An approach to adaptation to climate changes in Poland

Adopted by COP 10 (Dec 1/CP.10) and approved by the MOP1, the Buenos Aires programme of adaptation and response measures opens doors to intensify preparations for expected climate change. By this decision the COP, requested the SBSTA to develop a structured 5-year programme of work of the SBSTA on the scientific, technical and socio-economic aspects of impacts of, and vulnerability and adaptation to, climate change. Consequently, the COP, by its decision 2/CP.11, adopted the “Five-year programme of work of the Subsidiary Body for Scientific and Technological Advice on impacts, vulnerability and adaptation to climate change” Finally during COP12 this programme was approved as “Nairobi Work Programme on impacts, vulnerability and adaptation to climate change”. This programme has fundamental significance not only for developing countries, but also for industrialized nations in which some sectors of the or social life are particularly vulnerable to climate change, specifically, inter alia EIT countries and new EU Member States. Further development of this adaptation programme economy should contain steps that provide optimum economic and social effectiveness, risk management, identification of vulnerable sectors and gaps in knowledge, preparation of a list of policy options, including an analysis of cost effectiveness, selection of the most effective policies, and a preparedness implementation plan. In Poland the preliminary adaptation programme covered agriculture, water management, and coastal zone management. For the time being, gaps in knowledge and preparedness measures have been identified. An estimation of possible impact on these areas was based on chosen GCMs, and sea level rise IPCC scenarios. In conclusion, it was stated that the results achieved should be seen as a first step forward and a more comprehensive study is necessary to update the results and cover other sectors of the economy, such as health protection, spatial planning, ecosystems and forestry, and to develop specific guidelines and recommendations for policy-makers.     

Multi-Source Emission Determination Using an Inverse-Dispersion Technique

Inverse-dispersion calculations can be used to infer atmospheric emission rates through a combination of downwind gas concentrations and dispersion model predictions. With multiple concentration sensors downwind of a compound source (whose component positions are known) it is possible to calculate the component emissions. With this in mind, a field experiment was conducted to examine the feasibility of such multi-source inferences, using four synthetic area sources and eight concentration sensors arranged in different configurations. Multi-source problems tend to be mathematically ill-conditioned, as expressed by the condition number κ. In our most successful configuration (average κ = 4.2) the total emissions from all sources were deduced to within 10% on average, while component emissions were deduced to within 50%. In our least successful configuration (average κ = 91) the total emissions were calculated to within only 50%, and component calculations were highly inaccurate. Our study indicates that the most accurate multi-source inferences will occur if each sensor is influenced by only a single source. A “progressive” layout is the next best: one sensor is positioned to “see” only one source, the next sensor is placed to see the first source and another, a third sensor is placed to see the previous two plus a third, and so on. When it is not possible to isolate any sources κ is large and the accuracy of a multi-source inference is doubtful.     

Statistical estimation of high-resolution surface air temperature from MODIS over the Yangtze River Delta,China

High-resolution surface air temperature data are critical to regional climate modeling in terms of energy balance, urban climate change, and so on. This study demonstrates the feasibility of using Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) to estimate air temperature at a high resolution over the Yangtze River Delta region, China. It is found that daytime LST is highly correlated with maximum air temperature, and the linear regression coefficients vary with the type of land surface. The air temperature at a resolution of 1 km is estimated from the MODIS LST with linear regression models. The estimated air temperature shows a clear spatial structure of urban heat islands. Spatial patterns of LST and air temperature differences are detected, indicating maximum differences over urban and forest regions during summer. Validations are performed with independent data samples, demonstrating that the mean absolute error of the estimated air temperature is approximately 2.5°C, and the uncertainty is about 3.1°C, if using all valid LST data. The error is reduced by 0.4°C (15%) if using best-quality LST with errors of less than 1 K. The estimated high-resolution air temperature data have great potential to be used in validating high-resolution climate models and other regional applications.     

An Efficient Non-iterative Bulk Parametrization of Surface Fluxes for Stable Atmospheric Conditions Over Polar Sea-Ice

In climate and weather prediction models the near-surface turbulent fluxes of heat and momentum and related transfer coefficients are usually parametrized on the basis of Monin–Obukhov similarity theory (MOST). To avoid iteration, required for the numerical solution of the MOST equations, many models apply parametrizations of the transfer coefficients based on an approach relating these coefficients to the bulk Richardson number \(Ri_{b}\). However, the parametrizations that are presently used in most climate models are valid only for weaker stability and larger surface roughnesses than those documented during the Surface Heat Budget of the Arctic Ocean campaign (SHEBA). The latter delivered a well-accepted set of turbulence data in the stable surface layer over polar sea-ice. Using stability functions based on the SHEBA data, we solve the MOST equations applying a new semi-analytic approach that results in transfer coefficients as a function of \(Ri_{b}\) and roughness lengths for momentum and heat. It is shown that the new coefficients reproduce the coefficients obtained by the numerical iterative method with a good accuracy in the most relevant range of stability and roughness lengths. For small \(Ri_{b}\), the new bulk transfer coefficients are similar to the traditional coefficients, but for large \(Ri_{b}\) they are much smaller than currently used coefficients. Finally, a possible adjustment of the latter and the implementation of the new proposed parametrizations in models are discussed.     

Observing and Simulating Wind-Turbine Wakes During the Evening Transition

Wind-turbine-wake evolution during the evening transition introduces variability to wind-farm power production at a time of day typically characterized by high electricity demand. During the evening transition, the atmosphere evolves from an unstable to a stable regime, and vertical stratification of the wind profile develops as the residual planetary boundary layer decouples from the surface layer. The evolution of wind-turbine wakes during the evening transition is examined from two perspectives: wake observations from single turbines, and simulations of multiple turbine wakes using the mesoscale Weather Research and Forecasting (WRF) model. Throughout the evening transition, the wake’s wind-speed deficit and turbulence enhancement are confined within the rotor layer when the atmospheric stability changes from unstable to stable. The height variations of maximum upwind-downwind differences of wind speed and turbulence intensity gradually decrease during the evening transition. After verifying the WRF-model-simulated upwind wind speed, wind direction and turbulent kinetic energy profiles with observations, the wind-farm-scale wake evolution during the evening transition is investigated using the WRF-model wind-farm parametrization scheme. As the evening progresses, due to the presence of the wind farm, the modelled hub-height wind-speed deficit monotonically increases, the relative turbulence enhancement at hub height grows by 50%, and the downwind surface sensible heat flux increases, reducing surface cooling. Overall, the intensifying wakes from upwind turbines respond to the evolving atmospheric boundary layer during the evening transition, and undermine the power production of downwind turbines in the evening.     

Recent advances in accessibility research: Representation,methodology and applications

 In this article we examine recent advances in accessibility research and their implications for future studies. We base our discussion on three intersecting dimensions that are useful for evaluating the contribution of recent studies: representation, methodology and applications. Various examples are selected to show that research concerned with representation and methodological problem solving is often applied to issues of broad concern in policy and planning. It is, however, not clear that the simultaneous treatment of representation, methodological and application issues has ever been fully worked out. The questions raised in this article may serve as a foundation for addressing issues pertinent to accurate representation, improved model building, and more rigorous applications in accessibility research. Received: 9 December 2002 / Accepted: 10 February 2003     

Climate change impacts on the energy system: a review of trends and gaps

Major transformation of the global energy system is required for climate change mitigation. However, energy demand patterns and supply systems are themselves subject to climate change impacts. These impacts will variously help and hinder mitigation and adaptation efforts, so it is vital they are well understood and incorporated into models used to study energy system decarbonisation pathways. To assess the current state of understanding of this topic and identify research priorities, this paper critically reviews the literature on the impacts of climate change on the energy supply system, summarising the regional coverage of studies, trends in their results and sources of disagreement. We then examine the ways in which these impacts have been represented in integrated assessment models of the electricity or energy system.Studies tend to agree broadly on impacts for wind, solar and thermal power stations. Projections for impacts on hydropower and bioenergy resources are more varied. Key uncertainties and gaps remain due to the variation between climate projections, modelling limitations and the regional bias of research interests. Priorities for future research include the following: further regional impact studies for developing countries; studies examining impacts of the changing variability of renewable resources, extreme weather events and combined hazards; inclusion of multiple climate feedback mechanisms in IAMs, accounting for adaptation options and climate model uncertainty.     

Quasi-3-yr Cycle of Rainy Season Precipitation in Tibet Related to Different Types of ENSO during 1981–2015

The rainy season precipitation in Tibet (RSPT) is a direct cause for local floods/droughts. It also indirectly affects the thermal conditions of the Tibetan Plateau, which can result in anomalous patterns of atmospheric circulation over East Asia. The interannual variability of the RSPT is often linked with the El Niño–Southern Oscillation (ENSO), but the relevant mechanisms are far from being understood, particularly for different types of ENSO events. We investigated the interannual variation of the RSPT in association with different types of ENSO. A quasi-3-yr period of the RSPT (less–more–more precipitation) was significant at the 95% confidence level. A joint multi-taper method with singular value decomposition analysis of the coupled field between the RSPT and the sea surface temperature (SST) revealed that the developing eastern Pacific type El Niño was accompanied by a decrease in the RSPT. The shift from the central Pacific type El Niño to the eastern Pacific La Niña was accompanied by an increase in the RSPT. Weakening of the central Pacific La Niña was accompanied by an increase in the RSPT. Analysis of the mechanism of this coupling, using the same analysis method but other climatic factors, indicated that the gradually strengthening eastern Pacific El Niño can inhibit the Walker circulation, weakening the South Asian summer monsoon, and resulting in transport of less water vapor from the Bay of Bengal to Tibet. The change from the central Pacific El Niño to the eastern Pacific La Niña led to continued strengthening of the Walker circulation with westward movement of the ascending area. This enhanced the South Asian summer monsoon over the Arabian Sea and transported more water vapor to Tibet. The decreasing central Pacific La Niña accompanied by persistent cooling of SSTs in the equatorial Pacific led to a strong eastern North Pacific summer monsoon, causing an anomaly in the easterly transport of water vapor from the Sea of Japan to Tibet and increased RSPT.     

Operational climate prediction in the era of big data in China: Reviews and prospects

Big data has emerged as the next technological revolution in IT industry after cloud computing and the Internet of Things. With the development of climate observing systems, particularly satellite meteorological observation and high-resolution climate models, and the rapid growth in the volume of climate data, climate prediction is now entering the era of big data. The application of big data will provide new ideas and methods for the continuous development of climate prediction. The rapid integration, cloud storage, cloud computing, and full-sample analysis of massive climate data makes it possible to understand climate states and their evolution more objectively, thus predicting the future climate more accurately. This paper describes the application status of big data in operational climate prediction in China; it analyzes the key big data technologies, discusses the future development of climate prediction operations from the perspective of big data, speculates on the prospects for applying climatic big data in cloud computing and data assimilation, and puts forward the notion of big data-based super-ensemble climate prediction methods and computerbased deep learning climate prediction methods.     

The Role of Planetary Boundary-Layer Parameterizations in the Air Quality of an Urban Area with Complex Topography

The effect of different planetary boundary-layer (PBL) parameterization schemes on the spatial distribution of atmospheric pollution over the complex topography of the greater Athens area is investigated. Four PBL schemes originally implemented in a numerical meteorological model and a fifth one simulating the urban effect are examined. Two different atmospheric conditions are analyzed; a typical summer and a typical winter pollution episode. The relative importance of chemical and physical processes of the pollution predictions is discussed using process analysis. It is revealed that, for primary pollutants, a local scheme seems more adequate to represent the maximum observed concentrations while, completely different in structure, a non-local scheme reproduces the mean observed values in the basin. Concerning secondary pollutants, peak concentration differences, due to the different PBL schemes, are smoothed out. Nevertheless, the PBL scheme selection shapes the horizontal and the vertical extension of maximum values. The non-local and semi non-local schemes are superior to the others, favouring strong vertical mixing and transport towards the surface. The stronger turbulence accommodated effectively by the semi non-local urban scheme enhances ozone production along the sea-breeze axis and preserves the high ozone concentrations during the nighttime hours in the urban core.