Using the past to constrain the future: how the palaeorecord can improve estimates of global warming

Climate sensitivity is defined as the change in global mean equilibrium temperature after a doubling of atmospheric CO2 concentration and provides a simple measure of global warming. An early estimate of climate sensitivity, 1.5-4.5{\deg}C, has changed little subsequently, including the latest assessment by the Intergovernmental Panel on Climate Change. The persistence of such large uncertainties in this simple measure casts doubt on our understanding of the mechanisms of climate change and our ability to predict the response of the climate system to future perturbations. This has motivated continued attempts to constrain the range with climate data, alone or in conjunction with models. The majority of studies use data from the instrumental period (post-1850) but recent work has made use of information about the large climate changes experienced in the geological past. In this review, we first outline approaches that estimate climate sensitivity using instrumental climate observations and then summarise attempts to use the record of climate change on geological timescales. We examine the limitations of these studies and suggest ways in which the power of the palaeoclimate record could be better used to reduce uncertainties in our predictions of climate sensitivity.Comment: The final, definitive version of this paper has been published in Progress in Physical Geography, 31(5), 2007 by SAGE Publications Ltd, All rights reserved. \c{opyright} 2007 Edwards, Crucifix and Harriso

Monitoring Soil Quality to Assess the Sustainability of Harvesting Corn Stover

Harvesting feedstock for biofuel production must not degrade soil, water, or air resources. Our objective is to provide an overview of field research being conducted to quantify effects of harvesting corn (Zea mays L.) stover as a bioenergy feedstock. Coordinated field studies are being conducted near Ames, IA; St. Paul and Morris, MN; Mead, NE; University Park, PA; Florence, SC; and Brookings, SD., as part of the USDA-ARS Renewable Energy Assessment Project (REAP). A baseline soil quality assessment was made using the Soil Management Assessment Framework (SMAF). Corn grain and residue yield for two different stover harvest rates (∼50% and ∼90%) are being measured. Available soil data remains quite limited but sufficient for an initial SMAF analysis that confirms total organic carbon (TOC) is a soil quality indicator that needs to be closely monitored closely to quantify crop residue removal effects. Overall, grain yields averaged 9.7 and 11.7 Mg ha−1 (155 and 186 bu acre−1) in 2008 and 2009, values that are consistent with national averages for both years. The average amount of stover collected for the 50% treatment was 2.6 and 4.2 Mg ha−1 for 2008 and 2009, while the 90% treatment resulted in an average removal of 5.4 and 7.4 Mg ha−1, respectively. Based on a recent literature review, both stover harvest scenarios could result in a gradual decline in TOC. However, the literature value has a large standard error, so continuation of this long-term multi-location study for several years is warranted

A global reference database of crowdsourced cropland data collected using the Geo-Wiki platform

A global reference data set on cropland was collected through a crowdsourcing campaign using the Geo-Wiki crowdsourcing tool. The campaign lasted three weeks, with over 80 participants from around the world reviewing almost 36,000 sample units, focussing on cropland identification. For quality assessment purposes, two additional data sets are provided. The first is a control set of 1,793 sample locations validated by students trained in satellite image interpretation. This data set was used to assess the quality of the crowd as the campaign progressed. The second data set contains 60 expert validations for additional evaluation of the quality of the contributions. All data sets are split into two parts: the first part shows all areas classified as cropland and the second part shows cropland average per location and user. After further processing, the data presented here might be suitable to validate and compare medium and high resolution cropland maps generated using remote sensing. These could also be used to train classification algorithms for developing new maps of land cover and cropland extent