A method to diagnose boundary-layer type using Doppler lidar

A new technique for objective classification of boundary layers is applied to ground-based vertically pointing Doppler lidar and sonic anemometer data. The observed boundary layer has been classified into nine different types based on those in the Met Office ‘Lock’ scheme, using vertical velocity variance and skewness, along with attenuated backscatter coefficient and surface sensible heat flux. This new probabilistic method has been applied to three years of data from Chilbolton Observatory in southern England and a climatology of boundary-layer type has been created. A clear diurnal cycle is present in all seasons. The most common boundary-layer type is stable with no cloud (30.0% of the dataset). The most common unstable type is well mixed with no cloud (15.4%). Decoupled stratocumulus is the third most common boundary-layer type (10.3%) and cumulus under stratocumulus occurs 1.0% of the time. The occurrence of stable boundary-layer types is much higher in the winter than the summer and boundary-layer types capped with cumulus cloud are more prevalent in the warm seasons. The most common diurnal evolution of boundary-layer types, occurring on 52 days of our three-year dataset, is that of no cloud with the stability changing from stable to unstable during daylight hours. These results are based on 16393 hours, 62.4% of the three-year dataset, of diagnosed boundary-layer type. This new method is ideally suited to long-term evaluation of boundary-layer type parametrisations in weather forecast and climate models.

Tropical Atlantic variability modes (1979–2002). Part I: Time-evolving SST modes related to West African rainfall

This work presents a description of the 1979–2002 tropical Atlantic (TA) SST variability modes coupled to the anomalous West African (WA) rainfall during the monsoon season. The time-evolving SST patterns, with an impact on WA rainfall variability, are analyzed using a new methodology based on maximum covariance analysis. The enhanced Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) dataset, which includes measures over the ocean, gives a complete picture of the interannual WA rainfall patterns for the Sahel dry period. The leading TA SST pattern, related to the Atlantic El Niño, is coupled to anomalous precipitation over the coast of the Gulf of Guinea, which corresponds to the second WA rainfall principal component. The thermodynamics and dynamics involved in the generation, development, and damping of this mode are studied and compared with previous works. The SST mode starts at the Angola/Benguela region and is caused by alongshore wind anomalies. It then propagates westward via Rossby waves and damps because of latent heat flux anomalies and Kelvin wave eastward propagation from an off-equatorial forcing. The second SST mode includes the Mediterranean and the Atlantic Ocean, showing how the Mediterranean SST anomalies are those that are directly associated with the Sahelian rainfall. The global signature of the TA SST patterns is analyzed, adding new insights about the Pacific– Atlantic link in relation to WA rainfall during this period. Also, this global picture suggests that the Mediterranean SST anomalies are a fingerprint of large-scale forcing. This work updates the results given by other authors, whose studies are based on different datasets dating back to the 1950s, including both the wet and the dry Sahel periods.

Quantification of chemical and physical processes influencing ozone during long-range transport using a trajectory ensemble

During long-range transport, many distinct processes – including photochemistry, deposition, emissions and mixing – contribute to the transformation of air mass composition. Partitioning the effects of different processes can be useful when considering the sensitivity of chemical transformation to, for example, a changing environment or anthropogenic influence. However, transformation is not observed directly, since mixing ratios are measured, and models must be used to relate changes to processes. Here, four cases from the ITCT-Lagrangian 2004 experiment are studied. In each case, aircraft intercepted a distinct air mass several times during transport over the North Atlantic, providing a unique dataset and quantifying the net changes in composition from all processes. A new framework is presented to deconstruct the change in O3 mixing ratio (Δ O3) into its component processes, which were not measured directly, taking into account the uncertainty in measurements, initial air mass variability and its time evolution. The results show that the net chemical processing (Δ O3chem) over the whole simulation is greater than net physical processing (Δ O3phys) in all cases. This is in part explained by cancellation effects associated with mixing. In contrast, each case is in a regime of either net photochemical destruction (lower tropospheric transport) or production (an upper tropospheric biomass burning case). However, physical processes influence O3 indirectly through addition or removal of precursor gases, so that changes to physical parameters in a model can have a larger effect on Δ O3chem than Δ O3phys. Despite its smaller magnitude, the physical processing distinguishes the lower tropospheric export cases, since the net photochemical O3 change is −5 ppbv per day in all three cases. Processing is quantified using a Lagrangian photochemical model with a novel method for simulating mixing through an ensemble of trajectories and a background profile that evolves with them. The model is able to simulate the magnitude and variability of the observations (of O3, CO, NOy and some hydrocarbons) and is consistent with the time-average OH following air-masses inferred from hydrocarbon measurements alone (by Arnold et al., 2007). Therefore, it is a useful new method to simulate air mass evolution and variability, and its sensitivity to process parameters.

Climate change scenarios for precipitation extremes in Portugal

Precipitation indices are commonly used as climate change indicators. Considering four Climate Variability and Predictability-recommended indices, this study assesses possible changes in their spatial patterns over Portugal under future climatic conditions. Precipitation data from the regional climate model Consortium for Small-Scale Modelling–Climate version of the Local Model (CCLM) ensemble simulations with ECHAM5/MPI-OM1 boundary conditions are used for this purpose. For recent–past, medians and probability density functions of the CCLM-based indices are validated against station-based and gridded observational dataset from ENSEMBLES-based (gridded daily precipitation data provided by the European Climate Assessment & Dataset project) indices. It is demonstrated that the model is able to realistically reproduce not only precipitation but also the corresponding extreme indices. Climate change projections for 2071–2100 (A1B and B1 SRES scenarios) reveal significant decreases in total precipitation, particularly in autumn over northwestern and southern Portugal, though changes exhibit distinct local and seasonal patterns and are typically stronger for A1B than for B1. The increase in winter precipitation over northeastern Portugal in A1B is the most important exception to the overall drying trend. Contributions of extreme precipitation events to total precipitation are also expected to increase, mainly in winter and spring over northeastern Portugal. Strong projected increases in the dry spell lengths in autumn and spring are also noteworthy, giving evidence for an extension of the dry season from summer to spring and autumn. Although no coupling analysis is undertaken, these changes are qualitatively related to modifications in the large-scale circulation over the Euro-Atlantic area, more specifically to shifts in the position of the Azores High and associated changes in the large-scale pressure gradient over the area.

Cyclones causing wind storms in the Mediterranean: characteristics, trends and links to large-scale patterns

A climatology of cyclones with a focus on their relation to wind storm tracks in the Mediterranean region (MR) is presented. Trends in the frequency of cyclones and wind storms, as well as variations associated with the North Atlantic Oscillation (NAO), the East Atlantic/West Russian (EAWR) and the Scandinavian variability pattern (SCAND) are discussed. The study is based on the ERA40 reanalysis dataset. Wind storm tracks are identified by tracking clusters of adjacent grid boxes characterised by extremely high local wind speeds. The wind track is assigned to a cyclone track independently identified with an objective scheme. Areas with high wind activity – quantified by extreme wind tracks – are typically located south of the Golf of Genoa, south of Cyprus, southeast of Sicily and west of the Iberian Peninsula. About 69% of the wind storms are caused by cyclones located in the Mediterranean region, while the remaining 31% can be attributed to North Atlantic or Northern European cyclones. The North Atlantic Oscillation, the East Atlantic/West Russian pattern and the Scandinavian pattern all influence the amount and spatial distribution of wind inducing cyclones and wind events in the MR. The strongest signals exist for the NAO and the EAWR pattern, which are both associated with an increase in the number of organised strong wind events in the eastern MR during their positive phase. On the other hand, the storm numbers decrease over the western MR for the positive phase of the NAO and over the central MR during the positive phase of the EAWR pattern. The positive phase of the Scandinavian pattern is associated with a decrease in the number of winter wind storms over most of the MR. A third of the trends in the number of wind storms and wind producing cyclones during the winter season of the ERA40 period may be attributed to the variability of the North Atlantic Oscillation.

Is intrinsic energy efficiency reflected in the pricing of office leases?

This paper investigates whether the intrinsic energy efficiency rating of an office building has a significant impact on its rental value. A sample of 817 transactions for offices with Energy Performance Certificates (EPCs) in the UK is used to assess whether a pricing differential can be identified, depending on the energy rating. While previous analyses of this topic have typically relied on appraisal-based and/or asking rent data, the dataset used in this research contains actual contract rents as well as information on lease terms. The results indicate a significant rental premium for energy-efficient buildings. However, it is found that this premium appears to be mainly driven by the youngest cohort of state-of-the-art energy-efficient buildings. The results also show that tenants of more energy-efficient buildings tend to pay a lower service charge, but this link appears to be rather weak and limited to newer buildings. Hence, it is argued that the information contained in the EPC is still not fully taken into account in the UK commercial property market with the possible exception of both the highest and the lowest EPC ratings.

Pediastrum species as potential indicators of lake-level change in tropical South America

We explored the potential for using Pediastrum (Meyen), a genus of green alga commonly found in palaeoecological studies, as a proxy for lake-level change in tropical South America. The study site, Laguna La Gaiba (LLG) (17°45′S, 57°40′W), is a broad, shallow lake located along the course of the Paraguay River in the Pantanal, a 135,000-km2 tropical wetland located mostly in western Brazil, but extending into eastern Bolivia. Fourteen surface sediment samples were taken from LLG across a range of lake depths (2-5.2 m) and analyzed for Pediastrum. We found seven species, of which P. musteri (Tell et Mataloni), P. argentiniense (Bourr. et Tell), and P. cf. angulosum (Ehrenb.) ex Menegh. were identified as potential indicators of lake level. Results of the modern dataset were applied to 31 fossil Pediastrum assemblages spanning the early Holocene (12.0 kyr BP) to present to infer past lake level changes qualitatively. Early Holocene (12.0-9.8 kyr BP) assemblages do not show a clear signal, though abundance of P. simplex (Meyen) suggests relatively high lake levels. Absence of P. musteri, characteristic of deep, open water, and abundance of macrophyte-associated taxa indicate lake levels were lowest from 9.8 to 3.0 kyr BP. A shift to wetter conditions began at 4.4 kyr BP, indicated by the appearance of P. musteri, though inferred lake levels did not reach modern values until 1.4 kyr BP. The Pediastrum-inferred mid-Holocene lowstand is consistent with lower precipitation, previously inferred using pollen from this site, and is also in agreement with evidence for widespread drought in the South American tropics during the middle Holocene. An inference for steadily increasing lake level from 4.4 kyr BP to present is consistent with diatom-inferred water level rise at Lake Titicaca, and demonstrates coherence with the broad pattern of increasing monsoon strength from the late Holocene until present in tropical South America.

Multiscale geostatistical analysis of AVHRR, SPOT-VGT, and MODIS global NDVI products

Global NDVI data are routinely derived from the AVHRR, SPOT-VGT, and MODIS/Terra earth observation records for a range of applications from terrestrial vegetation monitoring to climate change modeling. This has led to a substantial interest in the harmonization of multisensor records. Most evaluations of the internal consistency and continuity of global multisensor NDVI products have focused on time-series harmonization in the spectral domain, often neglecting the spatial domain. We fill this void by applying variogram modeling (a) to evaluate the differences in spatial variability between 8-km AVHRR, 1-km SPOT-VGT, and 1-km, 500-m, and 250-m MODIS NDVI products over eight EOS (Earth Observing System) validation sites, and (b) to characterize the decay of spatial variability as a function of pixel size (i.e. data regularization) for spatially aggregated Landsat ETM+ NDVI products and a real multisensor dataset. First, we demonstrate that the conjunctive analysis of two variogram properties – the sill and the mean length scale metric – provides a robust assessment of the differences in spatial variability between multiscale NDVI products that are due to spatial (nominal pixel size, point spread function, and view angle) and non-spatial (sensor calibration, cloud clearing, atmospheric corrections, and length of multi-day compositing period) factors. Next, we show that as the nominal pixel size increases, the decay of spatial information content follows a logarithmic relationship with stronger fit value for the spatially aggregated NDVI products (R2 = 0.9321) than for the native-resolution AVHRR, SPOT-VGT, and MODIS NDVI products (R2 = 0.5064). This relationship serves as a reference for evaluation of the differences in spatial variability and length scales in multiscale datasets at native or aggregated spatial resolutions. The outcomes of this study suggest that multisensor NDVI records cannot be integrated into a long-term data record without proper consideration of all factors affecting their spatial consistency. Hence, we propose an approach for selecting the spatial resolution, at which differences in spatial variability between NDVI products from multiple sensors are minimized. This approach provides practical guidance for the harmonization of long-term multisensor datasets.

On the performance of the tick test

In financial research, the sign of a trade (or identity of trade aggressor) is not always available in the transaction dataset and it can be estimated using a simple set of rules called the tick test. In this paper we investigate the accuracy of the tick test from an analytical perspective by providing a closed formula for the performance of the prediction algorithm. By analyzing the derived equation, we provide formal arguments for the use of the tick test by proving that it is bounded to perform better than chance (50/50) and that the set of rules from the tick test provides an unbiased estimator of the trade signs. On the empirical side of the research, we compare the values from the analytical formula against the empirical performance of the tick test for fifteen heavily traded stocks in the Brazilian equity market. The results show that the formula is quite realistic in assessing the accuracy of the prediction algorithm in a real data situation.

Centennial-scale climate change in Ireland during the Holocene

We examine mid- to late Holocene centennial-scale climate variability in Ireland using proxy data from peatlands, lakes and a speleothem. A high degree of between-record variability is apparent in the proxy data and significant chronological uncertainties are present. However, tephra layers provide a robust tool for correlation and improve the chronological precision of the records. Although we can find no statistically significant coherence in the dataset as a whole, a selection of high-quality peatland water table reconstructions co-vary more than would be expected by chance alone. A locally weighted regression model with bootstrapping can be used to construct a ‘best-estimate’ palaeoclimatic reconstruction from these datasets. Visual comparison and cross-wavelet analysis of peatland water table compilations from Ireland and Northern Britain show that there are some periods of coherence between these records. Some terrestrial palaeoclimatic changes in Ireland appear to coincide with changes in the North Atlantic thermohaline circulation and solar activity. However, these relationships are inconsistent and may be obscured by chronological uncertainties. We conclude by suggesting an agenda for future Holocene climate research in Ireland.

A reprocessing for climate of sea surface temperature from the Along-Track Scanning Radiometers: basis in radiative transfer

We present new radiative transfer simulations to support determination of sea surface temperature (SST) from Along Track Scanning Radiometer (ATSR) imagery. The simulations are to be used within the ATSR Reprocessing for Climate project. The simulations are based on the “Reference Forward Model” line-by-line model linked with a sea surface emissivity model that accounts for wind speed and temperature, and with a discrete ordinates scattering model (DISORT). Input to the forward model is a revised atmospheric profile dataset, based on full resolution ERA-40, with a wider range of high-latitude profiles to address known retrieval biases in those regions. Analysis of the radiative impacts of atmospheric trace gases shows that geographical and temporal variation of N2O, CH4, HNO3, and CFC-11 and CFC-12 have effects of order 0.05, 0.2, 0.1 K on the 3.7, 11, 12 μm channels respectively. In addition several trace gases, neglected in previous studies, are included using fixed profiles contributing ~ 0.04 K to top-of-atmosphere BTs. Comparison against observations for ATSR2 and AATSR indicates that forward model biases have been reduced from 0.2 to 0.5 K for previous simulations to ~ 0.1 K.

Generalized Bayesian cloud detection for satellite imagery. part 1: technique and validation for night-time imagery over land and sea

Numerical Weather Prediction (NWP) fields are used to assist the detection of cloud in satellite imagery. Simulated observations based on NWP are used within a framework based on Bayes' theorem to calculate a physically-based probability of each pixel with an imaged scene being clear or cloudy. Different thresholds can be set on the probabilities to create application-specific cloud-masks. Here, this is done over both land and ocean using night-time (infrared) imagery. We use a validation dataset of difficult cloud detection targets for the Spinning Enhanced Visible and Infrared Imager (SEVIRI) achieving true skill scores of 87% and 48% for ocean and land, respectively using the Bayesian technique, compared to 74% and 39%, respectively for the threshold-based techniques associated with the validation dataset.

Generalised Bayesian cloud detection for satellite imagery. part 2: technique and validation for day-time imagery

Numerical Weather Prediction (NWP) fields are used to assist the detection of cloud in satellite imagery. Simulated observations based on NWP are used within a framework based on Bayes' theorem to calculate a physically-based probability of each pixel with an imaged scene being clear or cloudy. Different thresholds can be set on the probabilities to create application-specific cloud masks. Here, the technique is shown to be suitable for daytime applications over land and sea, using visible and near-infrared imagery, in addition to thermal infrared. We use a validation dataset of difficult cloud detection targets for the Spinning Enhanced Visible and Infrared Imager (SEVIRI) achieving true skill scores of 89% and 73% for ocean and land, respectively using the Bayesian technique, compared to 90% and 70%, respectively for the threshold-based techniques associated with the validation dataset.

Implications of the North Atlantic Oscillation for a UK–Norway renewable power system

UK wind-power capacity is increasing and new transmission links are proposed with Norway, where hydropower dominates the electricity mix. Weather affects both these renewable resources and the demand for electricity. The dominant large-scale pattern of Euro-Atlantic atmospheric variability is the North Atlantic Oscillation (NAO), associated with positive correlations in wind, temperature and precipitation over northern Europe. The NAO's effect on wind-power and demand in the UK and Norway is examined, focussing on March when Norwegian hydropower reserves are low and the combined power system might be most susceptible to atmospheric variations. The NCEP/NCAR meteorological reanalysis dataset (1948–2010) is used to drive simple models for demand and wind-power, and ‘demand-net-wind’ (DNW) is estimated for positive, neutral and negative NAO states. Cold, calm conditions in NAO− cause increased demand and decreased wind-power compared to other NAO states. Under a 2020 wind-power capacity scenario, the increase in DNW in NAO− relative to NAO neutral is equivalent to nearly 25% of the present-day average rate of March Norwegian hydropower usage. As the NAO varies on long timescales (months to decades), and there is potentially some skill in monthly predictions, we argue that it is important to understand its impact on European power systems.

Forecasting by factors, by variables, by both or neither?

We consider forecasting with factors, variables and both, modeling in-sample using Autometrics so all principal components and variables can be included jointly, while tackling multiple breaks by impulse-indicator saturation. A forecast-error taxonomy for factor models highlights the impacts of location shifts on forecast-error biases. Forecasting US GDP over 1-, 4- and 8-step horizons using the dataset from Stock and Watson (2009) updated to 2011:2 shows factor models are more useful for nowcasting or short-term forecasting, but their relative performance declines as the forecast horizon increases. Forecasts for GDP levels highlight the need for robust strategies, such as intercept corrections or differencing, when location shifts occur as in the recent financial crisis.