Reservoir timescales for anthropogenic CO2 in the atmosphere

Non-steady state timescales are complicated and their application to specific geophysical systems requires a common theoretical foundation. We first extend reservoir theory by quantifying the difference between turnover time and transit time (or residence time) for time-dependent systems under any mixing conditions. We explicitly demonstrate the errors which result from assuming these timescales are equal, which is only true at steady state. We also derive a new response function which allows the calculation of age distributions and timescales for well-mixed reservoirs away from steady state, and differentiate between timescales based on gross and net fluxes. These theoretical results are particularly important to tracer-calibrated "box models" currently used to study the carbon cycle, which usually approximate reservoirs as well-mixed. We then apply the results to the important case of anthropogenic CO2 in the atmosphere, since timescales describing its behavior are commonly used but ambiguously defined. All relevant timescales, including lifetime, transit time, and adjustment time, are precisely defined and calculated from data and models. Apparent discrepancies between the current, empirically determined turnover time of 30-60 years and longer model-derived estimates of expected lifetime and adjustment time are explained within this theoretical framework. We also discuss the results in light of policy issues related to global warming, in particular since any comparisons of the "lifetimes" of different greenhouse gases (CO2, CH4, N2O, CFC's etc.) must use a consistent definition to be meaningful.     

Impacts of extreme weather on wheat and maize in France: evaluating regional crop simulations against observed data

Extreme weather conditions can strongly affect agricultural production, with negative impacts that can at times be detected at regional scales. In France, crop yields were greatly influenced by drought and heat stress in 2003 and by extremely wet conditions in 2007. Reported regional maize and wheat yields where historically low in 2003; in 2007 wheat yields were lower and maize yields higher than long-term averages. An analysis with a spatial version (10?×?10?km) of the EPIC crop model was tested with regards to regional crop yield anomalies of wheat and maize resulting from extreme weather events in France in 2003 and 2007, by comparing simulated results against reported regional crops statistics, as well as using remotely sensed soil moisture data. Causal relations between soil moisture and crop yields were specifically analyzed. Remotely sensed (AMSR-E) JJA soil moisture correlated significantly with reported regional crop yield for 2002–2007. The spatial correlation between JJA soil moisture and wheat yield anomalies was positive in dry 2003 and negative in wet 2007. Biweekly soil moisture data correlated positively with wheat yield anomalies from the first half of June until the second half of July in 2003. In 2007, the relation was negative the first half of June until the second half of August. EPIC reproduced observed soil dynamics well, and it reproduced the negative wheat and maize yield anomalies of the 2003 heat wave and drought, as well as the positive maize yield anomalies in wet 2007. However, it did not reproduce the negative wheat yield anomalies due to excessive rains and wetness in 2007. Results indicated that EPIC, in line with other crop models widely used at regional level in climate change studies, is capable of capturing the negative impacts of droughts on crop yields, while it fails to reproduce negative impacts of heavy rain and excessively wet conditions on wheat yield, due to poor representations of critical factors affecting plant growth and management. Given that extreme weather events are expected to increase in frequency and perhaps severity in coming decades, improved model representation of crop damage due to extreme events is warranted in order to better quantify future climate change impacts and inform appropriate adaptation responses.     

U.S. Agriculture and Climate Change: New Results

We examined the impacts on U.S. agriculture of transient climate change assimulated by 2 global general circulation models focusing on the decades ofthe 2030s and 2090s. We examined historical shifts in the location of cropsand trends in the variability of U.S. average crop yields, finding thatnon-climatic forces have likely dominated the north and westward movement ofcrops and the trends in yield variability. For the simulated future climateswe considered impacts on crops, grazing and pasture, livestock, pesticide use,irrigation water supply and demand, and the sensitivity to international tradeassumptions, finding that the aggregate of these effects were positive for theU.S. consumer but negative, due to declining crop prices, for producers. Weexamined the effects of potential changes in El Niño/SouthernOscillation (ENSO) and impacts on yield variability of changes in mean climateconditions. Increased losses occurred with ENSO intensity and frequencyincreases that could not be completely offset even if the events could beperfectly forecasted. Effects on yield variability of changes in meantemperatures were mixed. We also considered case study interactions ofclimate, agriculture, and the environment focusing on climate effects onnutrient loading to the Chesapeake Bay and groundwater depletion of theEdward's Aquifer that provides water for municipalities and agriculture to theSan Antonio, Texas area. While only case studies, these results suggestenvironmental targets such as pumping limits and changes in farm practices tolimit nutrient run-off would need to be tightened if current environmentalgoals were to be achieved under the climate scenarios we examined