Legacy effects of grassland management on soil carbon to depth

The importance of managing land to optimise carbon sequestration for climate change mitigation is widely recognised, with grasslands being identified as having the potential to sequester additional carbon. However, most soil carbon inventories only consider surface soils, and most large scale surveys group ecosystems into broad habitats without considering management intensity. Consequently, little is known about the quantity of deep soil carbon and its sensitivity to management. From a nationwide survey of grassland soils to 1 m depth, we show that carbon in grasslands soils is vulnerable to management and that these management effects can be detected to considerable depth down the soil profile, albeit at decreasing significance with depth. Carbon concentrations in soil decreased as management intensity increased, but greatest soil carbon stocks (accounting for bulk density differences), were at intermediate levels of management. Our study also highlights the considerable amounts of carbon in sub-surface soil below 30cm, which is missed by standard carbon inventories. We estimate grassland soil carbon in Great Britain to be 2097 Tg C to a depth of 1 m, with ~60% of this carbon being below 30cm. Total stocks of soil carbon (t ha-1) to 1 m depth were 10.7% greater at intermediate relative to intensive management, which equates to 10.1 t ha-1 in surface soils (0-30 cm), and 13.7 t ha-1 in soils from 30-100 cm depth. Our findings highlight the existence of substantial carbon stocks at depth in grassland soils that are sensitive to management. This is of high relevance globally, given the extent of land cover and large stocks of carbon held in temperate managed grasslands. Our findings have implications for the future management of grasslands for carbon storage and climate mitigation, and for global carbon models which do not currently account for changes in soil carbon to depth with management.

The role of local atmospheric forcing on the modulation of the ocean mixed layer depth in reanalysis and a coupled single column ocean model

The role of the local atmospheric forcing on the ocean mixed layer depth (MLD) over the global oceans is studied using ocean reanalysis data products and a single-column ocean model coupled to an atmospheric general circulation model. The focus of this study is on how the annual mean and the seasonal cycle of the MLD relate to various forcing characteristics in different parts of the world's ocean, and how anomalous variations in the monthly mean MLD relate to anomalous atmospheric forcings. By analysing both ocean reanalysis data and the single-column ocean model, regions with different dominant forcings and different mean and variability characteristics of the MLD can be identified. Many of the global oceans' MLD characteristics appear to be directly linked to different atmospheric forcing characteristics at different locations. Here, heating and wind-stress are identified as the main drivers; in some, mostly coastal, regions the atmospheric salinity forcing also contributes. The annual mean MLD is more closely related to the annual mean wind-stress and the MLD seasonality is more closely to the seasonality in heating. The single-column ocean model, however, also points out that the MLD characteristics over most global ocean regions, and in particular the tropics and subtropics, cannot be maintained by local atmospheric forcings only, but are also a result of ocean dynamics that are not simulated in a single-column ocean model. Thus, lateral ocean dynamics are essentially in correctly simulating observed MLD.

Ordinal judgements of depth in monocularly- and binocularly-viewed photographs of complex natural scenes

This study investigated the contribution of stereoscopic depth cues to the reliability of ordinal depth judgments in complex natural scenes. Participants viewed photographs of cluttered natural scenes, either monocularly or stereoscopically. On each trial, they judged which of two indicated points in the scene was closer in depth. We assessed the reliability of these judgments over repeated trials, and how well they correlated with the actual disparities of the points between the left and right eyes' views. The reliability of judgments increased as their depth separation increased, was higher when the points were on separate objects, and deteriorated for point pairs that were more widely separated in the image plane. Stereoscopic viewing improved sensitivity to depth for points on the same surface, but not for points on separate objects. Stereoscopic viewing thus provides depth information that is complementary to that available from monocular occlusion cues.

Disparity-defined objects moving in depth do not elicit three-dimensional shape constancy

Observers generally fail to recover three-dimensional shape accurately from binocular disparity. Typically, depth is overestimated at near distances and underestimated at far distances [Johnston, E. B. (1991). Systematic distortions of shape from stereopsis. Vision Research, 31, 1351–1360]. A simple prediction from this is that disparity-defined objects should appear to expand in depth when moving towards the observer, and compress in depth when moving away. However, additional information is provided when an object moves from which 3D Euclidean shape can be recovered, be this through the addition of structure from motion information [Richards, W. (1985). Structure from stereo and motion. Journal of the Optical Society of America A, 2, 343–349], or the use of non-generic strategies [Todd, J. T., & Norman, J. F. (2003). The visual perception of 3-D shape from multiple cues: Are observers capable of perceiving metric structure? Perception and Psychophysics, 65, 31–47]. Here, we investigated shape constancy for objects moving in depth. We found that to be perceived as constant in shape, objects needed to contract in depth when moving toward the observer, and expand in depth when moving away, countering the effects of incorrect distance scaling (Johnston, 1991). This is a striking example of the failure of shape con- stancy, but one that is predicted if observers neither accurately estimate object distance in order to recover Euclidean shape, nor are able to base their responses on a simpler processing strategy.