Present-day and future precipitation in the Baltic Sea region as simulated in a suite of regional climate models

Here we investigate simulated changes in the precipitation climate over the Baltic Sea and surrounding land areas for the period 2071–2100 as compared to 1961–1990. We analyze precipitation in 10 regional climate models taking part in the European PRUDENCE project. Forced by the same global driving climate model, the mean of the regional climate model simulations captures the observed climatological precipitation over the Baltic Sea runoff land area to within 15% in each month, while single regional models have errors up to 25%. In the future climate, the precipitation is projected to increase in the Baltic Sea area, especially during winter. During summer increased precipitation in the north is contrasted with a decrease in the south of this region. Over the Baltic Sea itself the future change in the seasonal cycle of precipitation is markedly different in the regional climate model simulations. We show that the sea surface temperatures have a profound impact on the simulated hydrological cycle over the Baltic Sea. The driving global climate model used in the common experiment projects a very strong regional increase in summertime sea surface temperature, leading to a significant increase in precipitation. In addition to the common experiment some regional models have been forced by either a different set of Baltic Sea surface temperatures, lateral boundary conditions from another global climate model, a different emission scenario, or different initial conditions. We make use of the large number of experiments in the PRUDENCE project, providing an ensemble consisting of more than 25 realizations of climate change, to illustrate sources of uncertainties in climate change projections.     

Laboratory Studies Of Wind Stress Over Surface Waves

Simultaneous laboratory observations of wind speed, wind stress, and surfacewind-wave spectra are made under a variety of wind forcing patterns using cleanwater as well as water containing an artificial surfactant. Under typical experimentalconditions, more than half of the total stress is supported by the wave-induced stressrather than by the surface viscous stress. When the surfactant reduces the shortwind-wave spectra, the wind stress also decreases by as much as 20–30% at agiven wind speed. When the wind forcing is modulated in time, the wind stresstends to be higher under decreasing wind than under increasing wind at a givenwind speed, mainly because the response of short wind-wave spectra to varyingwind forcing is delayed in time. These examples clearly demonstrate that therelationship between the wind speed and the wind stress can be significantlymodified if the surface wave field is not in equilibrium with the wind forcing.Next, we examine whether the wind stress is estimated accurately if the wave-inducedstress by all surface wave components is explicitly evaluated by linear superpositionand is added to the surface viscous stress. It is assumed that the surface viscous stressis uniquely related to the wind speed, and that the wind input rate is determined by thelocal, reduced turbulent stress rather than the total stress. Our wind stress estimatesincluding the wave contributions agree well with observed wind stress values, evenif the surface wave field is away from its equilibrium with the wind in the presenceof surface films and/or under time-transient wind forcing. These observations stronglysuggest that the wind stress is accurately evaluated as a sum of the wave-induced stressand the surface viscous stress. At very high winds, our stress estimates tend to be lowerthan the observations. We suspect that this is because of the enhancement of wind stressover very steep (or breaking) short wind-waves.     

Recent progress in the operational forecasting of summer severe weather

Abstract

Summer severe weather (SSW) can strike suddenly and unexpectedly with disastrous consequences for human activity. Considerable progress has been made in the past ten years in the operational forecasting of SSW. Traditionally, SSW was defined to consist of tornadoes, strong winds, hail, lightning and heavy rain. Hazardous types of strong winds have recently been expanded to include microbursts, macrobursts and surfacing rear inflow jet damage behind mesoscale convective systems. Doppler radar was used to relate surface damage to the appropriate atmospheric phenomena, first diagnostically and then prognostically. This improvement in classification has fedback to and improved the forecast process. Concurrent progress has been made in the use of synoptic observations. The concept of helical wind profiles and improved knowledge of the role of dry mid‐level air has improved the forecasting of tornadoes and strong gusty winds. Moisture flux convergence, derived from surface measurements, shows great promise in identifying areas of storm initiation. Satellite imagery has been used to identify dynamical atmospheric boundaries. Numerical modelling of the interaction of environmental wind profiles and individual thunderstorms has greatly contributed to the understanding of SSW. Studies of spatial and temporal patterns of lightning, both specific cases and climatology, contribute to the forecasting of severe storms. Polarization radar results have shown progress in separating the signals of hail from those of rain and in the improved measurement of heavy rainfalls. Radar observation of clear air boundaries and their interactions show potential for the forecasting of thunderstorm initiation. Though not traditionally considered part of SSW, hurricanes that evolve into extra‐tropical storms share many of the same hazardous features. The progress in computing, communications and display technologies has also made substantial contributions to operational forecasting and to the dissemination of weather warnings.     

The Euro-Atlantic Circulation Response to the Madden-Julian Oscillation Cycle of Tropical Heating: Coupled GCM Intervention Experiments

Abstract

Intervention experiments using the Coupled Forecast System model, version 2 (CFSv2), have been performed in which various Madden-Julian Oscillation (MJO) evolutions were added to the model’s internally generated heating: Slow Repeated Cycles, Slow Single Cycle, Fast Repeated Cycles, and Fast Single Cycle. In each experiment, one of these specified MJO evolutions of tropical diabatic heating was added in multiple ensemble reforecasts of boreal winter (1 November to 31 March for 31 winters: 1980–2010). Since in each experiment, multiple re-forecasts were made with the identical heating evolution added, predictable component analysis is used to identify modes with the highest signal-to-noise ratio. Traditional MJO-phase analysis of total model heating (dominated by internally generated heating) shows that the MJO-related heating structure compares well with heating estimated from observed fast and slow episodes; however, the model heating is larger by a factor of two. The evolution of Euro-Atlantic circulation regimes indicates a clear response due to the added heating, with a robust increase in the frequency of occurrence of the negative phase of the North Atlantic Oscillation (NAO?) after the heating crosses into the Pacific and a somewhat less robust increase in the positive phase of the NAO (NAO+) following Indian Ocean heating. In the Fast Cycle experiments, the model response is somewhat muted compared with the Slow Cycle experiments. The Scandinavian Blocking regime becomes more frequent prior to the NAO? regime. The two leading modes in the predictable component analysis of 300?hPa height (Z300), synoptic scale feedback (DZ300), and planetary wave diabatic heating in all experiments form an oscillatory pair with high statistical significance. The oscillatory pair represents the cyclic response to the particular MJO signal (Fast or Slow, Single, or Repeated Cycles) in each case. The period is about 64 days for the Slow Cycle and 36 days for the Fast Cycle, consistent with the imposed periods. The time series of one of the leading modes of Z300 is highly anti-correlated with the frequency of occurrence of the NAO– in the Repeated Cycle experiments. A clear cycle involving the Z300 and DZ300 leading modes is identified.     

Technology transfer by CDM projects

Abstract

Technology development and transfer is an important feature of both the United Nations Framework Convention on Climate Change (UNFCCC) and its Kyoto Protocol. Although the Clean Development Mechanism (CDM) does not have an explicit technology transfer mandate, it may contribute to technology transfer by financing emission reduction projects using technologies currently not available in the host countries. This article analyses the claims about technology transfer made by CDM project participants in their project design documents. Roughly one-third of all CDM projects, accounting for almost two-thirds of the annual emission reductions, involve technology transfer. Technology transfer varies widely across project types and is more common for larger projects and projects with foreign participants. Equipment transfer is more common for larger projects, while smaller projects involve transfers of both equipment and knowledge or of knowledge alone. Technology transfer does not appear to be closely related to country size or per-capita GDP, but a host country can influence the extent of technology transfer involved in its CDM projects.     

Supervised classification of electric power transmission line nominal voltage from high-resolution aerial imagery

For many researchers, government agencies, and emergency responders, access to the geospatial data of US electric power infrastructure is invaluable for analysis, planning, and disaster recovery. Historically, however, access to high quality geospatial energy data has been limited to few agencies because of commercial licenses restrictions, and those resources which are widely accessible have been of poor quality, particularly with respect to reliability. Recent efforts to develop a highly reliable and publicly accessible alternative to the existing datasets were met with numerous challenges – not the least of which was filling the gaps in power transmission line voltage ratings. To address the line voltage rating problem, we developed and tested a basic methodology that fuses knowledge and techniques from power systems, geography, and machine learning domains. Specifically, we identified predictors of nominal voltage that could be extracted from aerial imagery and developed a tree-based classifier to classify nominal line voltage ratings. Overall, we found that line support height, support span, and conductor spacing are the best predictors of voltage ratings, and that the classifier built with these predictors had a reliable predictive accuracy (that is, within one voltage class for four out of the five classes sampled). We applied our approach to a study area in Minnesota.     

Methodology and use of tensor invariants for satellite gravity gradiometry

Although its use is widespread in several other scientific disciplines, the theory of tensor invariants is only marginally adopted in gravity field modeling. We aim to close this gap by developing and applying the invariants approach for geopotential recovery. Gravitational tensor invariants are deduced from products of second-order derivatives of the gravitational potential. The benefit of the method presented arises from its independence of the gradiometer instrument’s orientation in space. Thus, we refrain from the classical methods for satellite gravity gradiometry analysis, i.e., in terms of individual gravity gradients, in favor of the alternative invariants approach. The invariants approach requires a tailored processing strategy. Firstly, the non-linear functionals with regard to the potential series expansion in spherical harmonics necessitates the linearization and iterative solution of the resulting least-squares problem. From the computational point of view, efficient linearization by means of perturbation theory has been adopted. It only requires the computation of reference gravity gradients. Secondly, the deduced pseudo-observations are composed of all the gravitational tensor elements, all of which require a comparable level of accuracy. Additionally, implementation of the invariants method for large data sets is a challenging task. We show the fundamentals of tensor invariants theory adapted to satellite gradiometry. With regard to the GOCE (Gravity field and steady-state Ocean Circulation Explorer) satellite gradiometry mission, we demonstrate that the iterative parameter estimation process converges within only two iterations. Additionally, for the GOCE configuration, we show the invariants approach to be insensitive to the synthesis of unobserved gravity gradients.     

Nonlinear Adjustment of GPS Observations of Type Pseudo-Ranges

The nonlinear adjustment of GPS observations of type pseudo-ranges is performed in two steps. In step one a combinatorial minimal subset of observations is constructed which is rigorously converted into station coordinates by means of Groebner basis algorithm or the multipolynomial resultant algorithm. The combinatorial solution points in a polyhedron are reduced to their barycentric in step two by means of their weighted mean. Such a weighted mean of the polyhedron points in ℝ³ is generated via the Error Propagation law/variance-covariance propagation. The Fast Nonlinear Adjustment Algorithm (FNon Ad Al) has been already proposed by Gauss whose work was published posthumously and Jacobi (1841). The algorithm, here referred to as the Gauss-Jacobi Combinatorial algorithm, solves the over-determined GPS pseudo-ranging problem without reverting to iterative or linearization procedure except for the second moment (Variance-Covariance propagation). The results compared well with the solutions obtained using the linearized least squares approach giving legitimacy to the Gauss-Jacobi combinatorial procedure. ? 2002 Wiley Periodicals, Inc.     

Probability distribution of eigenspectra and eigendirections of a twodimensional,symmetric rank two random tensor

Let there be given a twodimensional symmetric rank two tensor of random type (examples:strain, stress) which is either directly observed or indirectly estimated from observations by an adjustment procedure. Under the assumption of normalityof tensor components we compute the joint probability density functionas well as the marginal probability density functionsof its eigenspectra (eigenvalues) and eigendirections (orientation parameters). Due to the nonlinearity of the relation between eigenspectra-eigendirections and the random tensor components, via the inverse nonlinear error propagationbiases and aliases of their first and centralized second moments (mean value, variance-covariance) are expressed in terms of Jacobianand Hessianmatrices. The joint probability density function and the first and second moments thus form the fundamental of hypothesis testing and qualify control of eigenspectra (eigenvalues, principal components) and eigendirections (orientation parameters, eigenvectors, principial direction) of a twodimensional, symmetric rank two random tensor.