Expansion of irrigated land can cause local cooling of daytime temperatures by up to several degrees Celsius. Here the authors compare the expected cooling associated with rates of irrigation expansion in developing countries for historical (1961-2000) and future (2000-30) periods with climate model predictions of temperature changes from other forcings, most notably increased atmospheric greenhouse gas levels, over the same periods. Indirect effects of irrigation on climate, via methane production in paddy rice systems, were not considered. In regions of rapid irrigation growth over the past 40 yr, such as northwestern India and northeastern China, irrigation's expected cooling effects have been similar in magnitude to climate model predictions of warming from greenhouse gases. A masking effect of irrigation can therefore explain the lack of significant increases in observed growing season maximum temperatures in these regions and the apparent discrepancy between observations and climate model simulations. Projections of irrigation for 2000-30 indicate a slowing of expansion rates, and therefore cooling from irrigation expansion over this time period will very likely be smaller than in recent decades. At the same time, warming from greenhouse gases will likely accelerate, and irrigation will play a relatively smaller role in agricultural climate trends. In many irrigated regions, therefore, temperature projections from climate models, which generally ignore irrigation, may be more accurate in predicting future temperature trends than their performance in reproducing past observed trends in irrigated regions would suggest.

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.

Effective strategies for managing the dangers of global climate change are proving very difficult to design and implement. They require governments to undertake a portfolio of efforts that are politically challenging because they require large expenditures today for uncertain benefits that accrue far into the future. That portfolio includes tasks such as putting a price on carbon, fixing the tendency for firms to under-invest in the public good of new technologies and knowledge that will be needed for achieving cost-effective and deep cuts in emissions; and preparing for a changing climate through investments in adaptation and climate engineering. Many of those efforts require international coordination that has proven especially difficult to mobilize and sustain because international institutions are usually weak and thus unable to force collective action...."

Carbon capture and storage (CCS) is among the technologies with greatest potential leverage to combat climate change. According to the PRISM analysis, a technology assessment performed by the Electric Power Research Institute (EPRI), wide deployment of CCS after 2020 in the US power sector alone could reduce emissions by approximately 350 million tonnes of CO₂ per year (Mt CO₂/yr) by 2030, a conclusion echoed by the McKinsey U.S. Mid-range Greenhouse Gas Abatement Curve 2030. But building CCS into such a formidable climate change mitigation “wedge” will require more than technological feasibility; it will also require the development of policies and business models that can enable wide adoption. Such business models, and the regulatory environments to support them, have as yet been largely undemonstrated. This, among other factors, has caused the gap between the technological potential and the actual pace of CCS development to remain large.

The purpose of the present work is to quantify actual progress in developing carbon storage projects (here defined as any projects that store carbon underground at any stage of their operation or development, for example through injection into oil fields for enhanced recovery or in saline aquifers or other geological formations). In this way, the real development ramp may be compared in scale and timing against the perceived need for and potential of the technology. Some very useful lists of carbon storage projects already exist – see, for example, the IPCC CCS database, the JP Morgan CCS project list, the MIT CCS database, and the IEA list. We seek to maintain an up-to-date database of all publicly-announced current and planned projects from which we can project a trajectory of carbon stored underground as a function of time. To do this, we estimate for each project the probability of completion as well as the potential volume of CO₂ that can be stored as of a given year.