Earth Observation Scientific Workflows in a Distributed Computing Environment

Geospatially Enabled Scientific Workflows offer a promising toolset to help researchers in the earth observation domain with many aspects of the scientific process. One such aspect is that of access to distributed earth observation data and computing resources. Earth observation research often utilizes large datasets requiring extensive CPU and memory resources in their processing. These resource intensive processes can be chained; the sequence of processes (and their provenance) makes up a scientific workflow. Despite the exponential growth in capacity of desktop computers, their resources are often insufficient for the scientific workflow processing tasks at hand. By integrating distributed computing capabilities into a geospatially enabled scientific workflow environment, it is possible to provide researchers with a mechanism to overcome the limitations of the desktop computer. Most of the effort on extending scientific workflows with distributed computing capabilities has focused on the web services approach, as exemplified by the OGC's Web Processing Service and by GRID computing. The approach to leveraging distributed computing resources described in this article uses instead remote objects via RPyC and the dynamic properties of the Python programming language. The Vistrails environment has been extended to allow for geospatial processing through the EO4Vistrails package ( http://code.google.com/p/eo4vistrails/ ). In order to allow these geospatial processes to be seamlessly executed on distributed resources such as cloud computing nodes, the Vistrails environment has been extended with both multi‐tasking capabilities and distributed processing capabilities. The multi‐tasking capabilities are required in order to allow Vistrails to run side‐by‐side processes, a capability it does not currently have. The distributed processing capabilities are achieved through the use of remote objects and mobile code through RPyC.     

The influence of negative emission technologies and technology policies on the optimal climate mitigation portfolio

Combining policies to remove carbon dioxide (CO₂) from the atmosphere with policies to reduce emissions could decrease CO₂ concentrations faster than possible via natural processes. We model the optimal selection of a dynamic portfolio of abatement, research and development (R&D), and negative emission policies under an exogenous CO₂ constraint and with stochastic technological change. We find that near-term abatement is not sensitive to the availability of R&D policies, but the anticipated availability of negative emission strategies can reduce the near-term abatement optimally undertaken to meet 2°C temperature limits. Further, planning to deploy negative emission technologies shifts optimal R&D funding from ??carbon-free?? technologies into ??emission intensity?? technologies. Making negative emission strategies available enables an 80% reduction in the cost of keeping year 2100 CO₂ concentrations near their current level. However, negative emission strategies are less important if the possibility of tipping points rules out using late-century net negative emissions to temporarily overshoot the CO₂ constraint earlier in the century.     

Comparison of the use of alternative UKCP09 products for modelling the impacts of climate change on flood frequency

Climate change could have significant impacts on hydrology. This paper uses UK Climate Projections 09 (UKCP09) products to assess the impacts on flood frequency in Britain. The main UKCP09 product comprises conditional probabilistic information on changes in a number of climate variables on a 25?×?25?km grid across the UK (the Sampled Data change factors). A second product is a Weather Generator which produces time-series of current weather variables and future weather variables based on the Sampled Data and consistent with the change factors. A third product comprises time-series from a Regional Climate Model (RCM) ensemble which were used to downscale Global Climate Models (GCMs) on which the projections are based and whose outputs were used in the production of the Sampled Data. This paper compares the use of Sampled Data change factors, Weather Generator time-series, RCM-derived change factors and RCM time-series. Each is used to provide hydrological model inputs for nine catchments, to assess impacts for the 2080s (A1B emissions). The results show relatively good agreement between methods for most catchments, with the four median values for a catchment generally being within 10% of each other. There are also some clear differences, with the use of time-series generally leading to a greater uncertainty range than the use of change factors because the latter do not allow for the effects of, or changes in, natural variability. Also, the use of Weather Generator time-series leads to much greater impacts than the other methods for one catchment. The results suggest that climate impact studies should not necessarily rely on the application of just one UKCP09 product, as each has different strengths and weaknesses.     

Past,present and future vegetation-cloud feedbacks in the Amazon Basin

To begin exploring the underlying mechanisms that couple vegetation to cloud formation processes, we derive the lifting condensation level (LCL) to estimate cumulus cloud base height. Using a fully coupled land–ocean–atmosphere general circulation model (HadCM3LC), we investigate Amazonian forest feedbacks on cloud formation over three geological periods; modern-day (a.d. 1970–1990), the last glacial maximum (LGM; 21 kya), and under a future climate scenario (IS92a; a.d. 2070–2090). Results indicate that for both past and future climate scenarios, LCL is higher relative to modern-day. Statistical analyses indicate that the 800 m increase in LCL during the LGM is related primarily to the drier atmosphere promoted by lower tropical sea surface temperatures. In contrast, the predicted 1,000 m increase in LCL in the future scenario is the result of a large increase in surface temperature and reduced vegetation cover.     

Preparing for climate change in Washington State

Climate change is expected to bring potentially significant changes to Washington State’s natural, institutional, cultural, and economic landscape. Addressing climate change impacts will require a sustained commitment to integrating climate information into the day-to-day governance and management of infrastructure, programs, and services that may be affected by climate change. This paper discusses fundamental concepts for planning for climate change and identifies options for adapting to the climate impacts evaluated in the Washington Climate Change Impacts Assessment. Additionally, the paper highlights potential avenues for increasing flexibility in the policies and regulations used to govern human and natural systems in Washington.     

Adaptive co-management and the paradox of learning

Much emphasis has been placed on the importance of learning to support collaborative environmental management and achieve sustainability under conditions of social–ecological change. Yet, on-going struggles to learn from experience and respond to complex social–ecological conditions reflect an emerging paradox. Despite widespread support of learning as a normative goal and process, core concepts, assumptions and approaches to learning have been applied in vague and sometimes uncritical ways. Greater specificity with respect to learning goals, approaches and outcomes is required. In response to this gap, we examine five dimensions of the learning paradox in the context of adaptive co-management, where the learning and linking functions of governance are stressed: (i) definitions of learning; (ii) learning goals and expectations; (iii) mechanisms by which learning takes place; (iv) questions regarding who is involved in the process of learning; and (v) the risks and ethical ambiguities faced by different actors expected to willingly participate in a learning process, whether formal or informal. Lessons from experience with a series of cases from the global North and South illustrate the implications of these dimensions. Resolving the dimensions of this learning paradox will require greater attention to capacity-building, recognition of the role of risk, and consideration of how incentives could be used to encourage learning. Further consideration of the role of power and marginality among groups participating in the learning process is also needed, as is more systematic evaluation to monitor and measure learning outcomes.     

The Influences of NAO and the Hudson Bay sea-ice on the climate of eastern Canada

Sea-ice cover over the Hudson Bay (HB) exhibits large variability in the freeze-up season normally starting in November. Its influence on the climate over eastern Canada has been studied with the Canadian Regional Climate Model (CRCM) in three steps. First, a 30-year continuous simulation from 1970 to 1999 was performed as a control run to evaluate the simulated climate variability over eastern Canada, in particularly variability associated with the North Atlantic oscillation (NAO). Then, 50 additional 1 month experiments were performed with modified sea-surface conditions prescribed over the HB. These integrations allowed us to quantify the contribution of HB sea-ice anomalies versus large scale NAO atmospheric variability (as defined by prescribed lateral boundary conditions) in inducing climate variability over eastern Canada. Results show that the NAO is the dominant factor controlling climate variability over eastern Canada. The contribution of HB sea-ice anomalies is significant only in the immediate coastal region. Under the influence of different phases of NAO, HB sea-ice anomalies do co-vary with temperature and precipitation anomalies downstream of the HB over eastern Canada. The ultimate cause of this co-variability is NAO variability which forces variability in both HB sea-ice cover as well as temperature/precipitation over eastern Canada.     

Comparison of uncertainty sources for climate change impacts: flood frequency in England

This paper investigates the uncertainty in the impact of climate change on flood frequency in England, through the use of continuous simulation of river flows. Six different sources of uncertainty are discussed: future greenhouse gas emissions; Global Climate Model (GCM) structure; downscaling from GCMs (including Regional Climate Model structure); hydrological model structure; hydrological model parameters and the internal variability of the climate system (sampled by applying different GCM initial conditions). These sources of uncertainty are demonstrated (separately) for two example catchments in England, by propagation through to flood frequency impact. The results suggest that uncertainty from GCM structure is by far the largest source of uncertainty. However, this is due to the extremely large increases in winter rainfall predicted by one of the five GCMs used. Other sources of uncertainty become more significant if the results from this GCM are omitted, although uncertainty from sources relating to modelling of the future climate is generally still larger than that relating to emissions or hydrological modelling. It is also shown that understanding current and future natural variability is critical in assessing the importance of climate change impacts on hydrology.     

Do superfluid instabilities prevent neutron star precession?

We discuss short wavelength (inertial wave) instabilities present in the standard two-fluid neutron star model when there is sufficient relative flow along the superfluid neutron vortex array. We demonstrate that these instabilities may be triggered in precessing neutron stars, since the angular velocity vectors of the neutron and proton fluids are misaligned during precession. Our results suggest that the standard (Eulerian) slow precession that results for weak drag between the vortices and the charged fluid (protons and electrons) is not seriously affected by the instability. In contrast, the fast precession, which results when vortices are strongly coupled to the charged component, is generally unstable. The presence of this instability renders the standard (solid body) rotation model for free precession inconsistent and makes unsafe conclusions that have recently been drawn regarding neutron star interiors based on observations of precession in radio pulsars.