determine the average effect of a 1 ppm increase of CO2 on yields of rice, wheat, and maize. Because the gradual increase in CO2 is highly correlated with major changes in technology, management, and other yield controlling factors, we focused on first differences of CO2 and yield time series. Estimates of CO2 responses obtained from this approach were highly uncertain, reflecting the relatively small importance of year-to-year CO2 changes for yield variability. Combining estimates from the top 20 countries for each crop resulted in estimates with substantially less uncertainty than from any individual country. The results indicate that while current datasets cannot reliably constrain estimates beyond previous experimental studies, an empirical approach supported by large amounts of data may provide a potentially valuable and independent assessment of this critical model parameter. For example, analysis of reliable yield records from hundreds of individual, independent locations (as opposed to national scale yield records with poorly defined errors) may result in empirical estimates with useful levels of uncertainty to complement estimates from experimental studies.

The potential impact of climate change on the world’s poor is a topic with wide and growing interest, but there remains much uncertainty about how specifically to adapt to a changing climate. Food security impacts are a particular concern, as hundreds of millions of people who struggle to get by in the current climate may be faced with more frequent droughts, flooding, and heat waves that can devastate crop harvests. The humanitarian, environmental, and security implications of these impacts could be enormous.

Climate change and land use change can affect multiple infectious diseases of humans, acting either independently or synergistically. Expanded efforts in empiric and future scenario-based risk assessment are required to anticipate problems. Moreover, the many health impacts of climate and land use change must be examined in the context of the myriad other environmental and behavioral determinants of disease. To optimize prevention capabilities, upstream environmental approaches must be part of any intervention, rather than assaults on single agents of disease. Clinicians must develop stronger ties, not only to public health officials and scientists, but also to earth and environmental scientists and policy makers. Without such efforts, we will inevitably benefit our current generation at the cost of generations to come.