Air Pollution Transport in an Alpine Valley: Results From Airborne and Ground-Based Observations

An observational dataset from a wintertime field campaign in the Inn Valley, Austria, is analysed in order to study mechanisms of air pollution transport in an Alpine valley. The results illustrate three types of mechanisms: transport by a density current, back-and-forth transport by valley winds, and transport by slope winds. The first type is associated with an air mass difference along the valley. Cooler air located in the lower part of the valley behaves like a density current and produces the advection of pollutants by upvalley winds. In the second type, strong horizontal gradients in pollution concentrations exist close to ground. Multiple wind reversals result in a back-and-forth transport of pollutants by weak valley winds. In the third type, upslope winds during daytime decrease low-level pollution concentrations and cause the formation of elevated pollution layers.     

An estimation of the surface solar ultraviolet irradiance during an extreme total ozone minimum

Summary A simple theoretical algorithm has been employed to estimate the solar ultraviolet irradiance at Athens, Greece (38.7°N, 23.4°E) during, summertime 1993, a year of extreme total ozone minimum in the existing data record. This estimation has been performed by using total ozone measurements as derived by both ground-based and satellite instrumentation. The utilization of the present investigation will assist to the various assesments for the risk of human health from the biologically-effective doses of the solar ultraviolet radiation arrived at the earth's surface during that time period.With 2 Figures     

Quantifying Arctic contributions to climate predictability in a regional coupled ocean-ice-atmosphere model

The relative importance of regional processes inside the Arctic climate system and the large scale atmospheric circulation for Arctic interannual climate variability has been estimated with the help of a regional Arctic coupled ocean-ice-atmosphere model. The study focuses on sea ice and surface climate during the 1980s and 1990s. Simulations agree reasonably well with observations. Correlations between the winter North Atlantic Oscillation index and the summer Arctic sea ice thickness and summer sea ice extent are found. Spread of sea ice extent within an ensemble of model runs can be associated with a surface pressure gradient between the Nordic Seas and the Kara Sea. Trends in the sea ice thickness field are widely significant and can formally be attributed to large scale forcing outside the Arctic model domain. Concerning predictability, results indicate that the variability generated by the external forcing is more important in most regions than the internally generated variability. However, both are in the same order of magnitude. Local areas such as the Northern Greenland coast together with Fram Straits and parts of the Greenland Sea show a strong importance of internally generated variability, which is associated with wind direction variability due to interaction with atmospheric dynamics on the Greenland ice sheet. High predictability of sea ice extent is supported by north-easterly winds from the Arctic Ocean to Scandinavia.     

A climate change simulation starting from 1935

Due to restrictions in the available computing resources and a lack of suitable observational data, transient climate change experiments with global coupled ocean-atmosphere models have been started from an initial state at equilibrium with the present day forcing. The historical development of greenhouse gas forcing from the onset of industrialization until the present has therefore been neglected. Studies with simplified models have shown that this cold start error leads to a serious underestimation of the anthropogenic global warming. In the present study, a 150-year integration has been carried out with a global coupled ocean-atmosphere model starting from the greenhouse gas concentration observed in 1935, i.e., at an early time of industrialization. The model was forced with observed greenhouse gas concentrations up to 1985, and with the equivalent C0₂ concentrations stipulated in Scenario A (Business as Usual) of the Intergovernmental Panel on Climate Change from 1985 to 2085. The early starting date alleviates some of the cold start problems. The global mean near surface temperature change in 2085 is about 0.3 K (ca. 10%) higher in the early industrialization experiment than in an integration with the same model and identical Scenario A greenhouse gas forcing, but with a start date in 1985. Comparisons between the experiments with early and late start dates show considerable differences in the amplitude of the regional climate change patterns, particularly for sea level. The early industrialization experiment can be used to obtain a first estimate of the detection time for a greenhouse-gas-induced near-surface temperature signal. Detection time estimates are obtained using globally and zonally averaged data from the experiment and a long control run, as well as principal component time series describing the evolution of the dominant signal and noise modes. The latter approach yields the earliest detection time (in the decade 1990–2000) for the time-evolving near-surface temperature signal. For global-mean temperatures or for temperatures averaged between 45°N and 45°S, the signal detection times are in the decades 2015–2025 and 2005–2015, respectively. The reduction of the cold start error in the early industrialization experiment makes it possible to separate the near-surface temperature signal from the noise about one decade earlier than in the experiment starting in 1985. We stress that these detection times are only valid in the context of the coupled model's internally-generated natural variability, which possibly underestimates low frequency fluctuations and does not incorporate the variance associated with changes in external forcing factors, such as anthropogenic sulfate aerosols, solar variability or volcanic dust.     

Subsurface influence on SST in the tropical Indian Ocean: structure and interannual variability

Interannual variations of subsurface influence on SST in the Indian Ocean show strong seasonality. The subsurface influence on SST confines to the southern Indian Ocean (SIO) in boreal winter and spring; it is observed on both sides of the equator in boreal summer and fall. Interannual long Rossby waves are at the heart of this influence, and contribute significantly to the coupled climate variability in the tropical Indian Ocean (TIO). Principal forcing mechanism for the generation of these interannual waves in the Indian Ocean and the relative influence of two dominant interannual signals in the tropics, namely El Niño and Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD), are also discussed. Two distinct regions dominated by either of the above climate signals are identified. IOD dominates the forcing of the off-equatorial Rossby waves, north of 10°S, and the forcing comes mainly from the anomalous Ekman pumping associated with the IOD. However, after the demise of IOD activity by December, Rossby waves are dominantly forced by ENSO, particularly south of 10°S.It is found that the subsurface feedback in the northern flank of the southern Indian Ocean ridge region (north of 10°S) significantly influences the central east African rainfall in boreal fall. The Indian Ocean coupled process further holds considerable capability of predicting the east African rainfall by one season ahead. Decadal modulation of the subsurface influence is also noticed during the study period. The subsurface influence north of 10°S coherently varies with the IOD, while it varies coherently with the ENSO south of this latitude.     

Boundary-layer depth and entrainment zone characterization with a boundary-layer profiler

A technique for determining the height of the convective atmospheric boundary layer (CBL) with a 915 MHz boundary-layer profiler is discussed. The results are compared with CBL heights determined from radiosonde measurements. The profiler provides continuous CBL height measurements with very good time resolution (30 minutes or less), allowing for detailed understanding of the growth and fluctuations of the CBL. In addition, the profiler provides information about the degree of definition of the CBL top and the thickness of the entrainment zone. The measurements discussed were taken during the Rural Oxidants in the Southern Environment II (ROSE II) experiment.     

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.     

IAP GCM模式大气波谱结构及其在厄尔尼诺年的异常

本文分析了中国科学院大气物理研究所大气环流模式(IAP GCM)模式大气波谱结构(沿纬圈的谐波波谱和时域上的频率谱)及其在厄尔尼诺年的异常。结果表明,超长波是低纬波动的主要空间形式,准40天振荡在一定地理区域内显著存在,并与观测结果有一定联系,它们在厄尔尼诺年均发生明显异常。本工作对鉴定GCM性能及开发GCM的研究领域可能是一种有益的尝试。     

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.