• Canada
• UK Research and Innovation close
• 2011 close
• 2014 close

Relative sea level (RSL) change reflects the interplay between a large number of variables operating at scales from global to local. Changes in RSL around the British Isles (BI) since the height of the last glaciation (ca. 24 000 years ago), are dominated by two key variables (i) the rise of ocean levels caused by climate warming and the melting of land-based ice; and (ii) the vertical adjustment of the Earth's surface due to the redistribution of this mass (unloading of formerly glaciated regions and loading of the ocean basins and margins). As a consequence RSL histories vary considerably across the region once covered by the British-Irish Ice Sheet (BIIS). The variable RSL history means that the BI is a globally important location for studying the interactions between land, ice and the ocean during the profound and rapid changes that followed the last glacial maximum. The BI RSL record is an important yardstick for testing global models of land-ice-ocean interactions and this in turn is important for understanding future climate and sea level scenarios. At present, the observational record of RSL change in the British Isles is limited to shallow water areas because of accessibility and only the later part of the RSL curve is well studied. In Northern Britain, where the land has been rising most, RSL indicators are close to or above present sea level and the RSL record is most complete. In southern locations, where uplift has been less, sea level was below the present for long periods of time but there is very little data on RSL position. There are varying levels of agreement between models and existing field data and we cannot be certain of model projections of former low sea levels. Getting the models right is important for understanding the whole global pattern of land-ice-ocean interactions in the past and into the future. To gather the missing data and thus improve the utility of the British RSL curves for testing earth-ice-ocean models, we will employ a specialised, interdisciplinary approach that brings together a unique team of experts in a multidisciplinary team. We have carefully selected sites where there is evidence of former sea levels is definitely preserved and we will use existing seabed geological data in British and Irish archives to plan our investigations. The first step is marine geophysical profiling of submerged seabed sediments and mapping of surface geomorphological features on the seabed. These features include the (usually) erosional surface (unconformity) produced by the rise in sea level, and surface geomorphological features that indicate former shorelines (submerged beaches, barriers and deltas). These allow us to identify the position (but not the age) of lower than present sea levels. The second step is to use this stratigraphic and geomorphological information to identify sites where we will take cores to acquire sediments and organic material from low sea-level deposits. We will analyse the sediments and fossil content of the cores to find material that can be closely related to former sea levels and radiocarbon dated. The third step in our approach is to extend the observed RSL curves using our new data and compare this to model predictions of RSL. We can then modify the parameters in the model to obtain better agreement with observations and thus better understand the earth-ice-ocean interactions. These data are also important for understanding the palaeogeography of the British Isles. Our data will allow a first order reconstruction of former coastlines, based upon the modern bathymetry, for different time periods during the deglaciation. This is of particular importance to the presence or absence of potential landbridges that might have enabled immigration to Ireland of humans and animals. They will also allow us to identify former land surfaces on the seabed. The palaeogeography is crucial to understanding the evolving oceanographic circulation of the Irish Sea.

Precipitation is unanimously recognized as one of the central variables of the global water and energy cycle, mainly because of its direct significance for the availability of water for human beings, agriculture and life on Earth in general, but also because of its impact on the energy budget and the atmospheric circulation through the associated latent heat release. Precipitation processes play a decisive role in controlling and thus predicting both weather phenomena and climate evolution in numerical weather prediction and general circulation models. Despite the importance of water to all creatures on Earth and to the Earth system as a whole, the life cycle of clouds and precipitation is not well understood; a seemingly simple process like the rapid formation of warm rain is still puzzling, and remains far from having a community-consensus explanation or model. The complexity of the microphysical processes underpinning the cloud evolution into the rain process represents the major obstacle for a considerable leap forward in this field and urgently calls for an effort towards combining modeling and observations. While the temporal and spatial scales of both Large-Eddy Simulation and Cloud Resolving Models are now suitable for studying cloud lifecycles, remote sensing observations (the only practically possible to look at such phenomena) have always suffered by the uncertainties deriving from ill-posed inversion problems. For instance the radar reflectivity signal is by definition strongly dependent on the drop size distribution of the scatterers, e.g., raindrops, in the beam volume and its interpretation is therefore related to the microphysical processes responsible for the formation of drop size distributions and their evolution. A unique deployment (to be completed by end of 2010) of multi-wavelength scanning radar with radiometric mode at all ARM facilities will provide unprecedented independent observations which should narrow down the uncertainties in the retrieval process and provide detailed observations of all phases of cloud evolution, from initiation, to development of updrafts and downdrafts, to hydrometeor evolution in time and space, to partitioning of condensate into precipitation and outflow anvils. We propose to take advantage of this upcoming opportunity by developing an optimal estimation approach capable of integrating different sensors in a consistent physical way. We will combine active (radar reflectivity) and passive (brightness temperatures) measurements because both yield different kinds of cloud microphysics information throughout the vertical extension: cloud and weather radars allow to range-resolve cloud structure, whereas passive microwave signals contain information about along-sight integrated water/ice contents. Our proposed technique combines measurements (and their error characteristics) with a priori information (and knowledge about its representativeness) into an optimal estimation framework to provide the atmospheric state together with uncertainty estimates. In order to optimally exploit the information content of remote sensing observations a first guess of the atmospheric state is iterated through the forward model - connecting atmospheric state with the measurement - up to a point where measurements and a priori information best match the retrieved atmospheric state. The ultimate product of the retrieval is represented by profiles of cloud and precipitation water content for the observed atmospheric columns, which will be extensively validated by independent methodologies during the MC3E campaign, planned for 2011 at the Oklahoma Southern Great Plain site. This cutting-edge product will help in developing, evaluating, and ultimately improving parameterization of cloud-precipitation processes in numerical models. As a test bed, a detailed cloud resolving model study oriented at the evaluation of different microphysical packages will be conducted in coincidence with MC3E.

Climate science demands on data management are growing rapidly as climate models grow in the precision with which they depict spatial structures and in the completeness with which they describe a vast range of physical processes. For the Climate Model Inter-comparison Project 5 (CMIP5), a distributed archive is being constructed to provide access to what is expected to be in excess of 10 Peta-bytes of global climate change projections. The data will be held at 30 or more computing centres and data archives around the world, but for users it will appear as a single archive described by one catalogue. In addition, the usability of the data will be enhanced by a three-step validation process and the publication of Digital Object Identifiers (doi) for all the data. For many users the spatial resolution provided by the global climate models (around 150km) is inadequate: the CORDEX project will provide data scaled down to around 10km. Evaluation of climate impacts often revolves around extremes and complex impact factors, requiring high volumes of data to be stored. At the same time, uncertainty about the optimal configuration of the models imposes the requirement that each scenario be explored with multiple models. This project will explore the challenges of developing a software management infrastructure which will scale to the multi-exabyte archives of climate data which are likely to be crucial to major policy decisions in by the end of the decade. Support for automated processing of the archived data and metadata will be essential. In the short term goal, strategies will be evaluated by applying them to the CORDEX project data.